| """ |
| Figure 1 (WORKING): the interaction network of one triadic run with a strong |
| attractor (gpt-oss) and a strong repeller (Magistral). Nodes = agents colored by |
| base model, sized by in-degree; directed edges = comment flow. Side panel: per-model |
| stats. Inset: a chat-window excerpt of the most-commented post. |
| Saves SVG (for redesign) + PNG (for the paper). |
| """ |
| import sqlite3, json, sys |
| from collections import defaultdict |
| from pathlib import Path |
| import numpy as np |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from matplotlib.patches import FancyBboxPatch |
| import networkx as nx |
| sys.path.insert(0, str(Path(__file__).parent)) |
| from housestyle import serif, mono, MODEL_COLOR |
|
|
| DB = "runs/sweep_tri_oss_mag_glm_s42.db" |
| OUT_SVG = Path(__file__).parent / "fig1_network.svg" |
| OUT_PNG = Path(__file__).parent / "fig1_network.png" |
| SHORT = {"Qwen/Qwen3-32B":"Qwen","openai/gpt-oss-20b":"gpt-oss", |
| "mistralai/Magistral-Small-2509":"Mag","zai-org/GLM-4-32B-0414":"GLM-4", |
| "google/gemma-4-31B-it":"Gemma"} |
|
|
| meta = json.load(open(DB.replace(".db",".metadata.json"))) |
| fam = {int(k)+1: SHORT.get(v["model"]) for k,v in meta["agent_model_map"].items()} |
| c = sqlite3.connect(DB) |
| posts = c.execute("SELECT post_id,user_id,content FROM post").fetchall() |
| coms = c.execute("SELECT post_id,user_id,content FROM comment").fetchall() |
| users = dict(c.execute("SELECT user_id,user_name FROM user").fetchall()) |
| c.close() |
| owner = {p:u for p,u,_ in posts} |
|
|
| |
| w = defaultdict(int); indeg = defaultdict(int); outdeg = defaultdict(int) |
| for pid,cu,_ in coms: |
| au = owner.get(pid) |
| if au and au != cu and cu in fam and au in fam: |
| w[(cu,au)] += 1; indeg[au]+=1; outdeg[cu]+=1 |
|
|
| |
| pc = defaultdict(int) |
| for pid,_,_ in coms: pc[pid]+=1 |
| agent_posts = [p for p in pc if owner.get(p) in fam] |
| top_pid = max(agent_posts, key=pc.get) |
| top_author = owner.get(top_pid) |
| top_text = next((t for p,u,t in posts if p==top_pid), "") |
| top_n = pc[top_pid] |
|
|
| |
| fams = ["gpt-oss","Mag","GLM-4","Qwen","Gemma"] |
| fams = [f for f in fams if f in fam.values()] |
| nf = {f:sum(1 for v in fam.values() if v==f) for f in fams} |
| ntot = sum(nf.values()); tot = sum(w.values()) |
| obs = defaultdict(int) |
| for (cu,au),ww in w.items(): obs[(fam[cu],fam[au])]+=ww |
| def Hin(Y): |
| hs=[] |
| for X in fams: |
| if X==Y: continue |
| o=obs.get((X,Y),0)/tot; e=(nf[X]/ntot)*((nf[Y])/(ntot-1)) |
| if e>0: hs.append(o/e-1) |
| return np.mean(hs) if hs else float("nan") |
| stats = {f: (np.mean([indeg[u] for u in fam if fam[u]==f]), Hin(f)) for f in fams} |
|
|
| |
| import math |
| |
| |
| order_for_anchor = [f for f in ["gpt-oss","Mag","GLM-4","Qwen","Gemma"] if f in set(fam.values())] |
| anchors = {} |
| for i,f in enumerate(order_for_anchor): |
| ang = 2*math.pi*i/len(order_for_anchor) + math.pi/2 |
| anchors[f] = (1.25*math.cos(ang), 1.25*math.sin(ang)) |
| rng = np.random.default_rng(3) |
| pos = {u: (anchors[fam[u]][0] + rng.normal(0,0.30), |
| anchors[fam[u]][1] + rng.normal(0,0.30)) for u in fam} |
|
|
| fig = plt.figure(figsize=(7.2, 4.2)) |
| gs = fig.add_gridspec(2, 2, width_ratios=[2.0, 1.0], height_ratios=[1.3, 1.0], |
| wspace=0.06, hspace=0.30) |
| axN = fig.add_subplot(gs[:, 0]); axN.axis("off"); axN.set_aspect("equal") |
| axS = fig.add_subplot(gs[0, 1]); axS.axis("off") |
| axC = fig.add_subplot(gs[1, 1]); axC.axis("off") |
|
|
| |
| wmax = max(w.values()) |
| for (cu,au),ww in w.items(): |
| if ww < 2: continue |
| x1,y1 = pos[cu]; x2,y2 = pos[au] |
| axN.annotate("", xy=(x2,y2), xytext=(x1,y1), |
| arrowprops=dict(arrowstyle="-|>", color="#9aa0a6", |
| alpha=0.18+0.55*(ww/wmax), lw=0.4+1.8*(ww/wmax), |
| shrinkA=5, shrinkB=6, connectionstyle="arc3,rad=0.08")) |
| |
| for u in fam: |
| x,y = pos[u] |
| s = 35 + 45*np.sqrt(indeg[u]) |
| axN.scatter([x],[y], s=s, c=MODEL_COLOR[fam[u]], edgecolors="white", linewidths=0.8, zorder=3) |
| |
| for f,(ax_,ay_) in anchors.items(): |
| axN.text(ax_*1.32, ay_*1.32, f, fontproperties=mono(8.5, bold=True), |
| color=MODEL_COLOR[f], ha="center", va="center", zorder=4) |
| xs=[p[0] for p in pos.values()]; ys=[p[1] for p in pos.values()] |
| m=0.45 |
| axN.set_xlim(min(xs)-m, max(xs)+m); axN.set_ylim(min(ys)-m, max(ys)+m) |
| axN.set_title("Interaction network: one three-way run", |
| fontproperties=serif(12, bold=True), pad=10) |
| |
| import matplotlib.lines as mlines |
| handles=[mlines.Line2D([],[],marker="o",ls="",mfc=MODEL_COLOR[f],mec="white",ms=9,label=f) for f in fams] |
| axN.legend(handles=handles, loc="lower left", prop=mono(8), frameon=False, handletextpad=0.2, borderpad=0.2) |
| axN.text(0.5,-0.03,"node size $\\propto$ in-degree · edge = comment flow", |
| transform=axN.transAxes, ha="center", fontproperties=mono(7.5), color="#555") |
|
|
| |
| axS.set_title("per-model", fontproperties=serif(10, bold=True), loc="left", pad=2) |
| axS.text(0, 0.86, f"{'model':<8}{'in-deg':>7}{'H':>7}", fontproperties=mono(8.5, bold=True), transform=axS.transAxes) |
| for i,f in enumerate(fams): |
| md, h = stats[f] |
| axS.text(0, 0.86-0.13*(i+1), |
| f"{f:<8}{md:>7.1f}{h:>+7.2f}", fontproperties=mono(8.5), |
| color=MODEL_COLOR[f], transform=axS.transAxes) |
| axS.text(0, 0.86-0.13*(len(fams)+1.4), |
| f"{tot} cross-comments\n{len(posts)} posts, n={ntot} agents", |
| fontproperties=mono(7.5), color="#444", transform=axS.transAxes, va="top") |
|
|
| |
| box = FancyBboxPatch((0.02,0.04),0.96,0.92, boxstyle="round,pad=0.02,rounding_size=0.04", |
| fc="#f3f4f6", ec="#cfd3da", lw=1.0, transform=axC.transAxes) |
| axC.add_patch(box) |
| def ascii_only(s): return s.encode("ascii","ignore").decode().strip() |
| auth_name = ascii_only(users.get(top_author, f"u{top_author}")) or f"u{top_author}" |
| auth_model = fam.get(top_author,"?") |
| header = f"{auth_name[:18]} ({auth_model})" |
| axC.text(0.07,0.85, header, fontproperties=mono(8.5,bold=True), |
| color=MODEL_COLOR.get(auth_model,"#333"), transform=axC.transAxes) |
| import textwrap |
| body = ascii_only(top_text.replace("\n"," ")) |
| wrapped = "\n".join(textwrap.fill(body, width=32).split("\n")[:4]) |
| if len(body) > len(wrapped): wrapped += " ..." |
| axC.text(0.07,0.72, wrapped, fontproperties=mono(8), color="#222", |
| transform=axC.transAxes, va="top") |
| axC.text(0.07,0.10, f">> {top_n} comments", fontproperties=mono(9,bold=True), |
| color="#1f77b4", transform=axC.transAxes) |
| axC.set_title("most-engaged post", fontproperties=serif(10, bold=True), loc="left", pad=2) |
|
|
| plt.savefig(OUT_SVG, bbox_inches="tight") |
| plt.savefig(OUT_PNG, dpi=300, bbox_inches="tight") |
| print(f"saved {OUT_SVG.name} + {OUT_PNG.name}") |
| print(f"run={Path(DB).stem} top_post nc={top_n} author={auth_name}({auth_model})") |
| for f in fams: print(f" {f}: in-deg={stats[f][0]:.1f} H={stats[f][1]:+.2f}") |
|
|
