arXiv:2608. 06144v1 Announce Type: new Abstract: Most agent benchmarks evaluate tasks independently and cannot measure whether experience from one task helps with later tasks.
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arXiv:2608. 04077v1 Announce Type: new Abstract: Evaluating financial AI agents requires criteria aligned with real professional work.
arXiv:2607. 05297v1 Announce Type: new Abstract: Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability.
FINSKILLOPS is a multi‑agent system designed to improve SEC filing question‑answering after deployment by creating reusable skills from evidence‑grounded failure diagnoses. It manages these skills through targeted validation, regression checks, negative controls, and versioned replacement or retirement, ensuring that new patches do not introduce regressions. Across six benchmarks, a frozen skill registry outperforms other systems, and in a 12‑round operational study the system reduced the non‑correct rate from 20.0% to 12.5% while promoting only six of 33 proposed skills.
arXiv:2608. 02636v1 Announce Type: cross Abstract: Self-evolving skill systems promise to improve agents by turning execution feedback into persistent skill updates without changing the underlying model.
arXiv:2606. 14239v1 Announce Type: new Abstract: Agent skills are structured procedural packages that guide frozen LLM agents in specialized workflows.
arXiv:2608. 03764v1 Announce Type: new Abstract: Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively.