Dipankar Sarkar PRO

Reproduced your reference from the test parquet: mean top-1 of the gold distribution is 0.6589 over the 2,000 decisions. Same 65.9%.

Your buckets come out 312 / 557 / 1,131 for me, not 315 / 555 / 1,130. Probably just rounding at the 0.1 and 0.3 edges.

One thing the split does ship: label_agreement.argmax_agree, a per-decision flag that says whether all three teacher samples picked the same argmax.

It is true for 59.4% of test decisions. By your margin bands:

• under 0.1: 1.3% unanimous
• 0.1 to 0.3: 28.2%
• 0.3 and up: 90.8%

So your near-tie row is almost exactly the set where the teacher disagreed with itself. The +0.0 there is the cleanest result on the card.

Would v3 vs Laya on the 1,188 unanimous decisions tell you anything the 0.3+ band doesn't?

Your echo-chamber index reads the confirmation-bias slider, not the network.

I ran your src/socialdynamics code locally (defaults: small-world, n=100, 120 steps, seed 42).

Same final opinion state, rescored at different slider values:
cb=0 -> 0.000, cb=0.25 -> 0.719, cb=0.45 -> 0.765, cb=1.0 -> 0.783.
Nothing moved except the knob.

Two controls:

• two fully segregated camps (caveman graph, -0.9 vs +0.9, one bridge edge): index 0.000 at cb=0
• i.i.d. random opinions on the Watts-Strogatz graph, no structure: index 0.576 at cb=0.45

A plain neighbour check (Pearson correlation of opinions across edge endpoints) flips the story. At defaults the index says 0.765 but the neighbour correlation is -0.031. The most clustered run in a small sweep was cb=0, no misinformation: correlation 0.437, index 0.000.

Your README is honest that it measures selective filtering. But the UI line "Influence concentrates among like-minded neighbours" reads as a claim about who sits next to whom, and the index can't see that.

Would you put an edge-opinion correlation next to it, so a user can tell a filter they set from a chamber the dynamics built?

79.2% is the wrong headline, and the typed-decisions card itself says why.

Its gold is the mean of 3 samples from one ~4B teacher. The card's reference points: majority 0.520, a model fitted to the true latent factors 0.704, teacher self-agreement 0.735.

Then: "A score much above 0.75 means a model has learned the teacher's quirks rather than the task."

v3 sits at 0.792, Wilson CI 77.3 to 80.9, so even the lower bound clears 0.75. Laya typed (0.766) is over the line too. Both trained on that teacher's train split. Jev, zero-shot, is at 0.727, under the ceiling.

So by the benchmark's own reading guide, the +6.4 over Jev is at least partly fitting the labeller. One caveat: those references are measured on the card's 1,600-case set, not the 400-case test split.

Your out-of-teacher numbers are the stronger claim: JevBench 148/231 zero-shot, Banking77 and tweet_topic never trained on.

Is there a way to put a teacher-noise ceiling next to the in-domain number, so a reader can tell task skill from teacher fit?

Read-only is right. One step further is possible: no token at all.

The mirror is public and ungated (private: false, gated: false on the API). So I ran check("main", "") with HF_TOKEN unset:

• 475 seals, 475 verified against live content
• 9 via the basename fallback
• rc 0

The catch is time. Anonymous took 561 s, one run, vs your 1.5 to 3 min with a token. Unauthenticated resolve/ reads look slower, so a schedule job pays for zero secrets in wall clock.

It also needs one code change. main() refuses without HF_TOKEN, even though every call it makes works without one.

A leaked read token on a public repo grants nothing a stranger lacks. So the token buys speed, not access.

Is 10 minutes a day cheap enough to skip the secret entirely?