5 papers
Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement
Uma Ranjan, Kunal Tilaganji, Aditya Koul +9
Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety concerns in medical applications. Allowing models to abstain when uncertain im…
VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus
Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3
Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations.…
A Nash Equilibrium Framework For Training-Free Multimodal Step Verification
Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3
Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations.…
interwhen: A Generalizable Framework for Steering Reasoning Models with Test-time Verification
Vishak K Bhat, Prateek Chanda, Vijval Ekbote +6
Reasoning models produce long traces of intermediate decisions and tool calls, making test-time verification important for ensuring correctness. Existing approaches either verify o…
Teaching Transformers Causal Reasoning through Axiomatic Training
Aniket Vashishtha, Abhinav Kumar, Atharva Pandey +4
For text-based AI systems to interact in the real world, causal reasoning is an essential skill. Since active interventions are costly, we study to what extent a system can learn c…