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.
arXiv preprint arXiv:2502.07978 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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OpenCEM is the first open-source digital twin that integrates unstructured contextual information with quantitative microgrid dynamics to enable context-aware energy management.
LLMs in a conjecturing-proving loop that conditions on their own prior verified Lean proofs discover more hard-to-prove theorems than baselines that generate statements and proofs together.
MATE uses permutation-invariant sum-aggregated memory of transition embeddings to solve CMDPs with online adaptation and computational advantages over Transformers and RNNs.
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.
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
citing papers explorer
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When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
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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Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset
OpenCEM is the first open-source digital twin that integrates unstructured contextual information with quantitative microgrid dynamics to enable context-aware energy management.
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Discovering New Theorems via LLMs with In-Context Proof Learning in Lean
LLMs in a conjecturing-proving loop that conditions on their own prior verified Lean proofs discover more hard-to-prove theorems than baselines that generate statements and proofs together.
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MATE: Solving Contextual Markov Decision Processes with Memory of Accumulated Transition Embeddings
MATE uses permutation-invariant sum-aggregated memory of transition embeddings to solve CMDPs with online adaptation and computational advantages over Transformers and RNNs.
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One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning
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.
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A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.