arXiv AI
WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents
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arXiv AI
Oct 15
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution
arXiv:2610.18067v1 Announce Type: new Abstract: Scaling interactive environments is important for training long-horizon agents through reinforcement learning. These environments often require further...
arXiv AI
Sep 3
UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents
UniToolCall introduces a unified framework for tool-use in large language model agents, standardizing toolset construction, dataset generation, and evaluation. The framework aggregates over 22,000 tools and creates a hybrid training corpus of more than 390,000 instances by combining ten public datasets with synthetically generated, structurally controlled trajectories. It models diverse interaction patterns—single‑hop vs. multi‑hop, single‑turn vs. multi‑turn, serial vs. parallel execution—and adds an Anchor Linkage mechanism to enforce cross‑turn dependencies, while converting seven public benchmarks into a common Query–Action–Observation–Answer format for fine‑grained evaluation.
arXiv AI
Jul 28
AgentOmnia: Scaling Agentic Models for Full-Scenario Applications
arXiv:2607. 23124v1 Announce Type: new Abstract: Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings.
arXiv AI
Oct 1
Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer
The paper introduces a self‑evolving harness framework where a frozen language‑model agent first solves tasks and then edits its own harness based on run records. Using a 49‑line seed harness, the evolved harness improves average scores on in‑distribution benchmarks by 4.48 points and on out‑of‑distribution benchmarks by 12.64 points, surpassing Codex on the former and matching it on the latter. Continued evolution on a specific out‑of‑distribution benchmark further raises performance, and the study analyzes emergent mechanisms such as output truncation and history compaction.
arXiv Machine Learning
Oct 7
EvoHarness-RL: Learning Runtime Harness Coordination for Self-Evolving Agents
arXiv:2608.05446v2 Announce Type: replace Abstract: Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, recover from failures, and reuse experie...
arXiv AI
Oct 1
CompoWorld: Compositional Environment Scaling for General Agents
arXiv:2609.33665v2 Announce Type: replace Abstract: Automatically generated environments provide a scalable source of interaction data for training general agents. However, existing approaches mainly...