Huaxiu Yao's banner

Huaxiu Yao

@HuaxiuYaoML • 7,097 subscribers

Assistant Professor @unccs @uncsdis | Postdoc @StanfordAILab | Author of RSI-Exam, MetaClaw, SkillRL, SimpleMem, AutoResearchClaw, Agent World Model, Agent0

Shorts

Day 3 building MetaClaw🦞 🚀 One-click deploy just dropped. No config; no setup. Just talk to your🦞 and watch it evolve. Try it 👉: Kudos to the team, Peng (Richard) Xia, Jianwen Chen, Xinyu Yang, Siwei Han, Shi Qiu, Haoqin Tu, Zeyu Zheng, Cihang Xie, Huaxiu Yao

Day 3 building MetaClaw🦞 🚀 One-click deploy just dropped. No config; no setup. Just talk to your🦞 and watch it evolve. Try it 👉: Kudos to the team, Peng (Richard) Xia, Jianwen Chen, Xinyu Yang, Siwei Han, Shi Qiu, Haoqin Tu, Zeyu Zheng, Cihang Xie, Huaxiu Yao

10,372 次观看

Videos

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

🧠 Can agent memory scale without losing reasoning? 🔥 We’re excited to share our latest work, SimpleMem, a principled memory framework for LLM agents built around semantic lossless compression. 📉 30× fewer inference tokens 📈 +26.4% avg F1 (vs Mem0) ⚡ 50.2% faster retrieval (vs Mem0) Instead of storing raw interaction history 🗂️ or relying on costly iterative reasoning loops 🔁, SimpleMem treats memory as a structured, evolving representation whose primary objective is 🎯 maximizing information density per token. 📄 Paper: 🔗 Code: 📦 Website： Nice work @JiaqiLiu835914, Yaofeng Su, Peng (Richard) Xia, Siwei Han, and great collab. w/ Cihang Xie, Zeyu Zheng, Mingyu Ding

🧠 Can agent memory scale without losing reasoning? 🔥 We’re excited to share our latest work, SimpleMem, a principled memory framework for LLM agents built around semantic lossless compression. 📉 30× fewer inference tokens 📈 +26.4% avg F1 (vs Mem0) ⚡ 50.2% faster retrieval (vs Mem0) Instead of storing raw interaction history 🗂️ or relying on costly iterative reasoning loops 🔁, SimpleMem treats memory as a structured, evolving representation whose primary objective is 🎯 maximizing information density per token. 📄 Paper: 🔗 Code: 📦 Website： Nice work @JiaqiLiu835914, Yaofeng Su, Peng (Richard) Xia, Siwei Han, and great collab. w/ Cihang Xie, Zeyu Zheng, Mingyu Ding

119,895 次观看 • 8 个月前

🚀 SimpleMem now supports Claude Skills! Use SimpleMem in to remember long-term information and project history across conversations. Register at 👉 🔗 Detailed Code: Try it out and let us know what you think! @JiaqiLiu835914, Yaofeng Su, Peng (Richard) Xia, Siwei Han, Cihang Xie, Zeyu Zheng, Mingyu Ding

🚀 SimpleMem now supports Claude Skills! Use SimpleMem in to remember long-term information and project history across conversations. Register at 👉 🔗 Detailed Code: Try it out and let us know what you think! @JiaqiLiu835914, Yaofeng Su, Peng (Richard) Xia, Siwei Han, Cihang Xie, Zeyu Zheng, Mingyu Ding

Huaxiu Yao ✈️ ICML 2026

22,937 次观看 • 8 个月前

🚀 SimpleMem MCP is now LIVE & Open Source! Experience SimpleMem as a cloud-hosted memory service with fast retrieval, fewer tokens, and persistent memory - all exposed via MCP. Easily integrate with Cursor, Claude Desktop, LM Studio, Cherry Studio, and more. 👉 Try it now: 🔗 Code: Nice work again @JiaqiLiu835914, Yaofeng Su, Peng (Richard) Xia, Siwei Han, and great collab. w/ Cihang Xie, Zeyu Zheng, Mingyu Ding

🚀 SimpleMem MCP is now LIVE & Open Source! Experience SimpleMem as a cloud-hosted memory service with fast retrieval, fewer tokens, and persistent memory - all exposed via MCP. Easily integrate with Cursor, Claude Desktop, LM Studio, Cherry Studio, and more. 👉 Try it now: 🔗 Code: Nice work again @JiaqiLiu835914, Yaofeng Su, Peng (Richard) Xia, Siwei Han, and great collab. w/ Cihang Xie, Zeyu Zheng, Mingyu Ding

16,015 次观看 • 8 个月前

Can vision-language-action (VLA) models generalize to diverse OOD tasks and align with customized objectives? 🤔 🚀 We introduce GRAPE, a plug-and-play algorithm to generalize robot policies via preference alignment. GRAPE unfolds three benefits to boost the generalizability of VLAs: 👉1. GRAPE aligns VLAs on a trajectory level and endows the model with the ability for global decision-making, instead of merely cloning behavior; 👉2. GRAPE implicitly models reward from both successful and failed trials to boost generalizability to diverse tasks; 👉3. GRAPE adopts a scalable preference synthesis algorithm to rank trajectories with preferences that align with arbitrary objectives. Our experiments on a diverse array of real-world and simulated robotic tasks reveal: 1⃣GRAPE enhances the performance of state-of-the-art VLA models, increasing success rates on in-domain and unseen manipulation tasks by 51.79% and 60.36%; 2⃣GRAPE is versatile to be aligned with diverse objectives and reduce collision rates by 44.31% or rollout length by 11.15% when aligning towards safer or more efficient manipulation policy, respectively. Check out our full project for more details: 🔥 Paper: 🔥 Project: 🔥 Code:

Can vision-language-action (VLA) models generalize to diverse OOD tasks and align with customized objectives? 🤔 🚀 We introduce GRAPE, a plug-and-play algorithm to generalize robot policies via preference alignment. GRAPE unfolds three benefits to boost the generalizability of VLAs: 👉1. GRAPE aligns VLAs on a trajectory level and endows the model with the ability for global decision-making, instead of merely cloning behavior; 👉2. GRAPE implicitly models reward from both successful and failed trials to boost generalizability to diverse tasks; 👉3. GRAPE adopts a scalable preference synthesis algorithm to rank trajectories with preferences that align with arbitrary objectives. Our experiments on a diverse array of real-world and simulated robotic tasks reveal: 1⃣GRAPE enhances the performance of state-of-the-art VLA models, increasing success rates on in-domain and unseen manipulation tasks by 51.79% and 60.36%; 2⃣GRAPE is versatile to be aligned with diverse objectives and reduce collision rates by 44.31% or rollout length by 11.15% when aligning towards safer or more efficient manipulation policy, respectively. Check out our full project for more details: 🔥 Paper: 🔥 Project: 🔥 Code:

20,071 次观看 • 1 年前

没有更多内容可加载