14 citations · 67 across the 56 of their papers we have counts for
66 papers
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing
Runang He, Tongya Zheng, Jing Cao +7
Pseudonymous accounts and complex smart-contract ecosystems substantially hinder the tracing of illicit cryptocurrency fund flows. Existing Graph Neural Networks (GNNs) have achiev…
Recall Begets Recall: Recurrent Memory Ranking for Personalized LLMs
Pengyue Jia, Shida Yan, Shunyu Liu +4
Memory ranking is the stage in personalized Large Language Models (LLMs) that decides which of the candidate memories returned by a retriever enter the limited context, so that the…
GraphCert: Bootstrap Agentic Graph Reasoning with Certified Evidence Rubrics
Weiqi Jiang, Yuchen Ying, Rui Wang +5
Graph agents extend large language models (LLMs) with the ability to actively explore and reason over knowledge graphs through multi-step interactions with graph tools. However, tr…
CodeTS: Verifiable Text-to-Time Series Generation via Executable Code
Xudong Yuan, Shunyu Liu, Tongya Zheng +3
Text-to-Time Series Generation (Text-to-TS) provides a promising paradigm for synthesizing time series from natural language, enabling scenario-specific generation when real observ…
SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them
Yang Zhou, Zixuan Huang, Sunzhu Li +10
Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that r…
Entropy Pacing Policy Optimization for Multi-Task Agentic Reinforcement Learning
Zetian Hu, Shunyu Liu, Junjie Zhang +4
Recent breakthroughs of Reinforcement Learning (RL) have highlighted its potential for complex agentic Large Language Model (LLM) tasks. However, existing efforts largely focus on…
OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models
Tianyi Lin, Chuanyu Sun, Jingyi Zhang +6
Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw. In this work, we aim to develop a framew…
STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning
Junjie Zhang, Guozheng Ma, Shunyu Liu +5
Recent advances in Reinforcement Learning (RL) have underscored its potential for incentivizing reasoning capabilities of Large Language Models (LLMs). However, existing step-level…
GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning
Yuchen Ying, Weiqi Jiang, Tongya Zheng +4
Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large language models (LLMs) with graph-b…
R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?
Jingyi Zhang, Tianyi Lin, Huanjin Yao +3
In this work, we aim to develop effective data synthesis techniques that autonomously synthesize multimodal training data for enhancing MLLMs in solving complex real-world tasks. T…
Physics-informed Diffusion Generation for Geomagnetic Map Interpolation
Wenda Li, Tongya Zheng, Kaixuan Chen +8
Geomagnetic map interpolation aims to infer unobserved geomagnetic data at spatial points, yielding critical applications in navigation and resource exploration. However, existing…
VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning
Yibo Wang, Yongcheng Jing, Shunyu Liu +5
Long-context reasoning has significantly empowered large language models (LLMs) to tackle complex tasks, yet it introduces severe efficiency bottlenecks due to the computational co…
RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation
Sunzhu Li, Jiale Zhao, Miteto Wei +6
Reinforcement Learning with Verifiable Rewards (RLVR) has driven substantial progress in reasoning-intensive domains like mathematics. However, optimizing open-ended generation rem…
Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following
Kongcheng Zhang, Qi Yao, Shunyu Liu +7
Reinforcement Learning (RL) has shown promise for aligning Large Language Models (LLMs) to follow instructions with various constraints. Despite the encouraging results, RL improve…
Tree of Preferences for Diversified Recommendation
Hanyang Yuan, Ning Tang, Tongya Zheng +7
Diversified recommendation has attracted increasing attention from both researchers and practitioners, which can effectively address the homogeneity of recommended items. Existing…
3MDiT: Unified Tri-Modal Diffusion Transformer for Text-Driven Synchronized Audio-Video Generation
Yaoru Li, Heyu Si, Federico Landi +8
Text-to-video (T2V) diffusion models have recently achieved impressive visual quality, yet most systems still generate silent clips and treat audio as a secondary concern. Existing…
Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning
Qinghong Guo, Yu Wang, Ji Cao +5
Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced…
Towards Efficient LLM-aware Heterogeneous Graph Learning
Wenda Li, Tongya Zheng, Shunyu Liu +7
Heterogeneous graphs are widely present in real-world complex networks, where the diversity of node and relation types leads to complex and rich semantics. Efforts for modeling com…
GeoZero: Incentivizing Reasoning from Scratch on Geospatial Scenes
Di Wang, Shunyu Liu, Wentao Jiang +10
Multimodal large language models (MLLMs) have undergone rapid development in advancing geospatial scene understanding. Recent studies have sought to enhance the reasoning capabilit…
A Survey on Agentic Multimodal Large Language Models
Huanjin Yao, Ruifei Zhang, Jiaxing Huang +8
With the recent emergence of revolutionary autonomous agentic systems, research community is witnessing a significant shift from traditional static, passive, and domain-specific AI…
CoVeR: Conformal Calibration for Versatile and Reliable Autoregressive Next-Token Prediction
Yuzhu Chen, Yingjie Wang, Shunyu Liu +2
Autoregressive pre-trained models combined with decoding methods have achieved impressive performance on complex reasoning tasks. While mainstream decoding strategies such as beam…
Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning
Yang Zhou, Sunzhu Li, Shunyu Liu +11
Recent advances in Large Language Models (LLMs) have underscored the potential of Reinforcement Learning (RL) to facilitate the emergence of reasoning capabilities. Despite the enc…
SPAZER: Spatial-Semantic Progressive Reasoning Agent for Zero-shot 3D Visual Grounding
Zhao Jin, Rong-Cheng Tu, Jingyi Liao +4
3D Visual Grounding (3DVG) aims to localize target objects within a 3D scene based on natural language queries. To alleviate the reliance on costly 3D training data, recent studies…
Intra-Trajectory Consistency for Reward Modeling
Chaoyang Zhou, Shunyu Liu, Zengmao Wang +4
Reward models are critical for improving large language models (LLMs), particularly in reinforcement learning from human feedback (RLHF) or inference-time verification. Current rew…