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Grammar Search for Multi-Agent Systems
Mayank Singh, Vikas Yadav, Shiva Krishna Reddy Malay +4
Automatic search for Multi-Agent Systems has recently emerged as a key focus in agentic AI research. Several prior approaches have relied on LLM-based free-form search over the cod…
ARM: Discovering Agentic Reasoning Modules for Generalizable Multi-Agent Systems
Bohan Yao, Shiva Krishna Reddy Malay, Vikas Yadav
Large Language Model (LLM)-powered Multi-agent systems (MAS) have achieved state-of-the-art results on various complex reasoning tasks. Recent works have proposed techniques to aut…
AU-Harness: An Open-Source Toolkit for Efficient and Unified Evaluation of AUdio-LLMs
Hoang Nguyen, Sidharth Surapaneni, Akshay Kalkunte +9
Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient and nonstandardized toolkits that limit fair comparison and sy…
Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA
Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan +5
Iterative RAG for multi-hop question answering faces challenges with lengthy contexts and the buildup of irrelevant information. This hinders a model's capacity to process and reas…
DNR Bench: Benchmarking Over-Reasoning in Reasoning LLMs
Masoud Hashemi, Oluwanifemi Bamgbose, Sathwik Tejaswi Madhusudhan +3
Test-time scaling has significantly improved large language model performance, enabling deeper reasoning to solve complex problems. However, this increased reasoning capability als…
Revitalizing Saturated Benchmarks: A Weighted Metric Approach for Differentiating Large Language Model Performance
Bryan Etzine, Masoud Hashemi, Nishanth Madhusudhan +4
Existing benchmarks are becoming saturated and struggle to separate model performances due to factors like data contamination and advancing LLM capabilities. This paper introduces…