4 citations · 4 across the 3 of their papers we have counts for
6 papers
Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
Subbarao Kambhampati, Karthik Valmeekam, Siddhant Bhambri +6
Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning task…
NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming
Upasana Biswas, Durgesh Kalwar, Subbarao Kambhampati +1
Mutual adaptation is a central challenge in human-AI teaming, as humans naturally adjust their strategies in response to an AI agent's behavior. Existing approaches attempt to appr…
Evaluating the False Trust Engendered by LLM Explanations
Vardhan Palod, Upasana Biswas, Subbarao Kambhampati
Large Language Models (LLMs) and Large Reasoning Models (LRMs) are increasingly used for critical tasks, yet they provide no guarantees about the correctness of their solutions. Us…
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation
Siddhant Bhambri, Upasana Biswas, Subbarao Kambhampati
Recent advances in reasoning-focused Large Language Models (LLMs) have introduced Chain-of-Thought (CoT) traces - intermediate reasoning steps generated before a final answer. Thes…
Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming
Upasana Biswas, Vardhan Palod, Siddhant Bhambri +1
State-of-the-art methods for Human-AI Teaming and Zero-shot Cooperation focus on task completion, i.e., task rewards, as the sole evaluation metric while being agnostic to how the…
Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?
Siddhant Bhambri, Upasana Biswas, Subbarao Kambhampati
Recent progress in reasoning-oriented Large Language Models (LLMs) has been driven by introducing Chain-of-Thought (CoT) traces, where models generate intermediate reasoning traces…