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ActiveSaddler 通过将动态课程学习建模为非平稳多臂老虎机问题，实现了 LLM Agent 优化中训练场景课程与 harness 的协同进化，在 GAIA2 和 Terminal-Bench2.0 上分别提升了 4.4 和 7.5 个百分点的 Pass@1。
The paper introduces Mara Chain, a refinement method that turns rejected AI-system configurations (prompts, skills, harnesses, code) into stepping stones instead of discarding them, achieving up to 20.5% better performance on AppWorld and large gains on TerminalBench 2.1 and MuSiQue with far fewer rollouts.
Microsoft introduces Coding-Agent Skill Distillation (CASD), a prompt optimization method where an off-the-shelf coding agent analyzes agent logs to write optimized prompts in one pass, outperforming previous techniques like GEPA and SkillOpt at a lower cost.
This article shares a prompt and practical guidelines for improving the token efficiency of LLM agent harnesses, based on lessons learned at Cursor, aiming to reduce costs without sacrificing task quality.
This paper introduces Self-Meta-Evolve, a hierarchical framework that personalizes prompts for each user in enterprise information extraction tasks, improving performance through continuous refinement based on interaction feedback.
tool-prune is a tool that prunes tool schemas before calling local LLM models, reducing prompt tokens by 92% and avoiding hallucinations with no extra LLM round-trips.
This paper presents an optimized prompt-based method for minimal-edit grammatical error correction that achieves state-of-the-art results by using taxonomy-based instructions, batching, and LLM-assisted prompt optimization.
The paper introduces RSM-full, an online clustered-memory pipeline for LLM agents that separates memory merge and retrieval assembly, achieving 83% of full-context quality at 32% of token cost under tight prompt budgets.
AgentGrad improves multi-agent prompt optimization by using sequential intervention to identify target agents and semantic clustering of textual gradients, achieving state-of-the-art performance and reducing optimization time by 2.5 times.
This paper introduces HarnessEvo to decompose LLM agent harnesses into separately-evolvable slots, revealing that optimization value is localized in specific components like reflection/control, and that uniform budget-splitting is sub-optimal, advocating for targeted budget concentration.
The paper demonstrates that prompt-space meta-learning for personalizing frozen large language models does not transfer across users, as the meta-validation objective is statistically invariant to user-support correspondence, leading to no significant improvement over seed prompts or controls.
This paper identifies failure modes in LLM-as-a-Judge systems for self-improving agents and introduces PROCTOR, an architecture with deterministic guardrails to mitigate these issues.
A paper tests NPO, a prompt optimization method relying on teacher feedback, and finds it achieves comparable or better results than GEPA with fewer rollouts, especially with stronger teacher models like GPT-5.5, and the optimized prompts transfer to other student models.
This paper introduces a control-data flow separation framework to stabilize prompt optimization in multi-agent LLM systems by decoupling execution protocols from language content, achieving 100% protocol validity while enhancing task performance.
A tweet discusses prompt optimization for MOLMO to enhance its point prompting for SAM3, with GEPA increasing performance by 122% but still falling short of perfect agreement.
This paper proposes a cost-aware framework using a multi-armed bandit controller to adaptively select prompting strategies for LLM essay scoring, reducing LLM calls by 78.4% while maintaining accuracy.
The article discusses prompt optimization as a key technique in event-driven development, mentioning frameworks like MLflow that support algorithms such as DSPy, GEPA, and MIProv2 to improve LLM prompts.
The paper proposes active inference as a framework for AI agents to efficiently acquire context by balancing information gain and cost, with benchmarks on language models and applications in question asking and prompt optimization.
DiSCO is a training-free, black-box defense for text-to-image models that uses distribution-guided contrastive prompt optimization to prevent generation of Not-Safe-For-Work content, significantly reducing attack success rates.
This paper introduces SAPO, a segment-level automatic prompt optimization method that decomposes prompts into role, context, tasks, and output format, then applies targeted improvements based on weak and strong examples. Evaluated across several benchmarks, SAPO outperforms strong APO baselines including APE, OPRO, EvoPrompt, GEPA, and StraGO on GPT-3.5-Turbo and GPT-4o-mini.