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Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents

Paper recorded by Signals 4 on 2026-09-22 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23

Category: cs.AI · 人工智能 · first seen 2026-09-23

Abstract

Large language model (LLM) agents often handle streams of related tasks, yet standard harnesses repeatedly ask the model to reconstruct the same control decisions inside each task's context. We study whether task feedback can instead turn recurring control into reusable executable code, while reserving LLM calls for task-specific semantic reasoning. We introduce Growing Harness, a failure-guided t

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#5 most recent of 360 cs.AI papers we have recorded · ↑ newer: A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem · ↓ older: Type-Safe Is Not Error-Free: A Constrained Decision Head Follows the O
Cite this page: Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents: the #5 most recent of 360 cs.AI papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/grow-the-harness-not-the-context-from-strategy-free-scaffolds-to-reusable-specia.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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