Long Task Success Rate Doubled: Alibaba's MEA Loop Fixes Context Decay
大模型之路 · wechat · 2026-08-26
Long-horizon agents often fail due to "context decay" and "goal drift," where errors accumulate over long conversations. Alibaba's open-source LongHorizon-Harness proposes a "Manage-Execute-Audit" (MEA) architecture:
- Core Mechanism: Decouples state management from execution context. The Manager holds a persistent task ledger, the Executor handles subtasks in fresh contexts, and the Auditor verifies results solely against environmental facts (e.g., file status).
- Performance: Using MEA, the same model improved success rates on WeaveBench from 51.8% to 80.7%, and Terminal-Bench from 69.7% to 77.2%.
- Engineering Insight: Reliability in long tasks depends more on system design than raw model power. The trade-off is increased token consumption (auditing costs 19%–38%), but it yields significantly higher stability.
More from coding & agent
- Musk amplifies dev's verdict: Grok Bot easier to use than Codex and Claude — elonmusk · 2026-10-15
- Blob0 launches: a macOS file-manager-style desktop client for S3 and Cloudflare R2 — yihui_indie · 2026-10-15
- The 6-Layer AI Engineer Skill Stack for 2026: Vectors to Multi-Agent Ops — mdancho84 · 2026-10-15
- Building a Mechanical Engineering Agent Workflow in ChatGPT: 30-Node Canvas Shows Promise — burhop · 2026-10-15
- Open-source Harness Engineering course hits 19k stars, shows $9 vs $200 gap between failing and working agent setups — thisguyknowsai · 2026-10-15
- A 6-person team shipped healthcare prior-auth AI agents in 3 months — here's the playbook — alex_verem · 2026-10-15