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The paper introduces LLM-Guided Contextual Action Evaluation (LCAE), a method that leverages a large language model to transform fixed industrial documents into a frozen action–observation–direction–delay relation basis. Recent numerical action–response history modulates the strength of these relations, creating a state‑conditioned nonlinear action‑effect field that both the actor and critic use for policy learning. LCAE integrates document semantics into maximum‑entropy policy learning without requiring the LLM or embedding model to run online during training or deployment.
The paper introduces PolicyLift, a three‑stage framework that improves decision policies using observational data for complex, structured actions such as text or images. Stage 1 learns discrete action codes that capture high‑level strategies, Stage 2 trains a context‑dependent policy over these codes, and Stage 3 generates full actions conditioned on the learned codes. The authors provide theoretical guarantees that improving the code‑level policy leads to improvement in the original action space and demonstrate consistent policy gains in synthetic and real‑world text generation experiments.
The paper introduces Agentic In-Context Learning (Agentic ICL), a framework where fixed‑parameter Transformers learn from interactions that shape the context they receive. It models an interactive learner with state, update, and decision rules, covering active learning, bandits, and online reinforcement learning, and demonstrates that causal Transformers can approximate these algorithms over a finite horizon. Experiments show that Transformers can learn to execute recursions and that different reward signals produce distinct decision policies, with alignment between process supervision and outcome‑optimal actions influencing learned behavior.
arXiv:2505.23323v3 Announce Type: replace Abstract: This position paper argues that many behaviours framed as either "stochastic parroting" or "advanced reasoning" are better explained as context-dir...
Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cloning. This supervises which motor command was dem...
arXiv:2608. 07148v1 Announce Type: new Abstract: Modern manufacturing imposes six coupled demands on adaptive control: local decisions with global consequences, partial observability, nonstationarity, reflex speed response with long horizon effects, delayed and diffuse outcomes, and dynamics that resist explicit modeling.