Upgrade canonical logbook from stronger peer evidence with attribution

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+outputs/distributed_dpo_audit.png filter=lfs diff=lfs merge=lfs -text
+packaged_replay/distributed_dpo_audit.png filter=lfs diff=lfs merge=lfs -text
+source_paper_v1.pdf filter=lfs diff=lfs merge=lfs -text

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SOURCE_PIN.txt

@@ -0,0 +1,19 @@
+Paper: Distributed Direct Preference Optimization
+OpenReview: ljNZyrAlaa
+arXiv: 2605.20696v1 (only revision at audit time)
+PDF SHA-256: ce6faba012d2e862d59aa6d5a05fccf6f004e331d4769ae95607ec63483904c5
+source archive SHA-256: 43d8384c31b601422addeba43f148391e8cf39f756a28a72fb9c4ec316b48ec4
+
+Stanford Human Preferences rows 0..599 from the Hugging Face datasets server:
+rows 0-99 SHA-256: ba49c5332c94e438dfa575c84d8389f32464b941b973a96a0120052c05d676fe
+rows 100-199 SHA-256: 3f4a7d7e1540b8b53ccf5f05d79de799de24a93fb0d4496fa2e9f842f2b28ac8
+rows 200-299 SHA-256: 57d07b51e1498f272feefcc9d9886d65e0730c47ad474537d58b633bf89ea52c
+rows 300-399 SHA-256: 01fa42003ab85554ff3d55d6152ecebb6775a835df6cef10acf13ca2792fdf8d
+rows 400-499 SHA-256: 31ada479d91a26ccc0ceadda09a1c2b3e97fa03ae1bc43784225dd3609cd4ea7
+rows 500-599 SHA-256: 17a8823add2699a4cbdcaab5e886926d37f9367b247a58534f9edcbed086ee39
+
+The sole revision matches every live theorem number. Scope retained:
+Corollary 5.2 removes partial-participation amplification but retains averaged-
+client variance; Theorem 5.4 assumes bounded drift; Theorem 5.5 is a
+constructed DPO-like family; and Theorem 6.1 assumes symmetric doubly
+stochastic mixing.

build_manifest.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import hashlib
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parent
+OUT = ROOT / "BUNDLE_SHA256SUMS.txt"
+
+
+def digest(path: Path) -> str:
+    h = hashlib.sha256()
+    with path.open("rb") as handle:
+        for block in iter(lambda: handle.read(1 << 20), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+rows = []
+for path in sorted(ROOT.rglob("*")):
+    if path.is_file() and path != OUT and "__pycache__" not in path.parts:
+        rows.append(f"{digest(path)}  {path.relative_to(ROOT).as_posix()}")
+OUT.write_text("\n".join(rows) + "\n", encoding="utf-8")
+print(f"wrote {len(rows)} entries to {OUT.name}")

code/dpo_exp.py

@@ -0,0 +1,164 @@
+"""Sharper tests for FedDPO / DecDPO convergence claims: fitted rate exponents
+and parameter-free ratio checks, at larger scale than the first pass.
+
+Log-linear DPO: reward r(x) = theta^T phi(x); on a preference pair (w, l) the
+loss is -log sigmoid(beta (r_w - r_l)). Clients hold heterogeneous preference
+data generated by client-specific ground-truth rewards.
+"""
+import json
+import numpy as np
+
+RESULTS = {}
+D, BETA = 32, 1.0
+
+
+def make_clients(N, kappa, seed, n_per=200):
+    rng = np.random.default_rng(seed)
+    base = rng.normal(size=D); base /= np.linalg.norm(base)
+    cl = []
+    for i in range(N):
+        t = base + kappa * rng.normal(size=D) / np.sqrt(D)
+        t /= np.linalg.norm(t)
+        W = rng.normal(size=(n_per, D)); L = rng.normal(size=(n_per, D))
+        flip = (W - L) @ t < 0
+        W2 = np.where(flip[:, None], L, W); L2 = np.where(flip[:, None], W, L)
+        cl.append((W2, L2, t))
+    return cl, base
+
+
+def grad(th, W, L):
+    z = BETA * ((W - L) @ th)
+    s = 1.0 / (1.0 + np.exp(z))
+    return -BETA * ((W - L) * s[:, None]).mean(axis=0)
+
+
+def loss(th, cl):
+    tot = 0.0
+    for W, L, _ in cl:
+        z = BETA * ((W - L) @ th)
+        tot += float(np.mean(np.log1p(np.exp(-z))))
+    return tot / len(cl)
+
+
+def fed_dpo(cl, R=200, E=5, S=None, lr=0.5, q_max=0, seed=0):
+    N = len(cl); S = S or N
+    rng = np.random.default_rng(seed)
+    th = np.zeros(D); buf = {}
+    hist = []
+    for r in range(R):
+        sel = rng.choice(N, size=S, replace=False)
+        deltas = []
+        for i in sel:
+            local = th.copy()
+            for _ in range(E):
+                local -= lr * grad(local, cl[i][0], cl[i][1])
+            d = local - th
+            delay = int(rng.integers(0, q_max + 1)) if q_max else 0
+            buf.setdefault(r + delay, []).append(d)
+        for d in buf.pop(r, []):
+            deltas.append(d)
+        if deltas:
+            th = th + np.mean(deltas, axis=0)
+        hist.append(loss(th, cl))
+    return np.array(hist)
+
+
+def metropolis(adj):
+    n = adj.shape[0]; deg = adj.sum(1)
+    Wm = np.zeros((n, n))
+    for i in range(n):
+        for j in range(n):
+            if i != j and adj[i, j]:
+                Wm[i, j] = 1.0 / (1 + max(deg[i], deg[j]))
+        Wm[i, i] = 1 - Wm[i].sum()
+    ev = np.sort(np.abs(np.linalg.eigvals(Wm)))[::-1]
+    return Wm, float(ev[1])
+
+
+def dec_dpo(cl, Wm, R=200, E=5, lr=0.5):
+    N = len(cl)
+    TH = np.zeros((N, D)); hist = []
+    for r in range(R):
+        for i in range(N):
+            for _ in range(E):
+                TH[i] -= lr * grad(TH[i], cl[i][0], cl[i][1])
+        TH = Wm @ TH
+        hist.append(float(np.mean(np.linalg.norm(TH - TH.mean(0), axis=1))))
+    return np.array(hist)
+
+
+def claim2_participation():
+    rows = []
+    N = 20
+    cl, _ = make_clients(N, 0.8, seed=1)
+    for S in (2, 5, 10, 20):
+        finals = [fed_dpo(cl, R=150, S=S, seed=100 + s)[-20:].mean() for s in range(5)]
+        var = [np.var(fed_dpo(cl, R=150, S=S, seed=200 + s)[-20:]) for s in range(5)]
+        rows.append({"S": S, "N": N, "final_loss": float(np.mean(finals)),
+                     "tail_variance": float(np.mean(var)),
+                     "one_over_S": 1.0 / S})
+        print("  S=%-3d final loss=%.6f  tail var=%.3e  (1/S=%.3f)" %
+              (S, rows[-1]["final_loss"], rows[-1]["tail_variance"], 1.0 / S), flush=True)
+    ls = np.log([r["one_over_S"] for r in rows]); lv = np.log([max(r["tail_variance"], 1e-16) for r in rows])
+    RESULTS["claim2_participation"] = {
+        "rows": rows, "loglog_slope_var_vs_1_over_S": round(float(np.polyfit(ls, lv, 1)[0]), 4),
+        "var_ratio_S2_over_SN": round(rows[0]["tail_variance"] / max(rows[-1]["tail_variance"], 1e-16), 2)}
+    print("  variance slope vs 1/S = %.3f ; S=2 vs S=N ratio = %.1fx" %
+          (RESULTS["claim2_participation"]["loglog_slope_var_vs_1_over_S"],
+           RESULTS["claim2_participation"]["var_ratio_S2_over_SN"]), flush=True)
+
+
+def claim3_staleness():
+    rows = []
+    cl, _ = make_clients(10, 0.8, seed=3)
+    for q in (0, 1, 2, 5, 10):
+        f = [fed_dpo(cl, R=150, q_max=q, seed=300 + s)[-20:].mean() for s in range(5)]
+        rows.append({"q_max": q, "final_loss": float(np.mean(f)),
+                     "sd": float(np.std(f))})
+        print("  q_max=%-3d final loss=%.6f +- %.6f" % (q, rows[-1]["final_loss"], rows[-1]["sd"]), flush=True)
+    base = rows[0]["final_loss"]
+    RESULTS["claim3_staleness"] = {
+        "rows": rows, "monotone_in_q": all(rows[i+1]["final_loss"] >= rows[i]["final_loss"] - 1e-9
+                                           for i in range(len(rows) - 1)),
+        "penalty_at_qmax10": round(rows[-1]["final_loss"] - base, 6)}
+
+
+def claim5_topology():
+    N = 8
+    cl, _ = make_clients(N, 0.8, seed=5)
+    tops = {}
+    ring = np.zeros((N, N), int)
+    for i in range(N):
+        ring[i, (i + 1) % N] = ring[(i + 1) % N, i] = 1
+    tops["ring"] = ring
+    star = np.zeros((N, N), int); star[0, 1:] = star[1:, 0] = 1
+    tops["star"] = star
+    full = np.ones((N, N), int) - np.eye(N, dtype=int)
+    tops["complete"] = full
+    path = np.zeros((N, N), int)
+    for i in range(N - 1):
+        path[i, i + 1] = path[i + 1, i] = 1
+    tops["path"] = path
+    rows = []
+    for name, adj in tops.items():
+        Wm, rho = metropolis(adj)
+        h = dec_dpo(cl, Wm, R=120)
+        rows.append({"topology": name, "rho": round(rho, 4),
+                     "one_over_1_minus_rho2": round(1.0 / (1 - rho ** 2), 3),
+                     "final_consensus_error": float(h[-1]),
+                     "mean_tail_consensus": float(h[-20:].mean())})
+        print("  %-9s rho=%.4f  1/(1-rho^2)=%8.2f  consensus err=%.4e" %
+              (name, rho, rows[-1]["one_over_1_minus_rho2"], rows[-1]["mean_tail_consensus"]), flush=True)
+    x = np.log([r["one_over_1_minus_rho2"] for r in rows])
+    y = np.log([max(r["mean_tail_consensus"], 1e-16) for r in rows])
+    sl, ic = np.polyfit(x, y, 1)
+    r2 = 1 - np.var(y - (sl * x + ic)) / np.var(y)
+    RESULTS["claim5_topology"] = {"rows": rows,
+                                  "loglog_slope_consensus_vs_1_over_1_minus_rho2": round(float(sl), 4),
+                                  "r2": round(float(r2), 4)}
+    print("  consensus error vs 1/(1-rho^2): slope %.3f, R2 %.3f" % (sl, r2), flush=True)
+
+
+if __name__ == "__main__":
+    claim2_participation(); claim3_staleness(); claim5_topology()
+    json.dump(RESULTS, open("dpo_results.json", "w"), indent=1)

code/dpo_exp2.py

@@ -0,0 +1,108 @@
+"""Redesigned tests: remove the two confounds in the first pass.
+
+Claim 2 (partial participation contributes a variance term that vanishes at S=N):
+  measure the variance of the ITERATE across seeds, not of the loss, and compare
+  against the finite-population correction (N-S)/(S(N-1)), which is the exact
+  sampling variance of a mean over S of N units drawn without replacement.
+  S=N is reported separately as an exact-zero check (no fit).
+
+Claim 5 (DecDPO consensus error grows with the spectral gap):
+  sweep rho CONTINUOUSLY on a FIXED graph with a FIXED client assignment using
+  lazy mixing W_a = (1-a) I + a W, so the only thing that changes is rho.
+  Any confound from topology structure or heterogeneity layout is eliminated.
+"""
+import json
+import numpy as np
+from dpo_exp import make_clients, grad, metropolis, D
+
+RESULTS = {}
+
+
+def fed_iterate(cl, R, S, lr=0.5, E=5, seed=0):
+    N = len(cl); rng = np.random.default_rng(seed)
+    th = np.zeros(D)
+    for _ in range(R):
+        sel = rng.choice(N, size=S, replace=False)
+        ds = []
+        for i in sel:
+            loc = th.copy()
+            for _ in range(E):
+                loc -= lr * grad(loc, cl[i][0], cl[i][1])
+            ds.append(loc - th)
+        th = th + np.mean(ds, axis=0)
+    return th
+
+
+def claim2():
+    N, R = 20, 120
+    cl, _ = make_clients(N, 0.8, seed=1)
+    rows = []
+    for S in (2, 4, 5, 10, 20):
+        TH = np.array([fed_iterate(cl, R, S, seed=1000 + s) for s in range(24)])
+        var = float(np.trace(np.cov(TH.T)))
+        fpc = (N - S) / (S * (N - 1))
+        rows.append({"S": S, "N": N, "iterate_variance": var,
+                     "finite_pop_correction_(N-S)/(S(N-1))": round(fpc, 6),
+                     "ratio_var_over_fpc": round(var / fpc, 6) if fpc > 0 else None})
+        print("  S=%-3d Var(theta)=%.4e   (N-S)/(S(N-1))=%.5f   ratio=%s" %
+              (S, var, fpc, rows[-1]["ratio_var_over_fpc"]), flush=True)
+    part = [r for r in rows if r["S"] < N]
+    x = np.log([r["finite_pop_correction_(N-S)/(S(N-1))"] for r in part])
+    y = np.log([r["iterate_variance"] for r in part])
+    sl, ic = np.polyfit(x, y, 1)
+    r2 = 1 - np.var(y - (sl * x + ic)) / np.var(y)
+    ratios = [r["ratio_var_over_fpc"] for r in part]
+    RESULTS["claim2"] = {
+        "rows": rows, "seeds_per_cell": 24, "rounds": R,
+        "loglog_slope_vs_finite_pop_correction": round(float(sl), 4),
+        "predicted_slope": 1.0, "r2": round(float(r2), 4),
+        "proportionality_const_spread": round(max(ratios) / min(ratios), 3),
+        "variance_at_S_equals_N": rows[-1]["iterate_variance"],
+        "vanishes_at_full_participation": bool(rows[-1]["iterate_variance"] < 1e-20)}
+    print("  fit slope %.3f (predicted 1.0), R2 %.3f ; const spread %.2fx ; Var(S=N)=%.2e" %
+          (sl, r2, RESULTS["claim2"]["proportionality_const_spread"],
+           rows[-1]["iterate_variance"]), flush=True)
+
+
+def dec_consensus(cl, Wm, R=200, E=5, lr=0.5):
+    N = len(cl); TH = np.zeros((N, D))
+    for _ in range(R):
+        for i in range(N):
+            for _ in range(E):
+                TH[i] -= lr * grad(TH[i], cl[i][0], cl[i][1])
+        TH = Wm @ TH
+    return float(np.mean(np.linalg.norm(TH - TH.mean(0), axis=1)))
+
+
+def claim5():
+    N = 8
+    cl, _ = make_clients(N, 0.8, seed=5)
+    ring = np.zeros((N, N), int)
+    for i in range(N):
+        ring[i, (i + 1) % N] = ring[(i + 1) % N, i] = 1
+    W0, _ = metropolis(ring)
+    rows = []
+    for a in (1.0, 0.8, 0.6, 0.45, 0.3, 0.2, 0.12):
+        Wm = (1 - a) * np.eye(N) + a * W0
+        rho = float(np.sort(np.abs(np.linalg.eigvals(Wm)))[::-1][1])
+        err = dec_consensus(cl, Wm, R=250)
+        rows.append({"lazy_alpha": a, "rho": round(rho, 5),
+                     "one_over_1_minus_rho": round(1 / (1 - rho), 3),
+                     "steady_state_consensus_error": err})
+        print("  alpha=%.2f rho=%.5f  1/(1-rho)=%7.2f  consensus err=%.5e" %
+              (a, rho, rows[-1]["one_over_1_minus_rho"], err), flush=True)
+    x = np.log([r["one_over_1_minus_rho"] for r in rows])
+    y = np.log([r["steady_state_consensus_error"] for r in rows])
+    sl, ic = np.polyfit(x, y, 1)
+    r2 = 1 - np.var(y - (sl * x + ic)) / np.var(y)
+    RESULTS["claim5"] = {
+        "graph": "8-node ring, fixed client assignment, lazy mixing sweeps rho",
+        "rows": rows, "rounds": 250,
+        "loglog_slope_vs_1_over_1_minus_rho": round(float(sl), 4),
+        "r2": round(float(r2), 4)}
+    print("  consensus error vs 1/(1-rho): slope %.3f, R2 %.4f" % (sl, r2), flush=True)
+
+
+if __name__ == "__main__":
+    claim2(); claim5()
+    json.dump(RESULTS, open("dpo_results2.json", "w"), indent=1)

code/dpo_exp4.py

@@ -0,0 +1,81 @@
+"""Claim 5, corrected: constant step size + local steps gives a BIASED fixed
+point, so the gap plateaus and the stated O(1/sqrt(R)) rate cannot appear.
+Use the decaying schedule the rate assumes, lr_r = lr0/sqrt(r), and measure the
+gap against the true pooled optimum (found by direct minimisation).
+"""
+import json
+import numpy as np
+from dpo_exp import make_clients, grad, metropolis, loss, D, BETA
+
+RESULTS = {}
+
+
+def pooled_opt(cl, iters=8000):
+    th = np.zeros(D)
+    for t in range(1, iters + 1):
+        g = np.mean([grad(th, W, L) for W, L, _ in cl], axis=0)
+        th -= (2.0 / np.sqrt(t)) * g
+    return th, loss(th, cl)
+
+
+def dec_gap(cl, Wm, marks, E=5, lr0=1.0):
+    N = len(cl); TH = np.zeros((N, D)); out = {}
+    ms = set(marks)
+    for r in range(1, max(marks) + 1):
+        lr = lr0 / np.sqrt(r)
+        for i in range(N):
+            for _ in range(E):
+                TH[i] -= lr * grad(TH[i], cl[i][0], cl[i][1])
+        TH = Wm @ TH
+        if r in ms:
+            out[r] = loss(TH.mean(0), cl)
+    return out
+
+
+def run():
+    N = 8
+    cl, _ = make_clients(N, 0.8, seed=5)
+    _, star = pooled_opt(cl)
+    print("  pooled optimum loss = %.8f" % star, flush=True)
+    ring = np.zeros((N, N), int)
+    for i in range(N):
+        ring[i, (i + 1) % N] = ring[(i + 1) % N, i] = 1
+    W0, _ = metropolis(ring)
+    Rs = [25, 50, 100, 200, 400, 800]
+    rows = []
+    for a in (1.0, 0.6, 0.3, 0.15):
+        Wm = (1 - a) * np.eye(N) + a * W0
+        rho = float(np.sort(np.abs(np.linalg.eigvals(Wm)))[::-1][1])
+        g = dec_gap(cl, Wm, Rs)
+        gaps = np.array([max(g[r] - star, 1e-14) for r in Rs])
+        A = np.stack([1 / np.sqrt(Rs), 1 / (np.array(Rs) * (1 - rho ** 2))], axis=1)
+        coef, *_ = np.linalg.lstsq(A, gaps, rcond=None)
+        r2 = 1 - np.var(gaps - A @ coef) / np.var(gaps)
+        # also: pure 1/sqrt(R) slope, to show the sqrt term is the asymptote
+        sl = float(np.polyfit(np.log(Rs), np.log(gaps), 1)[0])
+        rows.append({"lazy_alpha": a, "rho": round(rho, 5),
+                     "one_minus_rho2": round(1 - rho ** 2, 5),
+                     "c1_sqrtR": round(float(coef[0]), 6),
+                     "c2_transient": round(float(coef[1]), 6),
+                     "two_term_fit_r2": round(float(r2), 5),
+                     "raw_loglog_slope_gap_vs_R": round(sl, 4),
+                     "gaps": {str(r): round(float(x), 8) for r, x in zip(Rs, gaps)}})
+        print("  alpha=%.2f rho=%.4f 1-rho^2=%.4f  c1=%.5f c2=%.5f  R2=%.4f  raw slope=%.3f" %
+              (a, rho, 1 - rho ** 2, coef[0], coef[1], r2, sl), flush=True)
+    c2 = [r["c2_transient"] for r in rows]
+    pos = all(x > 0 for x in c2)
+    RESULTS["claim5_rate_decomposition"] = {
+        "pooled_optimum_loss": star, "R_grid": Rs, "step_size": "lr_r = 1/sqrt(r)",
+        "rows": rows,
+        "all_c2_positive": pos,
+        "c2_spread_max_over_min": round(max(c2) / min(c2), 3) if pos else None,
+        "all_fits_above_r2_0.99": all(r["two_term_fit_r2"] > 0.99 for r in rows),
+        "mean_raw_slope": round(float(np.mean([r["raw_loglog_slope_gap_vs_R"] for r in rows])), 4)}
+    print("  c2: %s ; all positive=%s ; mean raw slope %.3f (predicted -0.5)" %
+          ([round(x, 4) for x in c2], pos,
+           RESULTS["claim5_rate_decomposition"]["mean_raw_slope"]), flush=True)
+
+
+if __name__ == "__main__":
+    run()
+    json.dump(RESULTS, open("dpo_results4.json", "w"), indent=1)

code/dpo_real.py

@@ -0,0 +1,72 @@
+"""Federated / decentralized DPO on the paper's actual setup: DistilGPT-2 (82M)
+trained with the true DPO loss on Stanford Human Preferences pairs, split across
+N=5 clients. This replaces the 64-dim log-linear proxy used previously.
+"""
+import os, json, time, math, copy, argparse
+import numpy as np, torch, torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+DEV = "mps" if torch.backends.mps.is_available() else "cpu"
+BETA = 0.1
+MODEL = "distilgpt2"
+
+
+def load_pairs(n_pairs, maxlen=128, seed=0):
+    from datasets import load_dataset
+    ds = load_dataset("stanfordnlp/SHP", split="train", streaming=True)
+    tok = AutoTokenizer.from_pretrained(MODEL)
+    tok.pad_token = tok.eos_token
+    out, it = [], iter(ds)
+    while len(out) < n_pairs:
+        try: r = next(it)
+        except StopIteration: break
+        a, b = r["human_ref_A"], r["human_ref_B"]
+        w, l = (a, b) if r["labels"] == 1 else (b, a)
+        p = "Q: " + r["history"][:400] + "\nA:"
+        pw = tok(p + " " + w[:400], truncation=True, max_length=maxlen)["input_ids"]
+        pl = tok(p + " " + l[:400], truncation=True, max_length=maxlen)["input_ids"]
+        np_ = len(tok(p, truncation=True, max_length=maxlen)["input_ids"])
+        if len(pw) > np_ + 2 and len(pl) > np_ + 2:
+            out.append({"w": pw, "l": pl, "np": np_, "domain": r["domain"]})
+    return out, tok
+
+
+def batch_logps(model, seqs, nps, pad):
+    L = max(len(s) for s in seqs)
+    ids = torch.full((len(seqs), L), pad, dtype=torch.long)
+    msk = torch.zeros((len(seqs), L), dtype=torch.bool)
+    for i, s in enumerate(seqs):
+        ids[i, :len(s)] = torch.tensor(s); msk[i, nps[i]:len(s)] = True
+    ids, msk = ids.to(DEV), msk.to(DEV)
+    logits = model(ids).logits[:, :-1]
+    tgt, m = ids[:, 1:], msk[:, 1:]
+    lp = torch.log_softmax(logits.float(), -1).gather(2, tgt.unsqueeze(-1)).squeeze(-1)
+    return (lp * m).sum(-1)
+
+
+def dpo_loss(model, ref, batch, pad):
+    sw = [b["w"] for b in batch]; sl = [b["l"] for b in batch]
+    nw = [b["np"] for b in batch]; nl = [b["np"] for b in batch]
+    pw = batch_logps(model, sw, nw, pad); pl = batch_logps(model, sl, nl, pad)
+    with torch.no_grad():
+        rw = batch_logps(ref, sw, nw, pad); rl = batch_logps(ref, sl, nl, pad)
+    logits = BETA * ((pw - rw) - (pl - rl))
+    acc = (logits > 0).float().mean().item()
+    return -F.logsigmoid(logits).mean(), acc
+
+
+if __name__ == "__main__":
+    t0 = time.time()
+    pairs, tok = load_pairs(40)
+    print("loaded %d pairs in %.1fs; domains=%s" % (len(pairs), time.time()-t0,
+          sorted({p['domain'] for p in pairs})[:5]), flush=True)
+    m = AutoModelForCausalLM.from_pretrained(MODEL).to(DEV)
+    ref = AutoModelForCausalLM.from_pretrained(MODEL).to(DEV).eval()
+    for p in ref.parameters(): p.requires_grad_(False)
+    opt = torch.optim.SGD(m.parameters(), lr=1e-4)
+    t0 = time.time()
+    for step in range(5):
+        loss, acc = dpo_loss(m, ref, pairs[step*4:(step+1)*4], tok.pad_token_id)
+        opt.zero_grad(); loss.backward(); opt.step()
+        print("  step %d loss=%.4f acc=%.2f  (%.2fs)" % (step, loss.item(), acc, time.time()-t0), flush=True)
+    print("per-step: %.2fs on %s" % ((time.time()-t0)/5, DEV))

code/fed_real.py

@@ -0,0 +1,195 @@
+"""FedDPO / DecDPO on DistilGPT-2 (82M) with real SHP preference pairs,
+non-IID split across N=5 clients by SHP domain (the paper's heterogeneity).
+
+Design notes that make the theorems actually testable:
+ * Each client's batch sequence is fixed by its own seed, so with FULL
+   participation (S=N) the whole round is deterministic. Corollary 5.2 predicts
+   the partial-participation variance term vanishes at S=N -- here that becomes
+   an EXACT zero-variance check, not a noisy approximation.
+ * Client sampling uses a separate RNG, so S<N runs differ only in WHICH
+   clients are sampled -- isolating the 1/S term from data-order noise.
+"""
+import json, time, copy, itertools
+import numpy as np, torch, torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from dpo_real import batch_logps, dpo_loss, DEV, MODEL, BETA
+
+N_CLIENTS, E_DEF, R_DEF, BS = 5, 2, 40, 4
+RES = {}
+
+
+def build_clients(n_per_domain=90):
+    from datasets import load_dataset
+    ds = load_dataset("stanfordnlp/SHP", split="train", streaming=True)
+    tok = AutoTokenizer.from_pretrained(MODEL); tok.pad_token = tok.eos_token
+    doms, want = {}, N_CLIENTS
+    for r in itertools.islice(ds, 400000):
+        d = r["domain"]
+        if d not in doms and len(doms) >= want: continue
+        b = doms.setdefault(d, [])
+        if len(b) >= n_per_domain: 
+            if all(len(v) >= n_per_domain for v in doms.values()) and len(doms) >= want: break
+            continue
+        a, bb = r["human_ref_A"], r["human_ref_B"]
+        w, l = (a, bb) if r["labels"] == 1 else (bb, a)
+        p = "Q: " + r["history"][:400] + "\nA:"
+        pw = tok(p + " " + w[:400], truncation=True, max_length=128)["input_ids"]
+        pl = tok(p + " " + l[:400], truncation=True, max_length=128)["input_ids"]
+        npr = len(tok(p, truncation=True, max_length=128)["input_ids"])
+        if len(pw) > npr + 2 and len(pl) > npr + 2:
+            b.append({"w": pw, "l": pl, "np": npr})
+    names = sorted(doms)[:N_CLIENTS]
+    return [doms[d] for d in names], names, tok
+
+
+def flat(m): return torch.cat([p.detach().reshape(-1) for p in m.parameters()])
+def setflat(m, v):
+    i = 0
+    for p in m.parameters():
+        n = p.numel(); p.data.copy_(v[i:i+n].view_as(p)); i += n
+
+
+def local_train(model, ref, data, E, lr, pad, rng):
+    opt = torch.optim.SGD(model.parameters(), lr=lr)
+    for _ in range(E):
+        idx = rng.integers(0, len(data), size=BS)
+        loss, _ = dpo_loss(model, ref, [data[i] for i in idx], pad)
+        opt.zero_grad(); loss.backward(); opt.step()
+
+
+def evaluate(model, ref, clients, pad, nb=3):
+    tot, acc, k = 0.0, 0.0, 0
+    with torch.no_grad():
+        for d in clients:
+            for j in range(nb):
+                l, a = dpo_loss(model, ref, d[j*BS:(j+1)*BS], pad)
+                tot += l.item(); acc += a; k += 1
+    return tot/k, acc/k
+
+
+def fed_run(base, ref, clients, tok, S, R=R_DEF, E=E_DEF, lr=2e-5, q_max=0, seed=0):
+    m = copy.deepcopy(base).to(DEV)
+    th = flat(m).clone()
+    samp = np.random.default_rng(10_000 + seed)
+    crngs = [np.random.default_rng(777 + i) for i in range(len(clients))]  # fixed per client
+    dly = np.random.default_rng(50_000 + seed)
+    buf = {}
+    for r in range(R):
+        sel = samp.choice(len(clients), size=S, replace=False) if S < len(clients) else np.arange(len(clients))
+        for i in sel:
+            setflat(m, th)
+            local_train(m, ref, clients[i], E, lr, tok.pad_token_id, crngs[i])
+            d = flat(m) - th
+            k = r + (int(dly.integers(0, q_max+1)) if q_max else 0)
+            buf.setdefault(k, []).append(d)
+        ds = buf.pop(r, [])
+        if ds: th = th + torch.stack(ds).mean(0)
+    setflat(m, th)
+    return m, th
+
+
+def metropolis(adj):
+    n = adj.shape[0]; deg = adj.sum(1); W = np.zeros((n, n))
+    for i in range(n):
+        for j in range(n):
+            if i != j and adj[i, j]: W[i, j] = 1.0/(1+max(deg[i], deg[j]))
+        W[i, i] = 1 - W[i].sum()
+    return W, float(np.sort(np.abs(np.linalg.eigvals(W)))[::-1][1])
+
+
+def dec_run(base, ref, clients, tok, W, R=R_DEF, E=E_DEF, lr=2e-5):
+    n = len(clients)
+    m = copy.deepcopy(base).to(DEV)
+    TH = torch.stack([flat(m).clone() for _ in range(n)])
+    crngs = [np.random.default_rng(777+i) for i in range(n)]
+    Wt = torch.tensor(W, dtype=TH.dtype, device=TH.device)
+    for r in range(R):
+        new = []
+        for i in range(n):
+            setflat(m, TH[i]); local_train(m, ref, clients[i], E, lr, tok.pad_token_id, crngs[i])
+            new.append(flat(m).clone())
+        TH = Wt @ torch.stack(new)
+    cons = float(torch.norm(TH - TH.mean(0, keepdim=True), dim=1).mean())
+    setflat(m, TH.mean(0))
+    return m, cons
+
+
+def main():
+    t0 = time.time()
+    clients, names, tok = build_clients()
+    print("clients:", [(n, len(d)) for n, d in zip(names, clients)], flush=True)
+    base = AutoModelForCausalLM.from_pretrained(MODEL)
+    ref = AutoModelForCausalLM.from_pretrained(MODEL).to(DEV).eval()
+    for p in ref.parameters(): p.requires_grad_(False)
+    pad = tok.pad_token_id
+    l0, a0 = evaluate(base.to(DEV), ref, clients, pad)
+    RES["init"] = {"loss": l0, "acc": a0, "clients": dict(zip(names, [len(d) for d in clients]))}
+    print("init loss=%.4f acc=%.3f" % (l0, a0), flush=True)
+
+    # ---- Claim 2: participation sweep, variance of the global model ----
+    rows = []
+    for S in (1, 2, 3, 5):
+        ths, ls = [], []
+        for s in range(3):
+            m, th = fed_run(base, ref, clients, tok, S, seed=s)
+            l, a = evaluate(m, ref, clients, pad); ths.append(th); ls.append(l)
+            print("   S=%d seed=%d loss=%.4f acc=%.3f (%.0fs)" % (S, s, l, a, time.time()-t0), flush=True)
+        TH = torch.stack(ths)
+        var = float((TH - TH.mean(0, keepdim=True)).pow(2).sum(1).mean())
+        fpc = (N_CLIENTS-S)/(S*(N_CLIENTS-1))
+        rows.append({"S": S, "param_variance": var, "fpc": round(fpc, 5),
+                     "ratio": (var/fpc if fpc > 0 else None),
+                     "loss_mean": float(np.mean(ls)), "loss_sd": float(np.std(ls))})
+        print("  S=%d Var=%.4e fpc=%.4f loss=%.4f+-%.4f" % (S, var, fpc, np.mean(ls), np.std(ls)), flush=True)
+    RES["claim2_participation"] = {"rows": rows}
+    json.dump(RES, open("fed_real_results.json", "w"), indent=1)
+
+    # ---- Claim 3: staleness ----
+    rows = []
+    for q in (0, 2, 5):
+        ls = []
+        for s in range(2):
+            m, _ = fed_run(base, ref, clients, tok, N_CLIENTS, q_max=q, seed=s)
+            l, a = evaluate(m, ref, clients, pad); ls.append(l)
+        rows.append({"q_max": q, "loss_mean": float(np.mean(ls)), "loss_sd": float(np.std(ls))})
+        print("  q_max=%d loss=%.5f+-%.5f (%.0fs)" % (q, np.mean(ls), np.std(ls), time.time()-t0), flush=True)
+    RES["claim3_staleness"] = {"rows": rows,
+        "monotone": all(rows[i+1]["loss_mean"] >= rows[i]["loss_mean"]-1e-9 for i in range(len(rows)-1))}
+    json.dump(RES, open("fed_real_results.json", "w"), indent=1)
+
+    # ---- Claim 5: topology ----
+    n = N_CLIENTS
+    tops = {}
+    ring = np.zeros((n, n), int)
+    for i in range(n): ring[i, (i+1) % n] = ring[(i+1) % n, i] = 1
+    tops["ring"] = ring
+    star = np.zeros((n, n), int); star[0, 1:] = star[1:, 0] = 1; tops["star"] = star
+    path = np.zeros((n, n), int)
+    for i in range(n-1): path[i, i+1] = path[i+1, i] = 1
+    tops["path"] = path
+    tops["complete"] = np.ones((n, n), int) - np.eye(n, dtype=int)
+    rows = []
+    for nm, adj in tops.items():
+        W, rho = metropolis(adj)
+        m, cons = dec_run(base, ref, clients, tok, W)
+        l, a = evaluate(m, ref, clients, pad)
+        rows.append({"topology": nm, "rho": round(rho, 4),
+                     "one_over_1_minus_rho2": round(1/(1-rho**2), 3),
+                     "consensus_error": cons, "loss": l, "acc": a})
+        print("  %-9s rho=%.4f cons=%.4e loss=%.4f (%.0fs)" % (nm, rho, cons, l, time.time()-t0), flush=True)
+    x = np.log([r["one_over_1_minus_rho2"] for r in rows if r["consensus_error"] > 1e-9])
+    y = np.log([r["consensus_error"] for r in rows if r["consensus_error"] > 1e-9])
+    if len(x) > 2:
+        sl, ic = np.polyfit(x, y, 1)
+        RES["claim5_topology"] = {"rows": rows, "loglog_slope": round(float(sl), 4),
+            "r2": round(float(1-np.var(y-(sl*x+ic))/np.var(y)), 4),
+            "rank_corr_positive": bool(np.corrcoef(x, y)[0, 1] > 0)}
+    else:
+        RES["claim5_topology"] = {"rows": rows}
+    RES["wall_clock_s"] = time.time()-t0
+    json.dump(RES, open("fed_real_results.json", "w"), indent=1)
+    print("DONE %.0fs" % (time.time()-t0), flush=True)
+
+
+if __name__ == "__main__":
+    main()

code/fed_topo.py

@@ -0,0 +1,48 @@
+"""Topology arm only: DecDPO on DistilGPT-2 across four 5-node graphs."""
+import json, time, numpy as np, torch
+from transformers import AutoModelForCausalLM
+from dpo_real import DEV, MODEL
+import fed_real as FR
+
+RES = json.load(open("fed_real_results.json"))
+
+def main():
+    t0=time.time()
+    clients,names,tok = FR.build_clients()
+    base = AutoModelForCausalLM.from_pretrained(MODEL)
+    ref = AutoModelForCausalLM.from_pretrained(MODEL).to(DEV).eval()
+    for p in ref.parameters(): p.requires_grad_(False)
+    pad=tok.pad_token_id; n=FR.N_CLIENTS
+    tops={}
+    ring=np.zeros((n,n),int)
+    for i in range(n): ring[i,(i+1)%n]=ring[(i+1)%n,i]=1
+    tops["ring"]=ring
+    star=np.zeros((n,n),int); star[0,1:]=star[1:,0]=1; tops["star"]=star
+    path=np.zeros((n,n),int)
+    for i in range(n-1): path[i,i+1]=path[i+1,i]=1
+    tops["path"]=path
+    tops["complete"]=np.ones((n,n),int)-np.eye(n,dtype=int)
+    rows=[]
+    for nm,adj in tops.items():
+        W,rho=FR.metropolis(adj)
+        m,cons=FR.dec_run(base,ref,clients,tok,W)
+        l,a=FR.evaluate(m,ref,clients,pad)
+        rows.append({"topology":nm,"rho":round(rho,4),
+                     "one_over_1_minus_rho2":(round(1/(1-rho**2),3) if rho<0.999999 else None),
+                     "consensus_error":cons,"loss":l,"acc":a})
+        print("  %-9s rho=%.4f  1/(1-rho^2)=%s  cons=%.4e  loss=%.5f  (%.0fs)"%(
+            nm,rho,rows[-1]["one_over_1_minus_rho2"],cons,l,time.time()-t0),flush=True)
+        json.dump({**RES,"claim5_topology":{"rows":rows}},open("fed_real_results.json","w"),indent=1)
+    ok=[r for r in rows if r["consensus_error"]>1e-7 and r["one_over_1_minus_rho2"]]
+    out={"rows":rows,"n_in_fit":len(ok)}
+    if len(ok)>=3:
+        x=np.log([r["one_over_1_minus_rho2"] for r in ok]); y=np.log([r["consensus_error"] for r in ok])
+        sl,ic=np.polyfit(x,y,1)
+        out.update({"loglog_slope":round(float(sl),4),
+                    "r2":round(float(1-np.var(y-(sl*x+ic))/np.var(y)),4),
+                    "spearman_positive":bool(np.corrcoef(x,y)[0,1]>0)})
+    RES["claim5_topology"]=out
+    json.dump(RES,open("fed_real_results.json","w"),indent=1)
+    print("DONE %.0fs"%(time.time()-t0),flush=True)
+
+if __name__=="__main__": main()

index.html

@@ -3,7 +3,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
-    <title>Reproduction: Distributed Direct Preference Optimization</title>
+    <title>Reproduction: Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo</title>
     <link rel="stylesheet" href="./logbook.css" />
   </head>
   <body>
@@ -21,36 +21,6 @@
         </div>
       </aside>
       <main id="content">
-        <nav id="view-tabs" aria-label="Logbook views">
-          <a data-view="code" href="#/view/code/index">
-            <svg viewBox="0 0 24 24" aria-hidden="true">
-              <path d="m18 16 4-4-4-4" />
-              <path d="m6 8-4 4 4 4" />
-              <path d="m14.5 4-5 16" />
-            </svg>
-            <span>Logbook</span>
-          </a>
-          <a data-view="trace" href="#/view/trace">
-            <svg viewBox="0 0 24 24" aria-hidden="true">
-              <path d="M8 5h13" />
-              <path d="M13 12h8" />
-              <path d="M13 19h8" />
-              <path d="M3 10a2 2 0 0 0 2 2h3" />
-              <path d="M3 5v12a2 2 0 0 0 2 2h3" />
-            </svg>
-            <span>Traces</span>
-          </a>
-          <a data-view="workspace" href="#/view/workspace">
-            <svg viewBox="0 0 24 24" aria-hidden="true">
-              <path d="M20 20a2 2 0 0 0 2-2V8a2 2 0 0 0-2-2h-7.9a2 2 0 0 1-1.69-.9L9.6 3.9A2 2 0 0 0 7.93 3H4a2 2 0 0 0-2 2v13a2 2 0 0 0 2 2Z" />
-            </svg>
-            <span>Workspace</span>
-          </a>
-        </nav>
-        <header id="logbook-header">
-          <h1 id="logbook-title"></h1>
-          <div id="logbook-cli"></div>
-        </header>
         <div id="page"></div>
       </main>
     </div>

logbook.css

@@ -1,6 +1,6 @@
 :root {
   --bg: #ffffff;
-  --paper: #ffffff;
+  --paper: #fdfcf9;
   --panel: #ffffff;
   --ink: #1f2937;
   --muted: #6b7280;
@@ -9,11 +9,9 @@
   --accent-strong: #ea580c;
   --accent-soft: #fff7ed;
   --accent-line: rgba(249, 115, 22, 0.16);
-  --grid-line: rgba(31, 41, 55, 0.02);
+  --grid-line: rgba(31, 41, 55, 0.045);
   --code-bg: #f3f4f6;
   --radius: 12px;
-  --sidebar-width: 280px;
-  --content-gutter: 40px;
   --serif: ui-serif, "Iowan Old Style", "Palatino Linotype", Georgia, serif;
   --sans: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial,
     sans-serif;
@@ -33,7 +31,6 @@ body {
 
 html {
   scroll-behavior: smooth;
-  scrollbar-gutter: stable;
 }
 
 body {
@@ -50,15 +47,10 @@ body {
   min-height: 100vh;
 }
 
-body[data-view="trace"] #sidebar-foot,
-body[data-view="workspace"] #sidebar-foot {
-  display: none;
-}
-
 /* ---- sidebar (composition-book cover) ---- */
 #sidebar {
-  width: var(--sidebar-width);
-  flex: 0 0 var(--sidebar-width);
+  width: 280px;
+  flex: 0 0 280px;
   background: #17181c;
   color: #e7e7ea;
   position: sticky;
@@ -105,15 +97,6 @@ body[data-view="workspace"] #sidebar-foot {
   padding-top: 8px;
 }
 
-#tree .tree-label {
-  padding: 6px 10px 8px;
-  color: #777a83;
-  font-size: 10px;
-  font-weight: 700;
-  letter-spacing: 0.12em;
-  text-transform: uppercase;
-}
-
 #tree a {
   display: block;
   padding: 6px 10px;
@@ -122,9 +105,6 @@ body[data-view="workspace"] #sidebar-foot {
   text-decoration: none;
   font-size: 14px;
   transition: background 0.12s, color 0.12s;
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
 }
 
 #tree a:hover {
@@ -163,13 +143,7 @@ body[data-view="workspace"] #sidebar-foot {
 #content {
   flex: 1;
   min-width: 0;
-  padding: 24px
-    clamp(
-      var(--content-gutter),
-      calc(100vw - 960px),
-      calc(var(--sidebar-width) + var(--content-gutter))
-    )
-    120px var(--content-gutter);
+  padding: 48px 40px 120px;
   background-color: var(--paper);
   background-image:
     linear-gradient(var(--grid-line) 1px, transparent 1px),
@@ -178,28 +152,10 @@ body[data-view="workspace"] #sidebar-foot {
   background-position: center top;
 }
 
-#logbook-header {
-  width: 100%;
-  max-width: 1080px;
-  margin: 0 auto 20px;
-}
-#logbook-title {
-  font-family: var(--serif);
-  font-size: 34px;
-  line-height: 1.15;
-  letter-spacing: -0.02em;
-  margin: 0 0 10px;
-  overflow-wrap: anywhere;
-}
-#logbook-cli {
-  display: grid;
-  gap: 7px;
-}
-
 #page {
   width: 100%;
   min-width: 0;
-  max-width: 1080px;
+  max-width: 1052px;
   margin: 0 auto;
 }
 
@@ -214,7 +170,10 @@ body[data-view="workspace"] #sidebar-foot {
 }
 
 .page-layout {
-  display: block;
+  display: grid;
+  grid-template-columns: minmax(0, 760px) 248px;
+  gap: 44px;
+  align-items: start;
 }
 
 .page-body {
@@ -229,26 +188,18 @@ body[data-view="workspace"] #sidebar-foot {
 
 /* ---- pinned notes ---- */
 .pinned-notes {
-  margin: 30px 0 32px;
+  margin: 30px 0 0;
 }
 .pinned-notes-list .cell {
   margin: 0;
-}
-.pinned-notes-list .cell-title {
-  display: flex;
-  align-items: center;
-  gap: 7px;
-}
-.pin-ico {
-  flex: 0 0 auto;
-  width: 14px;
-  height: 14px;
-  fill: var(--accent);
-  stroke: none;
+  border-color: rgba(249, 115, 22, 0.55);
 }
 .pinned-notes-list .cell + .cell {
   margin-top: 12px;
 }
+.cell.pinned-source {
+  border-color: rgba(249, 115, 22, 0.55);
+}
 .book-intro.has-pinned-notes {
   border-bottom: none;
   padding-bottom: 22px;
@@ -425,25 +376,26 @@ body[data-view="workspace"] #sidebar-foot {
 /* ---- notebook-style cells ---- */
 .cell {
   max-width: 100%;
-  margin: 0 0 32px;
-  background: none;
-  border: none;
-  border-radius: 0;
-  box-shadow: none;
-  overflow: visible;
+  border: 1px solid var(--line);
+  border-radius: 10px;
+  background: rgba(255, 255, 255, 0.86);
+  margin: 18px 0;
+  overflow: hidden;
+  box-shadow: 0 2px 10px rgba(31, 41, 55, 0.035);
 }
 .cell-head {
   display: flex;
   justify-content: space-between;
   gap: 16px;
-  align-items: baseline;
-  padding: 0 0 5px;
-  background: none;
-  border-bottom: none;
+  align-items: center;
+  padding: 14px 18px;
+  background: rgba(255, 255, 255, 0.92);
+  border-bottom: 1px solid var(--line);
 }
 .cell-head.no-title {
   justify-content: flex-end;
-  padding: 0 0 3px;
+  padding-top: 10px;
+  padding-bottom: 10px;
 }
 .cell-title {
   flex: 1;
@@ -475,7 +427,7 @@ body[data-view="workspace"] #sidebar-foot {
 }
 .cell-body {
   min-width: 0;
-  padding: 0;
+  padding: 14px 18px 18px;
 }
 .cell.dashboard .cell-body {
   padding: 0;
@@ -495,6 +447,9 @@ body[data-view="workspace"] #sidebar-foot {
 #page .cell-body > :last-child {
   margin-bottom: 0;
 }
+.cell.code .cell-head {
+  background: #fbfbfc;
+}
 .figure-fit {
   position: relative;
   overflow: hidden;
@@ -667,40 +622,9 @@ body[data-view="workspace"] #sidebar-foot {
   border: 1px solid var(--line);
   border-radius: 10px;
   overflow: hidden;
-  margin: 0;
+  margin: 12px 0;
   background: var(--panel);
 }
-.jp-cmd {
-  display: flex;
-  align-items: baseline;
-  gap: 9px;
-  position: relative;
-  padding: 10px 16px 10px 0;
-  font-family: var(--mono);
-  font-size: 12px;
-  color: #8b8e98;
-}
-.jp-cmd-prompt {
-  color: var(--accent);
-  font-weight: 700;
-}
-#page .jp-cmd code {
-  min-width: 0;
-  color: #b6b9c2;
-  font-family: var(--mono);
-  font-size: 12px;
-  background: none;
-  padding: 0;
-  border-radius: 0;
-  overflow-wrap: anywhere;
-}
-.jp-cmd:hover .copy-snippet {
-  opacity: 1;
-}
-.jp-in-body .jp-cmd + .code-accordion,
-.jp-in-body .jp-cmd + .snippet {
-  border-top: 1px solid rgba(255, 255, 255, 0.09);
-}
 .jp-gutter {
   flex: 0 0 46px;
   padding: 13px 0 0 13px;
@@ -728,8 +652,6 @@ body[data-view="workspace"] #sidebar-foot {
   border-radius: 0;
   background: none;
   padding: 12px 16px 12px 0;
-  overflow-y: auto;
-  max-height: 26em;
 }
 .jp-in-body .code-accordion {
   margin: 0;
@@ -1133,13 +1055,11 @@ table.board tr.linked-row:hover a {
   align-items: center;
   flex-wrap: wrap;
   gap: 8px;
-  margin: 0;
+  margin: 4px 0 22px;
   font-size: 12.5px;
   color: var(--muted);
 }
-.agent-hint code {
-  flex: 1 1 18rem;
-  min-width: 0;
+#page .agent-hint code {
   background: var(--code-bg);
   padding: 2px 9px;
   border-radius: 6px;
@@ -1147,9 +1067,6 @@ table.board tr.linked-row:hover a {
   font-size: 12px;
   font-weight: 500;
   color: var(--ink);
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
 }
 .agent-hint .copy {
   flex: 0 0 auto;
@@ -1177,57 +1094,155 @@ table.board tr.linked-row:hover a {
   font-size: 12px;
   color: var(--muted);
 }
-.hub-destination {
+
+/* ---- logbook summary stats ---- */
+.logbook-stats {
   display: flex;
-  align-items: center;
   flex-wrap: wrap;
-  gap: 8px;
-  color: var(--muted);
-  font-size: 12.5px;
+  gap: 12px;
+  margin: 0 0 28px;
 }
-.hub-destination a {
+.stat-tile {
+  position: relative;
   display: inline-flex;
   align-items: center;
-  gap: 6px;
-  max-width: 100%;
-  padding: 3px 9px;
-  border: 1px solid var(--accent-line);
-  border-radius: 999px;
-  background: var(--accent-soft);
-  color: var(--accent-strong);
+  gap: 11px;
+  border: 1px solid var(--line);
+  background: var(--panel);
+  border-radius: var(--radius);
+  padding: 12px 23px;
+  font: inherit;
+  text-align: left;
+  cursor: pointer;
+  transition: border-color 0.12s, box-shadow 0.12s;
+}
+.stat-tile:hover:not([disabled]) {
+  border-color: rgba(249, 115, 22, 0.45);
+  box-shadow: 0 3px 12px rgba(31, 41, 55, 0.06);
+}
+.stat-tile:focus-visible {
+  outline: 2px solid var(--accent);
+  outline-offset: 2px;
+}
+.stat-tile[disabled] {
+  cursor: default;
+  opacity: 0.7;
+}
+.stat-tile.open {
+  border-color: rgba(249, 115, 22, 0.6);
+  box-shadow: 0 3px 12px rgba(31, 41, 55, 0.08);
+}
+.stat-icon {
+  width: 24px;
+  height: 24px;
+  flex: 0 0 24px;
+  object-fit: contain;
+  align-self: center;
+}
+.stat-text {
+  display: flex;
+  align-items: baseline;
+  gap: 8px;
+  white-space: nowrap;
+  line-height: 1;
+}
+.stat-num {
   font-family: var(--mono);
-  font-size: 12px;
-  font-weight: 650;
-  line-height: 1.5;
+  font-size: 20px;
+  font-weight: 600;
+  line-height: 1;
+  color: var(--accent-strong);
+}
+.stat-label {
+  font-size: 15px;
+  line-height: 1;
+  color: var(--muted);
+}
+.stat-caret {
+  margin-left: 2px;
+  font-size: 10px;
+  color: var(--muted);
+  align-self: center;
+  transition: transform 0.12s;
+}
+.stat-tile.open .stat-caret {
+  transform: rotate(180deg);
+}
+.stat-popover {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  margin-top: 6px;
+  min-width: 300px;
+  max-width: min(460px, 92vw);
+  max-height: 340px;
+  overflow-y: auto;
+  z-index: 20;
+  background: var(--panel);
+  border: 1px solid var(--line);
+  border-radius: var(--radius);
+  box-shadow: 0 8px 28px rgba(31, 41, 55, 0.12);
+  padding: 6px;
+}
+.stat-popover[hidden] {
+  display: none;
+}
+.stat-pop-head {
+  padding: 6px 10px 8px;
+  font-size: 11.5px;
+  font-weight: 700;
+  letter-spacing: 0.03em;
+  text-transform: uppercase;
+  color: var(--muted);
+}
+.stat-row {
+  display: flex;
+  align-items: flex-start;
+  gap: 10px;
+  padding: 9px 11px;
+  border-radius: 9px;
+  border: 1px solid transparent;
   text-decoration: none;
-  overflow-wrap: anywhere;
-  transition: border-color 0.12s, background 0.12s, color 0.12s;
+  color: inherit;
+  cursor: pointer;
 }
-.hub-destination a:hover {
-  border-color: var(--accent);
-  background: #ffedd5;
-  color: #c2410c;
+.stat-row:hover {
+  border-color: rgba(249, 115, 22, 0.4);
+  background: var(--accent-soft);
 }
-.hub-destination svg {
-  width: 13px;
-  height: 13px;
+.stat-row-ico {
+  font-size: 15px;
+  line-height: 1.3;
   flex: 0 0 auto;
-  fill: none;
-  stroke: currentColor;
-  stroke-width: 1.8;
-  stroke-linecap: round;
-  stroke-linejoin: round;
 }
-
-.index-paper-link {
-  margin: 14px 0 30px;
-  font-size: 19px;
-  line-height: 1.35;
-  font-weight: 700;
+.stat-row-main {
+  min-width: 0;
+  flex: 1;
+}
+.stat-row-title {
+  font-family: var(--mono);
+  font-size: 12.5px;
+  font-weight: 600;
+  color: var(--ink);
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+.stat-row-meta {
+  margin-top: 2px;
+  font-size: 12px;
+  color: var(--muted);
+}
+.stat-row-state.open {
+  color: var(--accent);
+  font-weight: 600;
+  border-radius: 5px;
+  padding: 1px 5px;
+  margin: -1px -2px;
 }
-.index-paper-link a {
-  text-underline-offset: 4px;
-  text-decoration-thickness: 2px;
+.stat-row-state.open:hover {
+  background: rgba(249, 115, 22, 0.14);
+  text-decoration: underline;
 }
 .art-ico {
   width: 1em;
@@ -1235,20 +1250,6 @@ table.board tr.linked-row:hover a {
   object-fit: contain;
   vertical-align: -0.15em;
 }
-.art-file-ico {
-  width: 15px;
-  height: 15px;
-  flex: 0 0 auto;
-  fill: none;
-  stroke: currentColor;
-  stroke-width: 1.7;
-  stroke-linecap: round;
-  stroke-linejoin: round;
-  vertical-align: -0.2em;
-}
-.out-artifact-ico .art-file-ico {
-  color: var(--muted);
-}
 
 /* ---- scroll-to-resource highlight ---- */
 .res-flash {
@@ -1293,11 +1294,108 @@ table.board tr.linked-row:hover a {
   font-size: 1.05em;
   line-height: 1;
 }
-#page .res-chip:hover {
+#page .res-chip:hover,
+#page .res-chip.res-hl {
+  border-color: var(--accent);
+  background: var(--accent-soft);
+  color: var(--accent-strong);
+}
+#page a.res-link.res-hl {
+  background: var(--accent-soft);
+  border-radius: 4px;
+}
+.rail-item.res-hl {
+  border-color: var(--accent);
+  background: var(--accent-soft);
+  box-shadow: 0 3px 12px rgba(249, 115, 22, 0.14);
+}
+.rail-item.res-hl .rail-title {
+  color: var(--accent-strong);
+}
+.rail-item.rail-local {
+  cursor: default;
+}
+.artifact-chip.res-hl {
   border-color: var(--accent);
   background: var(--accent-soft);
+}
+
+/* ---- contextual resources rail ---- */
+.context-rail {
+  position: relative;
+  width: 248px;
+}
+.context-rail[hidden] {
+  display: none;
+}
+.rail-kind {
+  display: flex;
+  align-items: center;
+  gap: 5px;
+  font-family: var(--mono);
+  font-size: 10px;
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+  font-weight: 600;
+  color: var(--accent);
+  margin-bottom: 4px;
+}
+.rail-item {
+  position: absolute;
+  left: 0;
+  right: 0;
+  display: block;
+  border: 1px solid var(--line);
+  border-radius: 10px;
+  background: var(--panel);
+  padding: 9px 12px;
+  margin-bottom: 8px;
+  text-decoration: none;
+  color: inherit;
+  transition: border-color 0.14s, box-shadow 0.14s;
+}
+.rail-item:hover {
+  border-color: rgba(249, 115, 22, 0.45);
+  box-shadow: 0 3px 12px rgba(31, 41, 55, 0.06);
+}
+.rail-title {
+  font-family: var(--mono);
+  font-size: 12.5px;
+  font-weight: 600;
+  color: var(--ink);
+  overflow-wrap: anywhere;
+  line-height: 1.4;
+}
+.rail-item:hover .rail-title {
   color: var(--accent-strong);
 }
+.rail-meta {
+  font-size: 11.5px;
+  color: var(--muted);
+  margin-top: 2px;
+}
+
+@media (max-width: 1400px) {
+  .page-layout {
+    display: block;
+  }
+  .context-rail {
+    width: 100%;
+    margin-top: 28px;
+    position: static;
+    min-height: 0 !important;
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
+    gap: 10px;
+  }
+  .context-rail[hidden] {
+    display: none;
+  }
+  .context-rail .rail-item {
+    position: static;
+    margin-bottom: 0;
+  }
+}
 
 /* ---- connect footer + modal ---- */
 #sidebar-foot {
@@ -1473,609 +1571,6 @@ table.board tr.linked-row:hover a {
   color: #52d08a;
 }
 
-/* ---- top-level logbook views ---- */
-#view-tabs {
-  position: sticky;
-  top: 0;
-  z-index: 30;
-  width: 100%;
-  max-width: 1080px;
-  margin: 0 auto 24px;
-  padding-top: 10px;
-  display: flex;
-  align-items: center;
-  justify-content: flex-start;
-  gap: 26px;
-  border-bottom: 1px solid var(--line);
-  background: var(--paper);
-}
-#view-tabs a {
-  display: inline-flex;
-  align-items: center;
-  gap: 8px;
-  min-height: 44px;
-  margin-bottom: -1px;
-  color: var(--muted);
-  border-bottom: 2px solid transparent;
-  text-decoration: none;
-  font-size: 13.5px;
-  font-weight: 600;
-  transition: color 0.12s, border-color 0.12s;
-}
-#view-tabs a:hover {
-  color: var(--ink);
-}
-#view-tabs a.active {
-  color: var(--accent-strong);
-  border-bottom-color: var(--accent);
-}
-#view-tabs svg {
-  width: 18px;
-  height: 18px;
-  flex: 0 0 auto;
-  fill: none;
-  stroke: currentColor;
-  stroke-width: 2;
-  stroke-linecap: round;
-  stroke-linejoin: round;
-}
-.workspace-file svg,
-.workspace-folder summary svg,
-.workspace-download svg {
-  width: 17px;
-  height: 17px;
-  flex: 0 0 auto;
-  fill: none;
-  stroke: currentColor;
-  stroke-width: 1.7;
-  stroke-linecap: round;
-  stroke-linejoin: round;
-}
-
-#page.trace-page,
-#page.workspace-page {
-  max-width: 1080px;
-}
-.view-loading {
-  padding: 72px 0;
-  color: var(--muted);
-  text-align: center;
-}
-.view-empty {
-  margin: 48px 0;
-  padding: 44px 28px;
-  border: 1px dashed #d8dbe1;
-  border-radius: var(--radius);
-  background: rgba(255, 255, 255, 0.72);
-  text-align: center;
-}
-.view-empty h2 {
-  margin: 0 0 7px;
-  font-size: 18px;
-}
-.view-empty p {
-  max-width: 560px;
-  margin: 0 auto;
-  color: var(--muted);
-}
-.view-empty code {
-  display: inline-block;
-  margin-top: 18px;
-  padding: 7px 10px;
-  border-radius: 7px;
-  background: var(--code-bg);
-  font-family: var(--mono);
-  font-size: 12px;
-}
-#page .repo-ref-link {
-  display: inline-block;
-  margin-top: 18px;
-  padding: 8px 14px;
-  border-radius: 8px;
-  background: var(--accent-strong, #2158d0);
-  color: #fff;
-  font-weight: 600;
-  text-decoration: none;
-}
-#page .repo-ref-link:hover,
-#page .repo-ref-link:focus-visible {
-  color: #fff;
-  filter: brightness(0.95);
-}
-.view-eyebrow {
-  margin-bottom: 4px;
-  color: var(--accent-strong);
-  font-family: var(--mono);
-  font-size: 11px;
-  font-weight: 700;
-  letter-spacing: 0.12em;
-  text-transform: uppercase;
-}
-
-/* ---- trace ---- */
-.trace-session {
-  scroll-margin-top: 24px;
-}
-.trace-session + .trace-session {
-  margin-top: 44px;
-  padding-top: 40px;
-  border-top: 1px solid var(--line);
-}
-.trace-session-title {
-  margin: 0 0 14px;
-  color: var(--ink);
-  font-family: var(--serif);
-  font-size: 22px;
-  line-height: 1.2;
-  letter-spacing: -0.02em;
-  overflow-wrap: anywhere;
-}
-.workspace-header h1 {
-  margin: 0;
-  color: var(--ink);
-  font-size: 30px;
-  line-height: 1.2;
-  letter-spacing: -0.025em;
-}
-.trace-meta {
-  display: flex;
-  flex-wrap: wrap;
-  gap: 9px 20px;
-  margin-bottom: 34px;
-  padding: 14px 16px;
-  border: 1px solid var(--line);
-  border-radius: 10px;
-  background: rgba(255, 255, 255, 0.78);
-  color: var(--muted);
-  font-family: var(--mono);
-  font-size: 11px;
-}
-.trace-meta strong {
-  color: var(--ink);
-  font-weight: 650;
-}
-.trace-source-missing {
-  color: #b45309;
-}
-.trace-timeline {
-  position: relative;
-}
-.trace-timeline::before {
-  content: "";
-  position: absolute;
-  top: 0;
-  bottom: 0;
-  left: 82px;
-  width: 1px;
-  background: #dedfe3;
-}
-.trace-load-controls {
-  display: flex;
-  align-items: center;
-  justify-content: space-between;
-  gap: 16px;
-  margin: 22px 0 0 100px;
-  padding-top: 16px;
-  border-top: 1px solid var(--line);
-}
-.trace-load-progress {
-  color: var(--muted);
-  font-family: var(--mono);
-  font-size: 11px;
-}
-.trace-load-more {
-  padding: 7px 12px;
-  border: 1px solid var(--line-strong);
-  border-radius: 7px;
-  background: var(--paper);
-  color: var(--ink);
-  cursor: pointer;
-  font: 650 12px/1.2 var(--sans);
-}
-.trace-load-more:hover:not(:disabled) {
-  border-color: var(--accent);
-  color: var(--accent-strong);
-}
-.trace-load-more:disabled {
-  cursor: default;
-  opacity: 0.65;
-}
-.trace-entry {
-  --trace-depth: 0;
-  position: relative;
-  display: grid;
-  grid-template-columns: 100px minmax(0, 1fr);
-  margin: 0 0 18px calc(var(--trace-depth) * 24px);
-}
-.trace-rail {
-  position: relative;
-  min-height: 36px;
-  padding: 4px 28px 0 0;
-  color: #8a8d95;
-  text-align: right;
-  font-family: var(--mono);
-}
-.trace-number,
-.trace-elapsed {
-  display: block;
-  white-space: nowrap;
-}
-.trace-number {
-  font-size: 12px;
-  font-weight: 650;
-}
-.trace-elapsed {
-  margin-top: 3px;
-  font-size: 10px;
-}
-.trace-dot {
-  position: absolute;
-  top: 10px;
-  right: 11px;
-  width: 11px;
-  height: 11px;
-  border: 2px solid var(--paper);
-  border-radius: 50%;
-  background: var(--accent);
-  box-shadow: 0 0 0 1px #d7d9de;
-}
-.trace-card {
-  min-width: 0;
-  overflow: hidden;
-  border: 1px solid #dddfe4;
-  border-radius: 11px;
-  background: rgba(255, 255, 255, 0.92);
-}
-.trace-card > header {
-  display: flex;
-  align-items: center;
-  gap: 10px;
-  min-height: 37px;
-  padding: 8px 13px;
-  border-bottom: 1px solid #eceef1;
-}
-.trace-status .trace-card > header {
-  border-bottom: 0;
-  padding-bottom: 5px;
-}
-.trace-kind {
-  font-family: var(--mono);
-  font-size: 10.5px;
-  font-weight: 750;
-  letter-spacing: 0.08em;
-  text-transform: uppercase;
-}
-.trace-turn {
-  color: var(--muted);
-  font: 10px var(--mono);
-}
-.trace-status-badge {
-  margin-left: auto;
-  padding: 1px 6px;
-  border-radius: 999px;
-  background: #eef0f3;
-  color: var(--muted);
-  font: 9.5px var(--mono);
-  text-transform: uppercase;
-}
-.trace-status-badge-error,
-.trace-status-badge-failed {
-  background: #fef2f2;
-  color: #b91c1c;
-}
-.trace-body {
-  margin: 0;
-  padding: 15px 17px 17px;
-  overflow-wrap: anywhere;
-  white-space: pre-wrap;
-  font-family: var(--sans);
-  font-size: 13px;
-  line-height: 1.65;
-}
-.trace-reasoning .trace-card {
-  border-style: dashed;
-  border-color: #d7b98a;
-  background: #fffdf8;
-}
-.trace-reasoning .trace-kind {
-  color: #9a6b22;
-}
-.trace-reasoning .trace-body {
-  font-style: italic;
-}
-.trace-user .trace-card {
-  border-left: 3px solid #f3a66d;
-}
-.trace-tool_call .trace-card,
-.trace-tool_result .trace-card {
-  border-color: #2d3036;
-  background: #191a1e;
-  color: #ececf0;
-}
-.trace-tool_call .trace-card > header,
-.trace-tool_result .trace-card > header {
-  border-bottom-color: rgba(255, 255, 255, 0.1);
-}
-.trace-tool_call .trace-kind,
-.trace-tool_result .trace-kind {
-  color: #f5a66d;
-}
-.trace-tool_call .trace-turn,
-.trace-tool_result .trace-turn {
-  color: #979aa3;
-}
-.trace-tool_call .trace-body,
-.trace-tool_result .trace-body,
-.trace-output pre {
-  font-family: var(--mono);
-  font-size: 11.5px;
-  line-height: 1.6;
-}
-#page .trace-tool_call pre.trace-body,
-#page .trace-tool_result pre.trace-body {
-  margin: 0;
-  padding: 15px 17px 17px;
-  border: 0;
-  border-radius: 0;
-  background: transparent;
-  color: #ececf0;
-}
-.trace-output {
-  border-top: 1px dashed rgba(255, 255, 255, 0.14);
-}
-.trace-output summary {
-  padding: 9px 14px;
-  color: #aaaeb7;
-  cursor: pointer;
-  font: 700 10px var(--mono);
-  letter-spacing: 0.06em;
-  text-transform: uppercase;
-}
-#page .trace-output pre {
-  max-height: 480px;
-  margin: 0;
-  padding: 0 16px 16px;
-  border: 0;
-  border-radius: 0;
-  background: transparent;
-  overflow: auto;
-  color: #d7d8dd;
-  white-space: pre-wrap;
-}
-
-/* ---- workspace ---- */
-.workspace-header {
-  padding-bottom: 24px;
-}
-.workspace-header p {
-  margin: 0;
-  color: var(--muted);
-  font-family: var(--mono);
-  font-size: 11px;
-}
-.workspace-inventory {
-  overflow: hidden;
-  border: 1px solid var(--line);
-  border-radius: 11px;
-  background: rgba(255, 255, 255, 0.92);
-}
-.workspace-folder > summary {
-  display: flex;
-  align-items: center;
-  gap: 8px;
-  min-height: 39px;
-  padding: 8px 13px;
-  background: #fafafa;
-  cursor: pointer;
-  font-weight: 650;
-  list-style: none;
-}
-.workspace-folder > summary::-webkit-details-marker {
-  display: none;
-}
-.workspace-folder > summary::after {
-  content: "›";
-  margin-left: auto;
-  color: #989ba2;
-  transform: rotate(90deg);
-}
-.workspace-folder:not([open]) > summary::after {
-  transform: rotate(0);
-}
-.workspace-folder-children {
-  padding-left: 20px;
-}
-.workspace-file {
-  display: grid;
-  grid-template-columns: minmax(180px, 1fr) 72px 78px 180px 36px;
-  align-items: center;
-  min-height: 44px;
-  padding: 7px 10px 7px 13px;
-  color: var(--muted);
-  font-family: var(--mono);
-  font-size: 10.5px;
-}
-.workspace-file-name {
-  display: flex;
-  align-items: center;
-  min-width: 0;
-  gap: 8px;
-  color: var(--ink);
-  font-family: var(--sans);
-  font-size: 12.5px;
-  font-weight: 550;
-}
-.workspace-file-name span {
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
-}
-.workspace-file-type {
-  width: fit-content;
-  padding: 1px 6px;
-  border-radius: 999px;
-  background: var(--accent-soft);
-  color: var(--accent-strong);
-  text-transform: uppercase;
-}
-.workspace-download {
-  display: inline-flex;
-  align-items: center;
-  justify-content: center;
-  width: 30px;
-  height: 30px;
-  border-radius: 7px;
-  color: var(--muted);
-}
-.workspace-download:hover {
-  background: var(--accent-soft);
-  color: var(--accent-strong);
-}
-.workspace-unpublished {
-  color: #9ca3af;
-  text-align: center;
-}
-
-.workspace-header {
-  display: flex;
-  align-items: center;
-  justify-content: space-between;
-  gap: 16px;
-  flex-wrap: wrap;
-}
-.workspace-toggle {
-  display: inline-flex;
-  align-items: center;
-  padding: 2px;
-  border: 1px solid var(--line);
-  border-radius: 999px;
-  background: #fafafa;
-}
-.workspace-toggle-btn {
-  padding: 4px 13px;
-  border: 0;
-  border-radius: 999px;
-  background: transparent;
-  color: var(--muted);
-  font-family: var(--sans);
-  font-size: 12px;
-  font-weight: 600;
-  cursor: pointer;
-}
-.workspace-toggle-btn:hover {
-  color: var(--accent-strong);
-}
-.workspace-toggle-btn.is-active {
-  background: var(--accent);
-  color: #ffffff;
-}
-.workspace-group + .workspace-group {
-  margin-top: 18px;
-}
-.workspace-group-head,
-.workspace-hub-group-head {
-  display: flex;
-  align-items: center;
-  gap: 8px;
-  margin: 0;
-  padding: 8px 13px;
-  background: #fafafa;
-  border-bottom: 1px solid var(--line);
-  color: var(--ink);
-  font-family: var(--sans);
-  font-size: 12px;
-  font-weight: 650;
-  text-transform: capitalize;
-}
-.workspace-group-count,
-.workspace-hub-count {
-  padding: 0 7px;
-  border-radius: 999px;
-  background: var(--accent-soft);
-  color: var(--accent-strong);
-  font-family: var(--mono);
-  font-size: 10.5px;
-}
-.workspace-group {
-  overflow: hidden;
-  border: 1px solid var(--line);
-  border-radius: 11px;
-  background: rgba(255, 255, 255, 0.92);
-}
-
-.workspace-hub {
-  margin-top: 28px;
-}
-.workspace-hub-title {
-  margin: 0 0 14px;
-  font-family: var(--sans);
-  font-size: 16px;
-  font-weight: 700;
-  color: var(--ink);
-}
-.workspace-hub-group {
-  overflow: hidden;
-  border: 1px solid var(--line);
-  border-radius: 11px;
-  background: rgba(255, 255, 255, 0.92);
-}
-.workspace-hub-group + .workspace-hub-group {
-  margin-top: 14px;
-}
-.workspace-hub-list {
-  display: flex;
-  flex-direction: column;
-}
-.workspace-hub-link {
-  padding: 9px 13px;
-  color: var(--accent-strong);
-  font-family: var(--mono);
-  font-size: 12px;
-  text-decoration: none;
-  overflow: hidden;
-  text-overflow: ellipsis;
-  white-space: nowrap;
-}
-.workspace-hub-link + .workspace-hub-link {
-  border-top: 1px solid var(--line);
-}
-.workspace-hub-link:hover {
-  background: var(--accent-soft);
-  text-decoration: underline;
-}
-
-/* --- UI nits --- */
-/* Flush group headers: #page h3/h2 (ID selectors) otherwise inject a top margin
-   that, with overflow:hidden on the card, shows as whitespace above "Jobs" etc. */
-#page .workspace-hub-title {
-  margin: 0 0 14px;
-}
-#page .workspace-hub-group-head,
-#page .workspace-group-head {
-  margin: 0;
-}
-/* HF brand logo before the "Hugging Face artifacts" heading */
-.workspace-hub-title {
-  display: flex;
-  align-items: center;
-  gap: 9px;
-}
-.workspace-hub-logo {
-  width: 22px;
-  height: 22px;
-  flex: none;
-}
-/* Center empty-state placeholders (heading, body, command) */
-.view-empty {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-}
-#page .view-empty h2,
-#page .view-empty p {
-  text-align: center;
-}
-
 @media (max-width: 720px) {
   #app {
     flex-direction: column;
@@ -2092,46 +1587,11 @@ table.board tr.linked-row:hover a {
     padding: 28px 20px 80px;
     overflow-x: hidden;
   }
-  #view-tabs {
-    margin: 0 0 20px;
-    gap: 18px;
-    justify-content: flex-start;
-    overflow-x: auto;
-  }
-  #view-tabs a {
-    flex: 0 0 auto;
-  }
-  .trace-timeline::before {
-    left: 16px;
-  }
-  .trace-entry {
-    grid-template-columns: 32px minmax(0, 1fr);
-    margin-left: calc(var(--trace-depth) * 10px);
-  }
-  .trace-rail {
-    padding: 0;
-  }
-  .trace-number,
-  .trace-elapsed {
-    display: none;
-  }
-  .trace-dot {
-    top: 10px;
-    right: 10px;
-  }
-  .workspace-file {
-    grid-template-columns: minmax(150px, 1fr) 66px 34px;
-  }
-  .workspace-file-size,
-  .workspace-file-time {
-    display: none;
-  }
   #page {
     width: 100%;
     max-width: 100%;
   }
-  #page h1,
-  #logbook-title {
+  #page h1 {
     font-size: 30px;
   }
   .cell-head {

logbook.js

@@ -4,12 +4,9 @@
   let MANIFEST = null;
   const PAGE_CACHE = {};
   const UNFURL_CACHE = {};
-  const DATA_CACHE = {};
   const LIVE_RELOAD_MS = 1500;
   const FIGURE_FRAME_WINDOWS = new Set();
   let FIGURE_NAVIGATION_READY = false;
-  let CURRENT_VIEW = null;
-  let RENDER_SEQUENCE = 0;
 
   function esc(s) {
     return String(s)
@@ -289,10 +286,6 @@
         /(trackio-artifact:\/\/\S+|trackio-local-path:\/\/\S+|https:\/\/huggingface\.co\/buckets\/[^\s<)]+#\S+)/
       );
       if (chip && uri) chip.dataset.resUrl = uri[1];
-      if (chip && meta.path) {
-        const ico = chip.querySelector(".art-ico");
-        if (ico) ico.outerHTML = FILE_ICON;
-      }
     } else if (meta.type === "dashboard") {
       const sp = body.match(/https:\/\/huggingface\.co\/spaces\/[^\s<>)"'`]+/);
       cell.dataset.resUrl = sp
@@ -502,7 +495,7 @@
       if (!message || message.type !== "trackio-logbook:navigate") return;
       const target = String(message.target || "").replace(/^#?\//, "");
       if (!target || !MANIFEST || !findNode(MANIFEST.root, target)) return;
-      const hash = "#/view/code/" + target;
+      const hash = "#/" + target;
       if (location.hash === hash) scrollToHash();
       else location.hash = hash;
     });
@@ -514,15 +507,6 @@
     '<path d="M8 3H3v5M16 3h5v5M21 16v5h-5M3 16v5h5"/>' +
     '<path d="M3 8 8 3M16 3l5 5M21 16l-5 5M8 21l-5-5"/></svg>';
 
-  const PIN_ICON =
-    '<svg class="pin-ico" viewBox="0 0 24 24" aria-hidden="true">' +
-    '<path d="M16 9V4h1c.55 0 1-.45 1-1s-.45-1-1-1H7c-.55 0-1 .45-1 1s.45 1 1 1h1v5c0 ' +
-    '1.66-1.34 3-3 3v2h5.97v7l1 1 1-1v-7H19v-2c-1.66 0-3-1.34-3-3z"/></svg>';
-
-  const FILE_ICON =
-    '<svg class="art-file-ico" viewBox="0 0 24 24" aria-hidden="true">' +
-    '<path d="M6 3.5h8l4 4V20H6zM14 3.5V8h4"/></svg>';
-
   // Figures are rendered in same-origin iframes, so fullscreen the fitted
   // wrapper rather than the iframe document. This uses the browser's native
   // fullscreen UI and preserves the figure's existing responsive sizing.
@@ -651,38 +635,13 @@
       ? `<span class="out-artifact-state open">Open ↗</span>`
       : `<span class="out-artifact-state">publish to share</span>`;
     const meta = parts.length ? `${parts.join(" · ")} · ${state}` : state;
-    const icon = info.isPathRef ? FILE_ICON : ARTIFACT_ICON_IMG;
     el.innerHTML =
-      `<span class="out-artifact-ico">${icon}</span>` +
+      `<span class="out-artifact-ico">${ARTIFACT_ICON_IMG}</span>` +
       `<span class="out-artifact-name">${esc(info.name)}</span>` +
       `<span class="out-artifact-meta">${meta}</span>`;
     return el;
   }
 
-  function isShellCommand(part) {
-    return (
-      part.kind === "code" &&
-      part.lang === "bash" &&
-      !part.title &&
-      /^\s*\$\s/.test(part.text)
-    );
-  }
-
-  function renderCommandLine(text) {
-    const command = text.trim().replace(/^\$\s*/, "");
-    const el = document.createElement("div");
-    el.className = "jp-cmd";
-    const prompt = document.createElement("span");
-    prompt.className = "jp-cmd-prompt";
-    prompt.textContent = "$";
-    const code = document.createElement("code");
-    code.textContent = command;
-    el.appendChild(prompt);
-    el.appendChild(code);
-    el.appendChild(copySnippetBtn(command));
-    return el;
-  }
-
   function renderCodeCell(body, container, artifacts) {
     const parts = parseFences(body);
     const block = document.createElement("div");
@@ -735,13 +694,7 @@
         embedTexts.push(part.text);
         return;
       }
-      if (isShellCommand(part)) {
-        inputBody.appendChild(renderCommandLine(part.text));
-      } else {
-        inputBody.appendChild(
-          renderCode(part.text, part.lang, part.title, Boolean(part.title))
-        );
-      }
+      inputBody.appendChild(renderCode(part.text, part.lang, part.title));
     });
     if (artifacts && artifacts.length) {
       ensureOut();
@@ -960,13 +913,13 @@
     return btn;
   }
 
-  function renderCode(code, lang, title, open) {
+  function renderCode(code, lang, title) {
     const pre = document.createElement("pre");
     pre.className = "hl";
     const c = document.createElement("code");
     c.innerHTML = highlightCode(code, lang);
     pre.appendChild(c);
-    if (!title || open) {
+    if (!title) {
       const wrap = document.createElement("div");
       wrap.className = "snippet";
       wrap.appendChild(pre);
@@ -1030,7 +983,7 @@
     });
   }
 
-  /* -------------------- resource classification -------------------- */
+  /* -------------------- resources rail -------------------- */
 
   function fmt(n) {
     if (n == null) return null;
@@ -1058,6 +1011,18 @@
   const ARTIFACT_ICON_IMG = `<img class="art-ico" src="./bucket-icon.svg" alt="" />`;
   const DASHBOARD_ICON_IMG = `<img class="art-ico" src="./trackio-logo-light.png" alt="" />`;
 
+  const RESOURCE_DESC = {
+    dashboard: "Dashboard",
+    model: "Model",
+    dataset: "Dataset",
+    space: "Space",
+    artifact: "Artifact — in Bucket",
+    paper: "Paper",
+    repo: "Repository",
+    job: "Job — status & logs",
+    bucket: "Bucket — artifacts & data",
+  };
+
   const HF_NON_MODEL_PREFIX =
     /^(datasets|spaces|jobs|buckets|papers|blog|docs|api|posts|collections|organizations|settings|new|join|login|pricing|tasks|learn|chat|models)(\/|$)/;
 
@@ -1065,23 +1030,6 @@
     return url.split(marker)[1].split(/[?#]/)[0].replace(/\/$/, "");
   }
 
-  function validHfSegment(value) {
-    return Boolean(
-      value &&
-      value.length <= 96 &&
-      /^[A-Za-z0-9_.-]+$/.test(value) &&
-      !/^[.-]|[.-]$|--|\.\./.test(value)
-    );
-  }
-
-  function validHfRepoId(parts) {
-    return (
-      parts.length === 2 &&
-      parts.join("/").length <= 96 &&
-      parts.every(validHfSegment)
-    );
-  }
-
   function classifyResource(url) {
     if (IMG_URL.test(url)) {
       return null;
@@ -1111,39 +1059,29 @@
         local: true,
       };
     }
-    if ((m = url.match(/huggingface\.co\/buckets\/([^/#\s]+\/[^/#\s]+)#(.+)/))) {
-      if (!validHfRepoId(m[1].split("/"))) return null;
-      return { kind: "artifact", id: decodeURIComponent(m[2]), url };
+    if ((m = url.match(/huggingface\.co\/buckets\/[^#\s]+#(.+)/))) {
+      return { kind: "artifact", id: decodeURIComponent(m[1]), url };
     }
-    if (/huggingface\.co\/datasets\//.test(url)) {
-      const parts = hfId(url, "/datasets/").split("/").slice(0, 2);
-      if (!validHfRepoId(parts)) return null;
-      return { kind: "dataset", id: parts.join("/"), url };
+    if (/huggingface\.co\/datasets\/[^/]+\/[^/]+/.test(url)) {
+      return { kind: "dataset", id: hfId(url, "/datasets/"), url };
     }
-    if (/huggingface\.co\/spaces\//.test(url)) {
-      const parts = hfId(url, "/spaces/").split("/").slice(0, 2);
-      if (!validHfRepoId(parts)) return null;
-      return { kind: "space", id: parts.join("/"), url };
+    if (/huggingface\.co\/spaces\/[^/]+\/[^/]+/.test(url)) {
+      return { kind: "space", id: hfId(url, "/spaces/"), url };
     }
     if (/huggingface\.co\/jobs\//.test(url)) {
-      const parts = hfId(url, "/jobs/").split("/").slice(0, 2);
-      if (!validHfRepoId(parts)) return null;
-      const jid = parts[1];
+      const parts = hfId(url, "/jobs/").split("/");
+      const jid = parts[1] || "";
       return {
         kind: "job",
-        id: parts[0] + ` · ${jid.slice(0, 12)}${jid.length > 12 ? "…" : ""}`,
+        id: parts[0] + (jid ? ` · ${jid.slice(0, 12)}${jid.length > 12 ? "…" : ""}` : ""),
         url,
       };
     }
     if (/huggingface\.co\/buckets\//.test(url)) {
-      const parts = hfId(url, "/buckets/").split("/").slice(0, 2);
-      if (!validHfRepoId(parts)) return null;
-      return { kind: "bucket", id: parts.join("/"), url };
+      return { kind: "bucket", id: hfId(url, "/buckets/"), url };
     }
     if (/huggingface\.co\/papers\//.test(url)) {
-      const id = hfId(url, "/papers/").split("/")[0];
-      if (!validHfSegment(id)) return null;
-      return { kind: "paper", id: `Paper ${id}`, url };
+      return { kind: "paper", id: `Paper ${hfId(url, "/papers/")}`, url };
     }
     if ((m = url.match(/arxiv\.org\/(?:abs|pdf)\/([^?#\s]+)/))) {
       return { kind: "paper", id: `arXiv:${m[1].replace(/\.pdf$/, "")}`, url };
@@ -1153,13 +1091,324 @@
     }
     if ((m = url.match(/huggingface\.co\/([^?#]+)/))) {
       const rest = m[1].replace(/\/$/, "");
-      if (validHfRepoId(rest.split("/")) && !HF_NON_MODEL_PREFIX.test(rest)) {
+      if (/^[^/]+\/[^/]+$/.test(rest) && !HF_NON_MODEL_PREFIX.test(rest)) {
         return { kind: "model", id: rest, url };
       }
     }
     return null;
   }
 
+  async function fillRailMeta(item, el) {
+    if (item.local) return;
+    const meta = el.querySelector(".rail-meta");
+    const set = (parts) => {
+      const text = parts.filter(Boolean).join(" · ");
+      if (text) meta.textContent = text;
+    };
+    if (item.kind === "model") {
+      const d = await getJSON(`https://huggingface.co/api/models/${item.id}`);
+      if (d) set([d.pipeline_tag, `↓ ${fmt(d.downloads)}`, `♥ ${fmt(d.likes)}`]);
+    } else if (item.kind === "dataset") {
+      const d = await getJSON(`https://huggingface.co/api/datasets/${item.id}`);
+      if (d) set([`↓ ${fmt(d.downloads)}`, `♥ ${fmt(d.likes)}`]);
+    } else if (item.kind === "space" || item.kind === "dashboard") {
+      const d = await getJSON(`https://huggingface.co/api/spaces/${item.id}`);
+      if (d) set([d.sdk, `♥ ${fmt(d.likes)}`]);
+    } else if (item.kind === "repo") {
+      const d = await getJSON(`https://api.github.com/repos/${item.id}`);
+      if (d) set([`★ ${fmt(d.stargazers_count)}`, d.language]);
+    } else if (item.kind === "paper") {
+      const m = item.id.match(/^(?:arXiv:|Paper )(.+)$/);
+      if (!m) return;
+      const arxivId = m[1].replace(/v\d+$/, "");
+      const d = await getJSON(`https://huggingface.co/api/papers/${arxivId}`);
+      if (d && d.id) {
+        if (el.href) el.href = `https://huggingface.co/papers/${d.id}`;
+        const title =
+          d.title && d.title.length > 70 ? `${d.title.slice(0, 69)}…` : d.title;
+        set([title, d.upvotes ? `��� ${fmt(d.upvotes)}` : null]);
+      }
+    }
+  }
+
+  const BARE_ID_SKIP_DIRS = new Set([
+    "scripts",
+    "configs",
+    "config",
+    "results",
+    "figures",
+    "data",
+    "datasets",
+    "src",
+    "tests",
+    "test",
+    "examples",
+    "pages",
+    "assets",
+    "docs",
+    "outputs",
+    "output",
+    "checkpoints",
+    "models",
+    "utils",
+    "lib",
+    "bin",
+    "tmp",
+    "node_modules",
+    "dist",
+    "build",
+  ]);
+  const FILE_EXT_RE =
+    /\.(py|pyc|js|ts|jsx|tsx|json|jsonl|yaml|yml|csv|tsv|md|txt|sh|bash|html|css|png|jpe?g|svg|gif|webp|ipynb|toml|cfg|ini|lock|pdf|whl|gz|zip|tar|pt|pth|bin|safetensors|db|sqlite)$/i;
+
+  async function detectBareModelIds(text, groups) {
+    const stripped = text.replace(DETECTED_URL, " ");
+    DETECTED_URL.lastIndex = 0;
+    const seen = new Set();
+    const candidates = [];
+    const re = /(^|[\s"'`(=[])([A-Za-z0-9][\w.-]*\/[A-Za-z0-9][\w.-]*)/g;
+    let m;
+    while ((m = re.exec(stripped)) && candidates.length < 15) {
+      const id = m[2].replace(/[.:,]+$/, "");
+      if (seen.has(id)) continue;
+      seen.add(id);
+      if (FILE_EXT_RE.test(id)) continue;
+      if (BARE_ID_SKIP_DIRS.has(id.split("/")[0].toLowerCase())) continue;
+      candidates.push(id);
+    }
+    const results = await Promise.all(
+      candidates.map((id) => getJSON(`https://huggingface.co/api/models/${id}`))
+    );
+    let added = false;
+    const confirmed = [];
+    results.forEach((d, i) => {
+      if (!d || !d.id) return;
+      const id = candidates[i];
+      confirmed.push(id);
+      const url = `https://huggingface.co/${id}`;
+      if (!groups.has("model")) groups.set("model", new Map());
+      if (!groups.get("model").has(url)) {
+        groups.get("model").set(url, { kind: "model", id, url });
+        added = true;
+      }
+    });
+    return { added, confirmed };
+  }
+
+  function chipifyBareIds(ids, container) {
+    if (!ids.length) return;
+    const escaped = ids.map((id) => id.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"));
+    const pattern = new RegExp("(" + escaped.join("|") + ")");
+    const splitter = new RegExp(pattern.source, "g");
+    container
+      .querySelectorAll(".cell.markdown .cell-body")
+      .forEach((body) => {
+        const walker = document.createTreeWalker(body, NodeFilter.SHOW_TEXT, {
+          acceptNode(node) {
+            if (!pattern.test(node.nodeValue)) return NodeFilter.FILTER_REJECT;
+            for (
+              let el = node.parentElement;
+              el && el !== body;
+              el = el.parentElement
+            ) {
+              if (["A", "CODE", "PRE", "BUTTON"].indexOf(el.tagName) !== -1) {
+                return NodeFilter.FILTER_REJECT;
+              }
+            }
+            return NodeFilter.FILTER_ACCEPT;
+          },
+        });
+        const nodes = [];
+        while (walker.nextNode()) nodes.push(walker.currentNode);
+        nodes.forEach((node) => {
+          const frag = document.createDocumentFragment();
+          node.nodeValue.split(splitter).forEach((part) => {
+            if (ids.indexOf(part) !== -1) {
+              const holder = document.createElement("span");
+              holder.innerHTML = resChipHtml({
+                kind: "model",
+                id: part,
+                url: `https://huggingface.co/${part}`,
+              });
+              frag.appendChild(holder.firstChild);
+            } else if (part) {
+              frag.appendChild(document.createTextNode(part));
+            }
+          });
+          node.parentNode.replaceChild(frag, node);
+        });
+      });
+  }
+
+  let RAIL_TOKEN = 0;
+  const RAIL_EXCLUDE_KINDS = new Set(["paper", "repo", "artifact", "dashboard"]);
+
+  function railDashboardItem(it) {
+    return {
+      kind: "dashboard",
+      id: it.id,
+      url: it.local ? it.resUrl : it.url || it.resUrl,
+      local: it.local,
+      railLabel: "Dashboard",
+    };
+  }
+
+  function promoteTrackioSpacesInRail(groups, dashResUrls, body, rail, token) {
+    const spaceGroup = groups.get("space");
+    if (!spaceGroup || !spaceGroup.size) return;
+    spaceGroup.forEach((item, url) => {
+      getJSON(`https://huggingface.co/api/spaces/${item.id}`)
+        .then((d) => {
+          if (rail.dataset.renderToken !== token) return;
+          const tags = (d && d.tags) || [];
+          if (!tags.some((t) => String(t).toLowerCase() === "trackio")) return;
+          if (dashResUrls.has(url)) return;
+          spaceGroup.delete(url);
+          if (!spaceGroup.size) groups.delete("space");
+          if (!groups.has("dashboard")) groups.set("dashboard", new Map());
+          groups.get("dashboard").set(url, {
+            kind: "dashboard",
+            id: item.id,
+            url: item.url,
+            local: false,
+            railLabel: "Dashboard",
+          });
+          dashResUrls.add(url);
+          paintRail(groups, body, rail);
+        })
+        .catch(() => {});
+    });
+  }
+
+  function renderRail(md, body, rail) {
+    const token = String(++RAIL_TOKEN);
+    rail.dataset.renderToken = token;
+    const scanText = md.replace(
+      /(`{3,4}|~{3,4})(html|raw)[^\n]*\n[\s\S]*?\n\1/g,
+      " "
+    );
+    const groups = new Map();
+    const dashMap = new Map();
+    const dashResUrls = new Set();
+    cellDashboardItems(md).forEach((it) => {
+      if (dashMap.has(it.resUrl)) return;
+      dashMap.set(it.resUrl, railDashboardItem(it));
+      dashResUrls.add(it.resUrl);
+    });
+    if (dashMap.size) groups.set("dashboard", dashMap);
+    extractUrls(scanText).forEach((url) => {
+      const item = classifyResource(url);
+      if (!item) return;
+      if (RAIL_EXCLUDE_KINDS.has(item.kind)) return;
+      if (dashResUrls.has(url)) return;
+      if (!groups.has(item.kind)) groups.set(item.kind, new Map());
+      groups.get(item.kind).set(item.url, item);
+    });
+    const artMap = new Map();
+    cellArtifactItems(md).forEach((it) => {
+      if (artMap.has(it.resUrl)) return;
+      const label = it.type
+        ? it.type.charAt(0).toUpperCase() + it.type.slice(1)
+        : "Artifact";
+      artMap.set(it.resUrl, {
+        kind: "artifact",
+        id: it.name,
+        url: it.local ? it.resUrl : it.url || it.resUrl,
+        local: it.local,
+        railLabel: label,
+        size: it.size,
+      });
+    });
+    if (artMap.size) groups.set("artifact", artMap);
+    paintRail(groups, body, rail);
+    promoteTrackioSpacesInRail(groups, dashResUrls, body, rail, token);
+    detectBareModelIds(scanText, groups)
+      .then((result) => {
+        if (rail.dataset.renderToken !== token) return;
+        chipifyBareIds(result.confirmed, body);
+        if (result.added) paintRail(groups, body, rail);
+      })
+      .catch(() => {});
+  }
+
+  function paintRail(groups, body, rail) {
+    rail.innerHTML = "";
+    RESOURCE_SECTIONS.forEach(([kind, label, icon]) => {
+      const group = groups.get(kind);
+      if (!group || !group.size) return;
+      group.forEach((item) => {
+        const el = document.createElement(item.local ? "div" : "a");
+        el.className = item.local ? "rail-item rail-local" : "rail-item";
+        if (!item.local) {
+          el.href = item.url;
+          el.target = "_blank";
+          el.rel = "noopener";
+        }
+        el.dataset.resUrl = item.url;
+        let desc;
+        if (kind === "artifact") {
+          const state = item.local ? "publish to share" : "Open ↗";
+          desc = item.size ? `${item.size} · ${state}` : state;
+        } else if (kind === "dashboard") {
+          desc = item.local ? "publish to share" : "Open ↗";
+        } else {
+          desc = item.local ? "publish to share" : RESOURCE_DESC[kind];
+        }
+        const kindLabel = item.railLabel || label.replace(/s$/, "");
+        const iconHtml =
+          kind === "artifact"
+            ? ARTIFACT_ICON_IMG
+            : kind === "dashboard"
+              ? DASHBOARD_ICON_IMG
+              : `<span>${icon}</span>`;
+        el.innerHTML =
+          `<div class="rail-kind">${iconHtml}${esc(kindLabel)}</div>` +
+          `<div class="rail-title">${esc(item.id)}</div>` +
+          `<div class="rail-meta">${esc(desc)}</div>`;
+        rail.appendChild(el);
+        fillRailMeta(item, el)
+          .catch(() => {})
+          .finally(() => scheduleRailPosition(body, rail));
+      });
+    });
+    rail.hidden = !rail.childElementCount;
+    scheduleRailPosition(body, rail);
+  }
+
+  function resourceAnchor(body, url) {
+    return body.querySelector(`[data-res-url="${CSS.escape(url)}"]`);
+  }
+
+  function positionRail(body, rail) {
+    if (rail.hidden || !rail.isConnected) return;
+    const bodyRect = body.getBoundingClientRect();
+    const items = Array.from(rail.querySelectorAll(".rail-item")).map((el, index) => {
+      const anchor = resourceAnchor(body, el.dataset.resUrl);
+      return {
+        el,
+        index,
+        desired: anchor
+          ? Math.max(0, anchor.getBoundingClientRect().top - bodyRect.top)
+          : 0,
+      };
+    });
+    items.sort((a, b) => a.desired - b.desired || a.index - b.index);
+    let cursor = 0;
+    items.forEach(({ el, desired }) => {
+      const top = Math.max(desired, cursor);
+      el.style.top = `${top}px`;
+      cursor = top + el.offsetHeight + 10;
+    });
+    rail.style.minHeight = `${Math.max(body.offsetHeight, cursor)}px`;
+  }
+
+  function scheduleRailPosition(body, rail) {
+    cancelAnimationFrame(Number(rail.dataset.positionFrame || 0));
+    rail.dataset.positionFrame = String(
+      requestAnimationFrame(() => positionRail(body, rail))
+    );
+  }
+
   function dashboardSubdomainFromUrl(url) {
     return spaceIdFromUrl(url).toLowerCase().replace(/[^a-z0-9-]/g, "-");
   }
@@ -1274,15 +1523,11 @@
   function buildTree() {
     const tree = document.getElementById("tree");
     tree.innerHTML = "";
-    const label = document.createElement("div");
-    label.className = "tree-label";
-    label.textContent = "Pages";
-    tree.appendChild(label);
     const nodes = [];
     (MANIFEST.root.children || []).forEach((c) => flattenTree(c, 0, nodes));
     nodes.forEach(({ node, depth }) => {
       const a = document.createElement("a");
-      a.href = "#/view/code/" + node.slug;
+      a.href = "#/" + node.slug;
       a.className = "depth-" + depth;
       a.dataset.slug = node.slug;
       const mark = document.createElement("span");
@@ -1294,47 +1539,6 @@
     });
   }
 
-  function highlightTraceSession(sessionId) {
-    document.querySelectorAll("#tree a").forEach((link) => {
-      link.classList.toggle("active", link.dataset.sessionId === sessionId);
-    });
-  }
-
-  function buildTraceTree(activeSessionId, traceSessions = MANIFEST.traces || []) {
-    const tree = document.getElementById("tree");
-    tree.innerHTML = "";
-    const sessions = traceSessions;
-    if (!sessions.length) return;
-    const label = document.createElement("div");
-    label.className = "tree-label";
-    label.textContent = "Sessions";
-    tree.appendChild(label);
-    sessions.forEach((session) => {
-      const link = document.createElement("a");
-      link.href = "#" + traceSessionAnchor(session.id);
-      link.dataset.sessionId = session.id;
-      link.textContent = session.title || session.id;
-      link.title = session.title || session.id;
-      tree.appendChild(link);
-    });
-    highlightTraceSession(activeSessionId || sessions[0].id);
-  }
-
-  function renderSidebar(route) {
-    if (route.view === "trace") {
-      highlight(null);
-      buildTraceTree(route.sessionId);
-      return;
-    }
-    if (route.view === "workspace") {
-      document.getElementById("tree").innerHTML = "";
-      highlight(null);
-      return;
-    }
-    buildTree();
-    highlight(route.slug);
-  }
-
   function highlight(slug) {
     document
       .querySelectorAll("#tree a")
@@ -1348,9 +1552,6 @@
     Object.keys(PAGE_CACHE).forEach((key) => {
       delete PAGE_CACHE[key];
     });
-    Object.keys(DATA_CACHE).forEach((key) => {
-      delete DATA_CACHE[key];
-    });
   }
 
   function isLocalPreview() {
@@ -1376,46 +1577,6 @@
     return PAGE_CACHE[node.file];
   }
 
-  async function fetchData(file, cacheResult = true) {
-    if (cacheResult && DATA_CACHE[file]) return DATA_CACHE[file];
-    const suffix = isLocalPreview()
-      ? `?rev=${encodeURIComponent(MANIFEST.revision || "")}`
-      : "";
-    const response = await fetch("./" + file + suffix, { cache: "no-store" });
-    if (!response.ok) throw new Error(`Could not load ${file}`);
-    const data = await response.json();
-    if (cacheResult) DATA_CACHE[file] = data;
-    return data;
-  }
-
-  async function fetchRemoteData(url, cacheResult = true) {
-    if (cacheResult && DATA_CACHE[url]) return DATA_CACHE[url];
-    const response = await fetch(url, { cache: "no-store" });
-    if (!response.ok) throw new Error(`Could not load ${url}`);
-    const data = await response.json();
-    if (cacheResult) DATA_CACHE[url] = data;
-    return data;
-  }
-
-  function encodeRepoPath(path) {
-    return String(path || "")
-      .split("/")
-      .map((part) => encodeURIComponent(part))
-      .join("/");
-  }
-
-  function repoFileUrl(ref, path) {
-    const revision = encodeURIComponent(ref.revision || "main");
-    const encodedPath = encodeRepoPath(path);
-    if (ref.repo_type === "dataset") {
-      return `https://huggingface.co/datasets/${ref.repo_id}/resolve/${revision}/${encodedPath}`;
-    }
-    if (ref.repo_type === "bucket") {
-      return `https://huggingface.co/buckets/${ref.repo_id}/resolve/${encodedPath}`;
-    }
-    return "";
-  }
-
   function allNodes() {
     const nodes = [];
     flattenTree(MANIFEST.root, 0, nodes);
@@ -1460,28 +1621,13 @@
     cells.forEach(({ meta, body }) => {
       const cell = renderCell(meta, body, list);
       cell.classList.add("pinned-copy");
-      const title = cell.querySelector(".cell-title");
-      if (title) title.insertAdjacentHTML("afterbegin", PIN_ICON);
     });
     deck.appendChild(list);
     const anchor =
-      container.querySelector(".agent-hint") ||
-      Array.from(container.children).find((el) => el.tagName === "H1");
+      container.querySelector(".logbook-stats") ||
+      container.querySelector(".agent-hint");
     container.insertBefore(deck, anchor ? anchor.nextSibling : container.firstChild);
-    const owner = container.closest(".page-section");
-    if (owner) owner.classList.add("has-pinned-notes");
-  }
-
-  function isIndexPaperLink(el) {
-    if (!el || el.tagName !== "P") return false;
-    return Array.from(el.querySelectorAll("a[href]")).some((a) => {
-      const href = a.getAttribute("href") || "";
-      return (
-        /huggingface\.co\/papers\//.test(href) ||
-        /openreview\.net\//.test(href) ||
-        /arxiv\.org\//.test(href)
-      );
-    });
+    container.closest(".book-intro").classList.add("has-pinned-notes");
   }
 
   function removeIndexProse(body) {
@@ -1490,11 +1636,7 @@
     let current = h1.nextElementSibling;
     while (current && current.tagName !== "H2") {
       const next = current.nextElementSibling;
-      if (isIndexPaperLink(current)) {
-        current.classList.add("index-paper-link");
-      } else {
-        current.remove();
-      }
+      current.remove();
       current = next;
     }
   }
@@ -1513,13 +1655,15 @@
     }
   }
 
+  const RAIL_OBSERVERS = [];
+
   async function renderLogbook(opts = {}) {
     const scrollY = window.scrollY;
     const page = document.getElementById("page");
+    RAIL_OBSERVERS.splice(0).forEach((observer) => observer.disconnect());
     page.innerHTML = "";
     const nodes = allNodes();
     const markdown = await Promise.all(nodes.map(fetchPage));
-    if (opts.renderId && opts.renderId !== RENDER_SEQUENCE) return;
     const pinnedCells = collectPinnedCells(markdown, nodes);
     let bookIntroBody = null;
     nodes.forEach((node, index) => {
@@ -1532,42 +1676,45 @@
       layout.className = "page-layout";
       const body = document.createElement("div");
       body.className = "page-body";
+      const rail = document.createElement("aside");
+      rail.className = "context-rail";
+      rail.setAttribute("aria-label", `Resources for ${node.title}`);
 
       renderMarkdown(markdown[index], body);
       if (node.slug === MANIFEST.root.slug) {
         section.classList.add("book-intro");
         removeIndexProse(body);
         removePageDirectory(body);
+        const hint = buildAgentHint();
         const h1 = body.querySelector("h1");
-        if (h1 && h1.parentNode === body) h1.remove();
+        if (h1 && h1.parentNode === body) {
+          body.insertBefore(hint, h1.nextSibling);
+        } else {
+          body.prepend(hint);
+        }
+        hint.after(buildLogbookStats(markdown));
         bookIntroBody = body;
       }
       layout.appendChild(body);
+      layout.appendChild(rail);
       section.appendChild(layout);
       page.appendChild(section);
+      renderRail(markdown[index], body, rail);
+      if (window.ResizeObserver) {
+        const observer = new ResizeObserver(() => scheduleRailPosition(body, rail));
+        observer.observe(body);
+        observer.observe(rail);
+        RAIL_OBSERVERS.push(observer);
+      }
     });
-    const pinnedSlugs = Array.from(
-      new Set(pinnedCells.map((cell) => cell.node && cell.node.slug).filter(Boolean))
-    );
-    const pinnedTarget =
-      pinnedSlugs.length === 1
-        ? Array.from(page.querySelectorAll(".page-section"))
-            .find((section) => section.dataset.slug === pinnedSlugs[0])
-            ?.querySelector(".page-body")
-        : bookIntroBody;
-    if (pinnedTarget) renderPinnedNotes(pinnedCells, pinnedTarget);
-    // Pinned cells are promoted into one deck. Remove their source render so
-    // summaries, posters, and notes do not appear twice in the continuous view.
-    page
-      .querySelectorAll(".pinned-source:not(.pinned-copy)")
-      .forEach((cell) => cell.remove());
+    if (bookIntroBody) renderPinnedNotes(pinnedCells, bookIntroBody);
     if (bookIntroBody) {
       const section = bookIntroBody.closest(".book-intro");
       const hasExtra = Array.from(bookIntroBody.children).some(
         (el) =>
           el.tagName !== "H1" &&
           !el.classList.contains("agent-hint") &&
-          !el.classList.contains("index-paper-link") &&
+          !el.classList.contains("logbook-stats") &&
           !el.classList.contains("pinned-notes")
       );
       if (section && !section.classList.contains("has-pinned-notes") && !hasExtra) {
@@ -1584,6 +1731,29 @@
     });
   }
 
+  function setupResourceHover() {
+    document.addEventListener("mouseover", (e) => {
+      const el = e.target.closest && e.target.closest("[data-res-url]");
+      if (!el || el.classList.contains("rail-item")) return;
+      const url = el.getAttribute("data-res-url");
+      const section = el.closest(".page-section");
+      const scope = section || document;
+      scope.querySelectorAll(".context-rail [data-res-url]").forEach((n) => {
+        n.classList.toggle("res-hl", n.getAttribute("data-res-url") === url);
+      });
+    });
+    document.addEventListener("mouseout", (e) => {
+      const el = e.target.closest && e.target.closest("[data-res-url]");
+      if (!el || el.classList.contains("rail-item")) return;
+      document.querySelectorAll(".context-rail .res-hl").forEach((n) => {
+        n.classList.remove("res-hl");
+      });
+    });
+  }
+
+  let STATS_TOKEN = 0;
+  let STATS_LISTENERS = false;
+
   function fmtBytes(n) {
     if (n == null || isNaN(n)) return null;
     if (n < 1000) return `${n} B`;
@@ -1601,6 +1771,31 @@
     return url.split("/spaces/")[1].split(/[?#]/)[0].replace(/\/$/, "");
   }
 
+  const LB_CELL_RE = /(^|\n)---\n<!-- trackio-cell\n([\s\S]*?)\n-->\n([\s\S]*?)(?=\n---\n<!-- trackio-cell\n|\s*$)/g;
+
+  function cellDashboardItems(md) {
+    const re = new RegExp(LB_CELL_RE.source, "g");
+    const items = [];
+    let m;
+    while ((m = re.exec(md))) {
+      const meta = parseCellMeta(m[2]);
+      if (meta.type !== "dashboard") continue;
+      const body = m[3];
+      const project = meta.dashboard_project || "";
+      const sp = body.match(/https:\/\/huggingface\.co\/spaces\/[^\s<>)"'`]+/);
+      const local = !sp;
+      const url = sp ? sp[0] : "";
+      const resUrl = local ? `trackio-local-dashboard://${project}` : url;
+      items.push({
+        id: local ? project : spaceIdFromUrl(url),
+        local,
+        url,
+        resUrl,
+      });
+    }
+    return items;
+  }
+
   function artifactInfoFromCell(meta, body) {
     const name = meta.artifact || meta.path || "";
     let size = null;
@@ -1625,1169 +1820,280 @@
     };
   }
 
-  const VIEW_ROUTE = { trace: "#/view/trace", workspace: "#/view/workspace" };
-  const VIEW_TOKENS = {
-    code: "agent_view_tokens",
-    trace: "trace_view_tokens",
-    workspace: "workspace_view_tokens",
-  };
-
-  function readTarget(view) {
-    const onSpaces =
-      /\.hf\.space$/.test(location.hostname) ||
-      /(^|\.)huggingface\.co$/.test(location.hostname);
-    let base = "";
-    if (onSpaces && MANIFEST.space_id) base = MANIFEST.space_id;
-    else if (/^https?:$/.test(location.protocol))
-      base = `${location.origin}${location.pathname}`;
-    if (!base) return "";
-    return base + (VIEW_ROUTE[view] || "");
-  }
-
-  function renderLogbookHeader(view) {
-    const title = document.getElementById("logbook-title");
-    if (title) title.textContent = MANIFEST.title;
-    const cli = document.getElementById("logbook-cli");
-    if (!cli) return;
-    cli.innerHTML = "";
-    cli.appendChild(buildAgentHint(view));
-    const destination = buildHubDestinationLink(view);
-    if (destination) cli.appendChild(destination);
-  }
-
-  function hubDestination(view) {
-    if (view === "trace" && MANIFEST.trace_dataset) {
-      return {
-        label: "View Hugging Face dataset:",
-        url: MANIFEST.trace_dataset,
-        fallback: "Agent Traces dataset",
-      };
-    }
-    if (view === "workspace") {
-      const bucketId = (MANIFEST.workspace || {}).bucket_id;
-      const url =
-        MANIFEST.workspace_bucket ||
-        (bucketId ? `https://huggingface.co/buckets/${bucketId}` : "");
-      if (url) {
-        return {
-          label: "View Hugging Face Bucket:",
-          url,
-          fallback: "Workspace Bucket",
-        };
+  function cellArtifactItems(md) {
+    const re = new RegExp(LB_CELL_RE.source, "g");
+    const items = [];
+    let m;
+    while ((m = re.exec(md))) {
+      const meta = parseCellMeta(m[2]);
+      const body = m[3];
+      const order = meta.created_at || "";
+      if (meta.type === "artifact") {
+        const info = artifactInfoFromCell(meta, body);
+        if (info.name) items.push({ ...info, order });
       }
     }
-    return null;
-  }
-
-  function buildHubDestinationLink(view) {
-    const destination = hubDestination(view);
-    if (!destination || !destination.url.startsWith("https://huggingface.co/")) {
-      return null;
-    }
-    const row = document.createElement("div");
-    row.className = "hub-destination";
-    const label = document.createElement("span");
-    label.textContent = destination.label;
-    const link = document.createElement("a");
-    link.href = destination.url;
-    link.target = "_blank";
-    link.rel = "noopener noreferrer";
-    link.textContent =
-      destination.url
-        .replace(/^https:\/\/huggingface\.co\/(?:datasets|buckets)\//, "")
-        .replace(/\/$/, "") || destination.fallback;
-    link.title = destination.url;
-    const icon = document.createElementNS("http://www.w3.org/2000/svg", "svg");
-    icon.setAttribute("viewBox", "0 0 24 24");
-    icon.setAttribute("aria-hidden", "true");
-    const path = document.createElementNS("http://www.w3.org/2000/svg", "path");
-    path.setAttribute("d", "M14 5h5v5M19 5l-8 8M19 13v5a1 1 0 0 1-1 1H6a1 1 0 0 1-1-1V6a1 1 0 0 1 1-1h5");
-    icon.appendChild(path);
-    link.appendChild(icon);
-    row.appendChild(label);
-    row.appendChild(link);
-    return row;
-  }
-
-  function buildAgentHint(view) {
-    const target = readTarget(view);
-    const command = `trackio logbook read${target ? ` ${target}` : ""}`;
-    const tokens = MANIFEST[VIEW_TOKENS[view] || VIEW_TOKENS.code];
-    const div = document.createElement("div");
-    div.className = "agent-hint";
-    const label = document.createElement("span");
-    label.className = "agent-hint-label";
-    label.textContent = "Read from the CLI:";
-    const code = document.createElement("code");
-    code.textContent = command;
-    const copy = document.createElement("button");
-    copy.className = "copy";
-    copy.type = "button";
-    copy.title = "Copy";
-    copy.textContent = "⧉";
-    copy.addEventListener("click", () => copyText(command, copy, "⧉"));
-    const note = document.createElement("span");
-    note.className = "agent-hint-note";
-    note.textContent =
-      "compact view for agents" + (tokens ? ` · ~${fmt(tokens)} tokens` : "");
-    div.appendChild(label);
-    div.appendChild(code);
-    div.appendChild(copy);
-    div.appendChild(note);
-    return div;
-  }
-
-  function routeState() {
-    const raw = (location.hash || "").replace(/^#\/?/, "");
-    if (!raw) return { view: "code", slug: MANIFEST.root.slug };
-    const parts = raw.split("/");
-    if (parts[0] !== "view") {
-      const slug = findNode(MANIFEST.root, raw) ? raw : MANIFEST.root.slug;
-      return { view: "code", slug };
-    }
-    if (parts[1] === "trace") {
-      return { view: "trace", sessionId: parts.slice(2).join("/") || null };
-    }
-    if (parts[1] === "workspace") return { view: "workspace" };
-    const candidate = parts.slice(2).join("/") || MANIFEST.root.slug;
-    return {
-      view: "code",
-      slug: findNode(MANIFEST.root, candidate) ? candidate : MANIFEST.root.slug,
-    };
-  }
-
-  function updateViewTabs(route = routeState()) {
-    document.querySelectorAll("#view-tabs a").forEach((tab) => {
-      const view = tab.dataset.view;
-      tab.classList.toggle("active", view === route.view);
-      tab.setAttribute("aria-current", view === route.view ? "page" : "false");
-      if (view === "code") {
-        const slug = route.view === "code" ? route.slug : MANIFEST.root.slug;
-        tab.href = `#/view/code/${slug}`;
-      } else if (view === "trace") {
-        tab.href = "#/view/trace";
+    return items;
+  }
+
+  function collectLogbookResources(markdownList) {
+    const re = new RegExp(LB_CELL_RE.source, "g");
+    const dashboards = new Map();
+    markdownList.forEach((md) => {
+      let m;
+      while ((m = re.exec(md))) {
+        const meta = parseCellMeta(m[2]);
+        const body = m[3];
+        if (meta.type !== "dashboard") continue;
+        const project = meta.dashboard_project || "";
+        const space = body.match(/https:\/\/huggingface\.co\/spaces\/[^\s<>)"'`]+/);
+        const local = !space;
+        const url = space ? space[0] : "";
+        const key = local ? `local:${project}` : `space:${spaceIdFromUrl(url)}`;
+        const resUrl = local ? `trackio-local-dashboard://${project}` : url;
+        if (!dashboards.has(key))
+          dashboards.set(key, { project, local, url, resUrl });
       }
     });
+    const artifacts = new Map();
+    markdownList.forEach((md) => {
+      cellArtifactItems(md).forEach((it) => {
+        const key = `${it.type}:${it.name}`;
+        const prev = artifacts.get(key);
+        if (!prev || it.order >= prev.order) artifacts.set(key, it);
+      });
+    });
+    return {
+      dashboards: Array.from(dashboards.values()).sort((a, b) =>
+        a.project.localeCompare(b.project)
+      ),
+      artifacts: Array.from(artifacts.values()).sort((a, b) =>
+        a.name.localeCompare(b.name)
+      ),
+    };
   }
 
-  function setActiveView(route) {
-    CURRENT_VIEW = route.view;
-    document.body.dataset.view = route.view;
-    updateViewTabs(route);
-    renderLogbookHeader(route.view);
-    renderSidebar(route);
-  }
-
-  function formatDuration(ms) {
-    if (ms == null || isNaN(ms)) return "—";
-    const total = Math.max(0, Math.floor(ms / 1000));
-    const hours = Math.floor(total / 3600);
-    const minutes = Math.floor((total % 3600) / 60);
-    const seconds = total % 60;
-    if (hours) return `${hours}h ${minutes}m ${seconds}s`;
-    if (minutes) return `${minutes}m ${seconds}s`;
-    return `${seconds}s`;
+  function closeStatPopovers() {
+    document
+      .querySelectorAll(".stat-popover")
+      .forEach((p) => (p.hidden = true));
+    document
+      .querySelectorAll(".stat-tile.open")
+      .forEach((t) => t.classList.remove("open"));
   }
 
-  function formatDate(value) {
-    if (!value) return "—";
-    const date = new Date(value);
-    if (isNaN(date.getTime())) return value;
-    return date.toLocaleString([], {
-      year: "numeric",
-      month: "short",
-      day: "numeric",
-      hour: "numeric",
-      minute: "2-digit",
-      second: "2-digit",
+  function ensureStatListeners() {
+    if (STATS_LISTENERS) return;
+    STATS_LISTENERS = true;
+    document.addEventListener("click", closeStatPopovers);
+    document.addEventListener("keydown", (e) => {
+      if (e.key === "Escape") closeStatPopovers();
     });
   }
 
-  function emptyView(title, text, command) {
-    const empty = document.createElement("div");
-    empty.className = "view-empty";
-    const heading = document.createElement("h2");
-    heading.textContent = title;
-    const body = document.createElement("p");
-    body.textContent = text;
-    empty.appendChild(heading);
-    empty.appendChild(body);
-    if (command) {
-      const code = document.createElement("code");
-      code.textContent = command;
-      empty.appendChild(code);
-    }
-    return empty;
-  }
-
-  function traceEventLabel(event) {
-    if (event.kind === "reasoning") return "Thought";
-    if (event.kind === "tool_call") return event.tool_name || event.title || "Tool";
-    if (event.kind === "tool_result") return "Tool output";
-    return event.title || event.kind || "Event";
-  }
-
-  function appendTraceResult(entry, result) {
-    if (!entry || !result || !result.output) return;
-    const card = entry.querySelector(".trace-card");
-    if (!card) return;
-    const details = document.createElement("details");
-    details.className = "trace-output";
-    const summary = document.createElement("summary");
-    summary.textContent = result.status === "error" ? "Error output" : "Output";
-    const output = document.createElement("pre");
-    output.textContent = result.output;
-    details.appendChild(summary);
-    details.appendChild(output);
-    card.appendChild(details);
-  }
-
-  function traceEventCard(event, result) {
-    const entry = document.createElement("div");
-    entry.className = `trace-entry trace-${event.kind || "status"}`;
-    entry.style.setProperty("--trace-depth", Math.min(Number(event.depth) || 0, 4));
-
-    const rail = document.createElement("div");
-    rail.className = "trace-rail";
-    const number = document.createElement("span");
-    number.className = "trace-number";
-    number.textContent = `#${event.sequence || ""}`;
-    const dot = document.createElement("span");
-    dot.className = "trace-dot";
-    const elapsed = document.createElement("span");
-    elapsed.className = "trace-elapsed";
-    elapsed.textContent = formatDuration(event.elapsed_ms);
-    rail.appendChild(number);
-    rail.appendChild(dot);
-    rail.appendChild(elapsed);
-
-    const card = document.createElement("article");
-    card.className = "trace-card";
-    const head = document.createElement("header");
-    const kind = document.createElement("span");
-    kind.className = "trace-kind";
-    kind.textContent = traceEventLabel(event);
-    head.appendChild(kind);
-    if (event.turn) {
-      const turn = document.createElement("span");
-      turn.className = "trace-turn";
-      turn.textContent = `turn ${event.turn}`;
-      head.appendChild(turn);
-    }
-    card.appendChild(head);
-
-    const bodyText = event.text || event.input || event.output;
-    if (bodyText) {
-      const body = document.createElement(
-        event.kind === "tool_call" || event.kind === "tool_result" ? "pre" : "div"
-      );
-      body.className = "trace-body";
-      body.textContent = bodyText;
-      card.appendChild(body);
-    }
-    if (event.status) {
-      const status = document.createElement("span");
-      status.className = `trace-status-badge trace-status-badge-${event.status}`;
-      status.textContent = String(event.status).replace(/_/g, " ");
-      head.appendChild(status);
-    }
-    entry.appendChild(rail);
-    entry.appendChild(card);
-    appendTraceResult(entry, result);
-    return entry;
-  }
-
-  function traceSessionAnchor(id) {
-    return "/view/trace/" + id;
+  function stateHtml(remote, url) {
+    return remote
+      ? `<a class="stat-row-state open" href="${esc(url)}" target="_blank" rel="noopener" title="Open in a new tab">Open ↗</a>`
+      : `<span class="stat-row-state">publish to share</span>`;
   }
 
-  function buildTraceSession(session, index, loadTraceData = fetchData) {
-    const sec = document.createElement("section");
-    sec.className = "trace-session";
-    sec.id = traceSessionAnchor(session.id);
-    sec.dataset.sessionId = session.id;
-
-    const title = document.createElement("h2");
-    title.className = "trace-session-title";
-    title.textContent = session.title || session.id;
-    sec.appendChild(title);
-
-    const meta = document.createElement("div");
-    meta.className = "trace-meta";
-    [
-      ["Started", formatDate(index.started_at)],
-      ["Ended", formatDate(index.ended_at)],
-      ["Duration", formatDuration(index.duration_ms)],
-      ["Events", String(index.event_count || 0)],
-    ].forEach(([label, value]) => {
-      const item = document.createElement("span");
-      const strong = document.createElement("strong");
-      strong.textContent = label;
-      item.appendChild(strong);
-      item.appendChild(document.createTextNode(` ${value}`));
-      meta.appendChild(item);
-    });
-    if (index.model) {
-      const model = document.createElement("span");
-      const strong = document.createElement("strong");
-      strong.textContent = "Model";
-      model.appendChild(strong);
-      model.appendChild(document.createTextNode(` ${index.model}`));
-      meta.appendChild(model);
-    }
-    if (index.source_available === false) {
-      const missing = document.createElement("span");
-      missing.className = "trace-source-missing";
-      missing.textContent = "Source file unavailable · showing last capture";
-      meta.appendChild(missing);
-    }
-    sec.appendChild(meta);
-
-    const timeline = document.createElement("section");
-    timeline.className = "trace-timeline";
-    sec.appendChild(timeline);
-
-    const chunks = index.chunks || [];
-    if (!chunks.length) {
-      timeline.appendChild(
-        emptyView("Empty trace", "No displayable events were found in this session.")
-      );
-      return sec;
-    }
-
-    const controls = document.createElement("div");
-    controls.className = "trace-load-controls";
-    const progress = document.createElement("span");
-    progress.className = "trace-load-progress";
-    const loadMore = document.createElement("button");
-    loadMore.type = "button";
-    loadMore.className = "trace-load-more";
-    controls.appendChild(progress);
-    controls.appendChild(loadMore);
-    sec.appendChild(controls);
-
-    let nextChunk = 0;
-    let loadedEvents = 0;
-    let loading = false;
-    const pendingCalls = new Map();
-
-    function updateLoadControls() {
-      const total = Number(index.event_count) || chunks.reduce(
-        (sum, chunk) => sum + (Number(chunk.count) || 0),
-        0
-      );
-      progress.textContent = `${Math.min(loadedEvents, total)} of ${total} events loaded`;
-      if (nextChunk >= chunks.length) {
-        loadMore.textContent = "All events loaded";
-        loadMore.disabled = true;
-        return;
-      }
-      const count = Number(chunks[nextChunk].count) || "next";
-      loadMore.textContent = `Load ${count} more events`;
-      loadMore.disabled = false;
-    }
-
-    async function loadNextTraceChunk() {
-      if (loading || nextChunk >= chunks.length) return;
-      loading = true;
-      loadMore.disabled = true;
-      loadMore.textContent = "Loading…";
-      const descriptor = chunks[nextChunk];
-      try {
-        // Event chunks can be large. Do not retain the parsed JSON in DATA_CACHE;
-        // the rendered DOM is the only long-lived copy.
-        const chunk = await loadTraceData(descriptor.file, false);
-        const events = chunk.events || [];
-        events.forEach((event) => {
-          if (
-            event.kind === "tool_result" &&
-            event.call_id &&
-            pendingCalls.has(event.call_id)
-          ) {
-            appendTraceResult(pendingCalls.get(event.call_id), event);
-            pendingCalls.delete(event.call_id);
+  function scrollToResource(resUrl) {
+    closeStatPopovers();
+    if (!resUrl) return;
+    const el = document.querySelector(
+      `#page .page-body [data-res-url="${CSS.escape(resUrl)}"]:not(.stat-row)`
+    );
+    if (!el) return;
+    el.scrollIntoView({ behavior: "smooth", block: "center" });
+    el.classList.add("res-flash");
+    setTimeout(() => el.classList.remove("res-flash"), 1500);
+  }
+
+  function dashRowHtml(d) {
+    const inner =
+      `<span class="stat-row-ico">${DASHBOARD_ICON_IMG}</span>` +
+      `<div class="stat-row-main"><div class="stat-row-title">${esc(d.project)}</div>` +
+      `<div class="stat-row-meta">${stateHtml(!d.local, d.url)}</div></div>`;
+    return `<div class="stat-row" data-res-url="${esc(d.resUrl)}" title="Jump to it in the logbook">${inner}</div>`;
+  }
+
+  function artRowHtml(a) {
+    const remote = !a.local && !!a.url;
+    const parts = [a.type, a.size].filter(Boolean).map(esc);
+    const meta = parts.length
+      ? `${parts.join(" · ")} · ${stateHtml(remote, a.url)}`
+      : stateHtml(remote, a.url);
+    const inner =
+      `<span class="stat-row-ico">${ARTIFACT_ICON_IMG}</span>` +
+      `<div class="stat-row-main"><div class="stat-row-title">${esc(a.name)}</div>` +
+      `<div class="stat-row-meta">${meta}</div></div>`;
+    return `<div class="stat-row" data-res-url="${esc(a.resUrl)}" title="Jump to it in the logbook">${inner}</div>`;
+  }
+
+  function statTile(icon, alt, singular, plural, head, rowFn) {
+    const tile = document.createElement("button");
+    tile.type = "button";
+    tile.className = "stat-tile";
+    const render = (items) => {
+      const count = items.length;
+      const label = count === 1 ? singular : plural;
+      const caret = count > 0 ? `<span class="stat-caret">▾</span>` : "";
+      tile.innerHTML =
+        `<img class="stat-icon" src="${icon}" alt="${esc(alt)}" />` +
+        `<div class="stat-text"><div class="stat-num">${count}</div>` +
+        `<div class="stat-label">${esc(label)}</div></div>` +
+        caret;
+      tile.disabled = count === 0;
+      if (count > 0) {
+        const pop = document.createElement("div");
+        pop.className = "stat-popover";
+        pop.hidden = true;
+        pop.innerHTML =
+          `<div class="stat-pop-head">${esc(head)}</div>` +
+          items.map(rowFn).join("");
+        pop.addEventListener("click", (e) => {
+          if (e.target.closest("a.stat-row-state")) {
+            e.stopPropagation();
             return;
           }
-          const entry = traceEventCard(event);
-          timeline.appendChild(entry);
-          if (event.kind === "tool_call" && event.call_id) {
-            pendingCalls.set(event.call_id, entry);
-          }
+          e.stopPropagation();
+          const row = e.target.closest(".stat-row");
+          if (row) scrollToResource(row.dataset.resUrl);
         });
-        loadedEvents += events.length;
-        nextChunk += 1;
-        sec.dataset.loadedChunks = String(nextChunk);
-        updateLoadControls();
-      } catch (error) {
-        progress.textContent = "Could not load the next trace segment.";
-        loadMore.textContent = "Retry";
-        loadMore.disabled = false;
-      } finally {
-        loading = false;
+        tile.appendChild(pop);
       }
-    }
-
-    sec.dataset.loadedChunks = "0";
-    sec.loadNextTraceChunk = loadNextTraceChunk;
-    loadMore.addEventListener("click", loadNextTraceChunk);
-    updateLoadControls();
-    return sec;
-  }
-
-  function ensureTraceSessionLoaded(sessionId) {
-    const target = document.getElementById(traceSessionAnchor(sessionId));
-    if (
-      target &&
-      target.dataset.loadedChunks === "0" &&
-      typeof target.loadNextTraceChunk === "function"
-    ) {
-      return target.loadNextTraceChunk();
-    }
-    return Promise.resolve();
-  }
-
-  function scrollToTraceSession(sessionId) {
-    if (!sessionId) {
-      window.scrollTo({ top: 0, behavior: "auto" });
-      return;
-    }
-    const target = document.getElementById(traceSessionAnchor(sessionId));
-    if (target) target.scrollIntoView({ behavior: "auto" });
-    else window.scrollTo({ top: 0, behavior: "auto" });
-  }
-
-  // A published static Space may store only a reference to a private (or
-  // public) repository instead of embedding trace/workspace content. These
-  // helpers render that reference: link-only for private/inaccessible repos,
-  // and a probe-then-render path for public ones.
-  function repoRefCard(ref, opts) {
-    const wrap = document.createElement("div");
-    wrap.className = "view-empty repo-ref-card";
-    const h = document.createElement("h2");
-    h.textContent = opts.title;
-    wrap.appendChild(h);
-    const p = document.createElement("p");
-    p.textContent = opts.message;
-    wrap.appendChild(p);
-    if (ref && ref.repo_url) {
-      const a = document.createElement("a");
-      a.className = "repo-ref-link";
-      a.href = ref.repo_url;
-      a.target = "_blank";
-      a.rel = "noopener noreferrer";
-      a.textContent = "Open on the Hub ↗";
-      wrap.appendChild(a);
-    }
-    return wrap;
-  }
-
-  async function probeRepoAccessible(ref) {
-    if (!ref || !ref.repo_id) return false;
-    let url = "";
-    if (ref.repo_type === "dataset") {
-      url = "https://huggingface.co/api/datasets/" + ref.repo_id;
-    } else if (ref.repo_type === "bucket") {
-      url = "https://huggingface.co/api/buckets/" + ref.repo_id;
-    }
-    if (!url) return ref.private === false;
-    try {
-      const response = await fetch(url, { cache: "no-store" });
-      if (!response.ok) return false;
-      const metadata = await response.json();
-      return metadata.private !== true;
-    } catch (error) {
-      return false;
-    }
-  }
-
-  async function renderRepoReference(ref, kind, page, renderId) {
-    const isTraces = kind === "traces";
-    const noun = isTraces ? "agent traces" : "workspace files";
-    const linkOnly = () =>
-      repoRefCard(ref, {
-        title: isTraces ? "Agent traces" : "Workspace",
-        message:
-          `These ${noun} live in a private repository. ` +
-          "Open it on the Hub to view them.",
-      });
-    const accessible = await probeRepoAccessible(ref);
-    if (renderId !== RENDER_SEQUENCE) return;
-    if (!accessible) {
-      page.appendChild(linkOnly());
-      return;
-    }
-    page.appendChild(
-      repoRefCard(ref, {
-        title: isTraces ? "Agent traces" : "Workspace",
-        message:
-          `These ${noun} are published to a public repository on the Hub.`,
-      })
-    );
-  }
-
-  async function loadPublicTraceSource(ref) {
-    const viewerPath = String(ref.viewer_path || "trackio/index.json")
-      .split("/")
-      .filter((part) => part && part !== "." && part !== "..")
-      .join("/");
-    const slash = viewerPath.lastIndexOf("/");
-    const root = slash >= 0 ? viewerPath.slice(0, slash + 1) : "";
-    const index = await fetchRemoteData(repoFileUrl(ref, viewerPath));
-    const sessions = Array.isArray(index.sessions) ? index.sessions : [];
-    if (!sessions.length) throw new Error("The trace dataset has no sessions");
-    return {
-      sessions,
-      loadData: (file, cacheResult = true) =>
-        fetchRemoteData(repoFileUrl(ref, root + file), cacheResult),
     };
-  }
-
-  async function renderTrace(route, renderId) {
-    const page = document.getElementById("page");
-    page.innerHTML = "";
-    page.className = "trace-page";
-    let sessions = MANIFEST.traces || [];
-    let loadTraceData = fetchData;
-    if (!sessions.length) {
-      updateViewTabs({ view: "trace" });
-      if (MANIFEST.traces_ref && MANIFEST.traces_ref.repo_url) {
-        const accessible = await probeRepoAccessible(MANIFEST.traces_ref);
-        if (renderId !== RENDER_SEQUENCE) return;
-        if (accessible) {
-          try {
-            const remote = await loadPublicTraceSource(MANIFEST.traces_ref);
-            sessions = remote.sessions;
-            loadTraceData = remote.loadData;
-            buildTraceTree(route.sessionId, sessions);
-          } catch (error) {
-            page.appendChild(
-              repoRefCard(MANIFEST.traces_ref, {
-                title: "Agent traces",
-                message:
-                  "These agent traces are published to a public repository on the Hub, but the web timeline could not be loaded.",
-              })
-            );
-            return;
-          }
-        } else {
-          await renderRepoReference(MANIFEST.traces_ref, "traces", page, renderId);
-          return;
-        }
-      } else {
-        page.appendChild(
-          emptyView(
-            "No agent sessions attached yet",
-            "Attach the active session once and Trackio will keep its trace refreshed here while you work. The session stays local until you explicitly publish it.",
-            "trackio logbook attach trace <session.jsonl>"
-          )
-        );
-        return;
+    tile.addEventListener("click", (e) => {
+      if (tile.disabled) return;
+      e.stopPropagation();
+      const pop = tile.querySelector(".stat-popover");
+      if (!pop) return;
+      const isOpen = !pop.hidden;
+      closeStatPopovers();
+      if (!isOpen) {
+        pop.hidden = false;
+        tile.classList.add("open");
       }
-    }
-    updateViewTabs({ view: "trace" });
-
-    const loading = document.createElement("div");
-    loading.className = "view-loading";
-    loading.textContent = "Loading traces…";
-    page.appendChild(loading);
-    let loaded;
-    try {
-      loaded = await Promise.all(
-        sessions.map(async (session) => {
-          const index = await loadTraceData(session.index_file);
-          return { session, index };
-        })
-      );
-    } catch (error) {
-      if (renderId !== RENDER_SEQUENCE) return;
-      page.innerHTML = "";
-      page.appendChild(
-        emptyView("Trace unavailable", "The normalized traces could not be loaded.")
-      );
-      return;
-    }
-    if (renderId !== RENDER_SEQUENCE) return;
-    page.innerHTML = "";
-
-    const shell = document.createElement("div");
-    shell.className = "trace-shell";
-    loaded.forEach(({ session, index }) => {
-      shell.appendChild(buildTraceSession(session, index, loadTraceData));
-    });
-    page.appendChild(shell);
-    const activeSessionId = route.sessionId || sessions[0].id;
-    await ensureTraceSessionLoaded(activeSessionId);
-    if (renderId !== RENDER_SEQUENCE) return;
-    scrollToTraceSession(route.sessionId);
-  }
-
-  function svgIcon(kind) {
-    const svg = document.createElementNS("http://www.w3.org/2000/svg", "svg");
-    svg.setAttribute("viewBox", "0 0 24 24");
-    svg.setAttribute("aria-hidden", "true");
-    const path = document.createElementNS("http://www.w3.org/2000/svg", "path");
-    path.setAttribute(
-      "d",
-      kind === "folder"
-        ? "M3.5 6.5h6l2 2h9v9a2 2 0 0 1-2 2h-13a2 2 0 0 1-2-2z"
-        : kind === "download"
-          ? "M12 3v12m0 0 4-4m-4 4-4-4M5 20h14"
-          : "M6 3.5h8l4 4V20H6zM14 3.5V8h4"
-    );
-    svg.appendChild(path);
-    return svg;
-  }
-
-  function workspaceTree(files) {
-    const root = { directories: new Map(), files: [] };
-    files.forEach((file) => {
-      const parts = file.path.split("/");
-      let cursor = root;
-      parts.slice(0, -1).forEach((name) => {
-        if (!cursor.directories.has(name)) {
-          cursor.directories.set(name, { directories: new Map(), files: [] });
-        }
-        cursor = cursor.directories.get(name);
-      });
-      cursor.files.push(file);
-    });
-    return root;
-  }
-
-  function workspaceFileRow(file) {
-    const row = document.createElement("div");
-    row.className = "workspace-file";
-    const name = document.createElement("div");
-    name.className = "workspace-file-name";
-    name.appendChild(svgIcon("file"));
-    const label = document.createElement("span");
-    label.textContent = file.name;
-    label.title = file.path;
-    name.appendChild(label);
-    const type = document.createElement("span");
-    type.className = "workspace-file-type";
-    type.textContent = file.type || "file";
-    const size = document.createElement("span");
-    size.className = "workspace-file-size";
-    size.textContent = fmtBytes(file.size) || "—";
-    const modified = document.createElement("time");
-    modified.className = "workspace-file-time";
-    modified.dateTime = file.modified_at || "";
-    modified.textContent = formatDate(file.modified_at);
-    row.appendChild(name);
-    row.appendChild(type);
-    row.appendChild(size);
-    row.appendChild(modified);
-    const url =
-      isLocalPreview() && file.local_url
-        ? file.local_url
-        : file.download_url || file.bucket_url;
-    if (url) {
-      const download = document.createElement("a");
-      download.className = "workspace-download";
-      download.href = url;
-      download.title = "Download";
-      download.setAttribute("aria-label", `Download ${file.name}`);
-      if (isLocalPreview() && file.local_url) download.download = file.name;
-      download.appendChild(svgIcon("download"));
-      row.appendChild(download);
-    } else {
-      const pending = document.createElement("span");
-      pending.className = "workspace-unpublished";
-      pending.textContent = "Local";
-      row.appendChild(pending);
-    }
-    return row;
-  }
-
-  function renderWorkspaceNode(node, container) {
-    Array.from(node.directories.entries())
-      .sort(([a], [b]) => a.localeCompare(b))
-      .forEach(([name, child]) => {
-        const details = document.createElement("details");
-        details.className = "workspace-folder";
-        details.open = true;
-        const summary = document.createElement("summary");
-        summary.appendChild(svgIcon("folder"));
-        const label = document.createElement("span");
-        label.textContent = name;
-        summary.appendChild(label);
-        details.appendChild(summary);
-        const children = document.createElement("div");
-        children.className = "workspace-folder-children";
-        renderWorkspaceNode(child, children);
-        details.appendChild(children);
-        container.appendChild(details);
-      });
-    node.files
-      .sort((a, b) => a.name.localeCompare(b.name))
-      .forEach((file) => container.appendChild(workspaceFileRow(file)));
-  }
-
-  const WORKSPACE_MODE_KEY = "trackio-logbook:workspace-mode";
-
-  function getWorkspaceMode() {
-    try {
-      return localStorage.getItem(WORKSPACE_MODE_KEY) === "type" ? "type" : "tree";
-    } catch (error) {
-      return "tree";
-    }
-  }
-
-  function setWorkspaceMode(mode) {
-    try {
-      localStorage.setItem(WORKSPACE_MODE_KEY, mode);
-    } catch (error) {
-      /* ignore storage failures (private mode, etc.) */
-    }
-  }
-
-  function fileGroupKey(file) {
-    if (file.type) return file.type;
-    const name = file.name || file.path || "";
-    const dot = name.lastIndexOf(".");
-    if (dot > 0 && dot < name.length - 1) return name.slice(dot + 1).toLowerCase();
-    return "other";
-  }
-
-  function renderWorkspaceByType(files, container) {
-    const groups = new Map();
-    files.forEach((file) => {
-      const key = fileGroupKey(file);
-      if (!groups.has(key)) groups.set(key, []);
-      groups.get(key).push(file);
-    });
-    Array.from(groups.keys())
-      .sort((a, b) => a.localeCompare(b))
-      .forEach((key) => {
-        const items = groups
-          .get(key)
-          .sort((a, b) => a.name.localeCompare(b.name));
-        const section = document.createElement("div");
-        section.className = "workspace-group";
-        const heading = document.createElement("h3");
-        heading.className = "workspace-group-head";
-        const label = document.createElement("span");
-        label.textContent = key;
-        const count = document.createElement("span");
-        count.className = "workspace-group-count";
-        count.textContent = String(items.length);
-        heading.appendChild(label);
-        heading.appendChild(count);
-        section.appendChild(heading);
-        items.forEach((file) => section.appendChild(workspaceFileRow(file)));
-        container.appendChild(section);
-      });
-  }
-
-  function buildWorkspaceToggle(current, onChange) {
-    const toggle = document.createElement("div");
-    toggle.className = "workspace-toggle";
-    toggle.setAttribute("role", "group");
-    toggle.setAttribute("aria-label", "Workspace layout");
-    const buttons = [];
-    [
-      ["tree", "Tree"],
-      ["type", "By type"],
-    ].forEach(([mode, text]) => {
-      const btn = document.createElement("button");
-      btn.type = "button";
-      btn.className = "workspace-toggle-btn";
-      btn.textContent = text;
-      const setActive = (active) => {
-        btn.classList.toggle("is-active", active);
-        btn.setAttribute("aria-pressed", active ? "true" : "false");
-      };
-      setActive(mode === current);
-      btn.addEventListener("click", () => {
-        buttons.forEach((entry) => entry.setActive(entry.mode === mode));
-        onChange(mode);
-      });
-      buttons.push({ mode, setActive });
-      toggle.appendChild(btn);
-    });
-    return toggle;
-  }
-
-  const HF_LOGO_DATA_URI = "data:image/svg+xml;base64,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";
-
-  function hubGroupId(type) {
-    return "ws-hub-" + String(type).toLowerCase().replace(/[^a-z0-9]+/g, "-");
-  }
-
-  const HUB_REF_GROUP_ORDER = [
-    "Jobs",
-    "Datasets",
-    "Models",
-    "Spaces",
-    "Buckets",
-    "Collections",
-    "Papers",
-  ];
-
-  function hubRefFromRepoRef(ref) {
-    if (!ref || !ref.repo_id || !ref.repo_url) return null;
-    if (ref.repo_type === "dataset") {
-      return { url: ref.repo_url, type: "Datasets", label: ref.repo_id };
-    }
-    if (ref.repo_type === "bucket") {
-      return { url: ref.repo_url, type: "Buckets", label: ref.repo_id };
-    }
-    return null;
-  }
-
-  async function publicAssociatedHubRefs() {
-    const refs = [MANIFEST.traces_ref, MANIFEST.workspace_ref].filter(Boolean);
-    const publicStates = await Promise.all(refs.map(probeRepoAccessible));
-    return refs
-      .filter((ref, index) => publicStates[index])
-      .map(hubRefFromRepoRef)
-      .filter(Boolean);
-  }
-
-  function mergeHubRefs(...groups) {
-    const merged = [];
-    const seen = new Set();
-    groups.flat().forEach((ref) => {
-      if (!validHubRef(ref)) return;
-      const key = `${ref.type || "Other"}:${ref.label || ref.url}`;
-      if (seen.has(key)) return;
-      seen.add(key);
-      merged.push(ref);
-    });
-    return merged;
-  }
-
-  function validHubRef(ref) {
-    if (!ref || !ref.url) return false;
-    if (classifyResource(ref.url)) return true;
-    const match = ref.url.match(/huggingface\.co\/collections\/([^/?#]+\/[^/?#]+)/);
-    return Boolean(match && validHfRepoId(match[1].split("/")));
-  }
-
-  function renderHubRefs(refs) {
-    const validRefs = Array.isArray(refs) ? refs.filter(validHubRef) : [];
-    if (!validRefs.length) return null;
-    const section = document.createElement("section");
-    section.className = "workspace-hub";
-    const heading = document.createElement("h2");
-    heading.className = "workspace-hub-title";
-    const hubLogo = document.createElement("img");
-    hubLogo.className = "workspace-hub-logo";
-    hubLogo.src = HF_LOGO_DATA_URI;
-    hubLogo.alt = "";
-    hubLogo.setAttribute("aria-hidden", "true");
-    heading.appendChild(hubLogo);
-    heading.appendChild(document.createTextNode("Linked Hugging Face artifacts"));
-    section.appendChild(heading);
-    const byType = new Map();
-    validRefs.forEach((ref) => {
-      const type = ref.type || "Other";
-      if (!byType.has(type)) byType.set(type, []);
-      byType.get(type).push(ref);
     });
-    const order = [
-      ...HUB_REF_GROUP_ORDER,
-      ...Array.from(byType.keys()).filter((t) => !HUB_REF_GROUP_ORDER.includes(t)),
-    ];
-    order.forEach((type) => {
-      const items = byType.get(type);
-      if (!items || !items.length) return;
-      const group = document.createElement("div");
-      group.className = "workspace-hub-group";
-      group.id = hubGroupId(type);
-      const gh = document.createElement("h3");
-      gh.className = "workspace-hub-group-head";
-      const label = document.createElement("span");
-      label.textContent = type;
-      const count = document.createElement("span");
-      count.className = "workspace-hub-count";
-      count.textContent = String(items.length);
-      gh.appendChild(label);
-      gh.appendChild(count);
-      group.appendChild(gh);
-      const list = document.createElement("div");
-      list.className = "workspace-hub-list";
-      items.forEach((ref) => {
-        const link = document.createElement("a");
-        link.className = "workspace-hub-link";
-        link.href = ref.url;
-        link.target = "_blank";
-        link.rel = "noopener noreferrer";
-        link.textContent = ref.label || ref.url;
-        link.title = ref.url;
-        list.appendChild(link);
-      });
-      group.appendChild(list);
-      section.appendChild(group);
-    });
-    return section;
+    return { tile, render };
   }
 
-  function workspaceSidebarEntries(files, hubRefs) {
-    const entries = [];
-    if (files && files.length) {
-      entries.push({ id: "ws-files", label: "Workspace files" });
-    }
-    const counts = new Map();
-    (Array.isArray(hubRefs) ? hubRefs : []).filter(validHubRef).forEach((ref) => {
-      const type = ref.type || "Other";
-      counts.set(type, (counts.get(type) || 0) + 1);
-    });
-    const order = [
-      ...HUB_REF_GROUP_ORDER,
-      ...Array.from(counts.keys()).filter((t) => !HUB_REF_GROUP_ORDER.includes(t)),
-    ];
-    order.forEach((type) => {
-      if (counts.get(type)) entries.push({ id: hubGroupId(type), label: type });
-    });
-    return entries;
-  }
+  function buildLogbookStats(markdownList) {
+    const token = ++STATS_TOKEN;
+    ensureStatListeners();
+    const { dashboards, artifacts } = collectLogbookResources(markdownList);
 
-  function buildWorkspaceSidebar(entries) {
-    const tree = document.getElementById("tree");
-    tree.innerHTML = "";
-    if (!entries || !entries.length) return;
-    const label = document.createElement("div");
-    label.className = "tree-label";
-    label.textContent = "Artifacts";
-    tree.appendChild(label);
-    entries.forEach((entry) => {
-      const a = document.createElement("a");
-      a.href = "#/view/workspace";
-      a.className = "depth-0";
-      a.dataset.section = entry.id;
-      a.textContent = entry.label;
-      a.addEventListener("click", (event) => {
-        event.preventDefault();
-        const target = document.getElementById(entry.id);
-        if (!target) return;
-        target.scrollIntoView({ behavior: "smooth", block: "start" });
-        tree
-          .querySelectorAll("a")
-          .forEach((link) => link.classList.toggle("active", link === a));
-      });
-      tree.appendChild(a);
-    });
-  }
-
-  function workspaceTypeFromPath(path) {
-    const dot = path.lastIndexOf(".");
-    const ext = dot >= 0 ? path.slice(dot).toLowerCase() : "";
-    if (
-      [
-        ".pt",
-        ".pth",
-        ".ckpt",
-        ".safetensors",
-        ".gguf",
-        ".onnx",
-        ".pkl",
-        ".joblib",
-        ".h5",
-        ".tflite",
-        ".pb",
-      ].includes(ext)
-    ) {
-      return "model";
-    }
-    if (
-      [
-        ".npz",
-        ".npy",
-        ".parquet",
-        ".csv",
-        ".tsv",
-        ".arrow",
-        ".jsonl",
-        ".feather",
-        ".msgpack",
-      ].includes(ext)
-    ) {
-      return "dataset";
-    }
-    return ext.replace(/^\./, "") || "file";
-  }
-
-  async function loadPublicWorkspace(ref) {
-    if (!(await probeRepoAccessible(ref))) return null;
-    const response = await fetch(
-      `https://huggingface.co/api/buckets/${ref.repo_id}/tree`,
-      { cache: "no-store" }
+    const el = document.createElement("div");
+    el.className = "logbook-stats";
+    const dash = statTile(
+      "./trackio-logo-light.png",
+      "Trackio",
+      "Trackio Dashboard",
+      "Trackio Dashboards",
+      "Dashboards created in this logbook",
+      dashRowHtml
     );
-    if (!response.ok) throw new Error("Could not load the public Bucket");
-    const entries = await response.json();
-    const prefix = "workspace/";
-    const files = (Array.isArray(entries) ? entries : [])
-      .filter(
-        (item) =>
-          item &&
-          item.type === "file" &&
-          typeof item.path === "string" &&
-          item.path.startsWith(prefix)
+    const art = statTile(
+      "./bucket-icon.svg",
+      "Bucket",
+      "Artifact",
+      "Artifacts",
+      "Artifacts created in this logbook",
+      artRowHtml
+    );
+    dash.render(dashboards);
+    art.render(artifacts);
+    el.appendChild(dash.tile);
+    el.appendChild(art.tile);
+
+    const scanText = markdownList
+      .map((md) =>
+        md.replace(/(`{3,4}|~{3,4})(html|raw)[^\n]*\n[\s\S]*?\n\1/g, " ")
       )
-      .map((item) => {
-        const path = item.path.slice(prefix.length);
-        return {
-          path,
-          name: path.split("/").pop() || path,
-          type: workspaceTypeFromPath(path),
-          size: Number(item.size) || 0,
-          modified_at: item.mtime || item.uploadedAt || "",
-          sessions: [],
-          bucket_url: `${ref.repo_url}#${encodeRepoPath(item.path)}`,
-          download_url: repoFileUrl(ref, item.path),
-        };
-      });
-    return {
-      schema_version: 1,
-      bucket_id: ref.repo_id,
-      file_count: files.length,
-      total_size: files.reduce((sum, file) => sum + file.size, 0),
-      files,
-    };
-  }
-
-  async function renderWorkspace(renderId) {
-    const page = document.getElementById("page");
-    page.innerHTML = "";
-    page.className = "workspace-page";
-    const loading = document.createElement("div");
-    loading.className = "view-loading";
-    loading.textContent = "Loading workspace…";
-    page.appendChild(loading);
-    let workspace;
-    try {
-      workspace = await fetchData((MANIFEST.workspace || {}).file || "workspace.json");
-    } catch (error) {
-      if (renderId !== RENDER_SEQUENCE) return;
-      page.innerHTML = "";
-      page.appendChild(emptyView("Workspace unavailable", "The workspace inventory could not be loaded."));
-      return;
-    }
-    if (renderId !== RENDER_SEQUENCE) return;
-    if (
-      !(workspace.files || []).length &&
-      MANIFEST.workspace_ref &&
-      MANIFEST.workspace_ref.repo_url
-    ) {
-      try {
-        const remoteWorkspace = await loadPublicWorkspace(MANIFEST.workspace_ref);
-        if (remoteWorkspace && remoteWorkspace.files.length) {
-          workspace = {
-            ...workspace,
-            ...remoteWorkspace,
-            hub_refs: workspace.hub_refs || [],
-          };
-        }
-      } catch (error) {
-        // Keep the repository card below as a graceful fallback.
+      .join("\n");
+    const seen = new Set(
+      dashboards.map((d) =>
+        d.local ? `local:${d.project}` : `space:${spaceIdFromUrl(d.url)}`
+      )
+    );
+    const remoteSpaces = new Map();
+    extractUrls(scanText).forEach((url) => {
+      const item = classifyResource(url);
+      if (item && item.kind === "space" && !item.local) {
+        remoteSpaces.set(item.url, item);
       }
-    }
-    if (renderId !== RENDER_SEQUENCE) return;
-    page.innerHTML = "";
-    const associatedHubRefs = await publicAssociatedHubRefs();
-    if (renderId !== RENDER_SEQUENCE) return;
-    const hubRefs = mergeHubRefs(workspace.hub_refs || [], associatedHubRefs);
-    const shell = document.createElement("div");
-    shell.className = "workspace-shell";
-    const header = document.createElement("header");
-    header.className = "workspace-header";
-    const summary = document.createElement("p");
-    summary.textContent = `${workspace.file_count || 0} files · ${fmtBytes(workspace.total_size || 0)}`;
-    header.appendChild(summary);
-    const files = workspace.files || [];
-    if (files.length) {
-      const inventory = document.createElement("section");
-      inventory.className = "workspace-inventory";
-      inventory.id = "ws-files";
-      const renderInventory = (mode) => {
-        inventory.innerHTML = "";
-        if (mode === "type") renderWorkspaceByType(files, inventory);
-        else renderWorkspaceNode(workspaceTree(files), inventory);
-      };
-      const toggle = buildWorkspaceToggle(getWorkspaceMode(), (mode) => {
-        setWorkspaceMode(mode);
-        renderInventory(mode);
-      });
-      header.appendChild(toggle);
-      shell.appendChild(header);
-      shell.appendChild(inventory);
-      renderInventory(getWorkspaceMode());
-    } else if (MANIFEST.workspace_ref && MANIFEST.workspace_ref.repo_url) {
-      shell.appendChild(header);
-      await renderRepoReference(
-        MANIFEST.workspace_ref,
-        "workspace",
-        shell,
-        renderId
-      );
-      if (renderId !== RENDER_SEQUENCE) return;
-    } else {
-      shell.appendChild(header);
-      shell.appendChild(
-        emptyView(
-          "No workspace files captured yet",
-          "Supported model and data files appear here as they are created or changed after a trace is attached. Outputs captured by trackio logbook run appear when the run finishes; logged Trackio artifacts appear immediately in the Logbook tab. Files stay local until you choose to publish."
-        )
-      );
-    }
-    const hub = renderHubRefs(hubRefs);
-    if (hub) shell.appendChild(hub);
-    page.appendChild(shell);
-    buildWorkspaceSidebar(workspaceSidebarEntries(files, hubRefs));
-    window.scrollTo({ top: 0, behavior: "auto" });
-  }
-
-  async function renderCurrentView(opts = {}) {
-    const route = routeState();
-    const renderId = ++RENDER_SEQUENCE;
-    setActiveView(route);
-    if (route.view === "trace") {
-      await renderTrace(route, renderId);
-    } else if (route.view === "workspace") {
-      await renderWorkspace(renderId);
-    } else {
-      document.getElementById("page").className = "code-page";
-      await renderLogbook({ ...opts, renderId });
-    }
+    });
+    remoteSpaces.forEach((s) => {
+      const key = `space:${s.id}`;
+      if (seen.has(key)) return;
+      getJSON(`https://huggingface.co/api/spaces/${s.id}`)
+        .then((d) => {
+          if (STATS_TOKEN !== token) return;
+          const tags = (d && d.tags) || [];
+          if (
+            !seen.has(key) &&
+            tags.some((t) => String(t).toLowerCase() === "trackio")
+          ) {
+            seen.add(key);
+            dashboards.push({
+              project: s.id,
+              local: false,
+              url: s.url,
+              resUrl: s.url,
+            });
+            dashboards.sort((a, b) => a.project.localeCompare(b.project));
+            dash.render(dashboards);
+          }
+        })
+        .catch(() => {});
+    });
+    return el;
   }
 
-  function handleRouteChange() {
-    const route = routeState();
-    if (
-      route.view === "code" &&
-      CURRENT_VIEW === "code" &&
-      document.querySelector("#page .page-section")
-    ) {
-      updateViewTabs(route);
-      scrollToHash();
-      return;
-    }
-    if (
-      route.view === "trace" &&
-      CURRENT_VIEW === "trace" &&
-      document.querySelector("#page .trace-session")
-    ) {
-      setActiveView(route);
-      const sessions = MANIFEST.traces || [];
-      const activeSessionId = route.sessionId || (sessions[0] || {}).id;
-      ensureTraceSessionLoaded(activeSessionId);
-      scrollToTraceSession(route.sessionId);
-      return;
-    }
-    renderCurrentView();
+  function buildAgentHint() {
+    const onSpaces =
+      /\.hf\.space$/.test(location.hostname) ||
+      /(^|\.)huggingface\.co$/.test(location.hostname);
+    let source = "";
+    if (onSpaces && MANIFEST.space_id) {
+      source = ` ${MANIFEST.space_id}`;
+    } else if (/^https?:$/.test(location.protocol)) {
+      source = ` ${location.origin}/`;
+    }
+    const command = `trackio logbook read${source}`;
+    const tokens = MANIFEST.agent_view_tokens;
+    const div = document.createElement("div");
+    div.className = "agent-hint";
+    const label = document.createElement("span");
+    label.className = "agent-hint-label";
+    label.textContent = "Read from the CLI:";
+    const code = document.createElement("code");
+    code.textContent = command;
+    const copy = document.createElement("button");
+    copy.className = "copy";
+    copy.type = "button";
+    copy.title = "Copy";
+    copy.textContent = "⧉";
+    copy.addEventListener("click", () => copyText(command, copy, "⧉"));
+    const note = document.createElement("span");
+    note.className = "agent-hint-note";
+    note.textContent =
+      "compact view for agents" + (tokens ? ` · ~${fmt(tokens)} tokens` : "");
+    div.appendChild(label);
+    div.appendChild(code);
+    div.appendChild(copy);
+    div.appendChild(note);
+    return div;
   }
 
   function currentSlug() {
-    const route = routeState();
-    return route.view === "code" ? route.slug : MANIFEST.root.slug;
+    const slug = (location.hash || "").replace(/^#\//, "") || MANIFEST.root.slug;
+    return findNode(MANIFEST.root, slug) ? slug : MANIFEST.root.slug;
   }
 
   function scrollToHash(opts = {}) {
-    if (routeState().view !== "code") return;
     const slug = currentSlug();
-    if (!location.hash || slug === MANIFEST.root.slug) {
+    if (!location.hash) {
       window.scrollTo({ top: 0, behavior: opts.behavior || "auto" });
       highlight(slug);
       return;
@@ -2800,7 +2106,7 @@
   function navigateToLogbookSlug(target) {
     const slug = String(target || "").replace(/^#?\//, "").trim();
     if (!slug || !findNode(MANIFEST.root, slug)) return;
-    const hash = "#/view/code/" + slug;
+    const hash = "#/" + slug;
     if (location.hash === hash) {
       scrollToHash({ behavior: "smooth" });
     } else {
@@ -2824,12 +2130,9 @@
 
   let SCROLL_FRAME = 0;
   function updateActiveSection() {
-    if (CURRENT_VIEW !== "code" && CURRENT_VIEW !== "trace") return;
     cancelAnimationFrame(SCROLL_FRAME);
     SCROLL_FRAME = requestAnimationFrame(() => {
-      const selector =
-        CURRENT_VIEW === "trace" ? ".trace-session" : ".page-section";
-      const sections = Array.from(document.querySelectorAll(selector));
+      const sections = Array.from(document.querySelectorAll(".page-section"));
       if (!sections.length) return;
       const marker = Math.min(window.innerHeight * 0.28, 180);
       let active = sections[0];
@@ -2842,11 +2145,7 @@
       ) {
         active = sections[sections.length - 1];
       }
-      if (CURRENT_VIEW === "trace") {
-        highlightTraceSession(active.dataset.sessionId);
-      } else {
-        highlight(active.dataset.slug);
-      }
+      highlight(active.dataset.slug);
     });
   }
 
@@ -2862,7 +2161,7 @@
         document.getElementById("book-title").textContent = MANIFEST.title;
         document.getElementById("book-head").setAttribute("aria-label", MANIFEST.title);
         buildTree();
-        renderCurrentView({ preserveScroll: true });
+        renderLogbook({ preserveScroll: true });
       } catch (e) {}
     }, LIVE_RELOAD_MS);
   }
@@ -2958,16 +2257,17 @@
     document.getElementById("book-title").textContent = MANIFEST.title;
     document.getElementById("book-head").setAttribute("aria-label", MANIFEST.title);
     document.getElementById("book-head").addEventListener("click", () => {
-      const target = "#/view/code/" + MANIFEST.root.slug;
+      const target = "#/" + MANIFEST.root.slug;
       if (location.hash === target) scrollToHash();
       else location.hash = target;
     });
     buildTree();
     setupConnect();
+    setupResourceHover();
     setupFigureNavigation();
-    window.addEventListener("hashchange", handleRouteChange);
+    window.addEventListener("hashchange", () => scrollToHash());
     window.addEventListener("scroll", updateActiveSection, { passive: true });
-    await renderCurrentView();
+    await renderLogbook();
     startLiveReload();
   }
 

logbook.json

@@ -1,5 +1,5 @@
 {
-  "schema_version": 2,
+  "schema_version": 1,
   "title": "Reproduction: Distributed Direct Preference Optimization",
   "emoji": "🎯",
   "space_id": "SabaPivot/repro-distributed-direct-preference-optimization",
@@ -10,13 +10,13 @@
     "url": "https://openreview.net/forum?id=ljNZyrAlaa"
   },
   "tags": [
-    "trackio",
-    "open-reproductions",
     "icml2026",
     "icml2026-repro",
-    "paper-ljNZyrAlaa"
+    "open-reproductions",
+    "paper-ljNZyrAlaa",
+    "trackio"
   ],
-  "updated_at": "2026-07-29T14:48:30+00:00",
+  "updated_at": "2026-07-30T02:11:55.819945+00:00",
   "root": {
     "slug": "index",
     "title": "Reproduction: Distributed Direct Preference Optimization",
@@ -29,39 +29,45 @@
         "children": []
       },
       {
-        "slug": "claim-1-feddpo-partial-participation-bound",
-        "title": "Claim 1: FedDPO partial participation bound",
-        "file": "pages/claim-1-feddpo-partial-participation-bound/page.md",
+        "slug": "claim-1",
+        "title": "Claim 1: Theorem 5.1 gives the first convergence bound for Federated DPO (FedDPO) under partial client participation, showing gradient-norm error scaling with local steps E, rounds R, sampled clients S, and gradient variance ζ²_g (Theorem 5.1).",
+        "file": "pages/claim-1/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-2",
+        "title": "Claim 2: Corollary 5.2 shows that under full participation (S=N) the 1/S variance-amplification term in the FedDPO bound vanishes, isolating the cost of partial participation (Corollary 5.2).",
+        "file": "pages/claim-2/page.md",
         "children": []
       },
       {
-        "slug": "claim-2-full-participation-corollary",
-        "title": "Claim 2: Full participation corollary",
-        "file": "pages/claim-2-full-participation-corollary/page.md",
+        "slug": "claim-3",
+        "title": "Claim 3: Theorem 5.4 introduces a staleness penalty term proportional to η·C_q·q_max, quantifying how delayed/asynchronous client updates degrade FedDPO convergence (Theorem 5.4).",
+        "file": "pages/claim-3/page.md",
         "children": []
       },
       {
-        "slug": "claim-3-staleness-penalty",
-        "title": "Claim 3: Staleness penalty",
-        "file": "pages/claim-3-staleness-penalty/page.md",
+        "slug": "claim-4",
+        "title": "Claim 4: Theorem 5.5 establishes a lower bound of Ω(Eκ²/S) showing that the dependence on client preference heterogeneity κ² and participation rate S cannot be removed by any FedDPO-style algorithm (Theorem 5.5).",
+        "file": "pages/claim-4/page.md",
         "children": []
       },
       {
-        "slug": "claim-4-feddpo-lower-bound",
-        "title": "Claim 4: FedDPO lower bound",
-        "file": "pages/claim-4-feddpo-lower-bound/page.md",
+        "slug": "claim-5",
+        "title": "Claim 5: Theorem 6.1 proves DecDPO converges at rate O(1/sqrt(R) + 1/(R(1-rho^2))) where rho is the spectral gap of the communication graph, with variance and heterogeneity terms scaled by 1/(1-rho^2) (Theorem 6.1).",
+        "file": "pages/claim-5/page.md",
         "children": []
       },
       {
-        "slug": "claim-5-decdpo-spectral-rate",
-        "title": "Claim 5: DecDPO spectral rate",
-        "file": "pages/claim-5-decdpo-spectral-rate/page.md",
+        "slug": "claim-6",
+        "title": "Claim 6: Numerical experiments on the Stanford Human Preferences dataset with N=5 agents empirically confirm the predicted effects of local step count, participation rate, staleness, and network topology on convergence (Section 7, Numerical Results).",
+        "file": "pages/claim-6/page.md",
         "children": []
       },
       {
-        "slug": "claim-6-shp-numerical-results",
-        "title": "Claim 6: SHP numerical results",
-        "file": "pages/claim-6-shp-numerical-results/page.md",
+        "slug": "claim-7-real-model-setup",
+        "title": "Real-model setup — DistilGPT-2 (82M) on Stanford Human Preferences",
+        "file": "pages/claim-7-real-model-setup/page.md",
         "children": []
       },
       {
@@ -72,30 +78,6 @@
       }
     ]
   },
-  "traces": [],
-  "workspace": {
-    "file": "workspace.json",
-    "file_count": 0,
-    "total_size": 0,
-    "bucket_id": null
-  },
-  "agent_view_tokens": 5866,
-  "trace_view_tokens": 17229,
-  "workspace_view_tokens": 85,
-  "revision": "e6d12b1ed894120a273d",
-  "traces_ref": {
-    "repo_id": "SabaPivot/repro-distributed-direct-preference-optimization-traces",
-    "repo_type": "dataset",
-    "repo_url": "https://huggingface.co/datasets/SabaPivot/repro-distributed-direct-preference-optimization-traces",
-    "private": true,
-    "viewer_path": "trackio/index.json"
-  },
-  "trace_dataset": "https://huggingface.co/datasets/SabaPivot/repro-distributed-direct-preference-optimization-traces",
-  "workspace_ref": {
-    "repo_id": "SabaPivot/repro-distributed-direct-preference-optimization-artifacts",
-    "repo_type": "bucket",
-    "repo_url": "https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts",
-    "private": true
-  },
-  "workspace_bucket": "https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts"
-}
+  "agent_view_tokens": 3400,
+  "revision": "1784656265000000000"
+}

official_claims.json

@@ -0,0 +1,8 @@
+[
+  "Theorem 5.1 gives the first convergence bound for Federated DPO (FedDPO) under partial client participation, showing gradient-norm error scaling with local steps E, rounds R, sampled clients S, and gradient variance ζ²_g (Theorem 5.1).",
+  "Corollary 5.2 shows that under full participation (S=N) the 1/S variance-amplification term in the FedDPO bound vanishes, isolating the cost of partial participation (Corollary 5.2).",
+  "Theorem 5.4 introduces a staleness penalty term proportional to η·C_q·q_max, quantifying how delayed/asynchronous client updates degrade FedDPO convergence (Theorem 5.4).",
+  "Theorem 5.5 establishes a lower bound of Ω(Eκ²/S) showing that the dependence on client preference heterogeneity κ² and participation rate S cannot be removed by any FedDPO-style algorithm (Theorem 5.5).",
+  "Theorem 6.1 proves DecDPO (decentralized DPO) converges at rate O(1/√R + 1/(R(1−ρ²))) where ρ is the spectral gap of the communication graph, with variance and heterogeneity terms scaled by 1/(1−ρ²) (Theorem 6.1).",
+  "Numerical experiments on the Stanford Human Preferences dataset with N=5 agents empirically confirm the predicted effects of local step count, participation rate, staleness, and network topology on convergence (Section 7, Numerical Results)."
+]

outputs/SHA256SUMS.json

@@ -0,0 +1,12 @@
+{
+  "corollary_5_2_full_participation.csv": "ff2d4278b79e9fdf103a48f907d694a11e39eda0369daf6076f0e269da141b86",
+  "dec_dpo_topology.csv": "691bdcc6ae25aa25d6598dbf87ad060255e9a0f802e03341da6a30eeb362b2ba",
+  "distributed_dpo_audit.png": "c08e9e3b7baf8e05e4a4190efd714a72d9122fd3cd4ecd645d640a9aea6ca1e4",
+  "fed_dpo_shp_ablations.csv": "31bfaabea77d3022b17e469b7b9f546a2fec8c40431b8177d1cf077ac06a9718",
+  "results.json": "bb7e2073a3d41e3c2fdb957ab423ee83a367d3fe1badcf5fbcb98e516a9b707b",
+  "shp_inventory.csv": "6460bb569c5ac46a0509503168290bb4b2bb03a5e6f566f282d0fe5a183e7732",
+  "source_claim_audit.csv": "c8464ba0cf666528438250e6fd581271d770a4c136c6a55377ca3361fd42b8e4",
+  "theorem_5_1_partial_bound.csv": "9accf57701e23d9e172978f895d6faf38f2a12f150d968d2ead593d3304fb896",
+  "theorem_5_4_staleness.csv": "be181a404639b2722e3a7905df2d4faf8bfb8e562ee42277b8e32652b724fdc1",
+  "theorem_5_5_lower_bound.csv": "8fc7b9c6775c5d7846a474089c727d0ae8a1de3bcf5db1abf76914e74878ad6d"
+}

outputs/corollary_5_2_full_participation.csv

@@ -0,0 +1,13 @@
+E,N,R,eta,initialization_term,partial_participation_variance_amplification,client_average_variance,heterogeneity_term,corollary_5_2_bound
+1,5,40,0.09486832980505137,0.36532064071556886,0.0,5.9786642628232746e-05,1.3337996821150978e-07,0.3653805607381653
+1,5,80,0.06708203932499368,0.25832070235739313,0.0,4.227554042680008e-05,6.668998410575486e-08,0.258363044587804
+1,5,160,0.04743416490252569,0.18266032035778443,0.0,2.9893321314116373e-05,3.3344992052877444e-08,0.1826902470240906
+1,5,320,0.03354101966249684,0.12916035117869656,0.0,2.113777021340004e-05,1.6672496026438715e-08,0.12918150562140598
+3,5,40,0.09486832980505137,0.12177354690518961,0.0,5.9786642628232746e-05,4.001399046345293e-07,0.12183373368772249
+3,5,80,0.06708203932499368,0.08610690078579769,0.0,4.227554042680008e-05,2.0006995231726458e-07,0.0861493763961768
+3,5,160,0.04743416490252569,0.06088677345259481,0.0,2.9893321314116373e-05,1.0003497615863233e-07,0.06091676680888508
+3,5,320,0.03354101966249684,0.043053450392898845,0.0,2.113777021340004e-05,5.0017488079316146e-08,0.043074638180600325
+6,5,40,0.09486832980505137,0.06088677345259481,0.0,5.9786642628232746e-05,8.002798092690587e-07,0.06094736037503231
+6,5,80,0.06708203932499368,0.043053450392898845,0.0,4.227554042680008e-05,4.0013990463452917e-07,0.04309612607323028
+6,5,160,0.04743416490252569,0.030443386726297404,0.0,2.9893321314116373e-05,2.0006995231726466e-07,0.030473480117563838
+6,5,320,0.03354101966249684,0.021526725196449423,0.0,2.113777021340004e-05,1.0003497615863229e-07,0.021547963001638984

outputs/dec_dpo_topology.csv

@@ -0,0 +1,5 @@
+topology,replicates,rho,one_over_one_minus_rho_squared,mean_tail_consensus_error,mean_tail_gradient_norm_sq,mean_final_loss
+path,8,0.872677996249965,4.194048299802485,0.0005454370424672539,0.0005829917140026842,0.6837910799762497
+ring,8,0.5393446629166316,1.4102237436136194,3.761684387776997e-05,0.0005826007264608975,0.6837903595432446
+star,8,0.8,2.7777777777777786,0.00041163500941361045,0.000582458508441908,0.683671120904709
+complete,8,8.284195946896153e-17,1.0,9.32317306912503e-34,0.0005820751105032884,0.6834946549569718

outputs/fed_dpo_shp_ablations.csv

@@ -0,0 +1,10 @@
+family,local_steps_E,sampled_clients_S,qmax,replicates,mean_final_loss,mean_loss_reduction,mean_tail_gradient_norm_sq,std_tail_gradient_norm_sq
+local_steps,1,3,0,8,0.6854464561081451,0.007700724451800131,0.0005880749016315657,1.0155468747282728e-06
+local_steps,3,3,0,8,0.6713684502828248,0.021778730277120517,0.0005443930037768237,2.4343579389877103e-06
+local_steps,6,3,0,8,0.6516766529857532,0.0414705275741922,0.0004827668972471771,2.1931322746900754e-06
+participation,3,1,0,8,0.6713264270553448,0.02182075350460043,0.0005435496190031553,4.033222976843732e-06
+participation,3,3,0,8,0.6711281026192374,0.02201907794070794,0.0005434856562567469,2.109517284475393e-06
+participation,3,5,0,8,0.6710800062252151,0.02206717433473021,0.0005436639929552561,1.0201377991962554e-06
+staleness,3,3,0,8,0.6710920419315259,0.022055138628419332,0.000543698019818161,2.69970703565243e-06
+staleness,3,3,2,8,0.6816140324392188,0.011533148120726491,0.0005762081162556293,1.0118713023835438e-06
+staleness,3,3,5,8,0.6863272721344135,0.006819908425531804,0.0005908721885661179,1.4644118928873132e-06

outputs/results.json

@@ -0,0 +1,56 @@
+{
+  "all_gates_pass": true,
+  "arxiv": "2605.20696v1",
+  "gates": {
+    "all_outputs_finite": true,
+    "claim1_actual_feddpo_executed": true,
+    "claim1_inverse_S_terms_exact": true,
+    "claim1_partial_bound_multiple_E_S_R": true,
+    "claim2_full_participation_amplification_removed": true,
+    "claim2_residual_client_average_variance_preserved": true,
+    "claim3_stale_algorithm_executed": true,
+    "claim3_staleness_formula_exact": true,
+    "claim3_staleness_zero_at_q_zero": true,
+    "claim4_full_participation_sampling_variance_zero": true,
+    "claim4_lower_bound_scaling_exact": true,
+    "claim5_connectivity_factor_varies": true,
+    "claim5_decdpo_executed": true,
+    "claim5_four_symmetric_graphs": true,
+    "claim6_all_four_ablation_families": true,
+    "claim6_exact_N5_times_120_SHP_rows": true,
+    "destructive_identical_clients_reduce_participation_effect": true,
+    "destructive_label_corruption_worsens_clean_objective": true,
+    "destructive_no_mixing_increases_consensus_error": true,
+    "source_pins_verified": true
+  },
+  "headline": {
+    "clean_final_loss": 0.670980521644771,
+    "clients": 5,
+    "decdpo_algorithm_runs": 32,
+    "decdpo_topology_cells": 4,
+    "fed_ablation_cells": 9,
+    "fed_ablation_ranges": {
+      "local_steps": 0.0001053080043843886,
+      "participation": 1.7833669850922645e-07,
+      "staleness": 4.7174168747956885e-05
+    },
+    "fed_algorithm_runs": 72,
+    "identical_client_S1_to_S5_ratio": 1.0004384715044146,
+    "kappa_squared": 0.001157812224058244,
+    "label_corrupted_final_loss": 0.690625282427891,
+    "lower_bound_cells": 20,
+    "no_mix_to_mix_consensus_ratio": 575.6464809616402,
+    "pairs_per_client": 120,
+    "shp_pairs": 600,
+    "smoothness_L_bound": 0.04000000000000001,
+    "staleness_formula_cells": 15,
+    "theorem_5_1_formula_cells": 36,
+    "topology_consensus_range": 0.0005454370424672539,
+    "topology_factor_consensus_spearman": 1.0,
+    "zeta_squared": 0.039387909241610605
+  },
+  "openreview_id": "ljNZyrAlaa",
+  "paper": "Distributed Direct Preference Optimization",
+  "scope": "finite log-linear SHP mechanism audit plus exact source-formula reconstruction",
+  "seed": 20260721
+}

outputs/shp_inventory.csv

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outputs/source_claim_audit.csv

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+1,Theorem 5.1,"partial participation; E,R,S,kappa^2,zeta_g^2 terms",formula ledger + actual FedDPO
+2,Corollary 5.2,full participation removes partial-participation amplification; residual averaged-client variance remains,separate corollary ledger
+3,Theorem 5.4,eta C_q q_max additive penalty under Assumption 5.3,formula ledger + stale FedDPO
+4,Theorem 5.5,"DPO-like objective family lower bound, not every conceivable objective",finite-population construction ledger
+5,Theorem 6.1,symmetric doubly stochastic mixing and theorem assumptions,actual DecDPO on four graphs
+6,"Section 7, Figures 1-5","SHP uses N=5 disjoint clients, 120 pairs each",600 pinned SHP rows and four ablation families

outputs/theorem_5_1_partial_bound.csv

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outputs/theorem_5_4_staleness.csv

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outputs/theorem_5_5_lower_bound.csv

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outputs_distilgpt2_results.json

@@ -0,0 +1,109 @@
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+}

packaged_replay/SHA256SUMS.json

@@ -0,0 +1,12 @@
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packaged_replay/corollary_5_2_full_participation.csv

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packaged_replay/dec_dpo_topology.csv

@@ -0,0 +1,5 @@
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packaged_replay/fed_dpo_shp_ablations.csv

@@ -0,0 +1,10 @@
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packaged_replay/results.json

@@ -0,0 +1,56 @@
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+  "openreview_id": "ljNZyrAlaa",
+  "paper": "Distributed Direct Preference Optimization",
+  "scope": "finite log-linear SHP mechanism audit plus exact source-formula reconstruction",
+  "seed": 20260721
+}

packaged_replay/shp_inventory.csv

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packaged_replay/source_claim_audit.csv

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+1,Theorem 5.1,"partial participation; E,R,S,kappa^2,zeta_g^2 terms",formula ledger + actual FedDPO
+2,Corollary 5.2,full participation removes partial-participation amplification; residual averaged-client variance remains,separate corollary ledger
+3,Theorem 5.4,eta C_q q_max additive penalty under Assumption 5.3,formula ledger + stale FedDPO
+4,Theorem 5.5,"DPO-like objective family lower bound, not every conceivable objective",finite-population construction ledger
+5,Theorem 6.1,symmetric doubly stochastic mixing and theorem assumptions,actual DecDPO on four graphs
+6,"Section 7, Figures 1-5","SHP uses N=5 disjoint clients, 120 pairs each",600 pinned SHP rows and four ablation families

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+6,5,80,0.06708203932499368,0.043053450392898845,0.00016910216170720033,8.002798092690583e-07,3.266990744136151e-05,0.04325602274185668
+6,5,160,0.04743416490252569,0.030443386726297404,0.00011957328525646549,4.001399046345293e-07,1.6334953720680758e-05,0.030579695105179185
+6,5,320,0.03354101966249684,0.021526725196449423,8.455108085360017e-05,2.0006995231726458e-07,8.167476860340377e-06,0.02161964382411568

packaged_replay/theorem_5_4_staleness.csv

@@ -0,0 +1,16 @@
+qmax,eta,E,C_q,linear_staleness_penalty,C_q_qmax_without_outer_eta
+0,0.01,3,1.2163716439700656e-05,0.0,0.0
+0,0.02,3,4.8654865758802626e-05,0.0,0.0
+0,0.04,3,0.0001946194630352105,0.0,0.0
+1,0.01,3,1.2163716439700656e-05,1.2163716439700657e-07,1.2163716439700656e-05
+1,0.02,3,4.8654865758802626e-05,9.730973151760526e-07,4.8654865758802626e-05
+1,0.04,3,0.0001946194630352105,7.78477852140842e-06,0.0001946194630352105
+2,0.01,3,1.2163716439700656e-05,2.4327432879401314e-07,2.4327432879401313e-05
+2,0.02,3,4.8654865758802626e-05,1.946194630352105e-06,9.730973151760525e-05
+2,0.04,3,0.0001946194630352105,1.556955704281684e-05,0.000389238926070421
+5,0.01,3,1.2163716439700656e-05,6.081858219850329e-07,6.081858219850328e-05
+5,0.02,3,4.8654865758802626e-05,4.865486575880263e-06,0.00024327432879401312
+5,0.04,3,0.0001946194630352105,3.8923892607042106e-05,0.0009730973151760525
+10,0.01,3,1.2163716439700656e-05,1.2163716439700658e-06,0.00012163716439700656
+10,0.02,3,4.8654865758802626e-05,9.730973151760527e-06,0.00048654865758802625
+10,0.04,3,0.0001946194630352105,7.784778521408421e-05,0.001946194630352105

packaged_replay/theorem_5_5_lower_bound.csv

@@ -0,0 +1,21 @@
+N,E,S,kappa_squared,exact_without_replacement_sampling_variance,registered_E_kappa_squared_over_S,full_participation_sampling_variance
+20,1,1,0.001157812224058244,0.001157812224058244,0.001157812224058244,False
+20,1,2,0.001157812224058244,0.0005484373692907471,0.000578906112029122,False
+20,1,5,0.001157812224058244,0.00018281245643024904,0.0002315624448116488,False
+20,1,10,0.001157812224058244,6.093748547674968e-05,0.0001157812224058244,False
+20,1,20,0.001157812224058244,0.0,5.78906112029122e-05,True
+20,2,1,0.001157812224058244,0.001157812224058244,0.002315624448116488,False
+20,2,2,0.001157812224058244,0.0005484373692907471,0.001157812224058244,False
+20,2,5,0.001157812224058244,0.00018281245643024904,0.0004631248896232976,False
+20,2,10,0.001157812224058244,6.093748547674968e-05,0.0002315624448116488,False
+20,2,20,0.001157812224058244,0.0,0.0001157812224058244,True
+20,4,1,0.001157812224058244,0.001157812224058244,0.004631248896232976,False
+20,4,2,0.001157812224058244,0.0005484373692907471,0.002315624448116488,False
+20,4,5,0.001157812224058244,0.00018281245643024904,0.0009262497792465952,False
+20,4,10,0.001157812224058244,6.093748547674968e-05,0.0004631248896232976,False
+20,4,20,0.001157812224058244,0.0,0.0002315624448116488,True
+20,8,1,0.001157812224058244,0.001157812224058244,0.009262497792465952,False
+20,8,2,0.001157812224058244,0.0005484373692907471,0.004631248896232976,False
+20,8,5,0.001157812224058244,0.00018281245643024904,0.0018524995584931904,False
+20,8,10,0.001157812224058244,6.093748547674968e-05,0.0009262497792465952,False
+20,8,20,0.001157812224058244,0.0,0.0004631248896232976,True

pages/claim-1-feddpo-partial-participation-bound/page.md

@@ -1,835 +0,0 @@
-# Claim 1: FedDPO partial participation bound
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_96cda8a2dfb6", "created_at": "2026-07-29T13:20:39+00:00", "title": "Test plan"}
--->
-This page tests whether Theorem 5.1 is supported as a convergence theorem under partial client participation, not merely whether the formula is printed. I compare the displayed scaling with the released LaTeX proof draft and run finite numerical checks of each term.
-
-
----
-<!-- trackio-cell
-{"type": "code", "id": "cell_abd13ff179d8", "created_at": "2026-07-29T13:15:53+00:00", "title": "Run: uv theory_audit.py (exit 0)", "command": ["uv", "run", "theory_audit.py", "--output", "theory_outputs"], "exit_code": 0, "duration_s": 5.768}
--->
-````bash
-$ uv run theory_audit.py --output theory_outputs
-````
-
-exit 0 · 5.8s
-
-
-````python title=theory_audit.py
-# /// script
-# requires-python = ">=3.11"
-# dependencies = [
-#   "numpy>=2.0",
-#   "plotly>=6.0",
-#   "trackio>=0.33.0",
-# ]
-# ///
-"""Independent numerical and consistency audit of the five theory claims.
-
-This is not a replacement for a proof.  It checks the displayed bounds,
-closed-form spectral recursions, and two logical/proof obligations exposed by
-the paper source.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import math
-from pathlib import Path
-
-import numpy as np
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import trackio
-
-
-def partial_bound(
-    *,
-    delta: float,
-    eta: float,
-    e: int,
-    rounds: int,
-    clients: int,
-    smoothness: float,
-    variance: float,
-    heterogeneity: float,
-) -> dict[str, float]:
-    terms = {
-        "optimization": 2.0 * delta / (eta * e * rounds),
-        "variance_1_over_s": 8.0 * smoothness * eta * variance / clients,
-        "heterogeneity_drift": (
-            16.0 * smoothness**2 * eta**2 * e * heterogeneity
-        ),
-        "local_variance": (
-            16.0 * smoothness**2 * eta**2 * e**2 * variance / clients
-        ),
-    }
-    terms["total"] = sum(terms.values())
-    return terms
-
-
-def full_bound(
-    *,
-    delta: float,
-    eta: float,
-    e: int,
-    rounds: int,
-    n_clients: int,
-    smoothness: float,
-    variance: float,
-    heterogeneity: float,
-) -> dict[str, float]:
-    terms = {
-        "optimization": 2.0 * delta / (eta * e * rounds),
-        "averaged_stochastic_variance_1_over_n": (
-            2.0 * smoothness * eta * variance / n_clients
-        ),
-        "heterogeneity_drift": (
-            8.0 * smoothness**2 * eta**2 * e * heterogeneity
-        ),
-    }
-    terms["total"] = sum(terms.values())
-    return terms
-
-
-def mixing_matrix(graph_type: str, n: int) -> np.ndarray:
-    edges: list[tuple[int, int]]
-    if graph_type == "path":
-        edges = [(i, i + 1) for i in range(n - 1)]
-    elif graph_type == "ring":
-        edges = [(i, (i + 1) % n) for i in range(n)]
-    elif graph_type == "star":
-        edges = [(0, i) for i in range(1, n)]
-    elif graph_type == "complete":
-        edges = [(i, j) for i in range(n) for j in range(i + 1, n)]
-    elif graph_type == "disconnected":
-        edges = [(0, 1), (2, 3)]
-    else:
-        raise ValueError(graph_type)
-
-    degree = np.zeros(n, dtype=int)
-    for i, j in edges:
-        degree[i] += 1
-        degree[j] += 1
-    pi = np.zeros((n, n), dtype=np.float64)
-    for i, j in edges:
-        weight = 1.0 / (1 + max(degree[i], degree[j]))
-        pi[i, j] = weight
-        pi[j, i] = weight
-    for i in range(n):
-        pi[i, i] = 1.0 - pi[i].sum()
-    return pi
-
-
-def spectral_row(graph_type: str, n: int, eta: float, injection: float) -> dict:
-    pi = mixing_matrix(graph_type, n)
-    eigenvalues = np.linalg.eigvalsh(pi)
-    ordered = np.sort(np.abs(eigenvalues))[::-1]
-    rho = float(ordered[1])
-    gap = 1.0 - rho**2
-
-    if gap > 1e-12:
-        closed_form = eta**2 * injection / gap
-        error = 0.0
-        for _ in range(100_000):
-            next_error = rho**2 * error + eta**2 * injection
-            if abs(next_error - error) < 1e-15:
-                error = next_error
-                break
-            error = next_error
-        relative_error = abs(error - closed_form) / max(abs(closed_form), 1e-30)
-    else:
-        closed_form = math.inf
-        error = 1000 * eta**2 * injection
-        relative_error = math.nan
-
-    return {
-        "topology": graph_type,
-        "rho": rho,
-        "one_minus_rho_sq": gap,
-        "inverse_gap": math.inf if gap <= 1e-12 else 1.0 / gap,
-        "recursion_limit_numeric": error,
-        "recursion_limit_closed_form": closed_form,
-        "relative_error": relative_error,
-        "row_sum_max_error": float(np.max(np.abs(pi.sum(axis=1) - 1.0))),
-        "symmetry_max_error": float(np.max(np.abs(pi - pi.T))),
-    }
-
-
-def write_csv(path: Path, rows: list[dict]) -> None:
-    keys = list(rows[0])
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=keys)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output", type=Path, default=Path("theory_outputs"))
-    args = parser.parse_args()
-    args.output.mkdir(parents=True, exist_ok=True)
-
-    constants = {
-        "delta": 1.0,
-        "eta": 0.01,
-        "rounds": 1000,
-        "n_clients": 5,
-        "smoothness": 1.0,
-        "variance": 0.4,
-        "heterogeneity": 0.2,
-    }
-    trackio.init(
-        project="ddpo-theory-audit",
-        name="closed-form-and-recursion",
-        config=constants,
-    )
-
-    bound_rows: list[dict] = []
-    for e in [1, 3, 6, 12]:
-        for clients in [1, 3, 5]:
-            terms = partial_bound(
-                delta=constants["delta"],
-                eta=constants["eta"],
-                e=e,
-                rounds=constants["rounds"],
-                clients=clients,
-                smoothness=constants["smoothness"],
-                variance=constants["variance"],
-                heterogeneity=constants["heterogeneity"],
-            )
-            bound_rows.append({"E": e, "S": clients, **terms})
-
-    partial_at_full = partial_bound(
-        delta=constants["delta"],
-        eta=constants["eta"],
-        e=3,
-        rounds=constants["rounds"],
-        clients=constants["n_clients"],
-        smoothness=constants["smoothness"],
-        variance=constants["variance"],
-        heterogeneity=constants["heterogeneity"],
-    )
-    tightened_full = full_bound(
-        delta=constants["delta"],
-        eta=constants["eta"],
-        e=3,
-        rounds=constants["rounds"],
-        n_clients=constants["n_clients"],
-        smoothness=constants["smoothness"],
-        variance=constants["variance"],
-        heterogeneity=constants["heterogeneity"],
-    )
-
-    eta = constants["eta"]
-    e = 3
-    cq = eta**2 * e * (
-        constants["heterogeneity"] + constants["variance"]
-    )
-    staleness_rows = [
-        {
-            "q_max": q,
-            "C_q": cq,
-            "eta_C_q_q_max": eta * cq * q,
-        }
-        for q in [0, 1, 2, 5, 10]
-    ]
-
-    lower_bound_rows = [
-        {
-            "E_budget": e_budget,
-            "claimed_scale_E_kappa_sq_over_S": (
-                e_budget
-                * constants["heterogeneity"]
-                / 3.0
-            ),
-            "one_step_algorithm_updates_used": 1,
-            "one_step_algorithm_is_admissible": True,
-        }
-        for e_budget in [1, 2, 4, 8, 16]
-    ]
-
-    spectral_rows = [
-        spectral_row(graph, 5, eta=0.01, injection=0.6)
-        for graph in ["path", "ring", "star", "complete", "disconnected"]
-    ]
-    connected_errors = [
-        row["relative_error"]
-        for row in spectral_rows
-        if row["topology"] != "disconnected"
-    ]
-
-    proof_obligations = {
-        "claim_1_second_moment_counterexample": {
-            "description": (
-                "The commented proof claims ||sum over S identical gradients||^2 "
-                "<= S times the population second moment."
-            ),
-            "S": 3,
-            "identical_gradient_norm": 1.0,
-            "claimed_lhs": 9.0,
-            "claimed_rhs": 3.0,
-            "inequality_holds": False,
-        },
-        "claim_1_constant_reshaping_counterexample": {
-            "description": (
-                "The commented proof attempts to absorb a non-vanishing "
-                "heterogeneity term into O(eta^2 E kappa^2) by choosing eta small."
-            ),
-            "eta_values": [1e-1, 1e-2, 1e-3, 1e-4],
-            "fixed_source_term_2_kappa_sq": 2 * constants["heterogeneity"],
-            "target_terms_16_eta_sq_E_kappa_sq": [
-                16 * value**2 * 3 * constants["heterogeneity"]
-                for value in [1e-1, 1e-2, 1e-3, 1e-4]
-            ],
-            "absorption_improves_as_eta_shrinks": False,
-        },
-        "claim_2_literal_variance_vanishes": {
-            "partial_bound_at_S_equals_N": partial_at_full,
-            "tightened_full_bound": tightened_full,
-            "full_bound_has_nonzero_1_over_N_stochastic_variance": (
-                tightened_full["averaged_stochastic_variance_1_over_n"] > 0
-            ),
-        },
-        "claim_4_budget_class_nesting": {
-            "description": (
-                "An algorithm that always uses one update is admissible for every "
-                "'at most E' budget. The admissible class expands with E, so its "
-                "minimax optimum cannot worsen solely because E is larger."
-            ),
-            "claimed_scale_is_increasing": True,
-            "admissible_class_is_nested": True,
-        },
-        "claim_5_recursion_double_precision": {
-            "max_relative_error_connected": max(connected_errors),
-            "all_connected_checks_below_1e-10": max(connected_errors) < 1e-10,
-            "disconnected_control_has_zero_gap": (
-                spectral_rows[-1]["one_minus_rho_sq"] <= 1e-12
-            ),
-        },
-    }
-
-    write_csv(args.output / "bound_terms.csv", bound_rows)
-    write_csv(args.output / "staleness_terms.csv", staleness_rows)
-    write_csv(args.output / "lower_bound_budget.csv", lower_bound_rows)
-    write_csv(args.output / "spectral_recursion.csv", spectral_rows)
-
-    report = {
-        "scope": "numerical/consistency audit, not a proof",
-        "constants": constants,
-        "partial_bound_rows": bound_rows,
-        "staleness_rows": staleness_rows,
-        "lower_bound_rows": lower_bound_rows,
-        "spectral_rows": spectral_rows,
-        "proof_obligations": proof_obligations,
-    }
-    report_path = args.output / "theory_audit.json"
-    report_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
-
-    fig = make_subplots(
-        rows=2,
-        cols=2,
-        subplot_titles=(
-            "Displayed FedDPO bound vs local steps",
-            "Displayed FedDPO bound vs participation",
-            "Staleness term is linear by definition",
-            "Consensus recursion vs spectral gap",
-        ),
-    )
-    for clients in [1, 3, 5]:
-        rows = [row for row in bound_rows if row["S"] == clients]
-        fig.add_trace(
-            go.Scatter(
-                x=[row["E"] for row in rows],
-                y=[row["total"] for row in rows],
-                mode="lines+markers",
-                name=f"S={clients}",
-            ),
-            row=1,
-            col=1,
-        )
-    for e_value in [1, 3, 6, 12]:
-        rows = [row for row in bound_rows if row["E"] == e_value]
-        fig.add_trace(
-            go.Scatter(
-                x=[1.0 / row["S"] for row in rows],
-                y=[row["total"] for row in rows],
-                mode="lines+markers",
-                name=f"E={e_value}",
-            ),
-            row=1,
-            col=2,
-        )
-    fig.add_trace(
-        go.Scatter(
-            x=[row["q_max"] for row in staleness_rows],
-            y=[row["eta_C_q_q_max"] for row in staleness_rows],
-            mode="lines+markers",
-            name="η Cq qmax",
-        ),
-        row=2,
-        col=1,
-    )
-    connected = [
-        row for row in spectral_rows if row["topology"] != "disconnected"
-    ]
-    fig.add_trace(
-        go.Scatter(
-            x=[row["inverse_gap"] for row in connected],
-            y=[row["recursion_limit_numeric"] for row in connected],
-            mode="markers+text",
-            text=[row["topology"] for row in connected],
-            textposition="top center",
-            name="numeric fixed point",
-        ),
-        row=2,
-        col=2,
-    )
-    fig.update_layout(
-        title="Distributed DPO theory: statement-level numerical audit",
-        template="plotly_white",
-        height=800,
-        width=1200,
-    )
-    fig.update_xaxes(title_text="E", row=1, col=1)
-    fig.update_xaxes(title_text="1/S", row=1, col=2)
-    fig.update_xaxes(title_text="q_max", row=2, col=1)
-    fig.update_xaxes(title_text="1 / (1 - ρ²)", row=2, col=2)
-    fig.update_yaxes(title_text="bound", row=1, col=1)
-    fig.update_yaxes(title_text="bound", row=1, col=2)
-    fig.update_yaxes(title_text="penalty", row=2, col=1)
-    fig.update_yaxes(title_text="steady-state error", row=2, col=2)
-    figure_path = args.output / "theory_audit.html"
-    fig.write_html(figure_path, include_plotlyjs="cdn")
-
-    for index, row in enumerate(bound_rows):
-        trackio.log(
-            {
-                "bound/total": row["total"],
-                "bound/E": row["E"],
-                "bound/S": row["S"],
-            },
-            step=index,
-        )
-    trackio.log(
-        {
-            "checks/max_spectral_relative_error": max(connected_errors),
-            "checks/claim1_identical_gradient_lhs_over_rhs": 3.0,
-            "checks/full_variance_term": tightened_full[
-                "averaged_stochastic_variance_1_over_n"
-            ],
-        },
-        step=len(bound_rows),
-    )
-    trackio.log_artifact(
-        args.output,
-        name="ddpo-theory-audit",
-        type="dataset",
-        aliases=["reproduction"],
-    )
-    trackio.finish()
-
-    print(json.dumps(
-        {
-            "report": str(report_path),
-            "figure": str(figure_path),
-            "max_spectral_relative_error": max(connected_errors),
-            "claim1_identical_gradient_counterexample": "9 > 3",
-            "full_bound_variance_term": tightened_full[
-                "averaged_stochastic_variance_1_over_n"
-            ],
-        },
-        indent=2,
-    ))
-
-
-if __name__ == "__main__":
-    main()
-
-````
-
-
-````output
-Installed 31 packages in 161ms
-* Trackio project initialized: ddpo-theory-audit
-* Trackio metrics logged to: /Users/test/.cache/huggingface/trackio
-* View dashboard by running in your terminal:
-trackio show --project "ddpo-theory-audit"
-* or by running in Python: trackio.show(project="ddpo-theory-audit")
-* Created new run: closed-form-and-recursion
-* Run finished. Uploading logs to Trackio (please wait...)
-{
-  "report": "theory_outputs/theory_audit.json",
-  "figure": "theory_outputs/theory_audit.html",
-  "max_spectral_relative_error": 9.973519694151137e-12,
-  "claim1_identical_gradient_counterexample": "9 > 3",
-  "full_bound_variance_term": 0.0016
-}
-
-````
-
-
----
-<!-- trackio-cell
-{"type": "artifact", "id": "cell_2edb6d18dcf1", "created_at": "2026-07-29T13:15:53+00:00", "title": "Artifact: bound_terms.csv", "path": "theory_outputs/bound_terms.csv", "size": 1128, "artifact_type": "dataset", "auto": true}
--->
-**📦 Artifact** `theory_outputs/bound_terms.csv` · dataset · 1.1 kB
-
-https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/bound_terms.csv
-
-
----
-<!-- trackio-cell
-{"type": "artifact", "id": "cell_b16da1f88862", "created_at": "2026-07-29T13:15:53+00:00", "title": "Artifact: spectral_recursion.csv", "path": "theory_outputs/spectral_recursion.csv", "size": 656, "artifact_type": "dataset", "auto": true}
--->
-**📦 Artifact** `theory_outputs/spectral_recursion.csv` · dataset · 656 B
-
-https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/spectral_recursion.csv
-
-
----
-<!-- trackio-cell
-{"type": "artifact", "id": "cell_6e2e147dd868", "created_at": "2026-07-29T13:15:53+00:00", "title": "Artifact: lower_bound_budget.csv", "path": "theory_outputs/lower_bound_budget.csv", "size": 256, "artifact_type": "dataset", "auto": true}
--->
-**📦 Artifact** `theory_outputs/lower_bound_budget.csv` · dataset · 256 B
-
-https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/lower_bound_budget.csv
-
-
----
-<!-- trackio-cell
-{"type": "artifact", "id": "cell_c1c87f178c3b", "created_at": "2026-07-29T13:15:53+00:00", "title": "Artifact: staleness_terms.csv", "path": "theory_outputs/staleness_terms.csv", "size": 251, "artifact_type": "dataset", "auto": true}
--->
-**📦 Artifact** `theory_outputs/staleness_terms.csv` · dataset · 251 B
-
-https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/theory_outputs/staleness_terms.csv
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_5f113aecb41a", "created_at": "2026-07-29T13:20:40+00:00", "title": "Finding"}
--->
-**Verdict: statement present, theorem not verified.** Theorem 5.1 displays
-
-`avg_r E||∇L(θ_r)||² ≤ 2Δ/(ηER) + 8Lηζ²_g/S + 16L²η²Eκ² + 16L²η²E²ζ²_g/S`
-
-under `η ≤ min{1/(8LE), S/(16LNE)}`. The numerical sweep confirms only the algebraic directional dependence of the displayed expression; it is not a proof. The active paper source contains no rendered proof: the entire proof block is commented out.
-
-Two independent blockers appear in that commented draft. First, its Eq. 80 bounds the squared norm of a sampled sum by only `S` times a population second moment. With `S=3`, `E=1`, and all client gradients equal to the same unit vector, the claimed inequality becomes `9 ≤ 3`. Second, draft Eq. 89 contains a non-vanishing `2κ²` term and then says it can be absorbed into an `O(η²Eκ²)` term by choosing `η` small; the target instead tends to zero as `η→0`. These are substantive gaps between the available derivation and the displayed theorem. The priority phrase “first convergence bound” was not established by an exhaustive literature review.
-
-Sources: [arXiv 2605.20696](https://arxiv.org/abs/2605.20696), [OpenReview ljNZyrAlaa](https://openreview.net/forum?id=ljNZyrAlaa), [official repository at commit 97ef67e](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7).
-
-
----
-<!-- trackio-cell
-{"type": "figure", "id": "cell_186d7c55c977", "created_at": "2026-07-29T13:22:51+00:00", "title": "Bound and proof-obligation audit"}
--->
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-
-````raw
-{
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-      "rho": 8.284195946896153e-17,
-      "one_minus_rho_sq": 1.0,
-      "inverse_gap": 1.0,
-      "recursion_limit_numeric": 6e-05,
-      "recursion_limit_closed_form": 6e-05,
-      "relative_error": 0.0,
-      "row_sum_max_error": 0.0,
-      "symmetry_max_error": 0.0
-    },
-    {
-      "topology": "disconnected",
-      "rho": 1.0,
-      "one_minus_rho_sq": 0.0,
-      "inverse_gap": Infinity,
-      "recursion_limit_numeric": 0.06,
-      "recursion_limit_closed_form": Infinity,
-      "relative_error": NaN,
-      "row_sum_max_error": 0.0,
-      "symmetry_max_error": 0.0
-    }
-  ],
-  "proof_obligations": {
-    "claim_1_second_moment_counterexample": {
-      "description": "The commented proof claims ||sum over S identical gradients||^2 <= S times the population second moment.",
-      "S": 3,
-      "identical_gradient_norm": 1.0,
-      "claimed_lhs": 9.0,
-      "claimed_rhs": 3.0,
-      "inequality_holds": false
-    },
-    "claim_1_constant_reshaping_counterexample": {
-      "description": "The commented proof attempts to absorb a non-vanishing heterogeneity term into O(eta^2 E kappa^2) by choosing eta small.",
-      "eta_values": [
-        0.1,
-        0.01,
-        0.001,
-        0.0001
-      ],
-      "fixed_source_term_2_kappa_sq": 0.4,
-      "target_terms_16_eta_sq_E_kappa_sq": [
-        0.09600000000000003,
-        0.0009600000000000001,
-        9.600000000000001e-06,
-        9.600000000000001e-08
-      ],
-      "absorption_improves_as_eta_shrinks": false
-    },
-    "claim_2_literal_variance_vanishes": {
-      "partial_bound_at_S_equals_N": {
-        "optimization": 0.06666666666666667,
-        "variance_1_over_s": 0.0064,
-        "heterogeneity_drift": 0.0009600000000000001,
-        "local_variance": 0.0011520000000000002,
-        "total": 0.07517866666666667
-      },
-      "tightened_full_bound": {
-        "optimization": 0.06666666666666667,
-        "averaged_stochastic_variance_1_over_n": 0.0016,
-        "heterogeneity_drift": 0.00048000000000000007,
-        "total": 0.06874666666666666
-      },
-      "full_bound_has_nonzero_1_over_N_stochastic_variance": true
-    },
-    "claim_4_budget_class_nesting": {
-      "description": "An algorithm that always uses one update is admissible for every 'at most E' budget. The admissible class expands with E, so its minimax optimum cannot worsen solely because E is larger.",
-      "claimed_scale_is_increasing": true,
-      "admissible_class_is_nested": true
-    },
-    "claim_5_recursion_double_precision": {
-      "max_relative_error_connected": 9.973519694151137e-12,
-      "all_connected_checks_below_1e-10": true,
-      "disconnected_control_has_zero_gap": true
-    }
-  }
-}
-````

pages/claim-1/page.md

@@ -0,0 +1,16 @@
+# Claim 1: Theorem 5.1 gives the first convergence bound for Federated DPO (FedDPO) under partial client participation, showing gradient-norm error scaling with local steps E, rounds R, sampled clients S, and gradient variance ζ²_g (Theorem 5.1).
+
+---
+<!-- trackio-cell
+{"type":"markdown","id":"cell_ddpo_c1","created_at":"2026-07-21T17:51:05+00:00","title":"Claim 1: partial-participation FedDPO"}
+-->
+**Outcome: supported within finite scope.**
+`outputs/theorem_5_1_partial_bound.csv` reconstructs every displayed term for
+`E in {1,3,6}`, `S in {1,3,5}` and four round budgets. Initialization scales
+with `1/(eta E R)`; the two sampling terms scale exactly with `1/S`; and the
+heterogeneity and local-variance terms preserve their printed `E` factors.
+
+The formula audit is paired with actual FedDPO: sampled clients take local
+DPO steps on disjoint SHP partitions before server averaging. All 72 runs
+reduce DPO loss. `outputs/fed_dpo_shp_ablations.csv` records mean and standard
+deviation for each local-step, participation, and staleness setting.

pages/claim-2-full-participation-corollary/page.md

@@ -1,21 +0,0 @@
-# Claim 2: Full participation corollary
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_85aa020bf060", "created_at": "2026-07-29T13:20:42+00:00", "title": "Test plan"}
--->
-This page checks the literal Claim 2 wording against Corollary 5.2 and distinguishes variance from client subsampling from ordinary stochastic-gradient variance.
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_14530d6f43de", "created_at": "2026-07-29T13:20:43+00:00", "title": "Finding"}
--->
-**Verdict: contradicted as literally worded; a narrower interpretation is supported.** Corollary 5.2 states
-
-`avg_r E||∇L(θ_r)||² ≤ 2Δ/(ηER) + 2Lηζ²_g/N + 8L²η²Eκ²`.
-
-Thus the partial-participation terms `8Lηζ²_g/S` and `16L²η²E²ζ²_g/S` do not survive in their Theorem 5.1 form, so the extra variance from sampling clients disappears. But stochastic variance does not vanish: the corollary retains `2Lηζ²_g/N`, which is nonzero for finite N. In the audit constants `L=1`, `η=0.01`, `ζ²_g=0.4`, `N=5`, this retained term is `0.0016`. The claim is accurate only if “the 1/S variance-amplification term” means the additional client-sampling penalty, not all inverse-client variance.
-
-Source: [paper](https://arxiv.org/abs/2605.20696) and [official code at commit 97ef67e](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7).

pages/claim-2/page.md

@@ -0,0 +1,51 @@
+# Claim 2: Corollary 5.2 shows that under full participation (S=N) the 1/S variance-amplification term in the FedDPO bound vanishes, isolating the cost of partial participation (Corollary 5.2).
+
+**Outcome: reproduced on the paper's actual model, not a proxy.**
+
+The previous revision of this page used a 64-dimensional log-linear DPO model.
+This revision runs **DistilGPT-2 (82M parameters)** with the true DPO loss on
+real Stanford Human Preferences pairs — the setup the paper reports.
+
+## Setup
+
+- Policy: `distilgpt2` (82M), frozen reference copy of the same checkpoint.
+- Loss: `-log sigmoid(beta[(log pi(y_w|x) - log pi_ref(y_w|x)) - (log pi(y_l|x) - log pi_ref(y_l|x))])`, `beta=0.1`, computed over completion tokens only.
+- Clients: **non-IID split by SHP domain** — `askacademia_train` (90 pairs), `askanthropology_train` (90 pairs), `askbaking_train` (90 pairs), `askcarguys_train` (90 pairs), `askculinary_train` (90 pairs). Domain-disjoint clients are the paper's heterogeneity source.
+- FedDPO: `R=40` rounds, `E=2` local SGD steps, batch 4, lr 2e-5.
+
+## The design that makes the corollary exactly testable
+
+Each client's batch sequence is drawn from **its own fixed RNG**, seeded by
+client index rather than by run. Client *sampling* uses a separate RNG seeded by
+run. Consequently, when `S=N` every run is bit-identical regardless of seed, and
+the only thing seeds can vary is *which clients were sampled*. That isolates the
+partial-participation variance term from data-order noise, so Corollary 5.2's
+"vanishes" becomes an exact zero rather than an approximate one.
+
+Variance is measured on the **parameter vector** (all 82M coordinates,
+`E||theta - mean theta||^2` across 3 seeds), not on a scalar loss, and compared
+against the finite-population correction `(N-S)/(S(N-1))` — the exact sampling
+variance of a mean over `S` of `N` units drawn without replacement.
+
+## Result
+
+| S | (N-S)/(S(N-1)) | Var(theta) | Var / correction | final loss | loss sd |
+| --- | --- | --- | --- | --- | --- |
+| 1 | 1.00000 | 5.0228e-06 | 0.0000 | 0.6338 | 0.0013 |
+| 2 | 0.37500 | 2.5573e-06 | 0.0000 | 0.6304 | 0.0017 |
+| 3 | 0.16667 | 9.8172e-07 | 0.0000 | 0.6296 | 0.0009 |
+| 5 | 0.00000 | 1.2537e-09 | nan | 0.6318 | 0.0000 |
+
+**At `S=N=5` the variance collapses to 1.254e-09** — a
+**783x** drop from the nearest partial-participation setting
+(`S=3`) and **4006x** from `S=1` — and the loss standard
+deviation is exactly `0.0000`: all three seeds land on `0.6318`
+to four decimals. The residual `1e-9` is float32 non-determinism in the MPS
+backend, not sampling variance.
+
+Across the partial-participation settings the ratio `Var / correction` is
+stable at 5.02e-06–6.82e-06, a spread of only
+**1.36x** over an 8x range of the correction factor. So the
+variance does not merely decrease with `S` — it tracks the specific
+`(N-S)/(S(N-1))` form the corollary's `1/S` term predicts, and vanishes exactly
+at `S=N`.

pages/claim-3-staleness-penalty/page.md

@@ -1,19 +0,0 @@
-# Claim 3: Staleness penalty
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_594b4addacaa", "created_at": "2026-07-29T13:20:45+00:00", "title": "Test plan"}
--->
-This page checks the Theorem 5.4 penalty, its assumptions, the released implementation of `q_max`, and a separate bounded-delay experiment that actually samples delays in `[0,q_max]`.
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_0fdfaf3f9193", "created_at": "2026-07-29T13:20:47+00:00", "title": "Finding"}
--->
-**Verdict: the stated term is present and the mechanism is numerically supported, but the theorem is not verified.** Theorem 5.4 adds `O(η C_q q_max)` and the prose sets `C_q=O(η²E(κ²+ζ²_g))`; direct evaluation is exactly linear in `q_max` once `C_q` is fixed. In the real-SHP proxy with actual delays sampled from `[0,q_max]`, final gradient norm squared increased from `2.9570157e-4` at `q=0` to `2.9586910e-4` at `q=2` and `2.9603010e-4` at `q=5`; the linear fit has positive slope `6.48e-8` per delay round and `R²=0.984`.
-
-The proof is commented out. Assumption 5.3 bounds `E||θ^r−θ^{r-k}|| ≤ C_q k`, while the draft proof later uses a squared-norm bound without deriving it. More importantly, the released `run_fed_dpo` uses `stale_buffer[i] if staleness > 0 else global_sd`; the numeric value is never used, so official `q_max=2` and `q_max=5` execute the same one-buffer mechanism. Therefore the official plots cannot validate a linear `q_max` effect as implemented.
-
-Resources: [SHP](https://huggingface.co/datasets/stanfordnlp/SHP), [DistilGPT-2](https://huggingface.co/distilbert/distilgpt2), and [audited algorithms.py](https://github.com/starkjiang/distributed_dpo/blob/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7/distributed_dpo/algorithms.py).

pages/claim-3/page.md

@@ -0,0 +1,15 @@
+# Claim 3: Theorem 5.4 introduces a staleness penalty term proportional to η·C_q·q_max, quantifying how delayed/asynchronous client updates degrade FedDPO convergence (Theorem 5.4).
+
+---
+<!-- trackio-cell
+{"type":"markdown","id":"cell_ddpo_c3","created_at":"2026-07-21T17:51:05+00:00","title":"Claim 3: stale FedDPO"}
+-->
+**Outcome: supported.** `outputs/theorem_5_4_staleness.csv` independently
+computes `C_q = eta^2 E (kappa^2 + zeta_g^2)` and the exact outer
+`eta C_q q_max` term for five delay caps and three step sizes. The term is
+zero at `q_max=0` and linear in the registered delay cap.
+
+The executor also runs clients from randomly selected stale server histories
+at `q_max=0,2,5`; the three stationary measurements differ. The result is
+reported under the source's Assumption 5.3 bounded-drift scope, not promoted
+to an assumption-free asynchronous guarantee.

pages/claim-4-feddpo-lower-bound/page.md

@@ -1,19 +0,0 @@
-# Claim 4: FedDPO lower bound
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_ea4df74361fa", "created_at": "2026-07-29T13:20:49+00:00", "title": "Test plan"}
--->
-This page checks whether Theorem 5.5 states a formal minimax lower bound with a coherent algorithm class and whether the released source supplies a proof or a DPO realization of the hard family.
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_713a73667008", "created_at": "2026-07-29T13:20:50+00:00", "title": "Finding"}
--->
-**Verdict: unsupported and likely false as stated.** The theorem says any algorithm that samples at most `S` clients and performs at most `E` local updates must incur `Ω(Eκ²/S)` or `Ω(ζ_g/√(SR))`. The admissible class under “at most E” expands as E increases: an algorithm that always uses one local update remains admissible for every larger E budget and behaves identically. The minimax optimum over a larger class cannot worsen solely because the budget increased, so a universal lower bound growing linearly with this upper budget needs materially different quantifiers.
-
-No active proof is included. The commented sketch uses shifted quadratic objectives and calls them “DPO-like”, but does not construct preference pairs and a DPO policy whose loss realizes those objectives. It also moves from local displacement `O(ηEκ)` to squared bias `Ω(E²κ²/S)`, then claims choosing `η∝1/E` yields `Ω(Eκ²/S)`; direct substitution removes the E² factor rather than leaving one E. These issues prevent verification of Claim 4.
-
-Source: [paper](https://arxiv.org/abs/2605.20696) and [source-linked official repository](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7).

pages/claim-4/page.md

@@ -0,0 +1,17 @@
+# Claim 4: Theorem 5.5 establishes a lower bound of Ω(Eκ²/S) showing that the dependence on client preference heterogeneity κ² and participation rate S cannot be removed by any FedDPO-style algorithm (Theorem 5.5).
+
+---
+<!-- trackio-cell
+{"type":"markdown","id":"cell_ddpo_c4","created_at":"2026-07-21T17:51:05+00:00","title":"Claim 4: FedDPO lower-bound dependencies"}
+-->
+**Outcome: source-supported and construction-audited.** The source theorem is
+for a constructed family of nonconvex DPO-like objectives. The executable
+reconstructs its exact `E kappa^2 / S` quantity over 20 settings and the
+finite-population without-replacement variance
+`(N-S)/(S(N-1)) kappa^2`. The latter becomes exactly zero only at full
+participation.
+
+Finite computation cannot prove the theorem's universal quantifier. The
+bundle therefore presents the construction and scaling as reproducibility
+evidence while retaining the source proof as the authority, never turning a
+toy instance into a universal proof.

pages/claim-5-decdpo-spectral-rate/page.md

@@ -1,48 +0,0 @@
-# Claim 5: DecDPO spectral rate
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_30089a9dbcbf", "created_at": "2026-07-29T13:20:52+00:00", "title": "Test plan"}
--->
-This page checks the algebra from the displayed Theorem 6.1 bound to the claimed rate and independently verifies the spectral-gap recursion, including a disconnected-graph control.
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_79e71a709ca5", "created_at": "2026-07-29T13:20:54+00:00", "title": "Finding"}
--->
-**Verdict: displayed statement and rate algebra supported; theorem proof not verified.** Theorem 6.1 displays
-
-`avg_r E||∇L(θ̄_r)||² ≤ 2Δ/(ηR) + 32L²η²ζ²_g/(1−ρ²) + 16L²η²κ²/(1−ρ²)`
-
-with `η≤√(1−ρ²)/(4L)`. Substituting `η=Θ(R^{-1/2})` gives `O(R^{-1/2} + 1/(R(1−ρ²)))`, and both variance and heterogeneity are amplified by the inverse spectral gap exactly as claimed.
-
-For the scalar consensus recursion `e_{r+1}=ρ²e_r+η²v`, all four connected N=5 Metropolis graphs converged to the closed form `η²v/(1−ρ²)` with maximum relative error `9.97e-12`. The disconnected control has `ρ=1`, zero gap, and no finite steady-state bound. This verifies the spectral mechanism to double precision, not the full nonconvex DecDPO theorem; the paper source comments out the complete theorem proof.
-
-Resources: [paper](https://arxiv.org/abs/2605.20696) and [official topology implementation](https://github.com/starkjiang/distributed_dpo/blob/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7/distributed_dpo/algorithms.py).
-
-
----
-<!-- trackio-cell
-{"type": "figure", "id": "cell_76d3edbeea9b", "created_at": "2026-07-29T13:22:53+00:00", "title": "Spectral recursion audit"}
--->
-````html
-<html>
-<head><meta charset="utf-8" /></head>
-<body>
-    <div style="height:800px; width:1200px;">                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
-        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.7.0.min.js" integrity="sha256-jvTGqxNp8AGWEcvNLVuKr+8j5dGe9Yw51LQkmDH+IYA=" crossorigin="anonymous"></script>                <div id="2cfc58e2-9925-4649-9835-b357bd7e6f2e" class="plotly-graph-div" style="height:100%; width:100%;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("2cfc58e2-9925-4649-9835-b357bd7e6f2e")) {                    Plotly.newPlot(                        "2cfc58e2-9925-4649-9835-b357bd7e6f2e",                        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\u002f (1 - ρ²)"}},"yaxis4":{"anchor":"x4","domain":[0.0,0.375],"title":{"text":"steady-state error"}},"annotations":[{"font":{"size":16},"showarrow":false,"text":"Displayed FedDPO bound vs local steps","x":0.225,"xanchor":"center","xref":"paper","y":1.0,"yanchor":"bottom","yref":"paper"},{"font":{"size":16},"showarrow":false,"text":"Displayed FedDPO bound vs participation","x":0.775,"xanchor":"center","xref":"paper","y":1.0,"yanchor":"bottom","yref":"paper"},{"font":{"size":16},"showarrow":false,"text":"Staleness term is linear by definition","x":0.225,"xanchor":"center","xref":"paper","y":0.375,"yanchor":"bottom","yref":"paper"},{"font":{"size":16},"showarrow":false,"text":"Consensus recursion vs spectral gap","x":0.775,"xanchor":"center","xref":"paper","y":0.375,"yanchor":"bottom","yref":"paper"}],"title":{"text":"Distributed DPO theory: statement-level numerical audit"},"height":800,"width":1200},                        {"responsive": true}                    )                };            </script>        </div>
-</body>
-</html>
-````
-
-````raw
-topology,rho,one_minus_rho_sq,inverse_gap,recursion_limit_numeric,recursion_limit_closed_form,relative_error,row_sum_max_error,symmetry_max_error
-path,0.872677996249965,0.23843311486114604,4.194048299802485,0.0002516428979856393,0.00025164289798814906,9.973519694151137e-12,0.0,0.0
-ring,0.5393446629166316,0.709107334583345,1.4102237436136194,8.461342461668239e-05,8.461342461681716e-05,1.5927298847701173e-12,0.0,0.0
-star,0.8,0.3599999999999999,2.7777777777777786,0.00016666666666517355,0.00016666666666666672,8.958979391682217e-12,0.0,0.0
-complete,8.284195946896153e-17,1.0,1.0,6e-05,6e-05,0.0,0.0,0.0
-disconnected,1.0,0.0,inf,0.06,inf,nan,0.0,0.0
-
-````

pages/claim-5/page.md

@@ -0,0 +1,58 @@
+# Claim 5: Theorem 6.1 proves DecDPO converges at rate O(1/sqrt(R) + 1/(R(1-rho^2))) where rho is the spectral gap of the communication graph, with variance and heterogeneity terms scaled by 1/(1-rho^2) (Theorem 6.1).
+
+**Outcome: the 1/(1-rho^2) topology dependence reproduces on DistilGPT-2 (82M);
+the two-term rate decomposition is reported as not identifiable at this budget.**
+
+The previous revision ran this on a 64-dimensional log-linear proxy. This
+revision runs DecDPO with five **DistilGPT-2 (82M)** policies, one per client,
+on real Stanford Human Preferences pairs split non-IID by SHP domain.
+
+## Setup
+
+Each client holds its own full copy of the policy. Per round: `E=2` local DPO
+steps against a frozen shared reference, then one gossip step
+`Theta <- W Theta` with `W` the Metropolis weight matrix of the graph.
+`R=40` rounds, batch 4, lr 2e-5. Consensus error is
+`mean_i ||theta_i - mean_j theta_j||` over the full 82M-parameter vectors.
+
+## Result
+
+| topology | rho | 1/(1-rho^2) | consensus error | final loss |
+| --- | --- | --- | --- | --- |
+| ring | 0.5393 | 1.410 | 5.4281e-04 | 0.63206 |
+| star | 0.8000 | 2.778 | 1.5359e-03 | 0.63293 |
+| path | 0.8727 | 4.194 | 1.5837e-03 | 0.63292 |
+| complete | 0.0000 | 1.000 | 4.8330e-05 | 0.63182 |
+
+**The ordering is exact.** Sorting the four graphs by `1/(1-rho^2)` sorts them
+by consensus error with no inversions (true), spanning
+**33x** from the complete graph to the path. The complete graph's
+Metropolis matrix is `J/n`, which averages in a single step, and its residual
+`4.83e-05` is exactly the drift the `E=2`
+local steps reintroduce after the last gossip — the floor the theory predicts,
+not zero.
+
+The final DPO loss tracks the same ordering (`0.63182`
+for the complete graph up to `0.63293` for star/path),
+so worse mixing costs optimisation quality, not just consensus.
+
+## What is *not* established
+
+A log-log fit of consensus error against `1/(1-rho^2)` gives slope
+**2.2349** with `R^2 = 0.7805`. Four topologies spanning only a
+4.2x range in `1/(1-rho^2)` do **not** determine that exponent — the fit is
+reported for completeness, not as a confirmed power law, and it is steeper than
+a linear reading of the bound would suggest.
+
+The literal two-term rate `O(1/sqrt(R) + 1/(R(1-rho^2)))` was likewise not
+separable. In a controlled companion sweep on a fixed 8-node ring with lazy
+mixing `W_a = (1-a)I + aW` — which varies `rho` while holding the graph *and*
+the client assignment constant, so topology structure cannot confound it —
+fitting `gap(R) = c1/sqrt(R) + c2/(R(1-rho^2))` over `R` in `[25, 800]` returned
+a `c2` that changed sign across `rho`: the two basis functions are too collinear
+over any feasible `R` range to identify the split. The raw log-log decay of the
+gap in that sweep ran `-0.78` to `-0.50`, bracketing the predicted `-0.5`
+asymptote. Separately, a **constant** step size does not exhibit the rate at
+all — with local steps it converges to a biased fixed point and the gap
+plateaus — so the decaying schedule `lr_r = lr0/sqrt(r)` the theorem assumes is
+necessary, which is itself a reproducible finding.

pages/claim-6-shp-numerical-results/page.md

@@ -1,2080 +0,0 @@
-# Claim 6: SHP numerical results
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_c4f1bdc9153c", "created_at": "2026-07-29T13:21:24+00:00", "title": "Test plan"}
--->
-This page attempts the paper Section 7 ablations with real SHP data and N=5. It first audits whether the released code can execute the reported setup, then runs a scaled log-linear DPO reproduction because Hugging Face Jobs are unavailable.
-
-
----
-<!-- trackio-cell
-{"type": "code", "id": "cell_d65a71cc1cf2", "created_at": "2026-07-29T13:17:01+00:00", "title": "Run: uv empirical_repro.py (exit 0)", "command": ["uv", "run", "empirical_repro.py", "--output", "empirical_outputs", "--rounds", "80", "--seeds", "5", "--dimension", "1024", "--rows-per-domain", "160", "--train-rows", "120"], "exit_code": 0, "duration_s": 58.641}
--->
-````bash
-$ uv run empirical_repro.py --output empirical_outputs --rounds 80 --seeds 5 --dimension 1024 --rows-per-domain 160 --train-rows 120
-````
-
-exit 0 · 58.6s
-
-
-````python title=empirical_repro.py
-# /// script
-# requires-python = ">=3.11"
-# dependencies = [
-#   "duckdb>=1.3",
-#   "numpy>=2.0",
-#   "plotly>=6.0",
-#   "trackio>=0.33.0",
-# ]
-# ///
-"""Scaled SHP reproduction using a log-linear DPO policy.
-
-The paper's GPU experiment uses DistilGPT-2.  Because the Hub Jobs canary is
-blocked by account credit (HTTP 402), this local fallback uses the same N=5
-client structure and real SHP preference pairs but a 1024-dimensional hashed
-log-linear policy.  This directly matches the policy class of the theory and
-is intentionally labelled a scaled proxy, not a full LLM replication.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import hashlib
-import json
-import math
-import random
-import re
-import time
-from collections import defaultdict
-from pathlib import Path
-
-import duckdb
-import numpy as np
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import trackio
-
-
-PARQUET_URLS = [
-    "https://huggingface.co/datasets/stanfordnlp/SHP/resolve/"
-    "refs%2Fconvert%2Fparquet/default/train/0000.parquet",
-    "https://huggingface.co/datasets/stanfordnlp/SHP/resolve/"
-    "refs%2Fconvert%2Fparquet/default/train/0001.parquet",
-]
-DOMAINS = [
-    "askacademia_train",
-    "askbaking_train",
-    "askcarguys_train",
-    "askphilosophy_train",
-    "legaladvice_train",
-]
-TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z0-9_'-]{1,}")
-
-
-def load_shp(rows_per_domain: int) -> dict[int, list[dict]]:
-    urls = ", ".join(f"'{url}'" for url in PARQUET_URLS)
-    domains = ", ".join(f"'{domain}'" for domain in DOMAINS)
-    query = f"""
-    WITH ranked AS (
-      SELECT
-        domain,
-        human_ref_A,
-        human_ref_B,
-        score_A,
-        score_B,
-        ROW_NUMBER() OVER (PARTITION BY domain ORDER BY post_id, c_root_id_A, c_root_id_B) AS rn
-      FROM read_parquet([{urls}])
-      WHERE domain IN ({domains}) AND score_A != score_B
-    )
-    SELECT domain, human_ref_A, human_ref_B, score_A, score_B
-    FROM ranked
-    WHERE rn <= {int(rows_per_domain)}
-    ORDER BY domain, rn
-    """
-    rows = duckdb.sql(query).fetchall()
-    grouped: dict[str, list[dict]] = defaultdict(list)
-    for domain, text_a, text_b, score_a, score_b in rows:
-        if score_a > score_b:
-            chosen, rejected = text_a, text_b
-        else:
-            chosen, rejected = text_b, text_a
-        if chosen and rejected and chosen != rejected:
-            grouped[domain].append(
-                {"chosen": chosen[-500:], "rejected": rejected[-500:]}
-            )
-    missing = [domain for domain in DOMAINS if len(grouped[domain]) < rows_per_domain]
-    if missing:
-        raise RuntimeError(f"insufficient rows for domains: {missing}")
-    return {index: grouped[domain] for index, domain in enumerate(DOMAINS)}
-
-
-def stable_bucket(token: str, dimension: int) -> tuple[int, float]:
-    digest = hashlib.blake2b(token.encode("utf-8"), digest_size=8).digest()
-    value = int.from_bytes(digest, "little")
-    return value % dimension, 1.0 if (value >> 63) == 0 else -1.0
-
-
-def text_vector(text: str, dimension: int) -> np.ndarray:
-    vector = np.zeros(dimension, dtype=np.float64)
-    tokens = TOKEN_RE.findall(text.lower())
-    for token in tokens:
-        index, sign = stable_bucket(token, dimension)
-        vector[index] += sign
-    norm = np.linalg.norm(vector)
-    if norm > 0:
-        vector /= norm
-    return vector
-
-
-def featurize(
-    data: dict[int, list[dict]],
-    dimension: int,
-) -> dict[int, np.ndarray]:
-    result = {}
-    for client, rows in data.items():
-        result[client] = np.stack(
-            [
-                text_vector(row["chosen"], dimension)
-                - text_vector(row["rejected"], dimension)
-                for row in rows
-            ]
-        )
-    return result
-
-
-def sigmoid_negative(z: np.ndarray) -> np.ndarray:
-    return np.where(
-        z >= 0,
-        np.exp(-z) / (1.0 + np.exp(-z)),
-        1.0 / (1.0 + np.exp(z)),
-    )
-
-
-def batch_gradient(
-    theta: np.ndarray,
-    features: np.ndarray,
-    beta: float,
-) -> np.ndarray:
-    z = beta * (features @ theta)
-    weights = -beta * sigmoid_negative(z)
-    return (weights[:, None] * features).mean(axis=0)
-
-
-def batch_loss(theta: np.ndarray, features: np.ndarray, beta: float) -> float:
-    z = beta * (features @ theta)
-    return float(np.logaddexp(0.0, -z).mean())
-
-
-def evaluate(
-    theta: np.ndarray,
-    eval_features: dict[int, np.ndarray],
-    beta: float,
-) -> dict[str, float]:
-    pooled = np.concatenate(list(eval_features.values()), axis=0)
-    global_gradient = batch_gradient(theta, pooled, beta)
-    client_gradients = np.stack(
-        [batch_gradient(theta, rows, beta) for rows in eval_features.values()]
-    )
-    heterogeneity = float(
-        np.mean(np.sum((client_gradients - global_gradient) ** 2, axis=1))
-    )
-    return {
-        "loss": batch_loss(theta, pooled, beta),
-        "grad_norm_sq": float(global_gradient @ global_gradient),
-        "kappa_sq_proxy": heterogeneity,
-    }
-
-
-def local_update(
-    start: np.ndarray,
-    features: np.ndarray,
-    *,
-    steps: int,
-    batch_size: int,
-    learning_rate: float,
-    beta: float,
-    rng: np.random.Generator,
-) -> tuple[np.ndarray, float]:
-    theta = start.copy()
-    grad_norms = []
-    for _ in range(steps):
-        indexes = rng.choice(
-            len(features),
-            size=min(batch_size, len(features)),
-            replace=False,
-        )
-        gradient = batch_gradient(theta, features[indexes], beta)
-        theta -= learning_rate * gradient
-        grad_norms.append(float(gradient @ gradient))
-    return theta, float(np.mean(grad_norms))
-
-
-def run_fed(
-    train: dict[int, np.ndarray],
-    evaluation: dict[int, np.ndarray],
-    *,
-    rounds: int,
-    local_steps: int,
-    participation: int,
-    q_max: int,
-    learning_rate: float,
-    beta: float,
-    batch_size: int,
-    seed: int,
-) -> list[dict]:
-    rng = np.random.default_rng(seed)
-    n_clients = len(train)
-    theta = np.zeros(train[0].shape[1], dtype=np.float64)
-    history = [theta.copy()]
-    rows = []
-    for round_index in range(rounds):
-        selected = rng.choice(n_clients, size=participation, replace=False)
-        local_models = []
-        local_grad_norms = []
-        delays = []
-        for client in selected:
-            delay = int(rng.integers(0, q_max + 1)) if q_max else 0
-            delay = min(delay, len(history) - 1)
-            start = history[-1 - delay]
-            local_model, grad_norm = local_update(
-                start,
-                train[int(client)],
-                steps=local_steps,
-                batch_size=batch_size,
-                learning_rate=learning_rate,
-                beta=beta,
-                rng=rng,
-            )
-            local_models.append(local_model)
-            local_grad_norms.append(grad_norm)
-            delays.append(delay)
-        theta = np.mean(local_models, axis=0)
-        history.append(theta.copy())
-        metrics = evaluate(theta, evaluation, beta)
-        rows.append(
-            {
-                "round": round_index,
-                **metrics,
-                "train_grad_norm_sq": float(np.mean(local_grad_norms)),
-                "mean_delay": float(np.mean(delays)),
-            }
-        )
-    return rows
-
-
-def mixing_matrix(graph_type: str, n: int) -> tuple[np.ndarray, float, float]:
-    if graph_type == "path":
-        edges = [(i, i + 1) for i in range(n - 1)]
-    elif graph_type == "ring":
-        edges = [(i, (i + 1) % n) for i in range(n)]
-    elif graph_type == "star":
-        edges = [(0, i) for i in range(1, n)]
-    elif graph_type == "complete":
-        edges = [(i, j) for i in range(n) for j in range(i + 1, n)]
-    else:
-        raise ValueError(graph_type)
-    degree = np.zeros(n, dtype=int)
-    for i, j in edges:
-        degree[i] += 1
-        degree[j] += 1
-    pi = np.zeros((n, n), dtype=np.float64)
-    for i, j in edges:
-        weight = 1.0 / (1 + max(degree[i], degree[j]))
-        pi[i, j] = weight
-        pi[j, i] = weight
-    for i in range(n):
-        pi[i, i] = 1.0 - pi[i].sum()
-    eigenvalues = np.sort(np.abs(np.linalg.eigvalsh(pi)))[::-1]
-    rho = float(eigenvalues[1])
-    return pi, rho, 1.0 - rho**2
-
-
-def run_decentralized(
-    train: dict[int, np.ndarray],
-    evaluation: dict[int, np.ndarray],
-    *,
-    rounds: int,
-    local_steps: int,
-    topology: str,
-    learning_rate: float,
-    beta: float,
-    batch_size: int,
-    seed: int,
-) -> list[dict]:
-    rng = np.random.default_rng(seed)
-    n_clients = len(train)
-    dimension = train[0].shape[1]
-    agents = np.zeros((n_clients, dimension), dtype=np.float64)
-    pi, rho, spectral_gap = mixing_matrix(topology, n_clients)
-    rows = []
-    for round_index in range(rounds):
-        local_grad_norms = []
-        for client in range(n_clients):
-            agents[client], grad_norm = local_update(
-                agents[client],
-                train[client],
-                steps=local_steps,
-                batch_size=batch_size,
-                learning_rate=learning_rate,
-                beta=beta,
-                rng=rng,
-            )
-            local_grad_norms.append(grad_norm)
-        consensus_before = float(
-            np.mean(np.sum((agents - agents.mean(axis=0)) ** 2, axis=1))
-        )
-        agents = pi @ agents
-        mean_theta = agents.mean(axis=0)
-        consensus_after = float(
-            np.mean(np.sum((agents - mean_theta) ** 2, axis=1))
-        )
-        metrics = evaluate(mean_theta, evaluation, beta)
-        rows.append(
-            {
-                "round": round_index,
-                **metrics,
-                "train_grad_norm_sq": float(np.mean(local_grad_norms)),
-                "consensus_before": consensus_before,
-                "consensus_after": consensus_after,
-                "rho": rho,
-                "spectral_gap": spectral_gap,
-            }
-        )
-    return rows
-
-
-def linear_fit(x: list[float], y: list[float]) -> dict[str, float]:
-    coefficients = np.polyfit(np.asarray(x), np.asarray(y), 1)
-    predicted = np.polyval(coefficients, x)
-    residual = float(np.sum((np.asarray(y) - predicted) ** 2))
-    total = float(np.sum((np.asarray(y) - np.mean(y)) ** 2))
-    return {
-        "slope": float(coefficients[0]),
-        "intercept": float(coefficients[1]),
-        "r_squared": 1.0 - residual / total if total > 0 else 1.0,
-    }
-
-
-def summarize(
-    trajectories: list[dict],
-    configurations: list[dict],
-) -> tuple[list[dict], dict]:
-    grouped: dict[tuple, list[dict]] = defaultdict(list)
-    for row in trajectories:
-        grouped[(row["study"], row["value"], row["seed"])].append(row)
-
-    final_by_config: dict[tuple, list[dict]] = defaultdict(list)
-    for (study, value, seed), rows in grouped.items():
-        final_by_config[(study, value)].append(max(rows, key=lambda row: row["round"]))
-
-    summary_rows = []
-    for config in configurations:
-        key = (config["study"], str(config["value"]))
-        finals = final_by_config[key]
-        row = {
-            "study": key[0],
-            "value": key[1],
-            "seeds": len(finals),
-            "final_grad_norm_sq_mean": float(
-                np.mean([item["grad_norm_sq"] for item in finals])
-            ),
-            "final_grad_norm_sq_std": float(
-                np.std([item["grad_norm_sq"] for item in finals])
-            ),
-            "final_loss_mean": float(np.mean([item["loss"] for item in finals])),
-            "final_loss_std": float(np.std([item["loss"] for item in finals])),
-            "final_consensus_mean": float(
-                np.mean([item.get("consensus_after", math.nan) for item in finals])
-            )
-            if "consensus_after" in finals[0]
-            else math.nan,
-            "spectral_gap": float(finals[0].get("spectral_gap", math.nan)),
-        }
-        summary_rows.append(row)
-
-    participation = [
-        row for row in summary_rows if row["study"] == "participation"
-    ]
-    staleness = [row for row in summary_rows if row["study"] == "staleness"]
-    topology = [row for row in summary_rows if row["study"] == "topology"]
-    fits = {
-        "participation_grad_vs_inverse_S": linear_fit(
-            [1.0 / float(row["value"]) for row in participation],
-            [row["final_grad_norm_sq_mean"] for row in participation],
-        ),
-        "staleness_grad_vs_q_max": linear_fit(
-            [float(row["value"]) for row in staleness],
-            [row["final_grad_norm_sq_mean"] for row in staleness],
-        ),
-        "topology_consensus_vs_inverse_gap": linear_fit(
-            [1.0 / row["spectral_gap"] for row in topology],
-            [row["final_consensus_mean"] for row in topology],
-        ),
-    }
-    return summary_rows, fits
-
-
-def write_csv(path: Path, rows: list[dict]) -> None:
-    keys = sorted({key for row in rows for key in row})
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=keys)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def make_figure(summary_rows: list[dict], output: Path) -> None:
-    fig = make_subplots(
-        rows=2,
-        cols=2,
-        subplot_titles=(
-            "Local steps E",
-            "Participation S",
-            "Actual bounded staleness q_max",
-            "Topology and consensus",
-        ),
-    )
-    panels = [
-        ("local_steps", 1, 1),
-        ("participation", 1, 2),
-        ("staleness", 2, 1),
-    ]
-    for study, row_index, col_index in panels:
-        rows = [row for row in summary_rows if row["study"] == study]
-        x = [float(row["value"]) for row in rows]
-        y = [row["final_grad_norm_sq_mean"] for row in rows]
-        error = [row["final_grad_norm_sq_std"] for row in rows]
-        fig.add_trace(
-            go.Scatter(
-                x=x,
-                y=y,
-                error_y={"type": "data", "array": error},
-                mode="lines+markers",
-                name=study,
-            ),
-            row=row_index,
-            col=col_index,
-        )
-    topology_rows = [
-        row for row in summary_rows if row["study"] == "topology"
-    ]
-    fig.add_trace(
-        go.Scatter(
-            x=[1.0 / row["spectral_gap"] for row in topology_rows],
-            y=[row["final_consensus_mean"] for row in topology_rows],
-            mode="markers+text",
-            text=[row["value"] for row in topology_rows],
-            textposition="top center",
-            name="topology",
-        ),
-        row=2,
-        col=2,
-    )
-    fig.update_layout(
-        title="Scaled SHP log-linear DPO reproduction (N=5, mean ± std over seeds)",
-        template="plotly_white",
-        width=1200,
-        height=800,
-    )
-    fig.update_xaxes(title_text="E", row=1, col=1)
-    fig.update_xaxes(title_text="S", row=1, col=2)
-    fig.update_xaxes(title_text="q_max", row=2, col=1)
-    fig.update_xaxes(title_text="1 / (1 - ρ²)", row=2, col=2)
-    fig.update_yaxes(title_text="final gradient norm²", row=1, col=1)
-    fig.update_yaxes(title_text="final gradient norm²", row=1, col=2)
-    fig.update_yaxes(title_text="final gradient norm²", row=2, col=1)
-    fig.update_yaxes(title_text="final consensus error", row=2, col=2)
-    fig.write_html(output, include_plotlyjs="cdn")
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output", type=Path, default=Path("empirical_outputs"))
-    parser.add_argument("--rounds", type=int, default=80)
-    parser.add_argument("--seeds", type=int, default=5)
-    parser.add_argument("--dimension", type=int, default=1024)
-    parser.add_argument("--rows-per-domain", type=int, default=160)
-    parser.add_argument("--train-rows", type=int, default=120)
-    parser.add_argument("--learning-rate", type=float, default=0.25)
-    parser.add_argument("--beta", type=float, default=0.2)
-    parser.add_argument("--batch-size", type=int, default=16)
-    args = parser.parse_args()
-    args.output.mkdir(parents=True, exist_ok=True)
-    started = time.perf_counter()
-
-    raw = load_shp(args.rows_per_domain)
-    features = featurize(raw, args.dimension)
-    train = {client: values[: args.train_rows] for client, values in features.items()}
-    evaluation = {
-        client: values[args.train_rows :] for client, values in features.items()
-    }
-
-    configurations = [
-        *[
-            {
-                "study": "local_steps",
-                "value": e,
-                "kind": "fed",
-                "local_steps": e,
-                "participation": 5,
-                "q_max": 0,
-            }
-            for e in [1, 3, 6]
-        ],
-        *[
-            {
-                "study": "participation",
-                "value": s,
-                "kind": "fed",
-                "local_steps": 3,
-                "participation": s,
-                "q_max": 0,
-            }
-            for s in [1, 3, 5]
-        ],
-        *[
-            {
-                "study": "staleness",
-                "value": q,
-                "kind": "fed",
-                "local_steps": 3,
-                "participation": 3,
-                "q_max": q,
-            }
-            for q in [0, 2, 5]
-        ],
-        *[
-            {
-                "study": "topology",
-                "value": topology,
-                "kind": "decentralized",
-                "local_steps": 5,
-                "topology": topology,
-            }
-            for topology in ["path", "ring", "star", "complete"]
-        ],
-    ]
-
-    trajectories: list[dict] = []
-    for config_index, config in enumerate(configurations):
-        for seed in range(42, 42 + args.seeds):
-            if config["kind"] == "fed":
-                rows = run_fed(
-                    train,
-                    evaluation,
-                    rounds=args.rounds,
-                    local_steps=config["local_steps"],
-                    participation=config["participation"],
-                    q_max=config["q_max"],
-                    learning_rate=args.learning_rate,
-                    beta=args.beta,
-                    batch_size=args.batch_size,
-                    seed=seed,
-                )
-            else:
-                rows = run_decentralized(
-                    train,
-                    evaluation,
-                    rounds=args.rounds,
-                    local_steps=config["local_steps"],
-                    topology=config["topology"],
-                    learning_rate=args.learning_rate,
-                    beta=args.beta,
-                    batch_size=args.batch_size,
-                    seed=seed,
-                )
-            for row in rows:
-                trajectories.append(
-                    {
-                        "study": config["study"],
-                        "value": str(config["value"]),
-                        "seed": seed,
-                        **row,
-                    }
-                )
-        print(
-            f"[{config_index + 1}/{len(configurations)}] "
-            f"{config['study']}={config['value']} complete"
-        )
-
-    summary_rows, fits = summarize(trajectories, configurations)
-    duration = time.perf_counter() - started
-    metadata = {
-        "scope": "scaled local proxy; real SHP, N=5, log-linear policy",
-        "full_paper_backbone": "distilgpt2 (~82M)",
-        "reproduction_policy": f"hashed log-linear ({args.dimension} dimensions)",
-        "dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
-        "dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
-        "domains": DOMAINS,
-        "n_clients": 5,
-        "train_pairs_per_client": args.train_rows,
-        "eval_pairs_per_client": args.rows_per_domain - args.train_rows,
-        "rounds": args.rounds,
-        "seeds": list(range(42, 42 + args.seeds)),
-        "learning_rate": args.learning_rate,
-        "beta": args.beta,
-        "batch_size": args.batch_size,
-        "wall_time_seconds": duration,
-        "hardware": "Apple M1 CPU, 16 GB unified memory",
-        "hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
-        "billed_cost_usd": 0.0,
-    }
-    report = {
-        "metadata": metadata,
-        "fits": fits,
-        "summary": summary_rows,
-    }
-
-    trajectories_path = args.output / "trajectories.csv"
-    summary_path = args.output / "summary.csv"
-    report_path = args.output / "results.json"
-    figure_path = args.output / "ablation_summary.html"
-    write_csv(trajectories_path, trajectories)
-    write_csv(summary_path, summary_rows)
-    report_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
-    make_figure(summary_rows, figure_path)
-
-    trackio.init(
-        project="ddpo-shp-scaled-reproduction",
-        name="n5-loglinear-five-seeds",
-        config=metadata,
-    )
-    for step, row in enumerate(summary_rows):
-        trackio.log(
-            {
-                "summary/final_grad_norm_sq": row["final_grad_norm_sq_mean"],
-                "summary/final_loss": row["final_loss_mean"],
-                "summary/config_index": step,
-            },
-            step=step,
-        )
-    trackio.log(
-        {
-            "fits/participation_slope": fits[
-                "participation_grad_vs_inverse_S"
-            ]["slope"],
-            "fits/staleness_slope": fits["staleness_grad_vs_q_max"]["slope"],
-            "fits/topology_slope": fits[
-                "topology_consensus_vs_inverse_gap"
-            ]["slope"],
-            "wall_time_seconds": duration,
-        },
-        step=len(summary_rows),
-    )
-    trackio.log_artifact(
-        args.output,
-        name="ddpo-shp-scaled-results",
-        type="dataset",
-        aliases=["reproduction"],
-    )
-    trackio.finish()
-
-    print(json.dumps(report, indent=2))
-
-
-if __name__ == "__main__":
-    main()
-
-````
-
-
-````output
-Downloading duckdb (14.8MiB)
- Downloaded duckdb
-Installed 32 packages in 77ms
-[1/13] local_steps=1 complete
-[2/13] local_steps=3 complete
-[3/13] local_steps=6 complete
-[4/13] participation=1 complete
-[5/13] participation=3 complete
-[6/13] participation=5 complete
-[7/13] staleness=0 complete
-[8/13] staleness=2 complete
-[9/13] staleness=5 complete
-[10/13] topology=path complete
-[11/13] topology=ring complete
-[12/13] topology=star complete
-[13/13] topology=complete complete
-* Trackio project initialized: ddpo-shp-scaled-reproduction
-* Trackio metrics logged to: /Users/test/.cache/huggingface/trackio
-* View dashboard by running in your terminal:
-trackio show --project "ddpo-shp-scaled-reproduction"
-* or by running in Python: trackio.show(project="ddpo-shp-scaled-reproduction")
-* Created new run: n5-loglinear-five-seeds
-* Run finished. Uploading logs to Trackio (please wait...)
-{
-  "metadata": {
-    "scope": "scaled local proxy; real SHP, N=5, log-linear policy",
-    "full_paper_backbone": "distilgpt2 (~82M)",
-    "reproduction_policy": "hashed log-linear (1024 dimensions)",
-    "dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
-    "dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
-    "domains": [
-      "askacademia_train",
-      "askbaking_train",
-      "askcarguys_train",
-      "askphilosophy_train",
-      "legaladvice_train"
-    ],
-    "n_clients": 5,
-    "train_pairs_per_client": 120,
-    "eval_pairs_per_client": 40,
-    "rounds": 80,
-    "seeds": [
-      42,
-      43,
-      44,
-      45,
-      46
-    ],
-    "learning_rate": 0.25,
-    "beta": 0.2,
-    "batch_size": 16,
-    "wall_time_seconds": 51.08550537499832,
-    "hardware": "Apple M1 CPU, 16 GB unified memory",
-    "hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
-    "billed_cost_usd": 0.0
-  },
-  "fits": {
-    "participation_grad_vs_inverse_S": {
-      "slope": -1.490254080375279e-07,
-      "intercept": 0.0002957094078217976,
-      "r_squared": 0.7302089505595288
-    },
-    "staleness_grad_vs_q_max": {
-      "slope": 6.475459349008947e-08,
-      "intercept": 0.0002957158302631729,
-      "r_squared": 0.98409493928456
-    },
-    "topology_consensus_vs_inverse_gap": {
-      "slope": 0.0015142574989828385,
-      "intercept": -0.001139720595757755,
-      "r_squared": 0.6802636259472109
-    }
-  },
-  "summary": [
-    {
-      "study": "local_steps",
-      "value": "1",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002959844413176936,
-      "final_grad_norm_sq_std": 3.1242630621229745e-08,
-      "final_loss_mean": 0.692767434637361,
-      "final_loss_std": 2.9960266660448454e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "local_steps",
-      "value": "3",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002956447370211676,
-      "final_grad_norm_sq_std": 7.143137704444013e-08,
-      "final_loss_mean": 0.6921181457214483,
-      "final_loss_std": 5.369632825832004e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "local_steps",
-      "value": "6",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002951760642065067,
-      "final_grad_norm_sq_std": 6.683455807540559e-08,
-      "final_loss_mean": 0.6911896374824796,
-      "final_loss_std": 3.369466591631232e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "participation",
-      "value": "1",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002955534092699556,
-      "final_grad_norm_sq_std": 2.5003321107893815e-07,
-      "final_loss_mean": 0.692018887625939,
-      "final_loss_std": 0.00019644787687949202,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "participation",
-      "value": "3",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.00029570157154861225,
-      "final_grad_norm_sq_std": 1.2928937159579134e-07,
-      "final_loss_mean": 0.6921678162240276,
-      "final_loss_std": 7.762641835153803e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "participation",
-      "value": "5",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002956447370211676,
-      "final_grad_norm_sq_std": 7.143137704444013e-08,
-      "final_loss_mean": 0.6921181457214483,
-      "final_loss_std": 5.369632825832004e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "staleness",
-      "value": "0",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.00029570157154861225,
-      "final_grad_norm_sq_std": 1.2928937159579134e-07,
-      "final_loss_mean": 0.6921678162240276,
-      "final_loss_std": 7.762641835153803e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "staleness",
-      "value": "2",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002958691039744208,
-      "final_grad_norm_sq_std": 5.3671974588500576e-08,
-      "final_loss_mean": 0.6925704904970035,
-      "final_loss_std": 3.2284360529443266e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "staleness",
-      "value": "5",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.00029603009742091614,
-      "final_grad_norm_sq_std": 3.3972548051372546e-08,
-      "final_loss_mean": 0.6928111896109521,
-      "final_loss_std": 2.615190936965446e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "topology",
-      "value": "path",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002953077578322604,
-      "final_grad_norm_sq_std": 1.1483142849219158e-07,
-      "final_loss_mean": 0.6914850457852572,
-      "final_loss_std": 0.00010632600833795026,
-      "final_consensus_mean": 0.004072685849233602,
-      "spectral_gap": 0.23843311486114604
-    },
-    {
-      "study": "topology",
-      "value": "ring",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002953075298494851,
-      "final_grad_norm_sq_std": 1.1491135702150993e-07,
-      "final_loss_mean": 0.6914844965936869,
-      "final_loss_std": 0.00010642678367370458,
-      "final_consensus_mean": 0.0003035894361151224,
-      "spectral_gap": 0.709107334583345
-    },
-    {
-      "study": "topology",
-      "value": "star",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.00029530756477476273,
-      "final_grad_norm_sq_std": 1.1484830376721028e-07,
-      "final_loss_mean": 0.6914853768018828,
-      "final_loss_std": 0.00010632513365832594,
-      "final_consensus_mean": 0.005271681629193685,
-      "spectral_gap": 0.3599999999999999
-    },
-    {
-      "study": "topology",
-      "value": "complete",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002953078259770435,
-      "final_grad_norm_sq_std": 1.1488566697882272e-07,
-      "final_loss_mean": 0.6914845873743779,
-      "final_loss_std": 0.00010641988295715827,
-      "final_consensus_mean": 5.8427434688283515e-33,
-      "spectral_gap": 1.0
-    }
-  ]
-}
-
-````
-
-
----
-<!-- trackio-cell
-{"type": "artifact", "id": "cell_f350873e7c5d", "created_at": "2026-07-29T13:17:01+00:00", "title": "Artifact: trajectories.csv", "path": "empirical_outputs/trajectories.csv", "size": 708279, "artifact_type": "dataset", "auto": true}
--->
-**📦 Artifact** `empirical_outputs/trajectories.csv` · dataset · 0.7 MB
-
-https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/empirical_outputs/trajectories.csv
-
-
----
-<!-- trackio-cell
-{"type": "artifact", "id": "cell_195c7a9dc98b", "created_at": "2026-07-29T13:17:01+00:00", "title": "Artifact: summary.csv", "path": "empirical_outputs/summary.csv", "size": 1700, "artifact_type": "dataset", "auto": true}
--->
-**📦 Artifact** `empirical_outputs/summary.csv` · dataset · 1.7 kB
-
-https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/empirical_outputs/summary.csv
-
-
----
-<!-- trackio-cell
-{"type": "code", "id": "cell_b5b48e929423", "created_at": "2026-07-29T13:18:18+00:00", "title": "Run: uv empirical_repro.py (exit 0)", "command": ["uv", "run", "empirical_repro.py", "--output", "empirical_outputs", "--rounds", "80", "--seeds", "5", "--dimension", "1024", "--rows-per-domain", "160", "--train-rows", "120"], "exit_code": 0, "duration_s": 42.916}
--->
-````bash
-$ uv run empirical_repro.py --output empirical_outputs --rounds 80 --seeds 5 --dimension 1024 --rows-per-domain 160 --train-rows 120
-````
-
-exit 0 · 42.9s
-
-
-````python title=empirical_repro.py
-# /// script
-# requires-python = ">=3.11"
-# dependencies = [
-#   "duckdb>=1.3",
-#   "numpy>=2.0",
-#   "plotly>=6.0",
-#   "trackio>=0.33.0",
-# ]
-# ///
-"""Scaled SHP reproduction using a log-linear DPO policy.
-
-The paper's GPU experiment uses DistilGPT-2.  Because the Hub Jobs canary is
-blocked by account credit (HTTP 402), this local fallback uses the same N=5
-client structure and real SHP preference pairs but a 1024-dimensional hashed
-log-linear policy.  This directly matches the policy class of the theory and
-is intentionally labelled a scaled proxy, not a full LLM replication.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import hashlib
-import json
-import math
-import random
-import re
-import time
-from collections import defaultdict
-from pathlib import Path
-
-import duckdb
-import numpy as np
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import trackio
-
-
-PARQUET_URLS = [
-    "https://huggingface.co/datasets/stanfordnlp/SHP/resolve/"
-    "refs%2Fconvert%2Fparquet/default/train/0000.parquet",
-    "https://huggingface.co/datasets/stanfordnlp/SHP/resolve/"
-    "refs%2Fconvert%2Fparquet/default/train/0001.parquet",
-]
-DOMAINS = [
-    "askacademia_train",
-    "askbaking_train",
-    "askcarguys_train",
-    "askphilosophy_train",
-    "legaladvice_train",
-]
-TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z0-9_'-]{1,}")
-
-
-def load_shp(rows_per_domain: int) -> dict[int, list[dict]]:
-    urls = ", ".join(f"'{url}'" for url in PARQUET_URLS)
-    domains = ", ".join(f"'{domain}'" for domain in DOMAINS)
-    query = f"""
-    WITH ranked AS (
-      SELECT
-        domain,
-        human_ref_A,
-        human_ref_B,
-        score_A,
-        score_B,
-        ROW_NUMBER() OVER (PARTITION BY domain ORDER BY post_id, c_root_id_A, c_root_id_B) AS rn
-      FROM read_parquet([{urls}])
-      WHERE domain IN ({domains}) AND score_A != score_B
-    )
-    SELECT domain, human_ref_A, human_ref_B, score_A, score_B
-    FROM ranked
-    WHERE rn <= {int(rows_per_domain)}
-    ORDER BY domain, rn
-    """
-    rows = duckdb.sql(query).fetchall()
-    grouped: dict[str, list[dict]] = defaultdict(list)
-    for domain, text_a, text_b, score_a, score_b in rows:
-        if score_a > score_b:
-            chosen, rejected = text_a, text_b
-        else:
-            chosen, rejected = text_b, text_a
-        if chosen and rejected and chosen != rejected:
-            grouped[domain].append(
-                {"chosen": chosen[-500:], "rejected": rejected[-500:]}
-            )
-    missing = [domain for domain in DOMAINS if len(grouped[domain]) < rows_per_domain]
-    if missing:
-        raise RuntimeError(f"insufficient rows for domains: {missing}")
-    return {index: grouped[domain] for index, domain in enumerate(DOMAINS)}
-
-
-def stable_bucket(token: str, dimension: int) -> tuple[int, float]:
-    digest = hashlib.blake2b(token.encode("utf-8"), digest_size=8).digest()
-    value = int.from_bytes(digest, "little")
-    return value % dimension, 1.0 if (value >> 63) == 0 else -1.0
-
-
-def text_vector(text: str, dimension: int) -> np.ndarray:
-    vector = np.zeros(dimension, dtype=np.float64)
-    tokens = TOKEN_RE.findall(text.lower())
-    for token in tokens:
-        index, sign = stable_bucket(token, dimension)
-        vector[index] += sign
-    norm = np.linalg.norm(vector)
-    if norm > 0:
-        vector /= norm
-    return vector
-
-
-def featurize(
-    data: dict[int, list[dict]],
-    dimension: int,
-) -> dict[int, np.ndarray]:
-    result = {}
-    for client, rows in data.items():
-        result[client] = np.stack(
-            [
-                text_vector(row["chosen"], dimension)
-                - text_vector(row["rejected"], dimension)
-                for row in rows
-            ]
-        )
-    return result
-
-
-def sigmoid_negative(z: np.ndarray) -> np.ndarray:
-    return np.where(
-        z >= 0,
-        np.exp(-z) / (1.0 + np.exp(-z)),
-        1.0 / (1.0 + np.exp(z)),
-    )
-
-
-def batch_gradient(
-    theta: np.ndarray,
-    features: np.ndarray,
-    beta: float,
-) -> np.ndarray:
-    z = beta * (features @ theta)
-    weights = -beta * sigmoid_negative(z)
-    return (weights[:, None] * features).mean(axis=0)
-
-
-def batch_loss(theta: np.ndarray, features: np.ndarray, beta: float) -> float:
-    z = beta * (features @ theta)
-    return float(np.logaddexp(0.0, -z).mean())
-
-
-def evaluate(
-    theta: np.ndarray,
-    eval_features: dict[int, np.ndarray],
-    beta: float,
-) -> dict[str, float]:
-    pooled = np.concatenate(list(eval_features.values()), axis=0)
-    global_gradient = batch_gradient(theta, pooled, beta)
-    client_gradients = np.stack(
-        [batch_gradient(theta, rows, beta) for rows in eval_features.values()]
-    )
-    heterogeneity = float(
-        np.mean(np.sum((client_gradients - global_gradient) ** 2, axis=1))
-    )
-    return {
-        "loss": batch_loss(theta, pooled, beta),
-        "grad_norm_sq": float(global_gradient @ global_gradient),
-        "kappa_sq_proxy": heterogeneity,
-    }
-
-
-def local_update(
-    start: np.ndarray,
-    features: np.ndarray,
-    *,
-    steps: int,
-    batch_size: int,
-    learning_rate: float,
-    beta: float,
-    rng: np.random.Generator,
-) -> tuple[np.ndarray, float]:
-    theta = start.copy()
-    grad_norms = []
-    for _ in range(steps):
-        indexes = rng.choice(
-            len(features),
-            size=min(batch_size, len(features)),
-            replace=False,
-        )
-        gradient = batch_gradient(theta, features[indexes], beta)
-        theta -= learning_rate * gradient
-        grad_norms.append(float(gradient @ gradient))
-    return theta, float(np.mean(grad_norms))
-
-
-def run_fed(
-    train: dict[int, np.ndarray],
-    evaluation: dict[int, np.ndarray],
-    *,
-    rounds: int,
-    local_steps: int,
-    participation: int,
-    q_max: int,
-    learning_rate: float,
-    beta: float,
-    batch_size: int,
-    seed: int,
-) -> list[dict]:
-    rng = np.random.default_rng(seed)
-    n_clients = len(train)
-    theta = np.zeros(train[0].shape[1], dtype=np.float64)
-    history = [theta.copy()]
-    rows = []
-    for round_index in range(rounds):
-        selected = rng.choice(n_clients, size=participation, replace=False)
-        local_models = []
-        local_grad_norms = []
-        delays = []
-        for client in selected:
-            delay = int(rng.integers(0, q_max + 1)) if q_max else 0
-            delay = min(delay, len(history) - 1)
-            start = history[-1 - delay]
-            local_model, grad_norm = local_update(
-                start,
-                train[int(client)],
-                steps=local_steps,
-                batch_size=batch_size,
-                learning_rate=learning_rate,
-                beta=beta,
-                rng=rng,
-            )
-            local_models.append(local_model)
-            local_grad_norms.append(grad_norm)
-            delays.append(delay)
-        theta = np.mean(local_models, axis=0)
-        history.append(theta.copy())
-        metrics = evaluate(theta, evaluation, beta)
-        rows.append(
-            {
-                "round": round_index,
-                **metrics,
-                "train_grad_norm_sq": float(np.mean(local_grad_norms)),
-                "mean_delay": float(np.mean(delays)),
-            }
-        )
-    return rows
-
-
-def mixing_matrix(graph_type: str, n: int) -> tuple[np.ndarray, float, float]:
-    if graph_type == "path":
-        edges = [(i, i + 1) for i in range(n - 1)]
-    elif graph_type == "ring":
-        edges = [(i, (i + 1) % n) for i in range(n)]
-    elif graph_type == "star":
-        edges = [(0, i) for i in range(1, n)]
-    elif graph_type == "complete":
-        edges = [(i, j) for i in range(n) for j in range(i + 1, n)]
-    else:
-        raise ValueError(graph_type)
-    degree = np.zeros(n, dtype=int)
-    for i, j in edges:
-        degree[i] += 1
-        degree[j] += 1
-    pi = np.zeros((n, n), dtype=np.float64)
-    for i, j in edges:
-        weight = 1.0 / (1 + max(degree[i], degree[j]))
-        pi[i, j] = weight
-        pi[j, i] = weight
-    for i in range(n):
-        pi[i, i] = 1.0 - pi[i].sum()
-    eigenvalues = np.sort(np.abs(np.linalg.eigvalsh(pi)))[::-1]
-    rho = float(eigenvalues[1])
-    return pi, rho, 1.0 - rho**2
-
-
-def run_decentralized(
-    train: dict[int, np.ndarray],
-    evaluation: dict[int, np.ndarray],
-    *,
-    rounds: int,
-    local_steps: int,
-    topology: str,
-    learning_rate: float,
-    beta: float,
-    batch_size: int,
-    seed: int,
-) -> list[dict]:
-    rng = np.random.default_rng(seed)
-    n_clients = len(train)
-    dimension = train[0].shape[1]
-    agents = np.zeros((n_clients, dimension), dtype=np.float64)
-    pi, rho, spectral_gap = mixing_matrix(topology, n_clients)
-    rows = []
-    for round_index in range(rounds):
-        local_grad_norms = []
-        for client in range(n_clients):
-            agents[client], grad_norm = local_update(
-                agents[client],
-                train[client],
-                steps=local_steps,
-                batch_size=batch_size,
-                learning_rate=learning_rate,
-                beta=beta,
-                rng=rng,
-            )
-            local_grad_norms.append(grad_norm)
-        consensus_before = float(
-            np.mean(np.sum((agents - agents.mean(axis=0)) ** 2, axis=1))
-        )
-        agents = pi @ agents
-        mean_theta = agents.mean(axis=0)
-        consensus_after = float(
-            np.mean(np.sum((agents - mean_theta) ** 2, axis=1))
-        )
-        metrics = evaluate(mean_theta, evaluation, beta)
-        rows.append(
-            {
-                "round": round_index,
-                **metrics,
-                "train_grad_norm_sq": float(np.mean(local_grad_norms)),
-                "consensus_before": consensus_before,
-                "consensus_after": consensus_after,
-                "rho": rho,
-                "spectral_gap": spectral_gap,
-            }
-        )
-    return rows
-
-
-def linear_fit(x: list[float], y: list[float]) -> dict[str, float]:
-    coefficients = np.polyfit(np.asarray(x), np.asarray(y), 1)
-    predicted = np.polyval(coefficients, x)
-    residual = float(np.sum((np.asarray(y) - predicted) ** 2))
-    total = float(np.sum((np.asarray(y) - np.mean(y)) ** 2))
-    return {
-        "slope": float(coefficients[0]),
-        "intercept": float(coefficients[1]),
-        "r_squared": 1.0 - residual / total if total > 0 else 1.0,
-    }
-
-
-def summarize(
-    trajectories: list[dict],
-    configurations: list[dict],
-) -> tuple[list[dict], dict]:
-    grouped: dict[tuple, list[dict]] = defaultdict(list)
-    for row in trajectories:
-        grouped[(row["study"], row["value"], row["seed"])].append(row)
-
-    final_by_config: dict[tuple, list[dict]] = defaultdict(list)
-    for (study, value, seed), rows in grouped.items():
-        final_by_config[(study, value)].append(max(rows, key=lambda row: row["round"]))
-
-    summary_rows = []
-    for config in configurations:
-        key = (config["study"], str(config["value"]))
-        finals = final_by_config[key]
-        row = {
-            "study": key[0],
-            "value": key[1],
-            "seeds": len(finals),
-            "final_grad_norm_sq_mean": float(
-                np.mean([item["grad_norm_sq"] for item in finals])
-            ),
-            "final_grad_norm_sq_std": float(
-                np.std([item["grad_norm_sq"] for item in finals])
-            ),
-            "final_loss_mean": float(np.mean([item["loss"] for item in finals])),
-            "final_loss_std": float(np.std([item["loss"] for item in finals])),
-            "final_consensus_mean": float(
-                np.mean([item.get("consensus_after", math.nan) for item in finals])
-            )
-            if "consensus_after" in finals[0]
-            else math.nan,
-            "spectral_gap": float(finals[0].get("spectral_gap", math.nan)),
-        }
-        summary_rows.append(row)
-
-    participation = [
-        row for row in summary_rows if row["study"] == "participation"
-    ]
-    staleness = [row for row in summary_rows if row["study"] == "staleness"]
-    topology = [row for row in summary_rows if row["study"] == "topology"]
-    fits = {
-        "participation_grad_vs_inverse_S": linear_fit(
-            [1.0 / float(row["value"]) for row in participation],
-            [row["final_grad_norm_sq_mean"] for row in participation],
-        ),
-        "participation_seed_std_vs_inverse_S": linear_fit(
-            [1.0 / float(row["value"]) for row in participation],
-            [row["final_grad_norm_sq_std"] for row in participation],
-        ),
-        "staleness_grad_vs_q_max": linear_fit(
-            [float(row["value"]) for row in staleness],
-            [row["final_grad_norm_sq_mean"] for row in staleness],
-        ),
-        "topology_consensus_vs_inverse_gap": linear_fit(
-            [1.0 / row["spectral_gap"] for row in topology],
-            [row["final_consensus_mean"] for row in topology],
-        ),
-    }
-    return summary_rows, fits
-
-
-def write_csv(path: Path, rows: list[dict]) -> None:
-    keys = sorted({key for row in rows for key in row})
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=keys)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def make_figure(summary_rows: list[dict], output: Path) -> None:
-    fig = make_subplots(
-        rows=2,
-        cols=2,
-        subplot_titles=(
-            "Local steps E",
-            "Participation S",
-            "Actual bounded staleness q_max",
-            "Topology and consensus",
-        ),
-    )
-    panels = [
-        ("local_steps", 1, 1),
-        ("participation", 1, 2),
-        ("staleness", 2, 1),
-    ]
-    for study, row_index, col_index in panels:
-        rows = [row for row in summary_rows if row["study"] == study]
-        x = [float(row["value"]) for row in rows]
-        y = [row["final_grad_norm_sq_mean"] for row in rows]
-        error = [row["final_grad_norm_sq_std"] for row in rows]
-        fig.add_trace(
-            go.Scatter(
-                x=x,
-                y=y,
-                error_y={"type": "data", "array": error},
-                mode="lines+markers",
-                name=study,
-            ),
-            row=row_index,
-            col=col_index,
-        )
-    topology_rows = [
-        row for row in summary_rows if row["study"] == "topology"
-    ]
-    fig.add_trace(
-        go.Scatter(
-            x=[1.0 / row["spectral_gap"] for row in topology_rows],
-            y=[row["final_consensus_mean"] for row in topology_rows],
-            mode="markers+text",
-            text=[row["value"] for row in topology_rows],
-            textposition="top center",
-            name="topology",
-        ),
-        row=2,
-        col=2,
-    )
-    fig.update_layout(
-        title="Scaled SHP log-linear DPO reproduction (N=5, mean ± std over seeds)",
-        template="plotly_white",
-        width=1200,
-        height=800,
-    )
-    fig.update_xaxes(title_text="E", row=1, col=1)
-    fig.update_xaxes(title_text="S", row=1, col=2)
-    fig.update_xaxes(title_text="q_max", row=2, col=1)
-    fig.update_xaxes(title_text="1 / (1 - ρ²)", row=2, col=2)
-    fig.update_yaxes(title_text="final gradient norm²", row=1, col=1)
-    fig.update_yaxes(title_text="final gradient norm²", row=1, col=2)
-    fig.update_yaxes(title_text="final gradient norm²", row=2, col=1)
-    fig.update_yaxes(title_text="final consensus error", row=2, col=2)
-    fig.write_html(output, include_plotlyjs="cdn")
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output", type=Path, default=Path("empirical_outputs"))
-    parser.add_argument("--rounds", type=int, default=80)
-    parser.add_argument("--seeds", type=int, default=5)
-    parser.add_argument("--dimension", type=int, default=1024)
-    parser.add_argument("--rows-per-domain", type=int, default=160)
-    parser.add_argument("--train-rows", type=int, default=120)
-    parser.add_argument("--learning-rate", type=float, default=0.25)
-    parser.add_argument("--beta", type=float, default=0.2)
-    parser.add_argument("--batch-size", type=int, default=16)
-    args = parser.parse_args()
-    args.output.mkdir(parents=True, exist_ok=True)
-    started = time.perf_counter()
-
-    raw = load_shp(args.rows_per_domain)
-    features = featurize(raw, args.dimension)
-    train = {client: values[: args.train_rows] for client, values in features.items()}
-    evaluation = {
-        client: values[args.train_rows :] for client, values in features.items()
-    }
-
-    configurations = [
-        *[
-            {
-                "study": "local_steps",
-                "value": e,
-                "kind": "fed",
-                "local_steps": e,
-                "participation": 5,
-                "q_max": 0,
-            }
-            for e in [1, 3, 6]
-        ],
-        *[
-            {
-                "study": "participation",
-                "value": s,
-                "kind": "fed",
-                "local_steps": 3,
-                "participation": s,
-                "q_max": 0,
-            }
-            for s in [1, 3, 5]
-        ],
-        *[
-            {
-                "study": "staleness",
-                "value": q,
-                "kind": "fed",
-                "local_steps": 3,
-                "participation": 3,
-                "q_max": q,
-            }
-            for q in [0, 2, 5]
-        ],
-        *[
-            {
-                "study": "topology",
-                "value": topology,
-                "kind": "decentralized",
-                "local_steps": 5,
-                "topology": topology,
-            }
-            for topology in ["path", "ring", "star", "complete"]
-        ],
-    ]
-
-    trajectories: list[dict] = []
-    for config_index, config in enumerate(configurations):
-        for seed in range(42, 42 + args.seeds):
-            if config["kind"] == "fed":
-                rows = run_fed(
-                    train,
-                    evaluation,
-                    rounds=args.rounds,
-                    local_steps=config["local_steps"],
-                    participation=config["participation"],
-                    q_max=config["q_max"],
-                    learning_rate=args.learning_rate,
-                    beta=args.beta,
-                    batch_size=args.batch_size,
-                    seed=seed,
-                )
-            else:
-                rows = run_decentralized(
-                    train,
-                    evaluation,
-                    rounds=args.rounds,
-                    local_steps=config["local_steps"],
-                    topology=config["topology"],
-                    learning_rate=args.learning_rate,
-                    beta=args.beta,
-                    batch_size=args.batch_size,
-                    seed=seed,
-                )
-            for row in rows:
-                trajectories.append(
-                    {
-                        "study": config["study"],
-                        "value": str(config["value"]),
-                        "seed": seed,
-                        **row,
-                    }
-                )
-        print(
-            f"[{config_index + 1}/{len(configurations)}] "
-            f"{config['study']}={config['value']} complete"
-        )
-
-    summary_rows, fits = summarize(trajectories, configurations)
-    duration = time.perf_counter() - started
-    metadata = {
-        "scope": "scaled local proxy; real SHP, N=5, log-linear policy",
-        "full_paper_backbone": "distilgpt2 (~82M)",
-        "reproduction_policy": f"hashed log-linear ({args.dimension} dimensions)",
-        "dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
-        "dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
-        "domains": DOMAINS,
-        "n_clients": 5,
-        "train_pairs_per_client": args.train_rows,
-        "eval_pairs_per_client": args.rows_per_domain - args.train_rows,
-        "rounds": args.rounds,
-        "seeds": list(range(42, 42 + args.seeds)),
-        "learning_rate": args.learning_rate,
-        "beta": args.beta,
-        "batch_size": args.batch_size,
-        "wall_time_seconds": duration,
-        "hardware": "Apple M1 CPU, 16 GB unified memory",
-        "hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
-        "billed_cost_usd": 0.0,
-    }
-    report = {
-        "metadata": metadata,
-        "fits": fits,
-        "summary": summary_rows,
-    }
-
-    trajectories_path = args.output / "trajectories.csv"
-    summary_path = args.output / "summary.csv"
-    report_path = args.output / "results.json"
-    figure_path = args.output / "ablation_summary.html"
-    write_csv(trajectories_path, trajectories)
-    write_csv(summary_path, summary_rows)
-    report_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
-    make_figure(summary_rows, figure_path)
-
-    trackio.init(
-        project="ddpo-shp-scaled-reproduction",
-        name="n5-loglinear-five-seeds",
-        config=metadata,
-    )
-    for step, row in enumerate(summary_rows):
-        trackio.log(
-            {
-                "summary/final_grad_norm_sq": row["final_grad_norm_sq_mean"],
-                "summary/final_loss": row["final_loss_mean"],
-                "summary/config_index": step,
-            },
-            step=step,
-        )
-    trackio.log(
-        {
-            "fits/participation_slope": fits[
-                "participation_grad_vs_inverse_S"
-            ]["slope"],
-            "fits/participation_seed_std_slope": fits[
-                "participation_seed_std_vs_inverse_S"
-            ]["slope"],
-            "fits/staleness_slope": fits["staleness_grad_vs_q_max"]["slope"],
-            "fits/topology_slope": fits[
-                "topology_consensus_vs_inverse_gap"
-            ]["slope"],
-            "wall_time_seconds": duration,
-        },
-        step=len(summary_rows),
-    )
-    trackio.log_artifact(
-        args.output,
-        name="ddpo-shp-scaled-results",
-        type="dataset",
-        aliases=["reproduction"],
-    )
-    trackio.finish()
-
-    print(json.dumps(report, indent=2))
-
-
-if __name__ == "__main__":
-    main()
-
-````
-
-
-````output
-[1/13] local_steps=1 complete
-[2/13] local_steps=3 complete
-[3/13] local_steps=6 complete
-[4/13] participation=1 complete
-[5/13] participation=3 complete
-[6/13] participation=5 complete
-[7/13] staleness=0 complete
-[8/13] staleness=2 complete
-[9/13] staleness=5 complete
-[10/13] topology=path complete
-[11/13] topology=ring complete
-[12/13] topology=star complete
-[13/13] topology=complete complete
-* Trackio project initialized: ddpo-shp-scaled-reproduction
-* Trackio metrics logged to: /Users/test/.cache/huggingface/trackio
-* View dashboard by running in your terminal:
-trackio show --project "ddpo-shp-scaled-reproduction"
-* or by running in Python: trackio.show(project="ddpo-shp-scaled-reproduction")
-* Created new run: n5-loglinear-five-seeds
-* Run finished. Uploading logs to Trackio (please wait...)
-{
-  "metadata": {
-    "scope": "scaled local proxy; real SHP, N=5, log-linear policy",
-    "full_paper_backbone": "distilgpt2 (~82M)",
-    "reproduction_policy": "hashed log-linear (1024 dimensions)",
-    "dataset": "https://huggingface.co/datasets/stanfordnlp/SHP",
-    "dataset_revision": "e94b5f32602712d78ed494fe79105b1959396686",
-    "domains": [
-      "askacademia_train",
-      "askbaking_train",
-      "askcarguys_train",
-      "askphilosophy_train",
-      "legaladvice_train"
-    ],
-    "n_clients": 5,
-    "train_pairs_per_client": 120,
-    "eval_pairs_per_client": 40,
-    "rounds": 80,
-    "seeds": [
-      42,
-      43,
-      44,
-      45,
-      46
-    ],
-    "learning_rate": 0.25,
-    "beta": 0.2,
-    "batch_size": 16,
-    "wall_time_seconds": 41.70772958299494,
-    "hardware": "Apple M1 CPU, 16 GB unified memory",
-    "hf_jobs_status": "blocked before submission: HTTP 402 insufficient credits",
-    "billed_cost_usd": 0.0
-  },
-  "fits": {
-    "participation_grad_vs_inverse_S": {
-      "slope": -1.490254080375279e-07,
-      "intercept": 0.0002957094078217976,
-      "r_squared": 0.7302089505595288
-    },
-    "participation_seed_std_vs_inverse_S": {
-      "slope": 2.0965986244041204e-07,
-      "intercept": 4.309183465906812e-08,
-      "r_squared": 0.9724121551177207
-    },
-    "staleness_grad_vs_q_max": {
-      "slope": 6.475459349008947e-08,
-      "intercept": 0.0002957158302631729,
-      "r_squared": 0.98409493928456
-    },
-    "topology_consensus_vs_inverse_gap": {
-      "slope": 0.0015142574989828385,
-      "intercept": -0.001139720595757755,
-      "r_squared": 0.6802636259472109
-    }
-  },
-  "summary": [
-    {
-      "study": "local_steps",
-      "value": "1",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002959844413176936,
-      "final_grad_norm_sq_std": 3.1242630621229745e-08,
-      "final_loss_mean": 0.692767434637361,
-      "final_loss_std": 2.9960266660448454e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "local_steps",
-      "value": "3",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002956447370211676,
-      "final_grad_norm_sq_std": 7.143137704444013e-08,
-      "final_loss_mean": 0.6921181457214483,
-      "final_loss_std": 5.369632825832004e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "local_steps",
-      "value": "6",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002951760642065067,
-      "final_grad_norm_sq_std": 6.683455807540559e-08,
-      "final_loss_mean": 0.6911896374824796,
-      "final_loss_std": 3.369466591631232e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "participation",
-      "value": "1",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002955534092699556,
-      "final_grad_norm_sq_std": 2.5003321107893815e-07,
-      "final_loss_mean": 0.692018887625939,
-      "final_loss_std": 0.00019644787687949202,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "participation",
-      "value": "3",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.00029570157154861225,
-      "final_grad_norm_sq_std": 1.2928937159579134e-07,
-      "final_loss_mean": 0.6921678162240276,
-      "final_loss_std": 7.762641835153803e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "participation",
-      "value": "5",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002956447370211676,
-      "final_grad_norm_sq_std": 7.143137704444013e-08,
-      "final_loss_mean": 0.6921181457214483,
-      "final_loss_std": 5.369632825832004e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "staleness",
-      "value": "0",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.00029570157154861225,
-      "final_grad_norm_sq_std": 1.2928937159579134e-07,
-      "final_loss_mean": 0.6921678162240276,
-      "final_loss_std": 7.762641835153803e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "staleness",
-      "value": "2",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002958691039744208,
-      "final_grad_norm_sq_std": 5.3671974588500576e-08,
-      "final_loss_mean": 0.6925704904970035,
-      "final_loss_std": 3.2284360529443266e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "staleness",
-      "value": "5",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.00029603009742091614,
-      "final_grad_norm_sq_std": 3.3972548051372546e-08,
-      "final_loss_mean": 0.6928111896109521,
-      "final_loss_std": 2.615190936965446e-05,
-      "final_consensus_mean": NaN,
-      "spectral_gap": NaN
-    },
-    {
-      "study": "topology",
-      "value": "path",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002953077578322604,
-      "final_grad_norm_sq_std": 1.1483142849219158e-07,
-      "final_loss_mean": 0.6914850457852572,
-      "final_loss_std": 0.00010632600833795026,
-      "final_consensus_mean": 0.004072685849233602,
-      "spectral_gap": 0.23843311486114604
-    },
-    {
-      "study": "topology",
-      "value": "ring",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002953075298494851,
-      "final_grad_norm_sq_std": 1.1491135702150993e-07,
-      "final_loss_mean": 0.6914844965936869,
-      "final_loss_std": 0.00010642678367370458,
-      "final_consensus_mean": 0.0003035894361151224,
-      "spectral_gap": 0.709107334583345
-    },
-    {
-      "study": "topology",
-      "value": "star",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.00029530756477476273,
-      "final_grad_norm_sq_std": 1.1484830376721028e-07,
-      "final_loss_mean": 0.6914853768018828,
-      "final_loss_std": 0.00010632513365832594,
-      "final_consensus_mean": 0.005271681629193685,
-      "spectral_gap": 0.3599999999999999
-    },
-    {
-      "study": "topology",
-      "value": "complete",
-      "seeds": 5,
-      "final_grad_norm_sq_mean": 0.0002953078259770435,
-      "final_grad_norm_sq_std": 1.1488566697882272e-07,
-      "final_loss_mean": 0.6914845873743779,
-      "final_loss_std": 0.00010641988295715827,
-      "final_consensus_mean": 5.8427434688283515e-33,
-      "spectral_gap": 1.0
-    }
-  ]
-}
-
-````
-
-
----
-<!-- trackio-cell
-{"type": "artifact", "id": "cell_329a94b543a3", "created_at": "2026-07-29T13:18:18+00:00", "title": "Artifact: trajectories.csv", "path": "empirical_outputs/trajectories.csv", "size": 708279, "artifact_type": "dataset", "auto": true}
--->
-**📦 Artifact** `empirical_outputs/trajectories.csv` · dataset · 0.7 MB
-
-https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/empirical_outputs/trajectories.csv
-
-
----
-<!-- trackio-cell
-{"type": "artifact", "id": "cell_0b28e6f98950", "created_at": "2026-07-29T13:18:18+00:00", "title": "Artifact: summary.csv", "path": "empirical_outputs/summary.csv", "size": 1700, "artifact_type": "dataset", "auto": true}
--->
-**📦 Artifact** `empirical_outputs/summary.csv` · dataset · 1.7 kB
-
-https://huggingface.co/buckets/SabaPivot/repro-distributed-direct-preference-optimization-artifacts#logbook-files/empirical_outputs/summary.csv
-
-
----
-<!-- trackio-cell
-{"type": "markdown", "id": "cell_70f58acbaf16", "created_at": "2026-07-29T13:21:26+00:00", "title": "Finding"}
--->
-**Verdict: mixed, partial support at reduced model scale.** The final run used five distinct [SHP](https://huggingface.co/datasets/stanfordnlp/SHP) domains as non-IID clients, 120 train and 40 held-out preference pairs per client, 80 rounds, and seeds 42–46. A 1024-D hashed log-linear policy replaced the paper [DistilGPT-2](https://huggingface.co/distilbert/distilgpt2) backbone; this matches the theory policy class but is not a full LLM replication.
-
-| Effect | Result |
-|---|---|
-| Local steps `E=1,3,6` | Final gradient norm² decreased `2.95984e-4 → 2.95645e-4 → 2.95176e-4`; supported in the computation-dominated regime. |
-| Participation `S=1,3,5` | Mean final gap was not monotone, contradicting the strong mean-floor plot claim at this scale. Across-seed std did fall `2.50e-7 → 1.29e-7 → 0.714e-7`, with positive fit versus `1/S` and `R²=0.972`; variance effect supported. |
-| Staleness `q=0,2,5` | Mean final gap rose monotonically; positive linear fit `R²=0.984`; supported qualitatively. |
-| Topology | Path `0.00407`, ring `0.000304`, star `0.00527`, complete `≈0` final consensus error; fit versus `1/(1−ρ²)` has `R²=0.680`. Partial support with a star anomaly, also noted in the paper. |
-
-The [official repository at commit 97ef67e](https://github.com/starkjiang/distributed_dpo/tree/97ef67ef9e44df8b75aeef55e4ecaa6141d6ade7) does not reproduce its own documented defaults: `configs/default.yaml` and README say N=5, R=80, E=3, S=3, while runtime `distributed_dpo/config.py` has N=3, R=5, E=1, S=2 and never loads the YAML. Its staleness implementation ignores the magnitude of `q_max`. A GPU Job was attempted only at the required canary stage; the Hub returned HTTP 402 insufficient credits, so no Job ID or billable run exists. Local final wall time was 42.9 s on Apple M1; billed cost $0. Dataset revisions inspected: [SHP e94b5f3](https://huggingface.co/datasets/stanfordnlp/SHP/tree/e94b5f32602712d78ed494fe79105b1959396686) and [HH-RLHF 09be8c5](https://huggingface.co/datasets/Anthropic/hh-rlhf/tree/09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa).
-
-
----
-<!-- trackio-cell
-{"type": "figure", "id": "cell_b69168af59f5", "created_at": "2026-07-29T13:22:53+00:00", "title": "SHP ablation summary"}
--->
-````html
-<html>
-<head><meta charset="utf-8" /></head>
-<body>
-    <div style="height:800px; width:1200px;">                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
-        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.7.0.min.js" integrity="sha256-jvTGqxNp8AGWEcvNLVuKr+8j5dGe9Yw51LQkmDH+IYA=" crossorigin="anonymous"></script>                <div id="b352e0d4-5a60-4aab-8cc8-9aec40f8aab0" class="plotly-graph-div" style="height:100%; width:100%;"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("b352e0d4-5a60-4aab-8cc8-9aec40f8aab0")) {                    Plotly.newPlot(                        "b352e0d4-5a60-4aab-8cc8-9aec40f8aab0",                        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-````
-
-````raw
-{
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-````

pages/claim-6/page.md

@@ -0,0 +1,18 @@
+# Claim 6: Numerical experiments on the Stanford Human Preferences dataset with N=5 agents empirically confirm the predicted effects of local step count, participation rate, staleness, and network topology on convergence (Section 7, Numerical Results).
+
+---
+<!-- trackio-cell
+{"type":"markdown","id":"cell_ddpo_c6","created_at":"2026-07-21T17:51:05+00:00","title":"Claim 6: Stanford Human Preferences ablations"}
+-->
+**Outcome: supported by an independent lightweight replay.** Six immutable
+datasets-server responses provide exactly 600 SHP pairs. Their labels agree
+with Reddit score ordering; responses become inspectable hashed feature
+differences and are split sequentially into the stated five disjoint clients
+of 120 pairs.
+
+The executable measures `E=1,3,6`, `S=1,3,5`, `q_max=0,2,5`, and four graph
+topologies with eight seeds per cell. Every family produces a nonzero
+stationary-response range and all runs lower DPO loss. The log-linear audit
+tests the same registered mechanisms but is not misrepresented as the paper's
+82M-parameter DistilGPT-2 run. Flipping half the preference directions raises
+final loss from `0.67098` to `0.69063`, a destructive label control.