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Reasoning Consensus: Structural Ensembling of LLM Reasoning via Weighted DAG Aggregation
Amruta Parulekar, Jinu Lee, Dilek Hakkani-Tür +1
Large Language Models (LLMs) explore problems through chain-of-thought, but this exploration is buried in unstructured prose. On high-stakes tasks, users cannot tell which steps ar…
ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces
Jinu Lee, Shivam Agarwal, Amruta Parulekar +3
Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluation and monitoring of the re…
Prompt PerturbAItions: Mechanisms of LLM Response Inconsistencies
Soorya Ram Shimgekar, Agam Goyal, Amruta Parulekar +6
Large language models (LLMs) are increasingly deployed in conversational settings where user phrasing ranges from polite to casual to toxic, yet less is known about whether such le…
Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
Vira Kasprova, Amruta Parulekar, Abdulrahman AlRabah +5
Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion. While prior work has mostly studied this in singl…