8 papers
Measuring and curing reasoning rigidity: from decorative chain-of-thought to genuine faithfulness
Abhinaba Basu, Pavan Chakraborty
Language models increasingly show their work by writing step-by-step reasoning before answering. But are these steps genuinely used, or is the answer rigid - fixed before reasoning…
ICE: Intervention-Consistent Explanation Evaluation with Statistical Grounding for LLMs
Abhinaba Basu, Pavan Chakraborty
Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions without statistical testing, making it…
When Names Change Verdicts: Intervention Consistency Reveals Systematic Bias in LLM Decision-Making
Abhinaba Basu, Pavan Chakraborty
Large language models (LLMs) are increasingly used for high-stakes decisions, yet their susceptibility to spurious features remains poorly characterized. We introduce ICE-Guard, a…
Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials
Abhinaba Basu, Pavan Chakraborty
Machine-learned interatomic potentials (MLIPs) are deployed for high-throughput materials screening without formal reliability guarantees. We show that a single MLIP used as a stab…
Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
Abhinaba Basu, Pavan Chakraborty
Scientific discovery increasingly relies on AI systems to select candidates for expensive experimental validation, yet no principled, budget-aware evaluation framework exists for c…
Contextual StereoSet: Stress-Testing Bias Alignment Robustness in Large Language Models
Abhinaba Basu, Pavan Chakraborty
A model that avoids stereotypes in a lab benchmark may not avoid them in deployment. We show that measured bias shifts dramatically when prompts mention different places, times, or…