arXiv:2606. 16330v1 Announce Type: new Abstract: Disruption recovery in industrial assembly lines requires timely decisions under machine faults, worker absence, and emergency orders.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2607. 03177v1 Announce Type: cross Abstract: Traditional reinforcement learning (RL) for recovery in autonomous systems lacks causal understanding and generalizes poorly to novel failure scenarios.
arXiv:2608. 11977v1 Announce Type: new Abstract: Tool-using LLM agents are commonly trained and evaluated in environments where tool calls succeed reliably, yet deployed tools can fail transiently, persistently, or silently.
arXiv:2609.18304v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly tackle long-horizon tasks through multi-step environment interaction, yet a single erroneous action can...
arXiv:2608. 14380v1 Announce Type: new Abstract: Many real-world tasks require LLM agents to interact with their environments over long execution horizons.
Large language model agents depend on external harnesses to exchange information with their environment and to recover from execution errors, but recovery is typically evaluated only by overall task success, masking a key trade‑off. The authors treat recovery as a causal decision problem, comparing outcomes with and without recovery from the same execution state to separate rescue from harm and analyze how its value evolves over time. They propose the Causal Intervention Router (CIR), a lightweight policy that uses pre‑recovery information to decide when intervention is beneficial, achieving a 3‑point increase in success on long‑horizon ALFWorld tasks with Qwen3‑14B while preserving correct observations and demonstrating that recovery’s benefit is not solely due to new observations.
arXiv:2608. 05080v1 Announce Type: new Abstract: Critic-free group-based reinforcement learning has become a scalable approach for post-training large language models.