Search Results for author:
Found 36 papers, 15 papers with code
The Singapore Consensus on Global AI Safety Research Priorities
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i. e., trustworthy, reliable, and secure.
RePO: Replay-Enhanced Policy Optimization
Reinforcement learning (RL) is vital for optimizing large language models (LLMs).
SafeCoT: Improving VLM Safety with Minimal Reasoning
Ensuring safe and appropriate responses from vision-language models (VLMs) remains a critical challenge, particularly in high-risk or ambiguous scenarios.
VLMs Can Aggregate Scattered Training Patches
Building on this, we simulate the adversarial data poisoning scenario mentioned above by using patches from dangerous images and replacing IDs with text descriptions like ``safe'' or ``unsafe'', demonstrating how harmful content can evade moderation in patches and later be reconstructed through visual stitching, posing serious VLM safety risks.
IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data
In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial.
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback
We then demonstrate that RL-finetuned models, even after exhibiting performance plateaus, can generate correct refinements on persistently failed problems by leveraging natural language feedback in the form of critiques.
Adversarial Preference Learning for Robust LLM Alignment
However, they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation, (2) the vast diversity of potential adversarial attacks, and (3) the risk of feedback bias and reward hacking.
Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks
Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency.
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search
Large language models (LLMs) have demonstrated impressive capabilities and are receiving increasing attention to enhance their reasoning through scaling test--time compute.
A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond
Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference.
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?
Despite the remarkable success of diffusion models (DMs) in data generation, they exhibit specific failure cases with unsatisfactory outputs.
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
Based on this hypothesis, we propose the Video Prediction Policy (VPP), which learns implicit inverse dynamics model conditioned on predicted future representations inside VDMs.
Towards AI-$45^{\circ}$ Law: A Roadmap to Trustworthy AGI
In this position paper, we propose the \textit{AI-\textbf{$45^{\circ}$} Law} as a guiding principle for a balanced roadmap toward trustworthy AGI, and introduce the \textit{Causal Ladder of Trustworthy AGI} as a practical framework.
Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension Ability
We further apply the ADCE to evaluate a series of mainstream LLMs, showing that most of them exhibit deep structure comprehension ability, which grows along with the prediction accuracy.
Prediction with Action: Visual Policy Learning via Joint Denoising Process
On the other line, diffusion models have also shown promise in robotic control tasks by denoising actions, known as diffusion policy.
OASIS: Open Agent Social Interaction Simulations with One Million Agents
There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i. e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems.
ADAM: An Embodied Causal Agent in Open-World Environments
ADAM is empowered by four key components: 1) an interaction module, enabling the agent to execute actions while documenting the interaction processes; 2) a causal model module, tasked with constructing an ever-growing causal graph from scratch, which enhances interpretability and diminishes reliance on prior knowledge; 3) a controller module, comprising a planner, an actor, and a memory pool, which uses the learned causal graph to accomplish tasks; 4) a perception module, powered by multimodal large language models, which enables ADAM to perceive like a human player.
From Imitation to Introspection: Probing Self-Consciousness in Language Models
Self-consciousness, the introspection of one's existence and thoughts, represents a high-level cognitive process.
Interpreting Low-level Vision Models with Causal Effect Maps
Based on the causal effect theory, the proposed diagnostic tool can refresh our common knowledge and bring a deeper understanding of low-level vision models.
CELLO: Causal Evaluation of Large Vision-Language Models
Previous work typically focuses on commonsense causality between events and/or actions, which is insufficient for applications like embodied agents and lacks the explicitly defined causal graphs required for formal causal reasoning.
CLEAR: Can Language Models Really Understand Causal Graphs?
Causal reasoning is a cornerstone of how humans interpret the world.
Causal Evaluation of Language Models
Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries into their potential for causal reasoning.
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
Within this framework, we conduct an in-depth causal analysis to assess the causal effect of these biases on MLLM predictions.
Distribution-consistency Structural Causal Models
In the field of causal modeling, potential outcomes (PO) and structural causal models (SCMs) stand as the predominant frameworks.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
In this work, we introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text, by leveraging the power of off-the-shelf text-to-image generation methods (e. g., Stable Diffusion).
Action-Sufficient State Representation Learning for Control with Structural Constraints
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.
Invariant Causal Representation Learning for Out-of-Distribution Generalization
Extensive experiments on both synthetic and real-world datasets show that our approach outperforms a variety of baseline methods.
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided.
Nonlinear Invariant Risk Minimization: A Causal Approach
Finally, in the discussion, we further explore the aforementioned assumption and propose a more general hypothesis, called the Agnostic Hypothesis: there exist a set of hidden causal factors affecting both inputs and outcomes.
Invariant Causal Representation Learning
As an alternative, we propose Invariant Causal Representation Learning (ICRL), a learning paradigm that enables out-of-distribution generalization in the nonlinear setting (i. e., nonlinear representations and nonlinear classifiers).
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
We propose counterfactual RL algorithms to learn both population-level and individual-level policies.
Interpreting Spatially Infinite Generative Models
In this paper we provide a firm theoretical interpretation for infinite spatial generation, by drawing connections to spatial stochastic processes.
Deconfounding Reinforcement Learning in Observational Settings
Using this benchmark, we demonstrate that the proposed algorithms are superior to traditional RL methods in confounded environments with observational data.
Flexible Spatio-Temporal Networks for Video Prediction
We describe a modular framework for video frame prediction.
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
For the first time, the human-level performance in face verification (97. 53%) on LFW is surpassed.
