New benchmark ICRL4AHT reveals that history-conditioned ICRL methods fail to show robust adaptation in multi-agent Overcooked-V2, underperforming random baselines on unseen teammates and layouts.
arXiv preprint arXiv:2502.07978 , year=
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Lightweight numerical bandits on text embeddings match or exceed LLM accuracy in contextual bandits at a fraction of the cost, with an embedding-based diagnostic to choose between them.
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Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.
A conjecture-prove-feedback loop lets an LLM rediscover and formally verify a known topology theorem, and reusing its own verified proofs as context improves proof success.
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
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Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
New benchmark ICRL4AHT reveals that history-conditioned ICRL methods fail to show robust adaptation in multi-agent Overcooked-V2, underperforming random baselines on unseen teammates and layouts.
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Lightweight numerical bandits on text embeddings match or exceed LLM accuracy in contextual bandits at a fraction of the cost, with an embedding-based diagnostic to choose between them.
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A conjecture-prove-feedback loop lets an LLM rediscover and formally verify a known topology theorem, and reusing its own verified proofs as context improves proof success.
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The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.