arXiv AI
Learning to Assign Prediction Tasks to Agents with Capacity Constraints
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arXiv AI
Jun 16
LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure
arXiv:2606. 15306v1 Announce Type: cross Abstract: We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared across those tasks and use it to improve future decisions.
arXiv AI
Oct 16
Learning to Steer LLM Agents and Multi-Agent Systems
The paper introduces a steering agent that supervises large language model (LLM)-based agents and multi‑agent systems during execution to prevent goal drift. By observing the execution context, the steering agent decides when to intervene using either a low‑cost fast path or a high‑cost slow path, and both paths are jointly trained with reinforcement learning. Experiments on information‑seeking search tasks demonstrate that the steering agent improves performance and efficiency for both single‑agent and multi‑agent systems, generalizes to out‑of‑domain tasks, and allows controllable intervention frequency without retraining.
arXiv Machine Learning
Oct 15
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories
arXiv:2610.19614v1 Announce Type: new Abstract: In this work, we reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations. We show that success...
arXiv AI
Jun 10
HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning
arXiv:2606. 10507v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degrades in multi-turn long-horizon agentic tasks.
arXiv AI
Jun 4
Can Generalist Agents Automate Data Curation?
arXiv:2606. 04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback.
arXiv Machine Learning
Sep 10
Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
arXiv:2603.02935v2 Announce Type: replace Abstract: Offline meta-reinforcement learning seeks to learn a policy that generalizes to new related tasks online. Context-based methods infer a task repres...