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AutoSaddler is a method for automatically patching agent harnesses from execution traces, ensuring updates generalize by testing on a held-out set to prevent regressions.
MEGA is a self-evolving infrastructure for coding-agent optimization that distills reusable wisdom from sessions, composes it via a typed Wisdom Graph, and uses operational evidence to continuously improve both the agents and the knowledge guiding their optimization.
Kubit launches product analytics aimed at optimizing AI agent actions by analyzing user behavior.
This paper introduces a two-phase continual-learning evaluation on Terminal-Bench 2.0 to test whether gains from agent-optimization methods compound when applied recursively. It finds that only RELAI-VCL, which incorporates regression control, achieves compounded improvement.
A detailed report on optimizing a production vLLM serving configuration on NVIDIA's DGX Spark, correcting flags that were costing 34% MTP acceptance after reviewing 90+ official NVIDIA documents and running a 69-scenario tool evaluation.
Introduces STRACE, a framework that performs structural trajectory analysis and causal extraction to construct high signal-to-noise optimization contexts for improving long-horizon agents, outperforming baselines on a formal verification task.
Bayesian-Agent presents a framework that treats reusable skills and SOPs as hypotheses, using Bayesian inference to guide agent behavior and improve task performance through posterior-guided harness optimization. It achieves significant improvements on multiple benchmarks with deepseek-v4-flash.
Hermes Agent has added native support for skill bundles, allowing multiple skills to be triggered together. The author advises bundling only logically chained workflows to avoid conflicting instructions.
The article highlights how verbose CLI output wastes tokens for LLM coding agents and introduces a pattern-based compressor that reduces shell command output noise while preserving essential information.
This paper addresses the missing old logits problem in asynchronous reinforcement learning for LLMs, proposing exact and approximate correction methods to improve training stability and performance.