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cs.AI2026

From Concentration to Differentiation and Back: Routing Effective Rank in MoE Reasoning Cohorts

Kang Chen, Sihan Zhao, Yixin Cao +1

Test-time scaling produces cohorts of reasoning rollouts, yet there is no standard label-free account of how their internal computation reorganizes as inference unfolds. We introdu…

cs.AI2026

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

Kang Chen, Sihan Zhao, Yixin Cao +1

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…

cs.AI2026

Disagree to Explore, Agree to Commit: Routing-Guided Test-Time Scaling for Software Agents

Kang Chen, Junjie Nian, Yixin Cao +1

Software-engineering agents solve repository-level tasks through long, stochastic tool-use trajectories, and repeated attempts often find fixes missed by one run. Test-time scaling…

cs.AI2026

TraceGraph: Shared Decision Landscapes for Diagnosing and Improving Agent Trajectories

Junjie Nian, Kang Chen, Ge Zhang +2

Agent benchmarks increasingly record rich interaction trajectories, yet evaluation often reduces each rollout to a pass rate or reward score. We introduce TraceGraph, a graph-based…

cs.AI2026

SliceGraph: Mapping Process Isomers in Multi-Run Chain-of-Thought Reasoning

Kang Chen, Junjie Nian, Yixin Cao +1

Multi-run chain-of-thought reasoning is usually collapsed to final-answer aggregates, which discard howsampled trajectories share, split, and rejoin through intermediate computatio…

cs.AI2026

NEX: Neuron Explore-Exploit Scoring for Label-Free Chain-of-Thought Selection and Model Ranking

Kang Chen, Zhuoka Feng, Sihan Zhao +5

Large language models increasingly spend inference compute sampling multiple chain-of-thought traces or searching over merged checkpoints. This shifts the bottleneck from generatio…