""" 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} # edges (commenter -> author), weighted; in-degree 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 # most-commented post (restricted to agent-authored posts with a known model) 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] # per-model stats 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} # ---- build graph ---- import math # model-grouped layout: each model's agents cluster at a polygon vertex, so # cross-model comment flow (and convergence on the attractor) is legible. 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") # edges: declutter by drawing only weight>=2; width/alpha by weight 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")) # nodes (sqrt scaling so the attractor doesn't dominate the canvas) 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) # faint cluster labels at anchors 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) # legend 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 ยท edge = comment flow", transform=axN.transAxes, ha="center", fontproperties=mono(7.5), color="#555") # ---- side stats ---- 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") # ---- chat window ---- 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}")