@TheTuringPost: Must-read papers of the week Harness Handbook: Making Evolving Agent Harnesses Readable, Navigable, and Editable Search…

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A curated list of must-read papers of the week covering agentic systems, long-context RL, visual reasoning, and more.

Must-read papers of the week Harness Handbook: Making Evolving Agent Harnesses Readable, Navigable, and Editable SearchOS-V1 KnowAct-GUIClaw LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning DeepLoop: Depth Scaling for Looped Transformers RoboTTT: Context Scaling for Robot Policies UniVR: Thinking in Visual Space for Unified Visual Reasoning Hierarchical Denoising For Multi-Step Visual Reasoning Partition, Prompt, Aggregate: Statistical Self-Consistency in LMs Tracing Agentic Failure from the Flow of Success Self-Improvements in Modern Agentic Systems: A Survey
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Must-read papers of the week

Harness Handbook: Making Evolving Agent Harnesses Readable, Navigable, and Editable SearchOS-V1 KnowAct-GUIClaw LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning DeepLoop: Depth Scaling for Looped Transformers RoboTTT: Context Scaling for Robot Policies UniVR: Thinking in Visual Space for Unified Visual Reasoning Hierarchical Denoising For Multi-Step Visual Reasoning Partition, Prompt, Aggregate: Statistical Self-Consistency in LMs Tracing Agentic Failure from the Flow of Success Self-Improvements in Modern Agentic Systems: A Survey

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Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable

arXiv cs.AI

The Harness Handbook is a behavior-centric representation synthesized from agent harness codebases using static program analysis and LLM assistance, helping developers and coding agents locate code implementing specific behaviors. It introduces Behavior-Guided Progressive Disclosure (BGPD) to guide agents from high-level descriptions to relevant implementation details, improving localization accuracy and edit-plan quality.