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Gavel reads skill-routing signals from a frozen model

Gavel reads task and skill representations from a frozen Qwen3-32B with two trained linear maps. It shortlists skills without loading the whole catalog into the prompt, then resumes model inference to judge candidates. On public benchmarks and a new 372-trajectory set, it improves routing by up to 13.4 points on written tasks and 21.9 points during agent rollouts against tested disclosure and retrieval baselines. No public implementation is linked yet.

Imported from the existing research archive. Migration preserves the recorded judgment and is not a fresh verification.

What this means for you

For teams with large skill catalogs, the result suggests routing can reuse a model’s internal signal and save prompt space. Trial only after code and held-out library tests are available; the reported comparison is still author-run.

FRONTIER WATCH

Public artifacts and validated results are different.

The recorded fact is the public work and its artifacts. Author-reported outcomes do not establish independent reproduction. Next checks include usable code, task conditions and third-party validation.

Evidence & related material

Background · Historical record, not rechecked

Gavel reads task and skill representations from a frozen Qwen3-32B with two trained linear maps. It shortlists skills without loading the whole catalog into the prompt, then resumes model inference to judge candidates. On public benchmarks and a new 372-trajectory set, it improves routing by up to 13.4 points on written tasks and 21.9 points during agent rollouts against tested disclosure and retrieval baselines. No public implementation is linked yet.

Technical details & scores
Backbone Qwen3-32B frozen
Trainable Parameters two linear maps
Benchmark three public sets plus SkillTraj, 372 trajectories
Score Gain up to +13.4 pp written tasks; +21.9 pp mid-rollout
Code not linked on abstract page
Technical impact 3
Engineering value 4
Adoption signal 1
Maturity 2
Verification cost 4
Risk 2