AI HOT (Curated Pool)Jun 1, 2026, 17:01 UTC+8

Tencent Hunyuan Releases Long-Term Memory Plugin Hy-Memory

腾讯混元发布智能体长期记忆插件Hy-Memory

Tencent Hunyuan released Hy-Memory for long-term collaborative agents such as OpenClaw, using a six-layer memory framework and System1/System2 dual system, with memory count reduced by over 70% and token consumption down 35% in ultra-long-context scenarios.

Why it matters: Tencent Hunyuan’s Hy-Memory clears HKR-H/K/R with a concrete memory architecture and cost-reduction figures. The score stays at the featured floor because the source is an official short post without reproducible tests, license details, or third-party benchmarks.

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Take

Hy-Memory takes the right bet: layered compression and retrieval for agent memory. I still would not call it a “second brain.” Tencent gives a six-layer memory framework, a System1/System2 split, and a three-stage evolution chain. The reported numbers are tidy: memory count down over 70%, per-memory information density up over 45%, token use down 35% in ultra-long-context runs, and update speed up 20%. That proves cleaner memory storage, not reliable memory behavior.

Long-running agents fail on bad compression, not just high token bills. Old preferences, task state, and user constraints get merged, stale, or recalled in the wrong context. Mem0, Letta, and OpenAI’s own memory features all ran into the same wall: memory needs auditability. Hy-Memory’s snippet gives no false-recall rate, forgetting rate, or correction workflow. The engineering story is credible; the trust story is unfinished.

Source: AI HOT (Curated Pool) · x.com
Published: Jun 1, 2026, 17:01 UTC+8 · Jun 1, 2026