Singapore

Medical images exhibit inherent community structures, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit.
While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffers from non-differentiability, training instability, and inability to model complex community structure. 
We present DCMM-Transformer, a novel Vision Transformer architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. 
Comprehensive experiments across diverse medical imaging datasets, including brain, chest, breast, and ocular modalities, demonstrate the superior performance and generalizability of the proposed approach. Furthermore, the learned group structure and structured attention modulation substantially enhance interpretability by yielding attention maps that are anatomically meaningful and semantically coherent.

AAAI 2026

DCMM-Transformer: Degree-Corrected Mixed-Membership Attention for Medical Imaging

transformer; dcmm; attention; image

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.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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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.

poster

Modern preference alignment methods, such as DPO, rely on divergence regularization to a reference model for training stability—but this creates a fundamental problem we call "reference mismatch." In this paper, we investigate the negative impacts of reference mismatch in aligning text-to-image (T2I) diffusion models, showing that larger reference mismatch hinders effective adaptation given the same amount of data, e.g., as when learning new artistic styles, or personalizing to specific objects. We demonstrate this phenomenon across text-to-image (T2I) diffusion models and introduce margin-aware preference optimization (MaPO), a reference-agnostic approach that breaks free from this constraint. By directly optimizing the likelihood margin between preferred and dispreferred outputs under the Bradley-Terry model without anchoring to a reference, MaPO transforms diverse T2I tasks into unified pairwise preference optimization. We validate MaPO's versatility across five challenging domains: (1) safe generation, (2) style adaptation, (3) cultural representation, (4) personalization, and (5) general preference alignment. Our results reveal that MaPO's advantage grows dramatically with reference mismatch severity, outperforming both DPO and specialized methods like DreamBooth while reducing training time by 15%. MaPO thus emerges as a versatile and memory-efficient method for generic T2I adaptation tasks. 
Warning: This paper contains examples of harmful content, including explicit text and images.

Margin-Aware Preference Optimization for Aligning Diffusion Models Without Reference

The latest advancements in scene relighting have been predominantly driven by inverse rendering with 3D Gaussian Splatting (3DGS). However, existing methods remain overly reliant on precise camera parameters under static illumination conditions, which is prohibitively expensive and even impractical in real-world scenarios. In this paper, we propose a novel learning from Unposed views under Varied illuminations Relightable 3D Gaussian Splatting (dubbed UV-RGS), to address this challenge by jointly optimizing camera poses, 3DGS representations, surface materials, and environment illuminations (i.e., unknown and varied lighting conditions in training) using only unposed views under varied lightings. Firstly, UV-RGS presents a viewpoint dividing strategy to group inputs into constituent units, enabling each unit can perform similar poses and illuminations. Next, for each unit, to get the constituent model, UV-RGS establishes incrementally pose learning module to estimate coarse camera parameters, which also enjoy a proxy-view refinement to alleviate the sparse view learning. Additionally, for all constituent unit models, we introduce a holistic model learning strategy that integrates progressive unit aggregation component and the 3DGS coupled with camera poses joint optimization, which realizes the scene high-fidelity perception by the physical-based rendering. Extensive experiments on both real-world and synthetic challenging datasets demonstrate the effectiveness of UV-RGS, achieving the state-of-the-art performance for scene inverse rendering by learning 3DGS from only unposed views under varied illuminations.

UV-RGS: Relightable 3D Gaussian Splatting from Unposed Views Under Varied Illuminations

Multiplex heterogeneous networks are common in real-world scenarios, where entities interact through diverse types of relations across multiple semantic layers. Recent advances in multiplex heterogeneous graph neural networks have achieved remarkable results by incorporating node and relation types into message passing and designing relation-aware architectures. However, most existing methods either decouple relations and risk losing complex semantics or require handcrafted relation patterns, which limit scalability. Moreover, prevailing models are typically restricted to Euclidean space, making it difficult to capture non-Euclidean topologies and to distinguish complex interactions among heterogeneous nodes and relations. Standard GNN message passing, grounded in the homophily assumption, also proves inadequate for the intricate, coupled structures in multiplex heterogeneous graphs. To address these challenges, we propose MRiemGNN, a novel multiplex heterogeneous graph neural network that synergizes Euclidean and Riemannian spaces through a geometry-aware, relation-specific message passing scheme and cross-space mutual learning. Experiments on multiple real-world datasets show that MRiemGNN achieves superior performance, efficiency, and scalability on both node classification and link prediction tasks. The code of our model is provided in the appendix.

Multiplex Heterogeneous Graph Neural Networks with Euclidean-Riemannian Mutual Space Synergy

Large language model (LLM) agents have demonstrated strong capabilities across diverse domains, yet automated agent design remains a significant challenge. Current automated agent design approaches are often constrained by limited search spaces that primarily optimize workflows but fail to integrate crucial human-designed components like memory, planning, and tool use. Furthermore, these methods are hampered by high evaluation costs, as evaluating even a single new agent on a benchmark can require tens of dollars. The difficulty of this exploration is further exacerbated by inefficient search strategies that struggle to navigate the large design space effectively, making the discovery of novel agents a slow and resource-intensive process. To address these challenges, we propose AgentSwift, a novel framework for automated agent design. We formalize a hierarchical search space that jointly models agentic workflow and composable functional components. This structure moves beyond optimizing workflows alone by co-optimizing functional components, which enables the discovery of more complex and effective agent architectures. To make exploration within this expansive space feasible, we mitigate high evaluation costs by training a value model on a high-quality dataset, generated via a novel strategy combining combinatorial coverage and balanced Bayesian sampling for low-cost evaluation. Guiding the entire process is a hierarchical Monte Carlo Tree Search (MCTS) strategy, which is informed by uncertainty to efficiently navigate the search space. Evaluated across a comprehensive set of seven benchmarks spanning embodied, math, web, tool, and game domains, AgentSwift discovers agents that achieve an average performance gain of 8.34\% over both existing automated agent search methods and manually designed agents. Moreover, our framework exhibits steeper and more stable search trajectories. By enabling the efficient, automated composition of workflow with functional components, AgentSwift provides a scalable methodology to explore complex agent designs. Our framework serves as a launchpad for researchers to rapidly prototype and discover powerful agent architectures without the impediment of prohibitive evaluation costs.

AgentSwift: Efficient LLM Agent Design via Value-Guided Hierarchical Search

Standard single-turn, static benchmarks fall short in evaluating the nuanced capabilities of Large Language Models (LLMs) on complex tasks such as software engineering. In this work, we propose a novel interactive evaluation framework that assesses LLMs on multi-requirement programming tasks through structured, feedback-driven dialogue.
Each task is modeled as a requirement dependency graph, and an "interviewer" LLM, aware of the ground-truth solution, provides minimal, targeted hints to an "interviewee" model to help correct errors and fulfill target constraints. This dynamic protocol enables fine-grained diagnostic insights into model behavior, uncovering strengths and systematic weaknesses that static benchmarks fail to measure.
We build on DevAI, a benchmark of 55 curated programming tasks, by adding ground-truth solutions and evaluating the relevance and utility of interviewer hints through expert annotation.
Our results highlight the importance of dynamic evaluation in advancing the development of collaborative code-generating agents.

Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks

Graphical User Interface (GUI) grounding, the task of mapping natural language instructions to precise screen coordinates, is fundamental to autonomous GUI agents. While existing methods achieve strong performance through extensive supervised training or reinforcement learning with labeled rewards, they remain constrained by the cost and availability of pixel-level annotations. 
We observe that when models generate multiple predictions for the same GUI element, the spatial overlap patterns reveal implicit confidence signals that can guide more accurate localization.
Leveraging this insight, we propose GUI-RC (Region Consistency), a test-time scaling method that constructs spatial voting grids from multiple sampled predictions to identify consensus regions where models show highest agreement. Without any training, GUI-RC improves accuracy by 2-3\% across various architectures on ScreenSpot benchmarks.
We further introduce GUI-RCPO (Region Consistency Policy Optimization), which transforms these consistency patterns into rewards for test-time reinforcement learning. By computing how well each prediction aligns with the collective consensus, GUI-RCPO enables models to iteratively refine their outputs on unlabeled data during inference. Extensive experiments demonstrate the generality of our approach: GUI-RC boosts Qwen2.5-VL-3B-Instruct from 80.11\% to 83.57\% on ScreenSpot-v2, while GUI-RCPO further improves it to 85.14\% through self-supervised optimization. Our approach reveals the untapped potential of test-time scaling and test-time reinforcement learning for GUI grounding, offering a promising path toward more robust and data-efficient GUI agents.

Test-Time Reinforcement Learning for GUI Grounding via Region Consistency

In multilabel classification, while only one ground truth label may be provided in the training data, multiple equally valid outputs may be possible, making reliable evaluation a persistent challenge. We postulate that human evaluators implicitly use task-specific invariants, e.g., object boundaries in colorized images or named entities in translations, to judge if an output is acceptable. Under this assumption, we introduce a notion of approximate task-specific invariants and use them as diagnostic tools to evaluate a variety of existing metrics for vision and language tasks. We use these task invariants as part of a framework to systematically test metric reliability by encouraging domain-relevant invariants in model outputs via an augmented loss function. In our experiments, we observe that enforcing invariants with an augmented loss yields substantial improvements in popular distributional metrics while more traditional metrics change only marginally. Through this invariants-driven evaluation, we expose where standard metrics fail to detect meaningful differences, and we highlight the conditions under which distributional metrics succeed or still fall short.

A Novel Approach to Evaluating Evaluation Metrics for Multi-Output Structured Prediction

Over the past years, memes have evolved from being exclusively a medium of humorous exchanges to one that allows users to express a range of emotions freely and easily. With the ever-growing utilization of memes in expressing depressive sentiments, we conduct a study on identifying depressive symptoms exhibited by memes shared by users of online social media platforms. We introduce RESTOREx as a vital resource for detecting depressive symptoms in memes on social media through the Large Language Model (LLM) generated and human-annotated explanations. We introduce MAMA-Memeia, a collaborative multi-agent multi-aspect discussion framework grounded in the clinical psychology method of Cognitive Analytic Therapy (CAT) Competencies. MAMA-Memeia improves upon the current state-of-the-art by 7.55% in macro-F1 and is established as the new benchmark compared to over 30 methods.

MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes

The rapid development of large language models (LLMs) has relied on access to high-quality, large-scale datasets, yet growing concerns around data privacy and security have spurred substantial research into pre-training data detection. While state-of-the-art (SOTA) methods such as RECALL and CON-RECALL leverage auxiliary prefixes to enhance detection performance, their dependence on individual prefixes introduces notable instability across varying prefix conditions. To address this, we first conduct a theoretical analysis to assess the impact of prefixes on existing prefix-based methods. Building on the analysis, we propose a novel prefix selection method to identify optimal prefixes. Specifically, our method derives two key criteria \textit{Discriminability} and \textit{Symmetry}. These criteria serve to quantify the effectiveness of prefixes in detecting pre-training data, enabling precise selection of high-performing candidate prefixes. Experiments on the WikiMIA dataset demonstrate that our method consistently improves the performance of RECALL and CON-RECALL, achieving gains of up to 21.1\% in AUC scores while significantly enhancing robustness.

Enhancing Pre-training Data Detection in LLMs Through Discriminative and Symmetric Prefix Selection

LLM-as-a-Judge refers to the automatic modeling of preferences for responses generated by Large Language Models (LLMs), which is of significant importance for both LLM evaluation and reward modeling. Although generative LLMs have made substantial progress in various tasks, their performance as LLM-Judge still falls short of expectations. In this work, we propose Think-J, which improves generative LLM-as-a-Judge by learning how to think. We first utilized a small amount of curated data to develop the model with initial judgment thinking capabilities. Subsequently, we optimize the judgment thinking traces based on reinforcement learning (RL). We propose two methods for judgment thinking optimization, based on offline and online RL, respectively. The offline method requires training a critic model to construct positive and negative examples for learning. The online method defines rule-based reward as feedback for optimization. Experimental results showed that our approach can significantly enhance the evaluation capability of generative LLM-Judge, surpassing both generative and classifier-based LLM-Judge without requiring extra human annotations.

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