Beyond the Trace: Coupling an Interpretable Reasoning-State Readout to Native MoE Routing

📅 2026-08-18
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
研究通过引入两层内部读出机制解决推理模型过程记录不全的问题,使用J64和R64方法增强推理状态的可解释性和性能。
📝 Abstract
What a reasoning model writes is only a partial record of the process that produces it. We introduce a two-level internal readout for mixture-of-experts reasoning. We first distill vocabulary-scale J-space into J64, a 64-axis semantic frame learned from the model's own reasoning states. J64 reveals readable process state that the emitted trace does not show: it separates inference effort from problem-induced strain. It also adds 0.096 to 0.135 held-out AUC over a baseline that reads the same rollout as token occupancy and aggregates it in exactly the same way. We then reconstruct J64 from native expert-routing statistics. The result is R64, a low-overhead proxy: its median per-axis correlation with J64 is 0.69 to 0.86 across three models and two families, and on gpt-oss-20b it preserves 95 to 100% of J64's predictive gain. The readout supports test-time decisions at two temporal resolutions. Over completed candidate sets, J64 and R64 improve single-branch selection, and R64-weighted voting improves plain majority voting in seven of eight settings. During generation, rolling readout windows drive a cumulative stop-and-resample policy whose operating point is fixed on training questions alone. J64 improves accuracy by 1.1 to 5.9 points over a sibling-permuted control, and the routing-only R64 proxy retains 0.9 to 3.2 of those points. Finally, router edits aimed at the mechanism J64 names induce the predicted reasoning behaviors and shift a diagnosed stall from numerical guessing toward exact symbolic execution. Together, J64 makes latent process state readable, while routing makes it deployable and actionable.
Problem

Research questions and friction points this paper is trying to address.

reasoning model
internal readout
inference effort
semantic frame
expert-routing
Innovation

Methods, ideas, or system contributions that make the work stand out.

interpretable reasoning-state readout
mixture-of-experts
semantic frame
low-overhead proxy
test-time decisions
🔎 Similar Papers
No similar papers found.