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Chunming Li, Shidong Wang, Haofeng Zhang

To address the limitations of transductive learning in evolving real-world scenarios where unknown categories may continuously emerge, Continual Generalized Category Discovery (C-GCD) presents a novel paradigm that extends conventional category discovery frameworks. Unlike traditional static learning environments, C-GCD requires models to incrementally discover novel categories across multiple operational phases while maintaining discrimination capabilities for previously learned classes, posing significant challenges in balancing stability and plasticity. Prior approaches typically employ parameter-level knowledge distillation from historical models to alleviate catastrophic forgetting, which effectively preserves prior knowledge and optimizes computational efficiency. However, our analysis reveals that the persistent availability of samples from previous stages enables more sophisticated knowledge preservation strategies. Specifically, we present a Fix and Explore strategy that employs distinct learning methodologies for different types of potential data, aiming to preserve the features of old categories as much as possible and gradually exploring the potential distribution of new class latent spaces, we can enhance the model's ability to discover novel categories. This paper investigates this effect and introduces a novel heuristic paradigm to solve the C-GCD problem, called Fix and Explore (FaE), which aims to provide sufficient imaginative space for new classes while preserving the classification ability for old tasks. We conducted experiments across multiple datasets and performed detailed comparisons. The results demonstrate that our method achieves state-of-the-art performance at each stage across all datasets.

Bingyang Guo, Qiang Zuo, Ruiyun Yu

The effective segmentation of 3D data is crucial for a wide range of industrial applications, especially for detecting subtle defects in the field of integrated circuits (IC). Ceramic package substrates (CPS), as an important electronic material, are essential in IC packaging owing to their superior physical and chemical properties. However, the complex structure and minor defects of CPS, along with the absence of a publically available dataset, significantly hinder the development of CPS surface defect detection. In this study, we construct a high-quality point cloud dataset for 3D segmentation of surface defects in CPS, i.e., CPS3D-Seg, which has the best point resolution and precision compared to existing 3D industrial datasets. CPS3D-Seg consists of 1300 point cloud samples under 20 product categories, and each sample provides accurate point-level annotations. Meanwhile, we conduct a comprehensive benchmark based on SOTA point cloud segmentation algorithms to validate the effectiveness of CPS3D-Seg. Additionally, we propose a novel 3D segmentation method based on causal inference (CINet), which quantifies potential confounders in point clouds through Structural Refine (SR) and Quality Assessment (QA) Modules. Extensive experiments demonstrate that CINet significantly outperforms existing algorithms in both mIoU and accuracy.

Lei Ding, Xibing Zuo, Haitao Guo, Jun Lu, Zhihui Gong, Xuanguang Liu, Jicang Lu

Unsupervised Change Detection (UCD) in Very High Resolution (VHR) Remote Sensing (RS) images remains to be a difficult challenge due to the inherent spatio-temporal complexity within data. Inspired by recent advancements in Visual Foundation Models (VFMs) and Contrastive Learning (CL), this research aims to develop CL methodologies to translate implicit knowledge in VFM into change representations, thus eliminating the need for explicit supervision. To this end, we introduce a Semantic-to-Change (S2C) learning framework for UCD in VHR RS images. Differently from existing CL methodologies that typically focus on learning multi-temporal similarities, we introduce a novel triplet learning strategy that explicitly models temporal differences, which are crucial to the CD task. Furthermore, random spatial and spectral perturbations are introduced during training to enhance robustness to temporal noise. In addition, a grid sparsity regularization is defined to suppress insignificant changes, and an IoU-matching algorithm is developed to refine the CD results. Experiments on three benchmark CD datasets demonstrate that the proposed S2C learning framework achieves significant improvements in accuracy, surpassing current state-of-the-art by over 31%, 9% and 23%, respectively. It also demonstrates robustness and sample efficiency, suitable for training and adaptation of various VFMs or backbone neural networks.

Hao-Long Yin, Jian-Ming Zhang, Ren-Jie Dai, Wei-Long Zheng, Qinyu Lv, Zhenghui Yi, Bao-Liang Lu

Depression is a prevalent mental health disorder characterized by persistent sadness and a diminished interest in daily activities, early detection of depression facilitates timely intervention, mitigating its adverse effects. Electroencephalography (EEG) signals and eye movements are emerging as promising biomarkers for depression detection due to their non-invasive nature and cost-effectiveness. Nevertheless, existing studies suffer from methodological constraints, including low specificity, insufficient sample sizes, limited generalizability, and difficulties in large-scale replication, which collectively undermine their clinical utility. To address these challenges, we collected a large-scale depression dataset comprising EEG and eye movements from 1,060 individuals diagnosed with depression and 1,308 healthy controls. To efficiently leverage multimodal data for automatic depression detection, we propose the EEG-Eye Movements Model (E2Mo). E2Mo employs modality-specific encoders to extract discriminative multi-view features from each modality and incorporates a mixture-of-modality-experts architecture with multi pretraining tasks to achieve efficient and robust modality alignment and fusion. Our approach achieves a 70.06% balanced accuracy by leveraging multi-modal data, demonstrating the effectiveness of integrating EEG signals and eye movements for automatic depression detection.

Ping Guo, Qingfu Zhang, Xi Lin

The discovery of symbolic solutions—mathematical expressions, logical rules, and algorithmic structures—is fundamental to advancing scientific and engineering progress. However, traditional methods often struggle with search efficiency and fail to integrate knowledge effectively. While recent large language model-based (LLM-based) approaches have demonstrated improvements in search efficiency, they lack the ability to continually refine and expand upon discovered solutions and their underlying knowledge, limiting their potential for \textit{open-ended innovation}. To address these limitations, we introduce CoEvo, a novel framework that leverages large language models within an evolutionary search methodology to continually generate and refine symbolic solutions. CoEvo integrates a dynamic knowledge library, enabling open-ended innovation of solutions through effective knowledge management. Additionally, CoEvo leverages multiple representations of solutions—including natural language, mathematical expressions, and code—to further enhance search efficiency. By combining the reasoning capabilities of LLMs with the exploratory power of evolutionary algorithms, CoEvo significantly improves the efficiency and scope of symbolic discovery. Our experimental results demonstrate that this method not only enhances the efficiency of searching for symbolic solutions but also supports the ongoing discovery process, akin to human scientific endeavors. This study represents a first effort in conceptualizing the search for symbolic solutions as a lifelong, iterative process, marking a significant step towards harnessing LLMs in the perpetual pursuit of scientific and engineering breakthroughs.

Changwen Xing, SamZaak Wong, Xinlai Wan, Yanfeng Lu, Mengli Zhang, Zebin Ma, Lei Qi, Zhengxiong Li, Nan Guan, Zhe Jiang 等

While Large Language Models (LLMs) demonstrate immense potential for automating integrated circuit (IC) development, their practical deployment is fundamentally limited by restricted context windows. Existing context-extension methods struggle to achieve effective semantic modeling and thorough multi-hop reasoning over extensive, intricate circuit specifications. To address this, we introduce ChipMind, a novel knowledge graph-augmented reasoning framework specifically designed for lengthy IC specifications. ChipMind first transforms circuit specifications into a domain-specific knowledge graph (ChipKG) through the Circuit Semantic-Aware Knowledge Graph Construction methodology. It then leverages the ChipKG-Augmented Reasoning mechanism, combining information-theoretic adaptive retrieval to dynamically trace logical dependencies with intent-aware semantic filtering to prune irrelevant noise, effectively balancing retrieval completeness and precision. Evaluated on an industrial-scale specification reasoning benchmark, ChipMind significantly outperforms state-of-the-art baselines, achieving an average improvement of 34.59% (up to 72.73%). Our framework bridges a critical gap between academic research and practical industrial deployment of LLM-aided Hardware Design (LAD).

Nguyen Thanh Tam, Khanh Quoc Tran, Dat Thanh Pham, Truong Phu Le, Nguyen Hoang Gia Han, Binh T. Nguyen

Regulatory compliance checking for online medical advertisements poses a critical public safety challenge distinct from traditional fact-checking, particularly in low-resource languages. Existing automated systems are ill-suited for the authorization-based, evidence-grounded, and explainable reasoning this task demands. To address this gap, we introduce VietCheckMed, a novel retrieval-augmented framework, and VietAestheticAds, the first large-scale, expert-validated benchmark for this task, comprising 8,329 advertisements paired with an authoritative regulatory corpus of 9,978 facilities. Comprehensive experiments demonstrate that our evidence-grounded approach is essential, substantially outperforming powerful unassisted LLM baselines by over 0.3805 F1-score. A detailed analysis reveals that the primary remaining challenges are nuanced failures in semantic and logical reasoning, defining a clear frontier for future research. To promote advances in regulatory technology and responsible AI, our dataset, code, and evaluation scripts will be made publicly available. This work contributes a foundational methodology and a vital public resource for developing responsible AI in high-stakes regulatory domains.

Yitian Kou, Yihe Gu, Chen Zhou, Dandan Zhu, Shu-Guang Kuai

Navigating human-populated environments without causing discomfort is a critical capability for socially-aware agents. While rule-based approaches offer interpretability through predefined psychological principles, they often lack generalizability and flexibility. Conversely, data-driven methods can learn complex behaviors from large-scale datasets, but are typically inefficient, opaque, and difficult to align with human intuitions. To bridge this gap, we propose RLSLM, a hybrid Reinforcement Learning framework that integrates a rule-based Social Locomotion Model, grounded in empirical behavioral experiments, into the reward function of a reinforcement learning framework. The social locomotion model generates an orientation-sensitive social comfort field that quantifies human comfort across space, enabling socially aligned navigation policies with minimal training. RLSLM then jointly optimizes mechanical energy and social comfort, allowing agents to avoid intrusions into personal or group space. A human-agent interaction experiment using an immersive VR-based setup demonstrates that RLSLM outperforms state-of-the-art rule-based models in user experience. Ablation and sensitivity analyses further show the model’s significantly improved interpretability over conventional data-driven methods. This work presents a scalable, human-centered methodology that effectively integrates cognitive science and machine learning for real-world social navigation.

Letian Chen, Runhan Shi, Gufeng Yu, Yang Yang

Aligning molecular sequence representations (e.g., SMILES notations) with textual descriptions is critical for applications spanning drug discovery, materials design, and automated chemical literature analysis. Existing methodologies typically treat molecular captioning (molecule-to-text) and text-based molecular design (text-to-molecule) as separate tasks, relying on supervised fine-tuning or contrastive learning pipelines. These approaches face three key limitations: (i) conventional metrics like BLEU prioritize linguistic fluency over chemical accuracy, (ii) training datasets frequently contain chemically ambiguous narratives with incomplete specifications, and (iii) independent optimization of generation directions leads to bidirectional inconsistency. To address these issues, we propose RTMol, a bidirectional alignment framework that unifies molecular captioning and text-to-SMILES generation through self-supervised round-trip learning. The framework introduces novel round-trip evaluation metrics and enables unsupervised training for molecular captioning without requiring paired molecule-text corpora. Experiments demonstrate that RTMol enhances bidirectional alignment performance by up to 47% across various LLMs, establishing an effective paradigm for joint molecule-text understanding and generation.

Alexandru-Mihai Apostu, Andrei Preda, Alexandra Daniela Damir, Diana Bolocan, Radu Tudor Ionescu, Ioana Croitoru, Mihaela Gaman

Generating thorough natural language explanations for threat detections remains an open problem in cybersecurity research, despite significant advances in automated malware detection systems. In this work, we present AutoMalDesc, an automated static analysis summarization framework that, following initial training on a small set of expert-curated examples, operates independently at scale. This approach leverages an iterative self-paced learning pipeline to progressively enhance output quality through synthetic data generation and validation cycles, eliminating the need for extensive manual data annotation. Evaluation across 3,600 diverse samples in five scripting languages demonstrates statistically significant improvements between iterations, showing consistent gains in both summary quality and classification accuracy. Our comprehensive validation approach combines quantitative metrics based on established malware labels with qualitative assessment from both human experts and LLM-based judges, confirming both technical precision and linguistic coherence of generated summaries. To facilitate reproducibility and advance research in this domain, we publish our complete dataset of more than 100K script samples, including annotated seed (900) and test (3.6K) datasets, along with our methodology and evaluation framework.

Muye Huang, Lingling Zhang, Yifei Li, Yaqiang Wu, Jun Liu

Charts are high-density visual carriers of complex data and medium for information extraction and analysis. Due to the need for precise and complex visual reasoning, automated chart understanding poses a significant challenge to existing Multimodal Large Language Models (MLLMs). Many MLLMs trained with reinforcement learning (RL) face the challenge of credit assignment. Their advantage estimation, typically performed at the trajectory level, cannot distinguish between correct and incorrect reasoning steps within a single generated response. To address this limitation, we introduce SketchVL, a novel MLLM that optimized with FinePO, a new RL algorithm designed for fine-grained credit assignment within each trajectory. SketchVL's methodology involves drawing its intermediate reasoning steps as markers on the image and feeding the annotated image back to itself, creating a robust, multi-step reasoning process. During training, the FinePO algorithm leverages a Fine-grained Process Reward Model (FinePRM) to score each drawing action within a trajectory, thereby precisely assigning credit for each step. This mechanism allows FinePO to more strongly reward correct tokens when a trajectory is globally successful, and more heavily penalize incorrect tokens when the trajectory is globally suboptimal, thus achieving fine-grained reinforcement signals. Experiments show that SketchVL learns to align its step-level behavior with the FinePRM, achieving an average performance gain of 7.23% over its base model across chart datasets, natural image datasets, and mathematics, providing a promising new direction for training powerful reasoning models.

Jong Wook Kim, Suyong Bahk, TaeHwa Lee, HyunDong Cho, Donghyun Kim, Sung-Chang Lim, Jin Soo Choi, Hui Yong Kim

Learned image compression (LIC) outperforms traditional codecs but suffers from excessive peak memory usage when handling high-resolution images. Consequently, block-based LIC has been studied to reduce peak memory and peak computational cost, but it often introduces blocking artifacts that degrade visual quality. To mitigate this, the JPEG-AI standard introduced a patch-based scheme in which overlapped blocks are coded independently using empirically determined overlap sizes. However, experimentally searching for optimal overlaps is time-consuming and does not guarantee blocking-free reconstruction. In this paper, we propose an analytic framework that models overlap propagation through convolution and transposed convolution layers to precisely determine the minimal overlaps required for blocking-free reconstruction. Based on the calculated minimum overlaps, we provide a block-based implementation methodology applicable to most CNN-based LIC models. Applied to four CNN-based LIC models on 4K images partitioned into various block sizes (256 x 256, 512 x 512), our method achieves rate-distortion performance identical to full-image coding while reducing average peak memory usage to 13.94% (encoder) and 13.33% (decoder), and average peak computational cost to 2.6% and 1.24%, respectively. Notably, the proposed block-based framework does not require any retraining of the original model. Furthermore, it can also be applied to most CNN-based image-processing neural networks without performance degradation.

Matthieu Dabrowski, Ouala Ben Jemaa, Benjamin Allaert

Current advancements in human motion understanding are strongly reliant on video data. Nevertheless, privacy regulations and operational constraints increasingly restrict the use of visual data in real-world scenarios. Inferring posture through wearable sensors, such as instrumented insoles measuring plantar activation, presents itself as a promising alternative. However, the absence of large-scale multimodal datasets hinders the rigorous benchmarking of these methodologies. We introduce HUMAPS-4D, a novel multimodal dataset designed for human motion analysis, effectively bridging computer vision and biomechanics. This dataset integrates synchronized motion capture, multi-view video, IMUs, plantar pressure signals, sEMG activation patterns, and high-level semantic annotations. The data was collected from 32 subjects performing 30 actions over a total duration of 14 hours. Participants demonstrate substantial anthropometric variability (age, body proportions, and morphology), which supports robust generalization across diverse body types. Distinct from existing resources, this collection offers a unique pairing of low-level physiological signals and high-level human motor descriptors. This capability enables the development of generative and inference models conditioned by both physical and semantic constraints, while simultaneously reducing the reliance on personally identifiable visual data. We establish benchmark tasks specifically targeting posture reconstruction from plantar pressure, semantic motion segmentation, physics-informed motricity analysis, and multimodal fusion under privacy-preserving conditions. The dataset, along with its associated annotation tools and visualization utilities, is scheduled for online release soon.

Zhifang Liao, Junhao Li, HaoKang Ding, Yucheng Song

Despite their impressive performance in multi-label classification of chest X-ray images (CXR), deep learning models are widely plagued by two types of spurious correlations: feature confounding arising from pathological co-occurrence and shortcut learning triggered by non-pathological visual confounders. These non-causal dependencies severely undermine the interpretability and robustness of models in real-world clinical settings. To address these challenges, we propose the Dual Adjustment Reasoning with Counterfactuals for Trustworthy Chest X-ray Classification (DARC) framework, the first to synergistically decouple both types of confounding sources from a causal mechanism perspective. At the data level, we construct CheXconf, the first pixel-level annotation dataset of non-pathological visual confounders in CXR, comprising 40,213 annotated instances across 11 categories. This provides a solid foundation for accurately modeling these confounders. At the methodological level, we design a novel dual-stream causal learning architecture. Its Global Stream leverages the back-door adjustment criterion with CheXconf to explicitly block spurious paths from non-pathological confounders. Concurrently, the Local Stream employs counterfactual reasoning, constrained by anatomical priors, to disentangle the visual coupling of co-occurring pathologies. Experiments on large-scale public benchmarks demonstrate that our method achieves significant improvements in task performance, interpretability, and robustness.

Longhua Li, Lei Qi, Qi Tian, Xin Geng

Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model without additional training. Despite extensive research, task interference remains a major obstacle that often undermines the performance of merged models. In this paper, we propose ESM (Essential Subspace Merging) , a robust framework for effective model merging. We begin by performing Principal Component Analysis (PCA) on feature shifts induced by parameter updates. The resulting principal directions span an essential subspace that dominantly influences feature representations. Each task's parameter update matrix is projected onto its respective essential subspace for low-rank decomposition before merging. This methodology mitigates inter-task interference while preserving core task-specific functionality. Furthermore, we introduce a multi-level polarized scaling strategy that amplifies parameters containing critical knowledge and suppresses redundant ones, preventing essential knowledge from being overwhelmed during fusion. Extensive experiments across multiple task sets and model scales demonstrate that our method achieves state-of-the-art performance in multi-task model merging.

Jiesong Lian, Ruizhe Zhong, Zixiang Zhou, Xiaoyue Mi, Long Hu, Yuan Zhou, Qinglin Lu, Yixue Hao, Junchi Yan

Post-training alignment of video generation models with human preferences is a critical goal. Developing effective Reward Models (RMs) for this process faces significant methodological hurdles. Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise. Concurrently, the architectural design of VLM-based RMs, particularly their output mechanisms, remains underexplored. Furthermore, RM is susceptible to reward hacking in post-training. To mitigate these limitations, we propose SoliReward, a systematic framework for video RM training. Our framework first sources high-quality, cost-efficient data via single-item binary annotations, then constructs preference pairs using a cross-prompt pairing strategy. Architecturally, we employ a Hierarchical Progressive Query Attention mechanism to enhance feature aggregation. Finally, we introduce a modified BT loss that explicitly accommodates win-tie scenarios. This approach regularizes the RM's score distribution for positive samples, providing more nuanced preference signals to alleviate over-focus on a small number of top-scoring samples. Our approach is validated on benchmarks evaluating physical plausibility, subject deformity, and semantic alignment, demonstrating improvements in direct RM evaluation metrics and in the efficacy of post-training on video generation models. Code and benchmark are available at https://github.com/lian700/SoliReward.

Charantej Pochimireddy, Subhasmita Sahoo, Apoorva Verma, Palavalli Shyam, Swapnil Malviya, Sarvesh Sarvesh, Raj Gadde

Recent advancements in deep neural networks (DNNs) have significantly improved visual quality of camera captures under low-light (<10lx) conditions, yet visual quality in extreme low-light (<1lx) remains inadequate. Existing DNN models are computationally intensive and suffer from large processing times, making them impractical for real-time enhancement of high-resolution video. Consequently, Ultra HD (UHD) videos (4K/8K) captured in extreme low-light environments exhibit elevated noise and diminished detail. Developing DNN-based solutions for UHD video enhancement faces challenges including paired dataset creation, temporal consistency, and efficient deployment under strict latency (<33ms) and power constraints (<250mA for 30fps video).We present a comprehensive methodology for developing a real-time raw to raw denoising solution for UHD video in extreme low-light, designed for seamless integration into existing ISP pipelines. Unlike ISP-replacement approaches, our solution enhances commercial camera stacks across sensor platforms. Our framework comprises: (1) Diverse dataset creation methodology; (2) A low-complexity model architecture optimized for mobile compute elements; (3) Efficient training and post-training optimizations (reparameterization, restructuring, quantization) to meet latency constraints while ensuring high-quality output. The result is a power-efficient real-time raw to raw video denoiser that improves extreme low-light video quality while preserving downstream ISP behavior.

Jingdong Zhang, Lingzhi Zhang, Qing Liu, Mang Tik Chiu, Connelly Barnes, Yizhou Wang, Haoran You, Xiaoyang Liu, Yuqian Zhou, Zhe Lin 等

Digital images are often degraded by soft effects such as lens flare, haze, shadows, and reflections, which reduce aesthetics even though the underlying pixels remain partially visible. The prevailing works address these degradations in isolation, developing highly specialized, specialist models that lack scalability and fail to exploit the shared underlying essences of these restoration problems. While specialist models are limited, recent large-scale pretrained generalist models offer powerful, text-driven image editing capabilities. while recent general-purpose systems (e.g., GPT-4o, Flux Kontext, Nano Banana) require detailed prompts and often fail to achieve robust removal on these fine-grained tasks or preserve identity of the scene. Leveraging the common essence of soft effects, i.e., semi-transparent occlusions, we introduce a foundational versatile model UniSER, capable of addressing diverse degradations caused by soft effects within a single framework. Our methodology centers on curating a massive 3.8M-pair dataset to ensure robustness and generalization, which includes novel, physically-plausible data to fill critical gaps in public benchmarks, and a tailored training pipeline that fine-tunes a Diffusion Transformer to learn robust restoration priors from this diverse data, integrating fine-grained mask and strength controls. This synergistic approach allows UniSER to significantly outperform both specialist and generalist models, achieving robust, high-fidelity restoration in the wild.

Haochen Niu, Kanyu Zhang, Shuyu Yin, Qinghai Guo, Peilin Liu, Fei Wen

In real-world robotic manipulation, states typically admit a neighborhood of near-equivalent actions. That is, for each state, there exist a feasible action neighborhood (FAN) rather than a single correct action, within which motions yield indistinguishable progress. However, prevalent VLA training methodologies are directly inherited from linguistic settings and do not exploit the FAN property, thus leading to poor generalization and low sample efficiency. To address this limitation, we introduce a FAN-guided regularizer that shapes the model's output distribution to align with the geometry of FAN. Concretely, we introduce a Gaussian prior that promotes locally smooth and unimodal predictions around the preferred direction and magnitude. In extensive experiments across both reinforced finetuning (RFT) and supervised finetuning (SFT), our method achieves significant improvement in sample efficiency, and success rate in both in-distribution and out-of-distribution (OOD) scenarios. By aligning with the intrinsic action tolerance of physical manipulation, FAN-guided regularization provides a principled and practical method for sample-efficient, and generalizable VLA adaptation. The source code is publicly available at: https://github.com/Haochen-Niu/FAN-VLA.

Jaehyun Park, Minyoung Ahn, Minkyu Kim, Jonghyun Lee, Jae-Gil Lee, Dongmin Park

Despite recent advances in diffusion models, AI generated images still often contain visual artifacts that compromise realism. Although more thorough pre-training and bigger models might reduce artifacts, there is no assurance that they can be completely eliminated, which makes artifact mitigation a highly crucial area of study. Previous artifact-aware methodologies depend on human-labeled artifact datasets, which are costly and difficult to scale, underscoring the need for an automated approach to reliably acquire artifact-annotated datasets. In this paper, we propose **ArtiAgent**, which efficiently creates pairs of real and artifact-injected images. It comprises three agents: a perception agent that recognizes and grounds entities and subentities from real images, a synthesis agent that introduces artifacts via artifact injection tools through novel patch-wise embedding manipulation within a diffusion transformer, and a curation agent that filters the synthesized artifacts and generates both local and global explanations for each instance. Using our pipeline, we collect 100K pairwise instances and demonstrate both efficacy and versatility across diverse applications. Code is available at https://github.com/krafton-ai/ArtiAgent.