File size: 3,601 Bytes
dd138ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | """(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)
# (A) which 'length' predicts engagement
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]))
# (B) per-regime raw vs length(mean)-adjusted attractiveness
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})
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