A great read if you an AI dev. Current context compactors retain 17% of the standing rules users give them. Session Constraints are instruct…

A great read if you an AI dev. Current context compactors retain 17% of the standing rules users give them. Session Constraints are instructions like "do not delete any emails until I confirm" that bind the agent for the rest of the session. It turns out that compaction drops them silently. Their evaluation suite, COMPINT, tests three settings, multi-turn chat, agentic trajectory, and long-horizon research. Compactors often leave the task worse off than running it without compaction at all. Retention swings with the compactor, the prompt, the context length, the phrasing, and where the constraint was injected, so the loss is structural. The fix is small. An SC-aware extractor running alongside the compactor recovers over 90% retention without touching the compactor or the model. Paper: https://arxiv.org/abs/2608.11242 Track more trending AI papers in our academy: https://academy.dair.ai/

// Agentic Context Management // Great read for the weekend. (bookmark it) Production agents fail less on reasoning and more on what sits in…
Good agent memory paper. And great insights on the benefits of structured memory for long-horizon behavior in LLMs. Why it matters: It treat…
Finally a good paper testing if file-system based memory for LLM agents is worth it. First, what does this look like? Deployed agents keep l…
// Programmatic Memory Enables Long-Horizon Reasoning // Keep the entire interaction log and search it. It works great and beats bespoke mem…

Source: DAIR.AI (X) | 2026-08-13

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