28 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.…
Real-Time Source-Free Object Detection
Sairam VCR, Varun Gopal, Poornima Jain +2
Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detec…
Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
Deepika SN Vemuri, Sayanta Adhikari, Ankit Saha +2
Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this by representing classes thr…
Swift Sampling: Selecting Temporal Surprises via Taylor Series
Dahye Kim, Bhuvan Sachdeva, Karan Uppal +3
While most frames in long-form video are redundant, the critical information resides in temporal surprises: moments where the actual visual features deviate from their predicted ev…
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.…