@yoheinakajima: i showcase "controlled" self improvement with a novel regime-to-seam approach where failures are categorized and allowe…
Summary
The author showcases a controlled self-improvement approach for AI agents using a regime-to-seam method where failures are categorized to fix targeted areas, built on activegraph.
View Cached Full Text
Cached at: 06/11/26, 05:39 PM
i showcase “controlled” self improvement with a novel regime-to-seam approach where failures are categorized and allowed to fix targeted areas of the agent
while interesting, it’s more to showcase the type of self-modification that’s easy to set up with activegraph https://t.co/4S4fmmNfxQ
Similar Articles
@yoheinakajima: in arxiv paper #2, i tackle the last topic from paper #1: @activegraphai as an architectural affordance for self-improv…
This paper introduces Regimes, an auditable, held-out-gated improvement loop built on the ActiveGraph runtime for self-improving agents. It demonstrates modest improvements on the LongMemEval dataset by autonomously discovering prompt repairs that pass static checks, sandbox execution, and held-out validation.
@yoheinakajima: less novel, but still very interesting impo is the gated approach to self-modification the agent basically forks itself…
Discusses a gated approach for AI agent self-modification where the agent forks itself, proposes a patch, and runs multiple tests before modification is applied.
Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph
Regimes is an auditable, held-out-gated improvement loop built on the ActiveGraph event-sourced runtime. It diagnoses failures in AI agents, proposes repairs, and promotes them only after passing multiple gates, improving accuracy on LongMemEval by up to +0.10.
@rohanpaul_ai: This paper shows an AI improving itself better when it rewrites its setup and updates its model. The problem is that mo…
This paper introduces SIA, a self-improving AI loop that combines scaffold rewriting and weight updates (via LoRA) to enhance task performance. Tested on three diverse tasks, it outperforms setups using only scaffold improvements.
@omarsar0: Great paper on self-improving agents. Why? We need to think more deeply about AI agent system design. The protocol spec…
A paper introduces a protocol framework for self-improving AI agents, enabling auditable improvement proposals, assessments, and rollbacks.