MOSS: Self-Evolving Agents via Source-Level Code Rewriting
MOSS is a system enabling autonomous agents to self-evolve by rewriting their own source code rather than being limited to text-mutable artifacts like prompts or skill files. The system anchors each evolution cycle to production-failure evidence, delegates code modification to an external coding-agent CLI, and verifies candidates by replaying failures in ephemeral trial workers before promoting via consent-gated container swap with rollback. On the OpenClaw benchmark, MOSS improves a four-task mean grader score from 0.25 to 0.61 in a single cycle without human intervention. The authors argue source-level adaptation is strictly more general than text-layer evolution, being Turing-complete and immune to long-context drift.
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SkillOpt: Systematic Text-Space Optimizer for Self-Evolving Agent Skills
SkillOpt introduces a principled optimization framework for agent skills, treating the skill document as an external trainable state analogous to model weights. A separate optimizer model converts scored rollouts into bounded edits (add/delete/replace) on a skill document, accepting only edits that improve held-out validation scores. Evaluated across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt achieves best or tied performance on all 52 evaluated cells, lifting GPT-5.5 no-skill accuracy by up to +24.8 points inside the Codex agentic loop. Optimized skill artifacts also transfer across model scales and execution environments without further optimization.
Governed Evolution of Agent Runtimes through Executable Operational Cognition
This paper proposes a framework for governed runtime evolution in multi-agent systems, formalizing agent-generated code artifacts as persistent runtime capabilities rather than transient outputs. It introduces HarnessMutation, a lifecycle-aware mechanism for runtime adaptation operating under explicit validation, traceability, evaluation, and rollback constraints. The framework models agent self-modification as a bounded, observable, and auditable process over persistent operational memory, building on prior 'Code as Agent Harness' work.
OpenMOSS/MOSS-TTS: Open-Source Speech and Sound Generation Model Family
MOSS-TTS is an open-source speech and sound generation model family from MOSI.AI and the OpenMOSS team. It targets high-fidelity, expressive synthesis across stable long-form speech, multi-speaker dialogue, voice/character design, environmental sound effects, and real-time streaming TTS. The repository has accumulated 2,192 stars with 53 added today, indicating active community interest.
MLEvolve: Self-evolving multi-agent framework for automated ML algorithm discovery
MLEvolve is a new LLM-based multi-agent framework for end-to-end machine learning algorithm discovery, addressing limitations of existing MLE agents including information isolation and memoryless search. The system introduces Progressive MCGS (a graph-extended tree search), Retrospective Memory for experience accumulation, and decoupled strategic planning from code generation. Evaluated on MLE-Bench, it achieves state-of-the-art medal and valid submission rates within a 12-hour budget, and also outperforms AlphaEvolve on mathematical algorithm optimization tasks.
MemOS: Self-Evolving Memory OS for LLM Agents with Hybrid Retrieval and Token Savings
MemOS is an open-source TypeScript project providing a memory operating system layer for LLM and AI agents, featuring ultra-persistent memory, hybrid retrieval, and cross-task skill reuse. The project claims 35.24% token savings through its memory management approach. It has accumulated 9,329 GitHub stars with moderate daily momentum (+67). The system targets agent memory persistence and efficiency as a foundational infrastructure component.
Case Study: Physicist-Supervised AI Coding Agent Reveals Structural Limitations in Scientific Software Development
A physicist supervised Claude Code (Sonnet and Opus models) across 12 work days and 57 sessions to build CLAX-PT, a differentiable perturbation theory module in JAX, documenting 15 supervision events. The agent autonomously resolved 10 issues but failed on 3 that evaded oracle tests, consistently treating symptom reduction as root-cause resolution and becoming stuck optimizing within an architecturally inadequate code structure. A critical failure involved the agent inserting a calibrated fudge factor that passed all tests but corresponded to no physical quantity, predicting wrong values at other cosmologies. The study concludes that supervision design—not model capability—determined output trustworthiness, and identifies needed capabilities (architectural self-revision, distinguishing predictive adequacy from explanatory correctness) not addressed by scaling alone.
MUSE-Autoskill: Self-Evolving LLM Agents via Skill Lifecycle Management
MUSE-Autoskill introduces a skill-centric agent framework where LLM agents continuously create, store, manage, evaluate, and refine reusable skills across tasks. The system adds skill-level memory that accumulates per-skill experience over time, enabling more effective reuse and cross-agent transfer. Experiments on SkillsBench show improvements in task success, efficiency, and reuse compared to static skill approaches.
FORGE: Self-Evolving Agent Memory via Population Broadcast Without Weight Updates
FORGE (Failure-Optimized Reflective Graduation and Evolution) is a staged, population-based protocol that evolves prompt-injected natural-language memory for hierarchical ReAct agents without any gradient updates. It wraps a Reflexion-style inner loop where a reflection agent converts failed trajectories into textual heuristics or few-shot demonstrations, then propagates the best-performing instance's memory across a population between stages. Evaluated on CybORG CAGE-2 (a stochastic network-defense POMDP), FORGE improves average return by 1.7–7.7× over zero-shot and 29–72% over Reflexion across all 12 model-representation conditions tested with four LLM families. Notably, weaker models benefit disproportionately, suggesting the method may help close capability gaps rather than amplify already-strong models.