arXiv AI
Jul 23
You Live More Than Once: Towards Hierarchical Skill Meta-Evolving
arXiv:2605. 28390v2 Announce Type: replace Abstract: Test-time skill evolving is regarded as a new paradigm for enhancing deployed agentic systems.
A Self-Evolving Agentic Framework for Metasurface Inverse Design
arXiv:2604. 01480v2 Announce Type: replace Abstract: Metasurface inverse design can realize complex optical functionality, but turning a target optical response into executable optimization code still requires substantial expertise in computational electromagnetics and solver-specific software engineering.
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arXiv Machine Learning
Jul 27
Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning
arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.
arXiv AI
Jun 29
Agentic Hardware Design as Repository-Level Code Evolution
arXiv:2606. 28279v1 Announce Type: cross Abstract: We present HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution.
arXiv AI
Jul 28
When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design
arXiv:2607. 23469v1 Announce Type: cross Abstract: Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled parameters requires costly full-wave simulations.
arXiv AI
6d ago
REVERE: Reflective Evolving Research Engineer
arXiv:2603. 20667v2 Announce Type: replace-cross Abstract: Existing prompt-optimization techniques rely on local signals, causing poor generalization across tasks.
arXiv AI
Jun 9
From 0-to-1 to 1-to-N: Reproducible Engineering Evidence for MetaAI Recursive Self-Design
arXiv:2606. 09663v1 Announce Type: new Abstract: Recursive self-design refers to AI-assisted modification of the mechanisms by which an AI system is built, evaluated, and improved.
arXiv AI
Aug 12
CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
arXiv:2604. 01687v3 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address.
arXiv Machine Learning
Jul 9
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
arXiv:2607. 07682v1 Announce Type: new Abstract: The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces.
arXiv AI
Jun 4
The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?
arXiv:2606. 04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows.
arXiv AI
Aug 11
Rethinking Self-Evolving Agents: Do We Still Need Prescribed Optimization Pipelines?
arXiv:2608. 09629v1 Announce Type: new Abstract: Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop.
arXiv AI
Jun 4
SePO: Self-Evolving Prompt Agent for System Prompt Optimization
arXiv:2606. 04465v1 Announce Type: cross Abstract: System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions.
Hugging Face Trending Papers
Jul 8
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often lack robustness and transferability, whereas evolutionary strategies are robust but struggle in high-dimensional spaces.