2 citations · 2 across the 17 of their papers we have counts for
7 papers · 1 filter
StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean
Idan Davidovich, Debargha Ganguly, Vikash Singh +1
Leading benchmarks for formal theorem proving with large language models are small collections drawn from competition math, such as the IMO and Putnam, that poorly represent field-…
Auditing Cross-Lingual Fairness in Language Model Watermarking
Alexander Nemecek, Osama Zafar, Debargha Ganguly +3
Watermarking schemes for large language model output are evaluated almost exclusively on English text using each scheme's detection threshold and a narrow set of quality measuremen…
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…
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…
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…
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…
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…