Research across multimodal foundation models, LLM agents, post-training, and responsible AI. See the selected publication list and Google Scholar.
EMNLP'26 Findings ·
CultureVLM characterizes and improves cultural understanding of vision-language models across more than 100 countries using culturally-grounded benchmarks and training procedures.
EMNLP 2026 Main Conference ·
MASCOT is a multi-agent framework for socio-collaborative companions that uses bi-level optimization—persona-aware behavioral alignment and collaborative dialogue optimization—to counter persona collapse and social sycophancy, improving role consistency and reducing redundant dialogue.
ACL'26 Industry ·
Structure-aware multimodal RAG for enterprise Q&A, combining layout-aware report parsing, page-level slide representations, and inference-time context assembly. Includes FastRAGEval for efficient answer evaluation.
ACL'26 ·
A systematic study showing that reasoning capabilities alone are insufficient for LLMs in multi-turn mental health conversations, isolating failure modes that demand additional safety and empathy-aware design.
ACL'26 ·
SARA is a unified RAG framework that balances local factual precision with global coverage by combining natural-language spans with compact semantic compression vectors, achieving consistent gains under strict context budgets.
ICWSM'26 ·
MedHalu is a fine-grained benchmark for studying hallucinations in LLM responses to consumer healthcare queries, analyzing hallucination patterns across models, query types, and medical specialties.
Preprint ·
Regularizes prompt optimization to limit prompt bloat and narrow, sample-specific rules. Combines gradient purification, semantic edit regularization, and regularization-guided updates to improve out-of-distribution generalization.
Preprint ·
A unified study of LLM self-distillation that combines teacher agreement, EMA stabilization, contrastive learning, feature matching, and divergence clipping to improve adaptation without stronger external teachers.
ICLR'26 ·
Sharpness-aware prompt evolution optimizes prompts for both performance and robustness by penalizing sharp regions in the prompt loss landscape, yielding prompts that transfer better across tasks and LLM families.
ICLR'26 ·
Sysformer learns adaptive, query-conditioned system prompts to safeguard frozen large language models, providing fine-grained safety control without modifying model weights.
NeurIPS'26 ·
Distills multi-agent debate into one LLM through reasoning-enhanced fine-tuning, trajectory-based augmentation, and process-aware distillation, moving computation from inference to training.
Preprint ·
A position paper arguing that consistency across views, modalities, and prompts should be the priority research target for unified multimodal models.
ACL'26 ·
We introduce SlideAgent, a versatile agentic framework for understanding multi-modal, multi-page, and multi-layout documents.
EMNLP'25 ·
A comprehensive survey of Protein Large Language Models, organizing the field along architectures, training objectives, datasets, and downstream tasks across biology, chemistry, and medicine.
KDD'25 TGL Workshop ·
DyGET converts temporal graph embeddings into interpretable trajectories that empower interdisciplinary insights through cross-disciplinary discovery and longitudinal community analysis.
AI and Ethics ·
A survey deconstructing the ethics of large language models — from long-standing issues such as bias and misinformation to newly emerging dilemmas around agentic behavior and cross-cultural alignment.
Preprint ·
An efficient approach to probing LLM knowledge that adapts pre-trained embeddings to query model knowledge with substantially reduced compute.
ACL'25 ·
A survey of efficient LLM training organized around data-centric techniques — selection, mixing, ordering, and synthesis — and their trade-offs with compute and downstream performance.
ICML'25 FM4LS Workshop ·
ProteinGPT is a multimodal LLM that integrates protein sequence and structural representations in a unified generative interface for property prediction and structure understanding.
Preprint ·
We propose a framework for developing topology-aware Multi-Agent Systems (MAS), emphasizing agent selection, structure profiling, and topology synthesis, to enhance coordination and efficiency in complex task.
A survey of multimodal foundation model evaluation from a hierarchical perspective, covering a broad range of applications.
WebConf'25 MM4SG ·
ScreenLLM introduces a stateful screen schema and key-frame extractor that compresses dynamic UI sessions into time-aware summaries, enabling efficient GUI understanding and action prediction with multimodal LLMs.
Preprint ·
Studies how rewriting YouTube video titles when sharing them on Reddit affects engagement, using controlled comparisons to isolate the effects of language and community context.
Preprint ·
A methodology-centered survey of LLM agents, connecting agent architectures, collaboration, and evolution with evaluation, tools, applications, and open challenges.
Good Data AAAI 2025 ·
SciEvo is a comprehensive dataset containing 2 million+ papers spanning 30 years (1995-2024) for temporal scientometric analysis.
AAAI'25 DAI Workshop ·
UniGuard is a universal safety guardrail for multimodal LLMs, defending against cross-modal jailbreak attacks across image and text channels with low utility cost.
NeurIPS'24 MLSB Workshop ·
RNA-GPT is a multimodal generative system that combines RNA sequence reasoning with structural cues for property prediction, retrieval, and natural-language interaction over RNA data.
EMNLP'24 ·
Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exploration and statistics of existing peer review data...
EMNLP'24 ·
PrivacyMind teaches LLMs to be contextual privacy protection learners that recognize sensitive content in context and adapt outputs accordingly, preserving utility while reducing leakage.
CIKM'24 ·
We address fairness issues in graph anomaly detection, providing benchmark datasets and comprehensive evaluation frameworks for fair anomaly detection on graphs....
ICML'24 ·
This work studies the competition dynamics among LLM-based agents, revealing emergent behaviors and strategic patterns in multi-agent systems....
WWW'24 ·
We present a framework and benchmark to evaluate LLMs' multilingual capabilities in healthcare queries, revealing significant performance gaps across languages and providing insights for improving hea...
ACL'24 ·
Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehensively understand the information. Multimodal...
ACL'24 ·
We propose a prototypical reward network that enables data-efficient reinforcement learning from human feedback (RLHF) for large language models....
ICML'23 ·
We propose a semi-offline reinforcement learning approach for optimizing text generation in language models, balancing exploration and exploitation effectively....
WWW'23 ·
CODER is a graph-based code recommendation framework for open source developers, modeling heterogeneous OSS contribution networks to deliver multi-modal recommendations across realistic workloads.
KDD'23 ·
We develop methods to predict how information spreads across different online communities, revealing patterns in cross-platform information diffusion....
AAAI'23 ·
We propose prototypical fine-tuning, a novel framework for fine-tuning pretrained language models that maintains robust performance across varying data sizes.
KDD'22 ·
A novel subgraph reasoning paradigm for fake news detection that provides explainability while improving generalization through reinforcement learning and hierarchical graph attention networks.
AAAI'22 ·
We propose a fine-grained reasoning framework for fake news detection by following the human information-processing model and designing a prior-aware bi-channel kernel graph network....