Singapore

Domain-adaptive person search (DAPS) aims to transfer pedestrian detection and re-identification capabilities from a labeled source domain to an unlabeled target domain, yet faces critical challenges from domain shift: semantic confusion among overlapping instances, over-reliance on shallow features for look-alike targets, and poor discriminability of small-scale instances. To address these issues, we propose the Localization-Anchored Instance Discrimination (LAID) framework, which leverages spatial relationships between bounding boxes as auxiliary signals to enhance instance identity learning.
LAID integrates three complementary strategies: 1) Cost-Aware Instance Matching (CAIM) uses IoU-based global optimal assignment to align current detections with historical identities, reducing overlap-induced misassociations; 2) Dual-Scope Contrastive Learning (DSCL) combines spatial separation constraints (for geometrically distant pairs) with global contrastive learning, prompting the model to learn deep discriminative features beyond superficial similarities; 3) Task-Sensitivity Alignment (TSA) aligns confidence distributions of detection and ReID heads via KL divergence, ensuring consistent pseudo-label generation.
Extensive experiments on CUHK-SYSU and PRW datasets demonstrate that LAID outperforms state-of-the-art DAPS methods, validating its effectiveness in mitigating domain shift and narrowing the performance gap between supervised and domain-adaptive person search.

AAAI 2026

Localization-Anchored Instance Discrimination for Domain Adaptive Person Search

technical paper

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.

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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.

This paper addresses the cooperative Multi-Vehicle Dynamic Pickup and Delivery Problem with Stochastic Requests (MVDPDPSR) and proposes an end-to-end centralized decision-making framework based on sequence-to-sequence, named Multi-Agent Pointer Transformer (MAPT).
MVDPDPSR is an extension of the vehicle routing problem and a spatio-temporal system optimization problem, widely applied in scenarios such as on-demand delivery.
Classical operations research methods face bottlenecks in computational complexity and time efficiency when handling large-scale dynamic problems.
Although existing reinforcement learning methods have achieved some progress, they still encounter several challenges: 1) Independent decoding across multiple vehicles fails to model joint action distributions; 2) The feature extraction network struggles to capture inter-entity relationships; 3) The joint action space is exponentially large.
To address these issues, we designed the MAPT framework, which employs a Transformer Encoder to extract entity representations, combines a Transformer Decoder with a Pointer Network to generate joint action sequences in an AutoRegressive manner, and introduces a Relation-Aware Attention module to capture inter-entity relationships.
Additionally, we guide the model's decision-making using informative priors to facilitate effective exploration. Experiments on 8 datasets demonstrate that MAPT significantly outperforms existing baseline methods in terms of performance and exhibits substantial computational time advantages compared to classical operations research methods.

Multi-Agent Pointer Transformer: Seq-to-Seq Reinforcement Learning for Multi-Vehicle Dynamic Pickup-Delivery Problems

Generating expressive and controllable human speech is one of the core goals of generative artificial intelligence, but its progress has long been constrained by two fundamental challenges: the deep entanglement of speech factors and the coarse granularity of existing control mechanisms. To overcome these challenges, we have proposed a novel framework called MF-Speech, which consists of two core components: MF-SpeechEncoder and MF-SpeechGenerator. MF-SpeechEncoder acts as a factor purifier, adopting a multi-objective optimization strategy to decompose the original speech signal into highly pure and independent representations of content, timbre, and emotion. Subsequently, MF-SpeechGenerator functions as a conductor, achieving precise, composable and fine-grained control over these factors through dynamic fusion and Hierarchical Style Adaptive Normalization (HSAN). Experiments demonstrate that in the highly challenging multi-factor compositional speech generation task, MF-Speech significantly outperforms current state-of-the-art methods, achieving a lower word error rate (WER=4.67\%), superior style control (SECS=0.5685, Corr=0.68), and the highest subjective evaluation scores (nMOS=3.96, sMOS$_t$=3.86, sMOS$_e$=3.78). Furthermore, the learned discrete factors exhibit strong transferability, demonstrating their significant potential as a general-purpose speech representation.

MF-Speech: Achieving Fine-Grained and Compositional Control in Speech Generation via Factor Disentanglement

3D Gaussian Splatting (3DGS) achieves high-fidelity novel view synthesis, but its application in online long-sequence scenarios is still restricted. Existing methods either rely on slow per-scene optimization or lack efficient frame-wise 3DGS updates, making them unsuitable for online long-sequence videos. In this paper, we propose LongSplat, an online real-time 3D Gaussian reconstruction framework designed for long-sequence image input. The core idea of LongSplat is to maintain a global 3DGS set and design a streaming 3DGS update mechanism that selectively compressing redundant historical Gaussians and introducing new Gaussians by comparing the current observations with the historical Gaussian. To achieve this goal, we design a Gaussian-Image Representation (GIR), which encodes 3D Gaussian parameters into a structured, image-like 2D format. GIR simultaneously enables identity-aware redundancy compression as well as the fusion of current view and historical Gaussians, which are used for online reconstruction and adapt the model to long sequences without overwhelming memory or computational costs. Extensive experiments demonstrate that LongSplat achieves state-of-the-art efficiency-quality trade-offs in real-time novel view synthesis, delivering real-time reconstruction while reducing Gaussian counts by 44% compared to our baseline methods DepthSplat.

LongSplat: Online Generalizable 3D Gaussian Splatting from Long Sequence Images

Synthesizing realistic 12-lead electrocardiogram (ECG) data is a complex task due to the intricate spatial and temporal dynamics of cardiac electrophysiology. Traditional generative models often struggle to capture the nuanced interdependencies among ECG leads, which are essential for accurate medical analysis. 
In this paper, we propose Physics-Inspired Partial Differential Equation GAN for Multilead ECG Synthesis (PhysioPDE-GAN), a generative framework designed to model the spatiotemporal structure of multilead ECG signals by incorporating physiological priors and spatial constraints directly into the generative process.
By embedding PDE-based representations directly into the generative process, our approach effectively captures both the temporal evolution and spatial relationships between ECG leads. 
We conduct extensive experiments to evaluate the performance of various base classifiers trained on the synthetic 12-lead ECG data generated by PhysioPDE-GAN. These classifiers outperform those trained on data produced by other conventional methods, achieving statistically significant improvements in detecting cardiac abnormalities. Our work highlights the potential of combining PDE-driven cardiac models with advanced generative techniques to enhance the quality and utility of synthetic biomedical datasets.

PDE-Driven Spatiotemporal Generative Modeling for Multilead ECG Synthesis

Graph Neural Networks (GNNs) have demonstrated impressive success across a range of graph-based tasks. However, their performance in node classification typically relies on enough high-quality labeled data which are difficult to obtain in practice. Self-training emerges as a promising solution to tackle the issue of label scarcity. Most existing studies in this direction mainly rely on classification scores to explore high-confidence unlabeled samples. Nevertheless, these methods often lead to false positive samples, which hinders the capability of GNNs. To this end, we propose a simple yet effective Topology-Aware Graph Self-Training (TA-GST) method. Specifically, we first explore the origin of false positives in pseudo-labeled samples. We then design a topology-aware scoring method, which considers both the classification score and connectivity pattern to enhance the reliability of pseudo-labeled samples. Besides, we depart TA-GST from the traditional teacher-student pattern and simplify it in an end-to-end manner. Extensive experiments on seven real-world datasets demonstrate the effectiveness of our method.

Can Pseudo-Label Be More Reliable? A Simple yet Effective Topology-Aware Graph Self-Training Method

To develop general-purpose collaborative agents, humans need reliable AI systems that can (1) adapt to new domains and (2) transparently reason with uncertainty to allow for verification and correction. Black-box models demonstrate powerful data processing abilities but do not satisfy these criteria due to their opaqueness, domain specificity, and lack of uncertainty awareness. We introduce Bonsai, a compositional and probabilistic reasoning system that generates adaptable inference trees by retrieving relevant grounding evidence and using it to compute likelihoods of sub-claims derived from broader natural language inferences. Bonsai's reasoning power is tunable at test-time via evidence scaling and it demonstrates reliable handling of varied domains including transcripts, photographs, videos, audio, and databases. Question-answering and human alignment experiments demonstrate that Bonsai matches the performance of domain-specific black-box methods while generating interpretable, grounded, and uncertainty-aware reasoning traces.

Bonsai: Interpretable Tree-Adaptive Grounded Reasoning

Scene graphs have emerged as a structured and serializable environment representation for grounded spatial reasoning with Large Language Models (LLMs).
In this work, we propose SG^2, an iterative Schema-Guided Scene-Graph reasoning framework based on multi-agent LLMs.
The agents are grouped into two modules: a (1) Reasoner module for abstract task planning and graph information queries generation, and a (2) Retriever module for extracting corresponding graph information based on code-writing following the queries.
Two modules collaborate iteratively, enabling sequential reasoning and adaptive attention to graph information.
The scene graph schema, prompted to both modules, serves to not only streamline both reasoning and retrieval process, but also guide the cooperation between two modules.
This eliminates the need to prompt LLMs with full graph data, reducing the chance of hallucination due to irrelevant information.
Through experiments in multiple simulation environments, we show that our framework surpasses existing LLM-based approaches and baseline single-agent, tool-based Reason-while-Retrieve strategy in numerical Q\&A and planning tasks.

Schema-Guided Scene-Graph Reasoning Based on Multi-Agent Large Language Model System

Federated Multi-View Clustering (FedMVC) has gained widespread attention for its ability to discover complementary clustering structures of distributed multi-view data while preserving data privacy. However, real-world clients often only have access to partial information, and the view incompleteness makes it more challenging for federated multi-view feature fusion to exploit consistency and complementary information. For another, efficiency is highly expected in federated scenarios, while existing federated incomplete multi-view clustering (FedIMVC) methods generally suffers from the curse of dimension. To alleviate these issues, we propose \textbf{F}ederated \textbf{I}ncomplete \textbf{M}ulti-\textbf{V}iew \textbf{C}lustering with \textbf{T}ensorized \textbf{L}ow-\textbf{R}ank \textbf{C}onstraint (FMVC-TLRC), which incorporates anchors to improve efficiency and is able to process ubiquitous view incompleteness issue in federated scenarios. FMVC-TLRC first aligns the local anchor graphs and employ tensorized low-rank constraint based on tensor Schatten p-norm to enforce the consistency of the data representations learned by each client. Besides, a federated unified framework is developed to jointly optimize the construction and alignment of anchor graphs, enabling collaborative model training. Experimental results on multiple datasets demonstrate the effectiveness of FIMVC-TLRC.

Federated Incomplete Multi-View Clustering with Tensorized Low-Rank Constraint

Compute structuring, a technique where AI developers split or modify compute workloads for the purpose of avoiding regulation, poses a challenge for AI governance techniques that rely on the computational properties of AI workloads. This work aims to explore the feasibility of detecting compute structuring and to propose robust detection methods. We do this by first exploring possible forms of compute structuring. Using realistic assumptions about cloud providers’ capabilities, we derive a potential detection approach. Further, we perform a comprehensive analysis of possible adversary scenarios and show that our method can detect them efficiently. Finally, we analyze potential future trends in AI compute workloads that could invalidate our proposed detection approach, and discuss possible adaptation and mitigation strategies. Overall, our study indicates that compute structuring detection is probably both feasible and practical to implement.

Detecting Compute Structuring in AI Governance Is Likely Feasible

Evaluating the quality of e-commerce search systems traditionally requires a significant number of human relevance annotations. In recent times, several deployed systems have explored the usage of Large Language Models (LLMs) as automated judges for this task while their inherent biases prevent direct use for metric estimation. We present a statistical framework extending Prediction-Powered Inference (PPI) that combines minimal human annotations with LLM judgments to produce reliable estimates of metrics which require sub-instance annotations. Our method requires as few as 100 human-annotated queries and 10,000 unlabeled examples, reducing annotation requirements significantly compared to traditional approaches. We formulate our proposed framework (PRECISE) for inference of relevance uplift for an LLM-based query reformulation application, extending PPI to sub-instance annotations at the query-document level. By reformulating the metric-integration space, we reduced the computational complexity from O(2^|C|) to O(2^K), where |C| represents corpus size (in order of millions). Detailed experiments across prominent retrieval datasets demonstrate that our method reduces the variance of estimates for the business-critical Precision@K metric, while effectively correcting for LLM bias in low-resource settings.

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Multi-Agent Pointer Transformer: Seq-to-Seq Reinforcement Learning for Multi-Vehicle Dynamic Pickup-Delivery Problems

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