{"version":1,"type":"story","url":"https://digestai.news/story/researchers-propose-successive-scale-racing-to-improve-agentic-harness","json":"https://digestai.news/story/researchers-propose-successive-scale-racing-to-improve-agentic-harness.json","markdown":"https://digestai.news/story/researchers-propose-successive-scale-racing-to-improve-agentic-harness.md","slug":"researchers-propose-successive-scale-racing-to-improve-agentic-harness","headline":"Researchers propose successive scale racing to improve agentic harness evolution","summary":"A new arXiv paper introduces Successive Scale Racing (SSR), a framework that adaptively selects evidence combinations for agentic harness evolution. The authors observe that expanding the task pool can paradoxically hurt existing methods, a failure mode revealed in their preliminary study. SSR builds diverse evidence sets based on task quality and coverage, then races candidate harnesses using a limited compute budget.\n\nAcross three benchmarks and six configurations, SSR attains state‑of‑the‑art results. When the task pool expands from 40 to 200 tasks, the pass rate rises from 64.3% to 68.3%, while baseline approaches all decline. The racing mechanism also trims validation executions by 60%, offering a more efficient optimization process.\n\nThe work suggests that careful evidence selection and adaptive racing can counteract the degradation seen in larger task sets, pointing to a scalable path for improving AI agents that rely on evolving instruction and tool harnesses.","keyPoints":["SSR adaptively searches evidence combos and races candidate harnesses under a small compute budget","Pass rate improves from 64.3% to 68.3% as tasks grow 40 → 200, while baselines drop","Validation executions are reduced by 60% compared with prior methods"],"whyItMatters":"Better evidence selection and efficient racing can keep AI agents improving as task libraries expand, making them more reliable for complex, real‑world applications.","category":{"slug":"agents","name":"Agents & Tools","url":"https://digestai.news/category/agents"},"entities":{"companies":[],"models":[],"people":[]},"firstPublishedAt":"2026-10-22T04:00:00Z","updatedAt":"2026-10-22T04:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"arXiv cs.AI","title":"Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing","url":"https://arxiv.org/abs/2610.28815","publishedAt":"2026-10-22T04:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Researchers propose successive scale racing to improve agentic harness evolution\", 22 October 2026, https://digestai.news/story/researchers-propose-successive-scale-racing-to-improve-agentic-harness","publisher":"Digest AI","title":"Researchers propose successive scale racing to improve agentic harness evolution","datePublished":"2026-10-22T04:00:00Z","url":"https://digestai.news/story/researchers-propose-successive-scale-racing-to-improve-agentic-harness"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}