9 papers
ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence
Wei He, Hengtao Li, Zhongrui Yu +20
Current embodied systems largely rely on pretrained capabilities that remain fixed after deployment, limiting their ability to learn from physical interaction. We introduce MachEmb…
ClawTrack: Towards Trace-Level Evaluation and Improvement of Real-World Autonomous Agents
Xingjian Wu, Xuhang Zhu, Xingchen Liu +6
As LLM-based agents are deployed in complex, multi-step workflows, a critical evaluation gap has emerged: most existing benchmarks judge only final outcomes, unable to distinguish…
Contrastive Reinforced Policy Optimization via Privileged Self-Distillation
Xingjian Wu, Junlin Liu, Xingchen Liu +6
Recent advances in post-training Large Language Models (LLMs) increasingly rely on Reinforcement Learning with Verifiable Rewards (RLVR) or On-Policy Self-Distillation (OPSD). Whil…
Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization
Hao Chen, Zhanming Shen, Liyao Li +8
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models. However, existing methods base…
Dual-Pathway Geometry-Aware MLLM for Spatial Intelligence
Yufei Zheng, Xuhan Zhu, Zide Liu +9
Spatial understanding of the physical world from 2D visual inputs hinges on two complementary forms of geometric knowledge: holistic 3D structural perception and fine-grained metri…
Self-Consistent Latent Reasoning: Long Latent Sequence Reasoning for Vision-Language Model
Chenfeng Wang, Wei He, Xuhan Zhu +10
In language reasoning, longer chains of thought consistently yield better performance, which naturally suggests that visual latent reasoning may likewise benefit from longer latent…
StreamingClaw Technical Report
Jiawei Chen, Zhe Chen, Chaoqun Du +21
Emerging applications such as embodied intelligence, AI hardware, autonomous driving, and intelligent cockpits rely on a real-time perception-decision-action closed loop, posing st…
EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning
Chengjun Yu, Xuhan Zhu, Chaoqun Du +4
Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-ter…
MindGPT-4ov: An Enhanced MLLM via a Multi-Stage Post-Training Paradigm
Wei Chen, Chaoqun Du, Feng Gu +14
We present MindGPT-4ov, a multimodal large language model (MLLM) that introduces a general post-training paradigm spanning data production, model training, and efficient deployment…