From the 1 of 30 linked papers with an AI index.
1 citations · 1 across the 15 of their papers we have counts for
5 papers · 1 filter
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
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…
CORE: Collaborative Reasoning via Cross Teaching
Kshitij Mishra, Mirat Aubakirov, Martin Takac +2
Large language models exhibit complementary reasoning errors: on the same instance, one model may succeed with a particular decomposition while another fails. We propose Collaborat…
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…