Tencent open-sources Hy-Memory 2.0 memory stack with 3B-active model

🚀 RELEASES & LAUNCHES

• Tencent open-sources Hy-Memory 2.0 memory stack: 3B-active model, engine, and MemHub app
  Tencent Hunyuan released Hy-Memory 2.0, an open-source agent memory stack whose dedicated 30B-A3B (3B active) memory model outperforms Claude Opus 5 on its memory benchmarks.
  • Aimed at agent developers and AI tool users across chat, work, and coding memory use cases, with the model, framework, and MemHub app all open-sourced
  • Hy-Memory-30B-A3B, trained specifically for memory read/write, scores 55.49 averaged across four memory systems — above Opus5 (54.62) and DeepSeek V4.1 Flash (53.64) — and leads on all four, including training-unseen EverMemOS
  • The Hy-Memory engine ranks #1 among open frameworks on 4 benchmarks: LoCoMo 72.6%, MemoryArena 55.31%, CodeMemoryBench 64.2%/35.6%, SWE-ContextBench 28.3%, with ~6% memory storage footprint
  • MemHub syncs one shared personal memory across 10 AI tools including Cursor, Codex, Claude Code, and OpenClaw
  • Already deployed in Tencent products (自选股, iWiki, 腾讯新闻), e.g. +40pt multi-hop recall in A-share research memory evaluation
  Sources 📄 Source: arxiv.org · 📎 Coverage: memory.hunyuan.tencent.com · 👁 via r/LocalLLaMA

🔬 RESEARCH

• Stanford, Together AI, UIUC and Meta introduce HybridAgent, which reasons in latent space and acts via text
  Researchers from Stanford, Together AI, UIUC, and Meta introduced HybridAgent, an agent that alternates between latent reasoning and text-based tool use.
  • Aimed at builders of tool-using LLM agents across math, science, medicine, search, and agentic reasoning tasks.
  • HybridAgent uses a lightweight HybridLink adapter to switch between latent mode for fast internal thinking and text mode for tool calls and answers, with continuous working memory carrying hybrid context across turns.
  • Trained via HybridSFT (stabilizes tool-integrated hybrid generation) and HybridRL (learns when to think latently vs act in text), built on Qwen3-Base models at 4B, 8B, and 14B scales.
  • Across 11 benchmarks it improves performance by 10.4% on average, cuts tokens by 35.5%-54.7%, runs 1.8x-2.6x faster, and costs 2.7x-3.7x less than text-based and latent baselines.
  • Latent thoughts are inspectable as readable pseudo-text, and code/data are released on GitHub.
  Sources 📄 Source: arxiv.org · 📎 Coverage: alphaxiv.org · 👁 via r/LocalLLaMA

📊 BENCHMARKS & EVALS

• 📄 Source for llama.cpp PR #30296 cuts AVX2 tiled accumulators for faster CPU inference — github.com