78 citations · 230 across the 36 of their papers we have counts for
37 papers
Towards Robust Process Reward Modeling via Noise-aware Learning
Bin Xie, Bingbing Xu, Xueyun Tian +2
Process Reward Models (PRMs) have achieved strong results in complex reasoning, but are bottlenecked by costly process-level supervision. A widely used alternative, Monte Carlo Est…
Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems
Zixu Wang, Bingbing Xu, Yige Yuan +2
Multi-Agent Systems (MAS) built on large language models typically solve complex tasks by coordinating multiple agents through workflows. Existing approaches generates workflows ei…
ROMA: Real-time Omni-Multimodal Assistant with Interactive Streaming Understanding
Xueyun Tian, Wei Li, Bingbing Xu +3
Recent Omni-multimodal Large Language Models show promise in unified audio, vision, and text modeling. However, streaming audio-video understanding remains challenging, as existing…
Beyond Entangled Planning: Task-Decoupled Planning for Long-Horizon Agents
Yunfan Li, Bingbing Xu, Xueyun Tian +2
Recent advances in large language models (LLMs) have enabled agents to autonomously execute complex, long-horizon tasks, yet planning remains a primary bottleneck for reliable task…
Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization
Xueyun Tian, Minghua Ma, Bingbing Xu +6
Supervised fine-tuning (SFT) on chain-of-thought (CoT) trajectories demonstrations is a common approach for enabling reasoning in large language models. Standard practices typicall…
Chain-of-Memory: Lightweight Memory Construction with Dynamic Evolution for LLM Agents
Xiucheng Xu, Bingbing Xu, Xueyun Tian +4
External memory systems are pivotal for enabling Large Language Model (LLM) agents to maintain persistent knowledge and perform long-horizon decision-making. Existing paradigms typ…
ACE-Align: Attribute Causal Effect Alignment for Cultural Values under Varying Persona Granularities
Jiatang Luo, Bingbing Xu, Rongxin Chen +5
Ensuring that large language models (LLMs) reflect diverse cultural values is important for globally deployed NLP systems. However, existing approaches often treat cultural groups…
RPO: Decoupling Rollout and Inference Policies for LLM Reasoning
Jingchu Wang, Bingbing Xu, Yige Yuan +4
Existing reinforcement learning methods for LLM reasoning implicitly assume that the policy generating training trajectories should coincide with the one producing inference respon…
HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation
Rongxin Chen, Tianyu Wu, Bingbing Xu +3
High-fidelity agent initialization is crucial for credible Agent-Based Modeling across diverse domains. A robust framework should be Topic-Adaptive, capturing macro-level joint dis…
GIFT: Games as Informal Training for Generalizable LLMs
Nuoyan Lyu, Bingbing Xu, Xueyun Tian +6
Recent LLMs excel at formal tasks such as mathematical reasoning and code generation, but still struggle with broader abilities such as planning, creativity, and social intelligenc…
Multi-Personality Generation of LLMs at Decoding-time
Rongxin Chen, Yunfan Li, Yige Yuan +2
Multi-personality generation for LLMs, enabling simultaneous embodiment of multiple personalization attributes, is a fundamental challenge. Existing retraining-based approaches are…
From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment
Bin Xie, Bingbing Xu, Yige Yuan +2
Inference-time alignment methods have gained significant attention for their efficiency and effectiveness in aligning large language models (LLMs) with human preferences. However,…
KnowCoder-V2: Deep Knowledge Analysis
Zixuan Li, Wenxuan Liu, Long Bai +13
Deep knowledge analysis tasks always involve the systematic extraction and association of knowledge from large volumes of data, followed by logical reasoning to discover insights.…
InfoNCE is a Free Lunch for Semantically guided Graph Contrastive Learning
Zixu Wang, Bingbing Xu, Yige Yuan +2
As an important graph pre-training method, Graph Contrastive Learning (GCL) continues to play a crucial role in the ongoing surge of research on graph foundation models or LLM as e…
Inference-time Alignment in Continuous Space
Yige Yuan, Teng Xiao, Li Yunfan +5
Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility. Existing methods rely on generating multiple response…
Incentivizing Strong Reasoning from Weak Supervision
Yige Yuan, Teng Xiao, Shuchang Tao +4
Large language models (LLMs) have demonstrated impressive performance on reasoning-intensive tasks, but enhancing their reasoning abilities typically relies on either reinforcement…
MIGE: Mutually Enhanced Multimodal Instruction-Based Image Generation and Editing
Xueyun Tian, Wei Li, Bingbing Xu +3
Despite significant progress in diffusion-based image generation, subject-driven generation and instruction-based editing remain challenging. Existing methods typically treat them…
Fact-Level Confidence Calibration and Self-Correction
Yige Yuan, Bingbing Xu, Hexiang Tan +5
Confidence calibration in LLMs, i.e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their o…
MITA: Bridging the Gap between Model and Data for Test-time Adaptation
Yige Yuan, Bingbing Xu, Teng Xiao +4
Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at ba…
Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
Zixu Wang, Bingbing Xu, Yige Yuan +2
Graph contrastive learning (GCL), standing as the dominant paradigm in the realm of graph pre-training, has yielded considerable progress. Nonetheless, its capacity for out-of-dist…
Graph Domain Adaptation: Challenges, Progress and Prospects
Boshen Shi, Yongqing Wang, Fangda Guo +3
As graph representation learning often suffers from label scarcity problems in real-world applications, researchers have proposed graph domain adaptation (GDA) as an effective know…
TEA: Test-time Energy Adaptation
Yige Yuan, Bingbing Xu, Liang Hou +3
Test-time adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution, offering the distinct advantage of not requiring access to tra…
Significant-attributed Community Search in Heterogeneous Information Networks
Yanghao Liu, Fangda Guo, Bingbing Xu +3
Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for communities w…
Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer
Wendong Bi, Xueqi Cheng, Bingbing Xu +3
The data-hungry problem, characterized by insufficiency and low-quality of data, poses obstacles for deep learning models. Transfer learning has been a feasible way to transfer kno…