Active Memory Agent for Long-Horizon Tasks
burny_tech · x · 2026-07-13
This paper proposes an Active Memory Agent for long-horizon agents: the main agent often forgets initial requirements, failed attempts, and useful discoveries during execution. To solve this, the authors introduce an independent memory agent to track key facts and alert the main agent only when these details might influence the next step.
The paper claims this design yields improvements on two benchmarks:
- Terminal-Bench: pass@1 increased by 8.3 points
- τ²-Bench: pass@1 increased by 6.8 points
- Additionally, a trained open-weights memory model boosted performance by 3.5 points
The core idea is to decouple "memory" from "execution," allowing long-chain agents to retain critical context more reliably.
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