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cs.CL2026

Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

Wang Yang, Debargha Ganguly, Xinpeng Li +5

Hybrid reasoning language models are commonly controlled through high-level Think/No-think instructions to regulate reasoning behavior, yet we found that such mode switching is lar…

cs.CL2026

Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separation

Shouren Wang, Wang Yang, Chuang Ma +7

Hybrid-thinking language models expose explicit /think and /no_think modes, but current designs do not separate them cleanly. Even in /no_think mode, models often emit long and sel…

cs.CL2026

VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning

Vikash Singh, Darion Cassel, Nathaniel Weir +2

Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamental challenge. We present a neurosymbolic…

cs.CL2026

Trust The Typical

Debargha Ganguly, Sreehari Sankar, Biyao Zhang +8

Current approaches to LLM safety fundamentally rely on a brittle cat-and-mouse game of identifying and blocking known threats via guardrails. We argue for a fresh approach: robust…

cs.CL2025

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

Debargha Ganguly, Vikash Singh, Sreehari Sankar +7

Large language models (LLMs) show remarkable promise for democratizing automated reasoning by generating formal specifications. However, a fundamental tension exists: LLMs are prob…