Hanlin Wang

78 posts

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Hanlin Wang

Hanlin Wang

@hanlinwang1024

PolyU CS PhD student LLM Agent/Reinforcement Learning/Embodied AI

Guangzhou Katılım Mart 2021
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Hanlin Wang
Hanlin Wang@hanlinwang1024·
🚀 Thrilled to announce our paper "STeCa: Step-Level Trajectory Calibration for LLM Agent Learning", featured in ACL 2025 Findings! 🎉 ✨ We tackle the challenge of long-horizon tasks by enabling real-time action calibration for LLM-based agents.
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Jian Wang
Jian Wang@jwanglvy·
Excited to share our latest work, "Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models"! 🚀 arXiv: arxiv.org/abs/2601.08955
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Yang Xiao
Yang Xiao@Yang_Xiao_nlp·
1/9 🔥 NEW PAPER: "LIMI: Less is More for Agency" The Age of AI Agency demands systems that don't just think, but work: vibe coding and automated research. We used just 78 samples to beat GPT-5 by 14.1% and discovered the Agency Efficiency Principle. See details below! 📊
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Zhe Hu
Zhe Hu@DDDerek666·
Our PraxisVLM paper is accepted at NeurIPS 2025! 🎉
Zhe Hu@DDDerek666

Imagine VLMs learning complex decision-making purely from text! 🤯 Our new paper introduces #PraxisVLM, which uses text-driven #ReinforcementLearning to instill robust reasoning skills. These text-acquired skills transfer to multimodal settings, achieving superior performance & generalizability, drastically reducing reliance on scarce image-text data. 🚀 📑Paper: arxiv.org/pdf/2503.16965 👨‍💻Code: github.com/Derekkk/Praxis… #EmbodiedAI #MultiModal #NLP #VLMs #RL

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Heming Xia
Heming Xia@hemingkx·
🎉Excited to share that TokenSkip has been accepted to the main conference of EMNLP 2025! Many thanks to all the coauthors for their hard work! Looking forward to seeing everyone in Suzhou😉. arxiv.org/abs/2502.12067
Heming Xia@hemingkx

Does every token in the CoT output contribute equally to deriving the answer? —— We say NO! 🚀 We are excited to introduce TokenSkip, which enables LLMs to skip less important tokens during Chain-of-Thought generation⚡️. 📄 Arxiv: arxiv.org/abs/2502.12067 🧵1/n

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Xin Zhang | 张鑫
Xin Zhang | 张鑫@xinzhangai·
New 1.5B embedding and reranking models 🤩 !!! New choice between Qwen3-embedding-0.6B and 4B We release **Lychee-embed** and **Lychee-rerank**, based-on Qwen2.5-1.5B and our multi-stage training framework in COLM 2025 paper. #NLP #LLM #RAG #COLM2025
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BangLiu
BangLiu@BangL93·
🤖Check The Hitchhiker’s Guide to Agents HERE🤖 Our Foundation Agents Survey V2 level up to 396 pages – every chapter is a full-on survey itself! 🧠 Agent Framework & Components 🌍 World Model & Memory 🔄 Self-Evolution 👥 Multi Agents 🛡️ Safety 1/4
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Jian Wang
Jian Wang@jwanglvy·
Excited to be in Vienna for #ACL2025! We will present 1 poster and 1 oral. Come say hi if you're around! 👋 📌Poster (Tutoring Agents) 🗓️Monday, July 28 18:00–19:30 | 📍Hall 4/5 (Session 5) 📌Oral (Safety Mechanisms) 🗓️Wednesday, July 30 09:00–10:30 |📍Room 1.85 (Session 11)
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Hanlin Wang
Hanlin Wang@hanlinwang1024·
🚀 Thrilled to announce our paper "STeCa: Step-Level Trajectory Calibration for LLM Agent Learning", featured in ACL 2025 Findings! 🎉 ✨ We tackle the challenge of long-horizon tasks by enabling real-time action calibration for LLM-based agents.
Hanlin Wang tweet media
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Hanlin Wang
Hanlin Wang@hanlinwang1024·
1️⃣ Detect deviated actions in real-time using step-level reward comparisons. 2️⃣ Perform self-reflection to revise these actions and construct calibrated trajectories. 3️⃣ Use these trajectories for reinforced training, significantly improving decision-making and robustness.
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Hanlin Wang
Hanlin Wang@hanlinwang1024·
We address the critical challenge of long-horizon tasks, where suboptimal actions accumulate over time, leading to task failures. Our solution, STeCa, enables LLM agents to:
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