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Evaluating a graphic design involves assessing it from multiple facets like alignment, composition, aesthetics and color choices. Holistic evaluation would involve aggregating feedback from individual expert reviewers. Towards this, we propose an Agentic Design Review System (Agentic-DRS), where multiple agents collaboratively analyze a design, orchestrated by a meta-agent. A novel in-context exemplar selection approach based on graph matching and a unique prompt expansion method plays central role towards making each agent design aware. In order to evaluate this framework, we propose DRS-BENCH. Thorough experimental evaluation against state-of-the-art baselines adapted to the problem setup, backed by critical ablations, demonstrates efficacy of Agentic-DRS in evaluating designs and generating actionable feedback.

AAAI 2026

Agentic Design Review System

poster

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.

Cytological images originate from exfoliated cells, collected via liquid-based slides and digitized into whole slide images (WSIs). Unlike histological WSIs that exhibit continuous and well-structured tissue, cytological WSIs are sparse in spatial distribution and unstructured in cellular relationships. Typically, the nucleus serves as the primary diagnostic feature, while surrounding cytoplasmic information plays a supportive role. These unique characteristics limit the development of effective foundation models and hinder the transferability of histology-based models for cytopathology. To address this, we propose **Cyto-SSL**, the first self-supervised pretraining framework for cytological images. It introduces **Nuclei-Centered Perturbation**, which highlights individual nuclei by perturbing non-nuclear regions. We also design an SR-Transformer module, which complements this by using sparse attention to concentrate on diagnostically relevant scattered cells, while iRPE helps model to capture local spatial relationships and avoids unnecessary attention to irrelevant global structures. Experimental results show that **Cyto-SSL** enhances performance across diverse cytological datasets and Multiple Instance Learning (MIL) methods. On a WSI-level dataset, it achieved 95.67\% accuracy and outperformed ImageNet-pretrained ResNet-50 by 11.33\%, demonstrating superior feature representation for cytological analysis. Additionally, **Cyto-SSL** modules are plug-and-play, easily integrated into other pretraining frameworks, yielding a 2.6\% accuracy gain across different SSL methods.

Cyto-SSL: A Self-Supervised Pretraining Framework for Cytology Foundation Model

Effective customer support requires not only accurate problem-solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service supporters to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines, defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv, an evaluation dataset of 1,855 real-world customer–agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv. Human evaluations further confirm gains in problem resolution. All code and data will be publicly available at https://github.com/aliyun/qwen-dianjin.

Evaluating, Synthesizing, and Enhancing for Customer Support Conversation

We address the challenge of integrating high-level semantic reasoning with low-level trajectory planning in end-to-end autonomous driving, where most existing frameworks decouple perception, decision-making, and control, leading to limited interpretability and poor instruction compliance. To bridge this gap, we propose Driving with Advice, a novel closed-loop framework that treats a vision-language model (VLM) as a motion advisor to provide interpretable, language-mediated guidance for trajectory generation. Our approach introduces three key innovations: (1) Semantic-Intentional Pretraining (SIP), which injects driving rationale into a compact VLM via machine-generated question-answering pairs; (2) a discrete action space grounded in directional and speed primitives, enabling structured and interpretable policy learning; and (3) an advice-following diffusion policy refined via Group Relative Policy Optimization under a multi-objective reward that ensures safety, comfort, and alignment with semantic intent. We evaluate our method on the NAVSIM benchmark in a closed-loop setting, achieving a state-of-the-art Predictive Driver Model Score (PDMS) of 91.5, outperforming strong baselines in safety (NC: 99.2). The results demonstrate that leveraging language as a cognitive interface between perception and control enhances both generalization and behavioral transparency, advancing the paradigm of language-conditioned driving.

Driving with Advice: Large Model as Motion Advisor for Joint Planning

Large language models have achieved substantial progress in mathematical reasoning, yet their advancement is limited by the scarcity of high-quality, high-difficulty training data. Existing synthesis methods largely rely on transforming human-written templates, limiting both diversity and scalability. We propose MathSmith, a novel framework for synthesizing challenging mathematical problems to enhance LLM reasoning. Rather than modifying existing problems, MathSmith constructs new ones from scratch by randomly sampling concept–explanation pairs from PlanetMath, ensuring data independence and avoiding contamination. To increase difficulty, we design nine predefined strategies as soft constraints during rationales. We further adopts reinforcement learning to jointly optimize structural validity, reasoning complexity, and answer consistency. The length of the reasoning trace generated under autoregressive prompting is used to reflect cognitive complexity, encouraging the creation of more demanding problems aligned with long-chain-of-thought reasoning. Experiments across five benchmarks, categorized as easy \& medium (GSM8K, MATH-500) and hard (AIME2024, AIME2025, OlympiadBench), show that MathSmith consistently outperforms existing baselines under both short and long CoT settings. Additionally, a weakness-focused variant generation module enables targeted improvement on specific concepts. Overall, MathSmith exhibits strong scalability, generalization, and transferability, highlighting the promise of high-difficulty synthetic data in advancing LLM reasoning capabilities.

MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy

Multimodal Instruction Following serves as a fundamental capability of multimodal language models, involving accurate comprehension and execution of user-provided instructions. However, existing multimodal instruction-following datasets and benchmarks face the shortcomings outlined below: (a) Lack of Difficulty Stratification, they collect diverse instruction categories but neglect the stratification of difficulty levels across these categories, which leads to overlap, bias, and low interpretability. (b) Lack of Fine-Grained Metrics, they conflate the model's ability to ``solve tasks" and ``follow constraints" into a single metric, which fails to accurately reflect its instruction-following capability. (c) Lack of Multi-Task Instructions, they overlook the fact that real-world user instructions often consist of multiple combined tasks. This paper proposes MMIFEvol, a framework for multimodal instruction evolving and benchmarking. First, we define the essential components of a carefully curated multimodal instruction set and establish corresponding difficulty levels, based on which we synthesize diverse instruction data. Next, we decouple the evaluation criteria for the instruction following into three different metrics to construct a high-quality benchmark and assess existing models. Experimental results demonstrate that current models still struggle with following complex instructions, while fine-tuning using MMIFEvol data effectively improves models' responsiveness to multimodal instructions.

MMIFEvol: Towards Evolutionary Multimodal Instruction Following

Magnetic Resonance Imaging (MRI) and its automatic segmentation are pivotal in assisting physicians with clinical diagnoses. In recent years, with the scarcity of labeled data, significant advancements have been made in semi-supervised segmentation. However, the prediction of many current methods is affected by the presence of false positive regions, which limits their reliability in clinical applications. To tackle this issue, we propose a pseudo-label optimization method based on polar coordinate modeling and prior constraints (PMPC), which refines false positive regions in pseudo-labels by leveraging prior knowledge within the polar coordinate system. Firstly, to improve the efficiency and rationality during polar coordinate modeling, the Adaptive Pole Selection (APS) algorithm is presented to ensure that the pole is located within the foreground region. Secondly, to mitigate false positive regions in pseudo-labels that violate medical anatomical priors, we propose the Prior Knowledge Constraint in Polar Coordinate System (KCP) module to reassign pixel categories in these regions. Finally, the Shape-Aware Weighting strategy is presented to evaluate the quality of the optimized pseudo-labels based on their shape and then determine their weight in guiding network parameter updates. Experiments on three MRI datasets demonstrate that the proposed method can be effectively integrated with existing pelvic MRI segmentation approaches, significantly reducing false positive rates and further improving segmentation quality.

A Pseudo-Label Optimization Method Based on Polar Coordinate Modeling and Prior Constraints

Trajectory prediction is a crucial task in modeling human behavior, especially in safety-critical fields such as social robotics and autonomous vehicle navigation. Traditional heuristics based on handcrafted rules often lack accuracy, while recently proposed deep learning approaches suffer from computational cost, slow inference speed, lack of explainability, and generalization issues that limit their practical adoption in such environments. In this paper, we introduce TrajEvo, a framework that leverages Large Language Models (LLMs) to automatically design trajectory prediction heuristics. TrajEvo employs an evolutionary algorithm to generate and refine prediction heuristics from past trajectory data. We introduce a Cross-Generation Elite Sampling to promote population diversity and a Statistics Feedback Loop allowing the LLM to analyze alternative predictions. Our evaluations show TrajEvo outperforms previous heuristic methods on various real-world datasets, and remarkably outperforms both heuristics and deep learning methods when generalizing to an unseen real-world dataset. TrajEvo represents a first step toward automated design of fast, explainable, and generalizable trajectory prediction heuristics. We make our source code publicly available to foster future research.

TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution

Knowledge editing aims to efficiently modify the internal knowledge of large language models (LLMs) without compromising their other capabilities. The prevailing editing paradigm, which appends an update matrix to the original parameter matrix, has been shown by some studies to damage key numerical stability indicators (such as condition number and norm), thereby reducing editing performance and general abilities, especially in sequential editing scenario. Although subsequent methods have made some improvements, they remain within the additive framework and have not fundamentally addressed this limitation. To solve this problem, we analyze it from both statistical and mathematical perspectives and conclude that multiplying the original matrix by an orthogonal matrix does not change the numerical stability of the matrix. Inspired by this, different from the previous additive editing paradigm, a multiplicative editing paradigm termed Multiplicative Orthogonal Sequential Editing (MOSE) is proposed. Specifically, we first derive the matrix update in the multiplicative form, the new knowledge is then incorporated into an orthogonal matrix, which is multiplied by the original parameter matrix. In this way, the numerical stability of the edited matrix is unchanged, thereby maintaining editing performance and general abilities. We compared MOSE with several current knowledge editing methods, systematically evaluating their impact on both editing performance and the general abilities across three different LLMs. Experimental results show that MOSE effectively limits deviations in the edited parameter matrix and maintains its numerical stability. Compared to current methods, MOSE achieves a 12.08% improvement in sequential editing performance, while retaining 95.73% of general abilities across downstream tasks.

Multiplicative Orthogonal Sequential Editing for Language Models

The Mamba architecture has been widely applied to various low-level vision tasks due to its exceptional adaptability and strong performance. Although the Mamba architecture has been adopted for spectral reconstruction, it still faces the following two challenges: (1) Single spatial perception limits the ability to fully understand and analyze hyperspectral images; (2) Single-scale feature extraction struggles to capture the complex structures and fine details present in hyperspectral images. To address these issues, we propose a multi-scale, multi-perceptual Mamba architecture for the spectral reconstruction task, called M3SR. Specifically, we design a multi-perceptual fusion block to enhance the ability of the model to comprehensively understand and analyze the input features. By integrating the multi-perceptual fusion block into a U-Net structure, M3SR can effectively extract and fuse global, intermediate, and local features, thereby enabling accurate reconstruction of hyperspectral images at multiple scales. Extensive quantitative and qualitative experiments demonstrate that the proposed M3SR outperforms existing state-of-the-art methods while incurring a lower computational cost.

M3SR: Multi-Scale Multi-Perceptual Mamba for Efficient Spectral Reconstruction

Recent studies have explored the capabilities of large language models (LLMs) in solving knowledge-intensive mathematical reasoning problems. However, existing benchmarks predominantly involve static theorems that LLMs have encountered during pretraining, making it difficult to assess whether these models can incorporate new or evolving knowledge into their reasoning processes. In this work, we introduce TaxReasoning, a novel benchmark designed to evaluate LLMs’ abilities in real-world tax calculation scenarios. These tasks require not only mathematical reasoning and numerical computation, but also the extraction and application of complex, frequently updated tax regulations. Through extensive experiments with state-of-the-art LLMs using diverse prompting strategies and knowledge augmentation techniques, we uncover substantial limitations in their ability to handle dynamic, knowledge-intensive questions—primarily due to missing domain-specific knowledge and ineffective retrieval. Even the best-performing models fall significantly short of human-level performance. Our analysis points to key avenues for improvement, including enhancing LLMs’ reasoning capabilities, developing more effective knowledge summarization techniques, and improving retrieval strategies. TaxReasoning offers a challenging new testbed for advancing LLMs toward more reliable reasoning in real-world, evolving, and knowledge-intensive domains.

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