| """(A) disentangle 'length': total-words (volume) vs mean post length, as engagement predictors. |
| (B) per-regime (dyad/triad/tetrad) raw vs length-adjusted per-model attractiveness (within-run z in-degree).""" |
| import sqlite3, json |
| from collections import defaultdict |
| from pathlib import Path |
| import numpy as np |
| RUNS=Path("runs");TET=RUNS/"tetratic" |
| 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"} |
| def load(globpat,tet=False): |
| out=[] |
| for db in sorted(globpat): |
| if not tet and any(x in db.name for x in ["archive","olm","hermes","glm47","pilot_","snapshot","qwq"]):continue |
| if tet and (TET/f"{db.stem}_checkpoint.json").exists():continue |
| mp=str(db).replace(".db",".metadata.json") |
| if not Path(mp).exists():continue |
| try:amm=json.load(open(mp)).get("agent_model_map",{}) |
| except:continue |
| if not amm:continue |
| fam={int(k)+1:SHORT.get(v["model"]) for k,v in amm.items()} |
| if any(v is None for v in fam.values()):continue |
| try: |
| c=sqlite3.connect(db);posts=c.execute("SELECT post_id,user_id,content FROM post").fetchall();coms=c.execute("SELECT post_id,user_id FROM comment").fetchall();c.close() |
| except:continue |
| if len(coms)<(100 if tet else 200):continue |
| owner={p:u for p,u,_ in posts};ind=defaultdict(int) |
| for p,cu in coms: |
| o=owner.get(p) |
| if o in fam and o!=cu:ind[o]+=1 |
| nw=defaultdict(int);npost=defaultdict(int) |
| for p,u,cont in posts: |
| if u in fam and cont:nw[u]+=len(cont.split());npost[u]+=1 |
| F=sorted(set(fam.values()));reg={2:"dyad",3:"triad",4:"tetrad"}.get(len(F)) |
| for u in fam: |
| if npost[u]>0:out.append({"run":db.stem,"model":fam[u],"indeg":ind.get(u,0),"meanlen":nw[u]/npost[u],"totw":nw[u],"reg":reg}) |
| return out |
| ag=load(RUNS.glob("sweep_*.db"))+load(TET.glob("tetra_s*_r*.db"),tet=True) |
| ag=[a for a in ag if a["reg"]] |
| def wz(x,run): |
| o=np.zeros(len(x));bd=defaultdict(list) |
| for i,r in enumerate(run):bd[r].append(i) |
| for r,idx in bd.items(): |
| v=x[np.array(idx)];m=v.mean();sd=v.std() or 1 |
| for j,i in enumerate(idx):o[i]=(v[j]-m)/sd |
| return o |
| run=np.array([a["run"]for a in ag]);model=np.array([a["model"]for a in ag]);reg=np.array([a["reg"]for a in ag]) |
| y=wz(np.array([a["indeg"]for a in ag],float),run) |
| lmean=wz(np.log1p(np.array([a["meanlen"]for a in ag])),run) |
| ltot=wz(np.log1p(np.array([a["totw"]for a in ag])),run) |
| |
| print("(A) within-run corr with engagement: mean-post-length r=%.3f total-words(volume) r=%.3f"%(np.corrcoef(lmean,y)[0,1],np.corrcoef(ltot,y)[0,1])) |
| |
| print("\n(B) per-model attractiveness (within-run z in-degree): raw vs length-adjusted, by regime") |
| print(f"{'model':8s} "+" ".join(f"{r}:raw {r}:adj " for r in ["dyad","triad","tetrad"])) |
| rows={} |
| for r in ["dyad","triad","tetrad"]: |
| m_=reg==r;b=np.polyfit(lmean[m_],y[m_],1)[0];resid=y.copy();resid[m_]=y[m_]-b*lmean[m_] |
| rows[r]=(b,resid) |
| for mod in ["gpt-oss","Qwen","GLM-4","Mag","Gemma"]: |
| cells=[] |
| for r in ["dyad","triad","tetrad"]: |
| b,resid=rows[r];m_=(reg==r)&(model==mod) |
| if m_.sum()==0:cells.append(" -- -- ") |
| else:cells.append(f"{y[m_].mean():+5.2f} {resid[m_].mean():+5.2f}") |
| print(f"{mod:8s} "+" ".join(cells)) |
| print("\nslopes (length->engagement) per regime:",{r:round(rows[r][0],2) for r in rows}) |
|
|
