173 citations · 644 across the 150 of their papers we have counts for
164 papers
UniGuard: Multi-View Guardrail Learning for Cross-Interface Safety Generalization
Viet-Thanh Pham, Thuy-Trang Vu, Junae Kim +4
Guardrail models are increasingly used as safety layers for LLMs and agentic systems, but most are trained on fixed prompt or response interfaces and become unreliable when the sam…
Anticipating Social Consequences with Distributional World Models
Viet-Thanh Pham, Thuy-Trang Vu, Zhuang Li +2
Social world models predict how interactions develop after an action, helping agents negotiate, cooperate and mediate. In these situations, one action can lead to several plausible…
SenseWeather: Multimodal Sensing for Robust Robotic Perception under Extreme Conditions
Fucai Ke, Sukai Huang, Junyi Hao +4
Robotic perception must remain reliable when extreme environmental conditions severely degrade the primary RGB observation. Although robotic platforms commonly carry complementary…
Stochastic Teacher Intervention for Agentic On-Policy Distillation
Junnan Liu, Linhao Luo, Zhijun Chen +3
On-policy distillation (OPD) efficiently transfers capabilities from a stronger teacher to a student language model through dense token-level supervision on student-generated rollo…
The Missing Coefficients: Bayesian Pairwise Merging for Model Personalization
Yaling Shen, Tongtong Wu, Siyuan Yan +1
How can we personalize a shared expert library from a user's pairwise choices? Prior work can realize different reward trade-offs by merging reward-specialized experts, given a vec…
Conformal Privacy Auditing: Calibrated Re-identification Attacks with Statistical Guarantees
Shuo Huang, Gholamreza Haffari, Xingliang Yuan +2
Empirical identity leakage from released text is increasingly driven by attackers that combine large language models (LLMs) with auxiliary knowledge to link documents to individual…
Code as Representation: A Compilable Parsing Paradigm for Academic Documents
Rihui Jin, Jun Wang, chengyuan zhu +11
Academic papers are a primary carrier of scientific knowledge, yet most of this knowledge remains locked in PDFs that are optimized for human reading rather than machine use. For M…
MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models
Linhao Luo, Thuy-Trang Vu, Van-Anh Nguyen +3
Aligning large language models (LLMs) with diverse and multifaceted user preferences is a fundamental challenge in personalized AI systems. Existing multi-objective alignment metho…
GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler
Minghan Wang, Ye Bai, Thuy-Trang Vu +2
Inference-time scaling (ITS) in latent reasoning models typically relies on heuristic perturbations, such as dropout or fixed Gaussian noise, to generate diverse candidate trajecto…
Resurfacing Paralinguistic Awareness in Large Audio Language Models
Hao Yang, Minghan Wang, Tongtong Wu +3
Large Audio Language Models (LALMs) have expanded the interaction with human to speech modality, which introduces great interactive potential, due to the paralinguistic cues implic…
Evidence-based Distributional Alignment for Large Language Models
Viet-Thanh Pham, Lizhen Qu, Zhuang Li +1
Distributional alignment enables large language models (LLMs) to predict how a target population distributes its responses across answer options, rather than collapsing disagreemen…
LiveCultureBench: a Multi-Agent, Multi-Cultural Benchmark for Large Language Models in Dynamic Social Simulations
Viet-Thanh Pham, Lizhen Qu, Thuy-Trang Vu +2
Large language models (LLMs) are increasingly deployed as autonomous agents, yet evaluations focus primarily on task success rather than cultural appropriateness or evaluator relia…
CARD: Towards Conditional Design of Multi-agent Topological Structures
Tongtong Wu, Yanming Li, Ziye Tang +5
Large language model (LLM)-based multi-agent systems have shown strong capabilities in tasks such as code generation and collaborative reasoning. However, the effectiveness and rob…
Environment-Aware Code Generation: How far are We?
Tongtong Wu, Rongyi Chen, Wenjie Du +6
Recent progress in large language models (LLMs) has improved code generation, but most evaluations still test isolated, small-scale code (e.g., a single function) under default or…
IntroLM: Introspective Language Models via Prefilling-Time Self-Evaluation
Hossein Hosseini Kasnavieh, Gholamreza Haffari, Chris Leckie +1
A major challenge for the operation of large language models (LLMs) is how to predict whether a specific LLM will produce sufficiently high-quality output for a given query. Existi…
Towards Inference-time Scaling for Continuous Space Reasoning
Minghan Wang, Thuy-Trang Vu, Ehsan Shareghi +1
Inference-time scaling through multiple sample generation in combination with Process- or Outcome-Reward Model (PRM or ORM) re-ranking has proven effective for text-based reasoning…
IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data
Tao Feng, Lizhen Qu, Niket Tandon +1
Causal discovery is fundamental to scientific research, yet traditional statistical algorithms face significant challenges, including expensive data collection, redundant computati…
Beyond Imitation: Recovering Dense Rewards from Demonstrations
Jiangnan Li, Thuy-Trang Vu, Ehsan Abbasnejad +1
Conventionally, supervised fine-tuning (SFT) is treated as a simple imitation learning process that only trains a policy to imitate expert behavior on demonstration datasets. In th…
Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search
Shuo Huang, Xingliang Yuan, Gholamreza Haffari +1
The increasing adoption of large language models (LLMs) in cloud-based services has raised significant privacy concerns, as user inputs may inadvertently expose sensitive informati…
G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Linhao Luo, Zicheng Zhao, Junnan Liu +9
Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by inc…
Explain Before You Answer: A Survey on Compositional Visual Reasoning
Fucai Ke, Joy Hsu, Zhixi Cai +10
Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground inte…
Physics-Grounded Motion Forecasting via Equation Discovery for Trajectory-Guided Image-to-Video Generation
Tao Feng, Xianbing Zhao, Zhenhua Chen +4
Recent advances in diffusion-based and autoregressive video generation models have achieved remarkable visual realism. However, these models typically lack accurate physical alignm…
Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models
Rihui Jin, Zheyu Xin, Xing Xie +6
Table reasoning (TR) requires structured reasoning over semi-structured tabular data and remains challenging, particularly for small language models (SLMs, e.g., LLaMA-8B) due to t…
Discrete Minds in a Continuous World: Do Language Models Know Time Passes?
Minghan Wang, Ye Bai, Thuy-Trang Vu +2
While Large Language Models (LLMs) excel at temporal reasoning tasks like event ordering and duration estimation, their ability to perceive the actual passage of time remains unexp…