arXiv AI
CB-Agent: Concept-Based In-Context Learning for Sequential Action Selection
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arXiv AI
Oct 2
JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces
JevSpawn is a new compositional policy that links natural language task specifications to finite probabilistic exploration, enabling LLM agents to generate actions more efficiently. It uses parallel action spawning, feedback‑driven branch selection, representation revision, and recovery from retained alternatives to adapt actions during interaction. Evaluations on eight benchmark tasks show that JevSpawn outperforms seven agent baselines and a TypeSafe Jev variant, improving task performance and speeding navigation.
arXiv AI
Jun 26
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.
arXiv AI
Jun 16
ACCORD: Action-Conditioned Contextual Grounding for Language Agents
arXiv:2606. 16432v1 Announce Type: cross Abstract: User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment.
arXiv Machine Learning
4d ago
From Copying to Composition: How Language Models Develop In-Context Learning
The study investigates how language models acquire in-context learning (ICL) by distinguishing between copying a nearby demonstration and integrating information across a prompt. Across 17 models, roughly one-third default to copied answers, a few are undecided, and the remainder favor rule-consistent answers that yield higher accuracy and are guided by the requested value rather than the nearest conflicting example. Analyses show that models prioritize task-relevant prompt tokens before accuracy surpasses chance, and that synthetic or fine-tuned training can shift models toward compositional responses or improve accuracy when prompts reassign label meanings.
arXiv Machine Learning
Jul 31
TAPO: Transition-Aware Policy Optimization for LLM Agents
arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.
arXiv AI
Sep 28
Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
Agentick is a unified benchmark for sequential decision‑making agents that evaluates RL, LLM, VLM, hybrid, and human agents on 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities via a single Gymnasium‑compatible interface. It includes a Coding API, oracle reference policies, pre‑built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes shows no single approach dominates, with GPT‑5 mini leading overall, PPO excelling in planning and multi‑agent tasks, and the reasoning harness boosting LLM performance by 3‑10×, while ASCII observations outperform natural language.