Agents
Recommended if you work on agent memory. Retrieval is the hardest memory problem in most harnesses because agents keep pulling stale context…
Recommended if you work on agent memory. Retrieval is the hardest memory problem in most harnesses because agents keep pulling stale context. AML tests this with a coding track of 150 software tasks,
Recommended if you work on agent memory. Retrieval is the hardest memory problem in most harnesses because agents keep pulling stale context. AML tests this with a coding track of 150 software tasks, each run with relevant history and again with noisy history. Agent Memory Challenge 2026 Cycle 2 is now open. Long-term memory is not just about storing more history. It is about retrieving the right evidence, recognizing what has changed, and avoiding stale context when an agent needs to act. Three tracks: Textual · Coding · Multimodal Op…
Related
- Impressive work from Alibaba. (bookmark it) If you build long-running agents and keep rewriting your memory schema, take a look at this appr…
- // Programmatic Memory Enables Long-Horizon Reasoning // Keep the entire interaction log and search it. It works great and beats bespoke mem…
- // OCR-Memory // Well this is a unique approach to store memory for long-horizon agents. Most of the agent memory systems compress trajector…
- Good work on improving memory for long-horizon agents. They separate two things that agent memory papers usually collapse into one. How memo…
- // Agent Zero Memory // This work separates three things that agent memory systems usually collapse into one. If you build agents with long-…
Source: DAIR.AI (X) | 2026-09-22