AgentEvolver is a system that enables agents to develop reusable capabilities while executing tasks, without altering the underlying foundation model. It uses eight entity families to expose operations, methods, agents, control flow, interfaces, and state for revision, coordinated by a shared Runtime and persistent planning. In evaluations on SWE-bench Pro Public, it achieved an 82.08% resolution rate, demonstrating retained capabilities across subsequent website, game, and research tasks, though some objectives remained incomplete.
arXiv:2610.34238v1 Announce Type: cross Abstract: In this paper, we introduce $\Delta\tau$-Bench, an executable agent benchmark that models the structural evolution of large tool ecosystems. In $\Del...
arXiv:2609.24663v1 Announce Type: new Abstract: Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As t...
Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As these artifacts are iteratively updated throughout...
DynaRev is a new benchmark comprising 181 tasks designed to evaluate web agents when users revise their goals during interaction. It introduces semantic milestones to trigger additive, unconditional, and runtime-conditioned revisions, and assesses agents on correctness, responsiveness, and efficiency via trajectory‑grounded evaluation. Experiments with five models show that agents often only partially satisfy revised tasks, with performance varying by task source, revision type, and model, and highlight the benefits of instruction rewriting versus browser resets.
The paper investigates how coding agents can self‑evolve their harnesses—software, configuration, and instructions—by reviewing completed sessions and proposing changes. Using Claude Code on 30 SWE‑bench issues, the authors analyzed 125 proposals from the etro skill, implemented 24, and evaluated 12 changes on subsequent tasks. The results show mixed effectiveness: some changes eliminated targeted problems, others reduced or had no impact, and some added unrelated work, indicating that self‑evolution requires mechanisms to assess and refine changes.
UniACE is a unified framework that standardizes the evaluation of large language model (LLM) agents by representing each benchmark as an instruction–tool–environment triplet and running models through a shared, task‑agnostic harness in isolated runtimes. It preserves native success criteria, offers an offline mode for dynamic‑resource tasks, and standardizes efficiency metrics, execution records, and failure attribution. Applying UniACE to 7 benchmarks across 24 domains and 15 models revealed significant score shifts, ranking reversals, and sensitivity to evidence representation, highlighting the impact of evaluation configuration on reported agent performance.
The paper introduces Goal-Aware Dynamic Multi-Agent Collaboration (GAD), a framework that assigns distinct duties to separate agents with individualized contexts to tackle long-horizon tasks. It employs a growing execution graph to structure subtasks and their dependencies, ensuring each agent retains the original instruction, task goal, and acceptance conditions. GAD dynamically adapts collaboration and information sharing, using verification and recovery checks to monitor subtask results and overall task completion, providing feedback for repair when checks fail.
arXiv:2605. 27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential.
Coding agents can adapt their harnesses, the software, configuration, and instructions used to run them, based on experience from earlier tasks, but whether these adaptations improve their behavior on...
arXiv:2607. 13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.
The paper introduces an online skill‑evolution framework that transforms interaction traces and evaluator feedback into a persistent, versioned library of reusable procedures for computer‑use agents. By executing each iteration against a frozen library snapshot, the system updates skills without altering the underlying model parameters. Experiments across four OSWorld domains show that the evolving library consistently outperforms an empty‑library baseline, with gains ranging from 5.7 to 18.6 percentage points, while also revealing domain‑specific temporal stability and challenges in skill retrieval and revision.
arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.