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A roundup of three notable AI papers: SkillOpt treats skill documents as trainable parameters to optimize frozen agents; a new method compiles agentic workflows into model weights for 100x cost reduction; and AutoScientists introduces a decentralized agent team for long-running science without a central planner.
Voxyz announces a new GBrain feature that enables agents to iteratively improve skills using LLM-as-judge evaluation and an overnight optimization cycle.
A new paper formalizes skill optimization for agents by treating markdown skill files as trainable parameters, using bounded edits validated against holdout sets. The approach transfers well between models and improves performance on procedural benchmarks.
SkillOpt introduces a systematic controllable text-space optimizer that enables AI agents to train and improve their own skills (like 'work instructions') through iterative edits and validation, outperforming human-crafted and one-shot prompts across multiple benchmarks and models.
Microsoft Research introduces SkillOpt, a method that treats agent skill documents as trainable external state, using an optimizer model to make bounded edits validated by a held-out set. The approach achieves best or tied results across 52 evaluation cells and improves accuracy by over 23 points on GPT-5.5, with zero extra inference cost and transferable skills.
Introducing SkillOpt, an optimizer that treats natural-language skills as trainable external parameters instead of finetuning model weights. It uses bounded edits and validation gating to enable stable, controllable skill updates, achieving best or tied-best results across 52 settings on 6 benchmarks with 7 models.
MOCHA introduces a multi-objective optimization method for LLM agent skills, using Chebyshev scalarization and exponential annealing to handle hard platform constraints and discover Pareto-optimal variants, achieving significant improvements over existing optimizers.