Today's edition highlights the infrastructure and tooling that support recursive improvement, from agents designing their own serving systems to frameworks for test-time harness evolution.
The loop: Coding agents design and optimize specialized LLM serving systems, which then run the agents themselves with higher efficiency and lower cost.
A benchmark for agentic design of LLM serving systems where coding agents optimize latency and cost, creating systems that can more efficiently serve the agents themselves.
The loop: The ReproBridge agent constructs reproducible research artifacts from raw code and papers, which improves the ability of successor agents to reproduce and build on ML research.
An agent workflow and benchmark for converting raw ML research code into standardized, reproducible artifacts that significantly increase downstream reproduction success by other agents.
The loop: Agent harness evolution methods modify the agent's prompts and components, which improves task reliability and coverage, making the agent more effective at its tasks.
An evaluation of four agent harness evolution methods revealing that while evolved agents gain new capabilities, they often lose previously covered tasks and incur higher deployment costs.
The loop: Agentic proposers reason over execution traces to evolve the agent's harness at test time, which then improves the agent's own execution and success rate on subsequent tasks.
A method that treats an executable agent harness as the state of test-time adaptation, iteratively evolving it on unlabeled test batches to handle shifts in task distributions and failure modes.
The loop: A learned optimizer searches for gridworld levels that induce reward-hacking, which improves the evaluation of PPO and makes the training process better at identifying and fixing cheating.
A project that trains a small optimizer using evolution strategies to efficiently find reward-hacking honeypots in gridworld levels to test PPO agents.
The loop: Self-improving agents attempt to modify their own code or environment, while an auditing system monitors for tampering to ensure the safety and reliability of the resulting improved agent.
An evaluation of auditing systems designed to monitor self-improving agents for harness tampering during their attempts to modify their own code.
The loop: An asynchronous framework treats agents as parameters and diffs as gradients, using an aggregator to optimize agent prompts and harnesses to improve the performance of the swarm.
A parallel framework for self-evolving agents that applies a gradient-descent-like process to optimize agent skills, prompts, and harnesses.
The loop: Claude uses a dreaming mechanism to organize memory and correct mistakes, which improves its internal state and makes the model better at learning from experience.
A look into how Claude organizes its memory and uses a dreaming mechanism to decide what to remember and correct mistakes from past experiences.
The loop: A learned optimizer searches for gridworld levels that induce reward-hacking, which improves the evaluation of PPO, which makes the training process better at identifying and fixing cheating.
I trained a small optimizer on synthetic functions, and used it to search gridworld levels where PPO cheats. TL;DR * Reward-hacking honeypots are levels that are built by hand where a reward is given to the agent for cheating (e.g. taking...
The loop: EvoSprig uses visual workflows and multi-agent collaboration to enable agents to perform recursive self-improvement on their own tools and harnesses.
Open-source framework for self-evolving agents: visual workflows, multi-agent collaboration, memory, tools and controlled recursive self-improvement (RSI).
The loop: agentdescent uses a gradient-descent-like process to optimize agent prompts and harnesses, which improves the performance of the agents in the swarm.
Gradient descent, but the parameters are agents - a parallel, asynchronous framework for self-evolving agents (skills, prompts, harnesses). Diffs are the gradients; the aggregator is the optimizer.
The loop: EmoReward uses self-evolving reward calibration to improve the reinforcement learning process for emotional video generation.
Official Implementation of EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL
