1 citations · 1 across the 12 of their papers we have counts for
13 papers
EvoSpatial: Experience-Driven Skill and Tool Evolution for Spatial Reasoning Agents
Bin Yu, Shijie Lian, Zhaolong Shen +10
Spatial reasoning agents can inspect tool outputs and repair errors, but reusing these lessons requires preserving both the computation and the conditions that make it valid. We in…
RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models
Bin Yu, Yao Zhang, Haishan Liu +9
Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should guide robot action prediction.…
HunterAgent: Neuro-Symbolic Attack Trace Reconstruction under Anti-Forensics
Guangze Zhao, Yongzheng Zhang, Weilin Gai +3
Modern alert-triage systems reduce SOC burden by filtering false positives, but flagging a high-risk alert is only the start of incident response. Threat hunting requires reconstru…
FrameSkip: Learning from Fewer but More Informative Frames in VLA Training
Bin Yu, Shijie Lian, Xiaopeng Lin +8
Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as i…
3D-Mix for VLA: A Plug-and-Play Module for Integrating VGGT-based 3D Information into Vision-Language-Action Models
Bin Yu, Shijie Lian, Xiaopeng Lin +8
Vision-Language-Action (VLA) models leverage Multimodal Large Language Models (MLLMs) for robotic control, but recent studies reveal that MLLMs exhibit limited spatial intelligence…
TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers
Bin Yu, Shijie Lian, Xiaopeng Lin +8
The fundamental premise of Vision-Language-Action (VLA) models is to harness the extensive general capabilities of pre-trained Vision-Language Models (VLMs) for generalized embodie…
TrajSelector: Harnessing Latent Representations for Efficient and Effective Best-of-N in Large Reasoning Model
Bin Yu, Xinming Wang, Shijie Lian +6
Large language models (LLMs) have shown remarkable progress in complex reasoning tasks, largely enabled by test-time scaling (TTS) paradigms that allocate additional compute during…
Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models
Bin Yu, Hang Yuan, Haotian Li +5
Recent advances in large language models have demonstrated that Supervised Fine-Tuning (SFT) with Chain-of-Thought (CoT) reasoning data distilled from large reasoning models (e.g.,…
Enhancing Knowledge Graph Completion with GNN Distillation and Probabilistic Interaction Modeling
Lingzhi Wang, Pengcheng Huang, Haotian Li +6
Knowledge graphs (KGs) serve as fundamental structures for organizing interconnected data across diverse domains. However, most KGs remain incomplete, limiting their effectiveness…
Deep Sparse Latent Feature Models for Knowledge Graph Completion
Haotian Li, Rui Zhang, Lingzhi Wang +6
Recent advances in knowledge graph completion (KGC) have emphasized text-based approaches to navigate the inherent complexities of large-scale knowledge graphs (KGs). While these m…
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
Haotian Li, Bin Yu, Yuliang Wei +3
Knowledge graph completion (KGC) revolves around populating missing triples in a knowledge graph using available information. Text-based methods, which depend on textual descriptio…
DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoning
Haotian Li, Hongri Liu, Yao Wang +2
Knowledge graphs (KGs), as structured representations of real world facts, are intelligent databases incorporating human knowledge that can help machine imitate the way of human pr…
An original model for multi-target learning of logical rules for knowledge graph reasoning
Yuliang Wei, Haotian Li, Guodong Xin +2
Large-scale knowledge graphs provide structured representations of human knowledge. However, as it is impossible to collect all knowledge, knowledge graphs are usually incomplete.…