← Back to all news
arXiv Machine Learning By Ali Atiah Alzahrani

Conservative Meta-Agent Search: Architecture Adaptation under Non-Stationarity

Read the original on arXiv Machine Learning →

The paper introduces Conservative Meta-Agent Search (CMAS), a method for adapting agent architectures in non‑stationary environments. CMAS retrieves archived designs, proposes typed graph edits, and evaluates them through paired shadow replay with variance‑penalized scoring and a simulated canary stage before promotion. Using the MetaInvest‑Bench simulator, CMAS reduces surrogate dynamic‑oracle regret and improves held‑out utility across 96 financial streams, while maintaining low false‑promotion rates and modest inference cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jul 10

TTHE: Test-Time Harness Evolution

arXiv:2607. 08124v1 Announce Type: cross Abstract: The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures.

By Jun Nie, Yonggang Zhang, Jun Song, Qianshu Cai, Dahai Yu, Yike Guo, Xinmei Tian, Bo Han
llmsagentsdata-engineering
More like this →
arXiv AI
Oct 12

Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits

The paper introduces Humanize MLE Agents (HMA), a workflow that alternates two agents across sessions on a shared workspace, limiting each session to at most k experiments and resetting context between sessions. This approach, modeled as a context‑reset semi‑Markov decision process, outperforms single‑agent baselines, achieving a 78.2% any‑medal rate on 75 Kaggle MLE‑bench tasks—8.0 points higher than the best open‑source baseline and using only a quarter of its time budget. Across six heterogeneous agent pairs, HMA improves the any‑medal rate by an average of 6.4 points, and a supplementary kernel‑optimization case reaches 878 cycles, within 1.6% of the community leaderboard leader.

By Changye Li, Zijian Zhang, Muheng Li, Yiduo Pang, Tingjun Wu, Menghan Li, Yitong Liu, Hongkai Guo, Mengchi Chen, Chenyu He, Kaisheng Ma, Wei Xu, Song Han, Sihao Liu, Ligeng Zhu
llmsagentsreinforcement-learningbenchmarks
More like this →