colibri/c/tools/expert_atlas

Expert Atlas — what does each of the 19,456 experts actually do?

Probe harness for #175. Runs a set of topic-tagged prompts, dumps each run's expert-routing histogram, and turns them into a per-expert topic-affinity vector.

cd c
export COLI_MODEL=/path/to/glm52_i4
./tools/expert_atlas/sweep.sh                             # 30 probes (10 topics x 3 prompts)
python3 tools/expert_atlas/analyze.py  --stats atlas_out/stats --out atlas_out/experts.json
python3 tools/expert_atlas/validate.py atlas_out/stats 200 # leave-one-prompt-out check

Read this before you trust any atlas

Four things silently corrupt this measurement. The sweep script controls all of them; if you roll your own, don't skip them.

trap	effect	control
--topp	prunes experts by cumulative probability — measured: it hides 38% of the distinct experts (7,587 → 4,687). It is also the recommended speed setting.	TOPP=0
speculative drafts	eusage is incremented inside moe(), before verification, so rejected drafts count. Those are experts routed for text the model never emitted.	MTP=0 DRAFT=0
.coli_usage	is loaded at startup and accumulates, so a naive STATS dump contains all prior history, not this run.	remove per run (script backs it up and restores)
autocorrelation	routing inside one run is highly correlated — the same context routes to the same experts token after token. An expert firing 38 times during one prompt is one observation, not 38. Chi-square/entropy on raw selections will certify single-prompt flukes as perfect specialists.	analyze.py requires the affinity to replicate across a category's independent prompts

The CUDA expert tier is also not run-to-run deterministic, so the sweep uses --gpu none. Tier config only decides where weights live, not what the router picks, so this costs nothing.

Method

analyze.py:

1. n[e][c] — selections of expert e while running category c
2. f[e][c] = n[e][c] / N[c] — normalise by category size (prefill routes the prompt too, so a verbose category would otherwise look busier)
3. p(c|e) — renormalise into a topic distribution per expert, i.e. base-rate corrected. Ranking by raw count instead just rediscovers which experts are popular in general.
4. spec(e) = 1 − H(p(c|e)) / log C — 0 = generalist, 1 = fires for exactly one topic
5. replication gate — an expert is only a candidate specialist for c if it fires in ≥2 of c's independent prompts

validate.py — leave-one-prompt-out. Build each category's top-K specialist set from its other prompts, then check which set the held-out prompt's routing actually lands in. If specialisation were an artifact of prompt wording, the held-out prompt would not prefer its own category.

Result on GLM-5.2 744B int4 (Zen5, CPU routing path)

• Leave-one-prompt-out: 29/30 = 96.7% (chance 10%). Specialisation is a property of the topic, not of prompt wording.
• The single miss is instructive: 写一首关于秋天的短诗 ("write a short poem about autumn") is classified poetry, not Chinese — routing follows the task over the language.
• Only 7.9% of experts are strong specialists (spec ≥ 0.5). The "one expert = one topic" picture is wrong for ~92% of them.
• Specialisation rises with depth: layer 3 ≈ 0.07 (generalist, token/syntax level) → layers 18–58 ≈ 0.19–0.27.
• The replication gate removed 587 experts that looked like flawless specialists on one prompt.

Extending it

probes.json is the whole probe set — add categories and prompts. Use 3+ prompts per category with varied phrasing, or the replication gate has nothing to check and you are back to measuring one prompt. Keep prompt lengths in a similar band across categories.