4 papers · 1 filter
Breaking the Martingale Curse: Multi-Agent Debate via Asymmetric Cognitive Potential Energy
Yuhan Liu, Juntian Zhang, Yichen Wu +4
Multi-Agent Debate (MAD) has emerged as a promising paradigm for enhancing large language model reasoning. However, recent work reveals a limitation:standard MAD cannot improve bel…
SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
Ahmed Heakl, Yahia Salaheldin Shaaban, Martin Takac +2
Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present SVRPBench, the first open benchmark to captur…
LLM-BABYBENCH: Understanding and Evaluating Grounded Planning and Reasoning in LLMs
Omar Choukrani, Idriss Malek, Daniil Orel +4
Assessing the capacity of Large Language Models (LLMs) to plan and reason within the constraints of interactive environments is crucial for developing capable AI agents. We introdu…
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou +4
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficie…