Singapore

This paper focuses on the task of speech-driven 3D facial animation, which aims to generate realistic and synchronized facial motions driven by speech inputs.Recent methods have employed audio-conditioned diffusion models for 3D facial animation, achieving impressive results in generating expressive and natural animations.However, these methods process the whole audio sequences in a single pass, which poses two major challenges: they tend to perform poorly when handling audio sequences that exceed the training horizon and will suffer from significant latency when processing long audio inputs. To address these limitations, we propose a novel autoregressive diffusion model that processes input audio in a streaming manner. This design ensures flexibility with varying audio lengths and achieves low latency independent of audio duration. Specifically, we select a limited number of past frames as historical motion context and combine them with the audio input to create a dynamic condition. This condition guides the diffusion process to iteratively generate facial motion frames, enabling real-time synthesis with high-quality results. 
Additionally, we implemented a real-time interactive demo, highlighting the effectiveness and efficiency of our approach. We will release the code to facilitate reproducibility and further research.

AAAI 2026

StreamingTalker: Audio-driven 3D Facial Animation with Autoregressive Diffusion Model

cv: diffusion models for vision

cv: 3d computer vision

cv: multi-modal vision

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Dual-system theory distinguishes between fast, intuitive System 1 and slow, deliberative System 2. While this dichotomy describes many forms of reasoning, it oversimplifies the reality of expert legal reasoning. Legal reasoning is not merely a process of slow, logical deliberation. It is intrinsically normative, embedding precedent analysis, statutory interpretation, policy balancing, and social values. This paper envisions a reasoning architecture for legal reasoning, System L (Legal System 2), which extends traditional System 2 by integrating domain-specific normative frameworks in a structured manner. Using the IRAC (Issue–Rule–Application–Conclusion) structure as a backbone model, System L represents a blueprint for the next generation of cognitive and AI systems capable of human-like legal reasoning.

System L: Toward System 2-Style Legal Reasoning

This paper surveys studies on the use of neural networks
for optimization in the training-data-free setting.
Specifically, we examine the dataless application of neural
network architectures in optimization by re-parameterizing
problems using fully connected (or MLP), convolutional,
graph, and quadratic neural networks. Although MLPs have
been used to solve linear programs a few decades ago, this
approach has recently gained increasing attention due to
its promising results across diverse applications,
including those based on combinatorial optimization,
inverse problems, and partial differential equations. The
motivation for this setting stems from two key (possibly
over-lapping) factors: (\textit{i}) data-driven learning
approaches are still underdeveloped and have yet to
demonstrate strong results, as seen in combinatorial
optimization, and (\textit{ii}) the availability of
training data is inherently limited, such as in medical
image reconstruction and other scientific applications. In
this paper, we define the dataless setting and categorize
it into two variants based on how a problem
instance—defined by a single datum—is encoded onto the
neural network: (\textit{i}) architecture-agnostic methods
and (\textit{ii}) architecture-specific methods.
Additionally, we discuss similarities and clarify
distinctions between the dataless neural network (dNN)
settings and related concepts such as zero-shot learning,
one-shot learning, lifting in optimization, and
over-parameterization.

On the Dataless Training of Neural Networks

Reinforcement learning (RL) has been recognized as a powerful tool for robot control tasks. RL typically employs reward functions to define task objectives and guide agent learning.
However, since the reward function serves the dual purpose of defining the optimal goal and guiding learning, it is challenging to design the reward function manually, which often results in a suboptimal task representation. 
To tackle the reward design challenge in RL, inspired by the satisficing theory, we propose a Test-driven Reinforcement Learning (TdRL) framework. In the TdRL framework, multiple test functions are used to represent the task objective rather than a single reward function. Test functions can be categorized as pass-fail tests and indicative tests, each dedicated to defining the optimal objective and guiding the learning process, respectively, thereby making defining tasks easier.
Building upon such a task definition, we first prove that if a trajectory return function assigns higher returns to trajectories closer to the optimal trajectory set, maximum entropy policy optimization based on this return function will yield a policy that is closer to the optimal policy set. Then, we introduce a lexicographic heuristic approach to compare the relative distance relationship between trajectories and the optimal trajectory set for learning the trajectory return function. Furthermore, we develop an algorithm implementation of TdRL.
Experimental results on the DeepMind Control Suite benchmark demonstrate that TdRL matches or outperforms handcrafted reward methods in policy training, with greater design simplicity and inherent support for multi-objective optimization.
We argue that TdRL offers a novel perspective for representing task objectives, which could be helpful in addressing the reward design challenges in RL applications.

Test-driven Reinforcement Learning in Continuous Control

The widespread deployment of large vision models such as Stable Diffusion raises significant legal and ethical concerns, as these models can memorize and reproduce copyrighted content without authorization. Existing detection approaches often lack robustness and fail to provide rigorous theoretical underpinnings. To address these gaps, we formalize the concept of copyright infringement and its detection from the perspective of Differential Privacy (DP), and introduce the conditional sensitivity metric, a concept analogous to sensitivity in DP, that quantifies the deviation in a diffusion model's output caused by the inclusion or exclusion of a specific training data point. To operationalize this metric, we propose **D-Plus-Minus (DPM)**, a novel post-hoc detection framework that identifies copyright infringement in text-to-image diffusion models. Specifically, DPM simulates inclusion and exclusion processes by fine-tuning models in two opposing directions: learning or unlearning. Besides, to disentangle concept-specific influence from the global parameter shifts induced by fine-tuning, DPM computes confidence scores over orthogonal prompt distributions using statistical metrics. Moreover, to facilitate standardized benchmarking, we also construct the **C**opyright **I**nfringement **D**etection **D**ataset (CIDD), a comprehensive resource for evaluating detection across diverse categories. Our results demonstrate that DPM reliably detects infringement content without requiring access to the original training dataset or text prompts, offering an interpretable and practical solution for safeguarding intellectual property in the era of generative AI.

Copyright Infringement Detection in Text-to-Image Diffusion Models via Differential Privacy

The proliferation of multi-person sports and collaborative training paradigms has underscored the necessity for comprehensive whole-field motion data integration. However, existing vision/WiFi-based approaches suffer from occlusion-induced tracking errors and interference-driven signal instability, compromising multi-person activity recognition performance. Notably, Electromyography (EMG) recognition, pivotal in Wearable Human Activity Recognition (WHAR) for muscle activity analysis and motion intent decoding, still struggles to balance performance and real-time efficiency in multi-person scenarios. Unlike channel-expansion approaches, we propose a wireless wearable Single-Dimensional Sparse EMG (2SEMG) Sensor for efficient personal sampling. These motion-unaffected sensors leverage the proposed lightweight One-Dimensional Motion Network (OMONet) to facilitate whole-field motion sensing. Experimental results indicate that the OMONet achieves state-of-the-art performance and efficiency in EMG and other physiological signal recognition, with its core components validated through ablation studies. The motion detection results of two representative badminton matches further validate the performance, robustness, and real-time efficiency of the whole-field motion sensing network based on 2SEMG Sensors and OMONet in real-world multi-person sports.

Whole-Field Action Sensing via Wearable Single-Channel EMG Sensors and Resource-Efficient Motion Network

Universal graph pre-training has emerged as a key paradigm in graph representation learning, offering a promising way to train encoders to learn transferable representations from unlabeled graphs and to effectively generalize across a wide range of downstream tasks. However, recent explorations in universal graph pre-training primarily focus on homogeneous graphs and it remains unexplored for heterogeneous graphs, which exhibit greater structural and semantic complexity. This heterogeneity makes it highly challenging to train a universal encoder for diverse heterogeneous graphs: (i) the diverse types with dataset-specific semantics hinder the construction of a unified representation space; (ii) the number and semantics of meta-paths vary across datasets, making encoding and aggregation patterns learned from one dataset difficult to apply to others. To address these challenges, we propose a novel Meta-path-aware Universal heterogeneous Graph pre-training (MUG) approach. Specifically, for challenge (i), MUG introduces a input unification module to bridge heterogeneous types by enriching original attributes with contextual structure information and aligning them within a unified representation space. Furthermore, for challenge (ii), MUG combines meta-path reconstruction with global scattering objective, enabling a shared encoder to capture transferable high-order relational patterns while mitigating dataset-specific biases.
Extensive experiments demonstrate the effectiveness of MUG on some real datasets.

MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training

Unsupervised Domain Adaptation (UDA) is a challenging task in person search. It adapts a well-trained model from a labeled source domain to an unlabeled target domain for privacy and efficiency. Currently, most of the state-of-the-art UDA person search methods adopt multi-scale feature alignment techniques to learn domain-invariant representations. However, person search is a multi-granularity task, and such an indiscriminate method of bridging the differences between domains misleads the identity learning process, which significantly limits the model's performance. In this paper, we propose an Instance-Guided Scene Adaptation (IGSA) framework by eradicating scene disparities and focusing the tasks on instances, effectively eliminating the contradiction between person search and domain adaptation. In IGSA, a Scene-Aware Bidirectional Filter (SABF) is desgned to divide the image features into background and foreground to perform bidirectional modulations, thereby achieving simultaneous scene elimination and instance enhancement. To further improve the reliability of identity learning, we also propose an Instance Refinement Consistency Contrastive Learning (IRCCL) method. By performing cross-epoch updates on the instance-level memory bank and re-initializing the cluster-level memory bank, the problem of inconsistent training across epochs caused by instance identity drift can be alleviated. Through the above designs, our method can achieve state-of-the-art performance on two benchmark datasets, with 82.1\% mAP and 83.8\% top-1 on the CUHK-SYSU dataset, 41.1\% mAP and 82.3\% top-1 on the PRW dataset, which is even better than some supervised methods.

Instance-Guided Scene Adaptation for Unsupervised Person Search

Adapting Large Multimodal Models (LMMs) to real-world scenarios poses the dual challenges of learning from sequential data streams while handling frequent modality incompleteness, a task known as Continual Missing Modality Learning (CMML). However, existing works on CMML have predominantly relied on prompt tuning, a technique that struggles with this task due to cross-task interference between its learnable prompts in their shared embedding space. A naive application of Low-Rank Adaptation (LoRA) with modality-shared module will also suffer modality interference from competing gradients. To this end, we propose DeLo, the first framework to leverage a novel dual-decomposed low-rank expert architecture for CMML. Specifically, this architecture resolves modality interference
through decomposed LoRA expert, dynamically composing LoRA updates matrix with rank-one factors from disentangled modality-specific factor pools. Embedded within a task-partitioned framework that structurally prevents catastrophic forgetting, this expert system is supported by two key mechanisms: a Cross-Modal Guided Routing strategy to handle incomplete data and a Task-Key Memory for efficient, task-agnostic inference. Extensive experiments on established CMML benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches. This highlights the value of a principled, architecturally-aware LoRA design for real-world multimodal challenges.

DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning

Adaptive treatment strategies (ATS) are sequential decision-making processes that enable personalized care by dynamically adjusting treatment decisions in response to evolving patient symptoms. While reinforcement learning (RL) offers a promising approach for optimizing ATS, its conventional online trial-and-error learning mechanism is not permissible in clinical settings due to risks of harm to patients. Offline RL tackles this limitation by learning policies exclusively from historical treatment data, but its performance is often constrained by data scarcity—a pervasive challenge in clinical domains. To overcome this, we propose Treatment Stitching (TreatStitch), a novel data augmentation framework that generates clinically valid treatment trajectories by intelligently stitching segments from existing treatment data. Specifically, TreatStitch identifies similar intermediate patient states across different trajectories and stitches their respective segments. Even when intermediate states are too dissimilar to stitch directly, TreatStitch leverages the Schrödinger bridge method to generate smooth and shortest possible bridging trajectories that connect dissimilar states. By augmenting these synthetic trajectories into the original dataset, offline RL can learn from a more diverse dataset, thereby improving its ability to optimize ATS. Extensive experiments across multiple treatment datasets demonstrate the effectiveness of TreatStitch in enhancing offline RL performance. Furthermore, we provide a theoretical justification showing that TreatStitch maintains clinical validity by avoiding out-of-distribution transitions.

Treatment Stitching with Schrödinger Bridge for Enhancing Offline Reinforcement Learning in Adaptive Treatment Strategies

User retention is a critical objective for online platforms like Pinterest, as it strengthens user loyalty and drives growth through repeated engagement. A key indicator of retention is revisitation, i.e., when users return to view previously saved content, a behavior often sparked by personalized recommendations and user satisfaction. However, modeling and optimizing revisitation poses significant challenges. One core difficulty is accurate attribution: it is often unclear which specific user actions or content exposures trigger a revisit, since many confounding factors (e.g., content quality, user interface, notifications, or even changing user intent) can influence return behavior. Additionally, the scale and timing of revisitations introduce further complexity; users may revisit content days or even weeks after their initial interaction, requiring the system to maintain and associate extensive historical records across millions of users and sessions. These complexities render existing methods insufficient for robustly capturing and optimizing long-term revisitation.

To address these gaps, we introduce a novel, lightweight, and interpretable framework for modeling revisitation behavior and optimizing long-term user retention in Pinterest’s search-based recommendation context. By defining a surrogate attribution process that links saves to subsequent revisitations, we reduce noise in the causal relationship between user actions and return visits. Our scalable event aggregation pipeline enables large-scale analysis of user revisitation patterns and enhances the ranking system’s ability to surface items with high retention value. Deployed on Pinterest’s Related Pins surface to serve 500+ million users, the framework led to a significant lift of 0.1% in daily active users (DAU) and 0.08% in weekly active users (WAU) without additional computational costs. Our data analysis reveals novel insights, such as the impact of content topics on revisitation rates; for example, users are more likely to revisit aesthetically pleasing topics.

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System L: Toward System 2-Style Legal Reasoning

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