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SkillOpt introduces a systematic controllable text-space optimizer that enables AI agents to train and improve their own skills (like 'work instructions') through iterative edits and validation, outperforming human-crafted and one-shot prompts across multiple benchmarks and models.
Microsoft Research introduces SkillOpt, a method that treats agent skill documents as trainable external state, using an optimizer model to make bounded edits validated by a held-out set. The approach achieves best or tied results across 52 evaluation cells and improves accuracy by over 23 points on GPT-5.5, with zero extra inference cost and transferable skills.
MOSS introduces source-level rewriting for self-evolving agents, enabling fixes to structural failures that text-layer evolution cannot reach. It lifts a four-task mean grader score from 0.25 to 0.61 in a single cycle on OpenClaw without human intervention.
Hermes Agent is promoted as an AI project featuring multi-layer memory, self-evolving skills, and autonomous 24/7 operation with cross-session recall, positioning it more as an operator than a tool.
FlyRoute is a self-evolving profiling framework that improves LLM-based task routing in multi-agent systems by dynamically updating agent capability descriptions from real traffic, achieving significant accuracy gains over static profiles.
GenEvolve is a self-evolving image generation framework that uses tool-orchestrated trajectories and visual experience distillation to iteratively improve generative capabilities, achieving state-of-the-art performance.
SEMA-RAG is a self-evolving multi-agent RAG framework for medical question answering that decouples interpretation, exploration, and adjudication into three specialist agents, achieving significant accuracy improvements over baselines across multiple benchmarks.
flux-genotype is an open-source AI kernel that orchestrates local LLMs on CPU, allowing self-modification of its architecture via a MetaDesigner module.
DrugSAGE is a framework that accumulates and reuses cross-task memory to build state-of-the-art drug discovery models efficiently, outperforming baseline agents by 10-30% on held-out tasks.
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that couples graph representation learning with structured, topology-constrained reasoning. It achieves absolute improvements of up to 3.44% in root cause localization accuracy and boosts fault-type classification performance by 4.39% to 16.81% across diverse datasets.
PaSaMaster is a self-evolving agentic literature retrieval system that iteratively refines search intent and produces evidence-grounded paper rankings, outperforming GPT-5.2 by 30% at 1% cost with zero hallucinations.
TACO introduces a self-evolving compression framework that automatically learns to shrink redundant terminal interaction history, cutting token overhead ~10% while boosting accuracy 1-4% across TerminalBench and other code-agent benchmarks.
NVIDIA researchers present the first self-evolving logic synthesis framework where multi-agent LLMs autonomously refine the ABC EDA tool codebase.
Agent-World introduces a self-evolving training framework for general agent intelligence that autonomously discovers real-world environments and tasks via the Model Context Protocol, enabling continuous learning. Agent-World-8B and 14B models outperform strong proprietary models across 23 challenging agent benchmarks.
EvoMaster is a scalable, self-evolving agent framework for large-scale scientific discovery that enables iterative hypothesis refinement and knowledge accumulation across experimental cycles. It achieves state-of-the-art results on four benchmarks including Humanity's Last Exam (41.1%) and MLE-Bench Lite (75.8%), outperforming general-purpose baselines by up to 316%.
This paper introduces GenericAgent, a self-evolving LLM agent system designed to maximize context information density. It addresses long-horizon limitations through hierarchical memory, reusable SOPs, and efficient compression, achieving better performance with fewer tokens compared to leading agents.
Researchers introduce BEHEMOTH benchmark and CluE cluster-based prompt optimization to enable LLMs to extract and retain heterogeneous memory across diverse tasks, achieving 9% gains over prior self-evolving frameworks.