TRACE is a transition‑aware residual control framework designed to improve multi‑objective materials discovery with large language model agents. It records each local refinement as a parent‑edit‑child transition, aggregates evidence to estimate reusable edit effects, and ranks future edits by their predicted ability to reduce remaining constraint violations while protecting already satisfied objectives. In a controlled comparison, TRACE outperforms the state‑of‑the‑art LLEMA baseline, raising the macro‑average hit rate from 18.13% to 25.96%.
arXiv:2610.19951v1 Announce Type: cross Abstract: Agent skills adapt frozen models through editable external instructions. Revising several rules at once makes changes difficult to inspect, while jud...
arXiv:2610.16491v1 Announce Type: cross Abstract: Improving an agent through successive revisions requires balancing exploration and evaluation under a limited budget. A narrow search can miss useful...
The paper introduces RIPPLE, a method for adapting workflow-synthesizing agents through prompt-policy editing without retraining the underlying model. RIPPLE diagnoses failed execution trajectories, maps failures to specific policy segments, and restricts edits to those segments. It then evaluates candidate edits in isolation and replays only those that remain safe after composition, achieving up to a 23.1% improvement in validation success on a synthetic benchmark and positive gains on additional language‑model backbones.
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
arXiv:2610.16725v1 Announce Type: new Abstract: Improving an LLM agent requires knowing what to change when it fails. Similar failures can require different fixes, and resolving one problem can expos...
arXiv:2606. 30840v1 Announce Type: new Abstract: LLM agents are becoming central to information retrieval: they issue retrieval queries, synthesize answers, and increasingly serve as judges for IR evaluation.
arXiv:2610.22661v1 Announce Type: new Abstract: Human-AI interactions are becoming a rich source of data for improving AI systems: while many model responses are already satisfactory, others expose f...
arXiv:2609. 05019v1 Announce Type: new Abstract: Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observed.
arXiv:2608. 01678v1 Announce Type: new Abstract: Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources.
TraceSpec is a framework that links trajectory evaluation with adaptive draft training to improve speculative decoding in large language model agents. It introduces a dynamic execution strategy combined with fixed‑prefix replay and provides a new corpus (TraceSpec‑Traj) of 10,000 training and 2,500 test states covering browsing and software engineering tasks. Experiments show that TraceSpec’s state‑conditioned draft allocation boosts replay throughput by 12.5%–22.6% and increases accepted length by up to 10.0% compared to fixed‑turn supervision.
The paper introduces Hindsight Meta-Experience Distillation (HMED), a method that lets self‑improving agents evaluate how revisions to their Meta‑Skills affect skill discovery by re‑executing both the original and revised Meta‑Skills from the same initial state. By comparing these outcomes, HMED creates structured Meta‑Experiences that provide learning signals even for revisions that are not ultimately kept. Experiments on three interactive benchmarks show that HMED consistently outperforms strong baselines, improving skill discovery by focusing on the consequences of the improvement process rather than just branch outcomes.
arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.