R$^2$ Flow is a recursive self‑improvement framework for large‑language‑model agents that alternates policy learning, independent verification, and versioned skill‑library updates on a shared‑state orchestration graph. By merging histories that differ only in the order of independent steps, it pools evidence across equivalent executions and uses flow‑share readouts, signed utility ranks, verifier evidence, and residual‑variance plateaus to decide when and which skills to edit. Experiments on question answering, mathematical reasoning, interactive decision making, and code generation show that R$^2$ Flow improves task accuracy and library‑edit precision compared to heuristic orchestration, reinforcement learning, and other skill‑evolution baselines, and it transfers across different executors.
By Mingda Zhang, Qiang Huang, Yanjin Li, Zijia Wang, Qika Lin, Xiaoying Tang, Tiesunlong Shen
arXiv:2606.28876v4 Announce Type: replace-cross
Abstract: Memory-Mediated Learning Architecture (MMLA) separates slow base parameters theta, a bounded numerical policy carrier Phi, and a bounded auth...
By Junyi Zou, Avrova Donz
arXiv:2609.06396v2 Announce Type: new
Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet...
By Zihan Tan, Leixin Sun, Zitong Shi, Yitao Liu, Jiajun Wu, Nathaniel Brooks, Jiaru Qian, Xiaoran Shang, Suyuan Huang, Yi Ding, Yangxu Liao, Mukai Li, Qiushi Sun, Shudong Liu, Xuankun Rong, Xiaohang Yu, Zhuo Chen, Hejia Geng, Chenxin Li, Aozhou Wang, Zengji Tu, Robert Tang, Yuxin Zhan, Eric Jiang, Yuxin Wu, Jianqing Zhang, Xiao Liang, Fang Wu, Haochi Zhang, Alexander Marlow, Guancheng Wan
The paper investigates how large language model (LLM) agents can improve performance by storing and reusing experience in external memory. It identifies a problem called error reproduction, where mistakes in stored abstractions propagate through cycles of abstraction, execution, and re-abstraction, leading to persistent errors. To mitigate this, the authors propose Anti-Persistence, a memory design that retains raw trajectories and outcomes but reconstructs task-specific guidance on demand, discarding it after use. Experiments across multiple benchmarks show that Anti-Persistence yields higher held‑out success rates and lower LLM‑call costs compared to other memory designs, supporting the idea of separating experience accumulation from abstraction persistence.
By Ruhan Wang, Kishan Panaganti, Dongruo Zhou
arXiv:2607. 01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifier and updates the policy from that episode-level signal.
By Ye Liu, Srijan Bansal, Bo Pang, Yang Li, Zeyu Leo Liu, Yifei Ming, Zixuan Ke, Shafiq Joty, Semih Yavuz
arXiv:2610.00592v1 Announce Type: cross
Abstract: In partially observable reinforcement learning (RL), a later observation can make stored information obsolete or change what it implies for the next...
By Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov, Alexey K. Kovalev