Hugging Face Trending Papers
The View from Nowhere: Population-Centered Agent Evolution
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
1d ago
The View from Nowhere: Population-Centered Agent Evolution
arXiv:2610.36660v1 Announce Type: new Abstract: Self-improving AI systems can revise the designs that govern their own behavior. Yet existing approaches center improvement on selected parent agents a...
arXiv Computation and Language
Oct 22
Evolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting
arXiv:2610.29784v1 Announce Type: new Abstract: Most contemporary artificial intelligence (AI) systems are released with frozen parameters and improve through external updates, whereas an ideal syste...
arXiv AI
Jun 26
The Red Queen G\"odel Machine: Co-Evolving Agents and Their Evaluators
arXiv:2606. 26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains.
arXiv AI
Aug 11
DarwinX: Evolving Agent Harnesses Through Natural Selection
arXiv:2608. 07545v1 Announce Type: cross Abstract: An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow.
arXiv Machine Learning
Oct 8
FreeEvolve: Learning to Evolve Beyond Fixed Loops
FREEEVOLVE automates the optimization loop used by agent evolvers, allowing the system to decide what to test, how much evidence to gather, which candidates to pursue, and when to stop, all within specified resource limits. By treating these decisions as an editable evolution skill and improving them through meta‑evolution, the method learns a capability that replaces hand‑crafted search loops. Experiments on several benchmarks show that FREEEVOLVE improves primary metrics by an average of 13.6 points and that meta‑evolved skills add further gains, indicating transferability across environments.
arXiv AI
6d ago
Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing
The paper introduces Successive Scale Racing (SSR), a framework that improves agentic harness evolution by adaptively selecting optimal evidence combinations during each evolution round. SSR constructs diverse evidence sets based on task quality and coverage, then efficiently races candidate harnesses under a limited compute budget. Across three benchmarks, SSR outperforms baselines, steadily raising pass rates as the task pool expands from 40 to 200 while cutting validation executions by 60%.