6 citations · 12 across the 11 of their papers we have counts for
11 papers
ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward
Jingpei Wu, Xiao Han, Weixiang Shen +3
Visual question answering increasingly requires multi-step reasoning. Recent post-training with reinforcement learning under verifiable rewards (RLVR) and Group Relative Policy Opt…
ReEXplore: Improving MLLMs for Embodied Exploration with Contextualized Retrospective Experience Replay
Gengyuan Zhang, Mingcong Ding, Jingpei Wu +2
Embodied exploration is a target-driven process that requires embodied agents to possess fine-grained perception and knowledge-enhanced decision making. While recent attempts lever…
GroundedPRM: Tree-Guided and Fidelity-Aware Process Reward Modeling for Step-Level Reasoning
Yao Zhang, Yu Wu, Haowei Zhang +6
Process Reward Models (PRMs) aim to improve multi-step reasoning in Large Language Models (LLMs) by supervising intermediate steps and identifying errors. However, building effecti…
Strong but Brittle: Simple Attacks Subvert Reasoning-based Safety Guardrails
Shuo Chen, Zhen Han, Haokun Chen +6
Open-weight Large Reasoning Models (LRMs) are approaching the capabilities of their frontier counterparts but pose significant safety concerns, as they are difficult to patch or mo…
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
Zifeng Ding, Heling Cai, Jingpei Wu +4
Modeling evolving knowledge over temporal knowledge graphs (TKGs) has become a heated topic. Various methods have been proposed to forecast links on TKGs. Most of them are embeddin…
Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs
Zifeng Ding, Jingcheng Wu, Jingpei Wu +2
Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs) provide additional key-value pairs (i.e., qualifiers) for each KG fact that help to better restrict th…
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Zifeng Ding, Jingpei Wu, Zongyue Li +2
Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knwoledge graph (TKG). Most previous TKGC methods only consider predic…
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Zifeng Ding, Jingpei Wu, Bailan He +3
Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only de…
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
Zifeng Ding, Zongyue Li, Ruoxia Qi +8
Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information f…
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
Zifeng Ding, Bailan He, Yunpu Ma +2
Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly b…
A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
Zifeng Ding, Yunpu Ma, Bailan He +1
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions…