most citedFAIRE: Assessing Racial and Gender Bias in AI-Driven Resume Evaluations

2 citations · 3 across the 12 of their papers we have counts for

collaborators

22 papers

cs.AI2025

Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoning

Leo Lu, Jonathan Zhang, Sean Chua +4

Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of large language models (LLMs). While prior work focuses on improving model performance thro…

cs.MA2025

WOLF: Werewolf-based Observations for LLM Deception and Falsehoods

Mrinal Agarwal, Saad Rana, Theo Sundoro +5

Deception is a fundamental challenge for multi-agent reasoning: effective systems must strategically conceal information while detecting misleading behavior in others. Yet most eva…

cs.CL2025

Interpreting the Latent Structure of Operator Precedence in Language Models

Dharunish Yugeswardeenoo, Harshil Nukala, Ved Shah +4

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities but continue to struggle with arithmetic tasks. Prior works largely focus on outputs or prompting s…

cs.AI2025

SMAGDi: Socratic Multi Agent Interaction Graph Distillation for Efficient High Accuracy Reasoning

Aayush Aluru, Myra Malik, Samarth Patankar +4

Multi-agent systems (MAS) often achieve higher reasoning accuracy than single models, but their reliance on repeated debates across agents makes them computationally expensive. We…

cs.AI2025

SwiftSolve: A Self-Iterative, Complexity-Aware Multi-Agent Framework for Competitive Programming

Adhyayan Veer Singh, Aaron Shen, Brian Law +4

Correctness alone is insufficient: LLM-generated programs frequently satisfy unit tests while violating contest time or memory budgets. We present SwiftSolve, a complexity-aware mu…

cs.CL2025

ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models

Haziq Mohammad Khalid, Athikash Jeyaganthan, Timothy Do +4

Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally. Given that multi-turn conversations…