Low-Light Image Enhancement (LLIE) remains a challenging task, particularly in preserving object edge continuity and fine structural details under extreme illumination degradation. In this paper, we propose a novel model, DRWKV (Detailed Receptance Weighted Key Value), which integrates our proposed Global Edge Retinex (GER) theory, enabling effective decoupling of illumination and edge structures for enhanced edge fidelity. Secondly, we introduce Evolving WKV Attention, a spiral-scanning mechanism that captures spatial edge continuity and models irregular structures more effectively. Thirdly, we design the Bilateral Spectrum Aligner Block (Bi-SAB) and a tailored MS2-Loss to jointly align luminance and chrominance features, improving visual naturalness and mitigating artifacts. Extensive experiments on five LLIE benchmarks demonstrate that DRWKV achieves leading performance in PSNR, SSIM, and NIQE while maintaining low computational complexity. Furthermore, DRWKV enhances downstream performance in low-light multi-object tracking tasks, validating its generalization capabilities. The code are available at: https://github.com/JackBaixue/DRWKV

CAD drawing restoration is one of the most urgent needs in industrial manufacturing. The existing research focuses on the digitization of CAD drawings, However, there are actually many problems in digitized CAD drawings due to the upgrading of engineering drafting software, and it is difficult to repair reliably. In this paper, we use multi-modal large language models (MLLMs) to carry out digital CAD drawing restoration, and we use Retrieval-Augmented Generation (RAG) technology to inject engineering domain knowledge into MLLMs. In addition, a complete set of multiagent systems is constructed to realize restoration in accordance with the CAD drawing review process in the mechanical field. We collected 1,639 CAD drawings of bearing seats to evaluate our multi-agent system, verifying its reliability and robustness to various problematic drawings. Notably, ChatCAD also has a much simpler implementation than alternative methods trained on a huge dataset.

Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual annotation and suffer from limited character diversity, contextual scenarios, and emotional expressiveness. To address these issues, we propose DialogueAgents, a novel hybrid agent-based speech synthesis framework, which integrates three specialized agents—a script writer, a speech synthesizer, and a dialogue critic—to collaboratively generate dialogues. Grounded in a diverse character pool, the framework iteratively refines dialogue scripts and synthesizes speech based on speech review, boosting emotional expressiveness and paralinguistic features of the synthesized dialogues. Using DialogueAgent, we contribute MultiTalk, a bilingual, multi-party, multi-turn speech dialogue dataset covering diverse topics. Extensive experiments demonstrate the effectiveness of our framework and the high quality of the MultiTalk dataset. We release the dataset and code1to facilitate future research on advanced speech synthesis models and customized data generation.

Recent advancements in autonomous driving have seen a paradigm shift towards end-to-end learning paradigms, which map sensory inputs directly to driving actions, thereby enhancing the robustness and adaptability of autonomous vehicles. However, these models often sacrifice interpretability, posing significant challenges to trust, safety, and regulatory compliance. To address these issues, we introduce DRIVE – Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving, a comprehensive framework designed to improve the dependability and stability of explanations in end-to-end unsupervised autonomous driving models. Our work specifically targets the inherent instability problems observed in the Driving through the Concept Gridlock (DCG) model, which undermine the trustworthiness of its explanations and decisionmaking processes. We define four key attributes of DRIVE: consistent interpretability, stable interpretability, consistent output, and stable output. These attributes collectively ensure that explanations remain reliable and robust across different scenarios and perturbations. Through extensive empirical evaluations, we demonstrate the effectiveness of our framework in enhancing the stability and dependability of explanations, thereby addressing the limitations of current models. Our contributions include an in-depth analysis of the dependability issues within the DCG model, a rigorous definition of DRIVE with its fundamental properties, a framework to implement DRIVE, and novel metrics for evaluating the dependability of concept-based explainable autonomous driving models. These advancements lay the groundwork for the development of more reliable and trusted autonomous driving systems, paving the way for their broader acceptance and deployment in real-world applications. “We can only see a short distance ahead, but we can see plenty there that needs to be done.” – Alan Turing

While diffusion models advance text-to-motion generation, their static semantic conditioning ignores temporal-frequency demands: early denoising requires structural semantics for motion foundations while later stages need localized details for text alignment. This mismatch mirrors biological morphogenesis where developmental phases demand distinct genetic programs. Inspired by epigenetic regulation governing morphological specialization, we propose **(ANT)**, an **A**daptive **N**eural **T**emporal-Aware architecture. ANT orchestrates semantic granularity through: **(i) Semantic Temporally Adaptive (STA) Module:** Automatically partitions denoising into low-frequency structural planning and high-frequency refinement via spectral analysis. **(ii) Dynamic Classifier-Free Guidance scheduling (DCFG):** Adaptively adjusts conditional to unconditional ratio enhancing efficiency while maintaining fidelity. Extensive experiments show that ANT can be applied to various baselines, significantly improving model performance, and achieving state-of-the-art semantic alignment on StableMoFusion.

With the rapid development of wireless communication technology, the efficient utilization of spectrum resources, optimization of communication quality, and intelligent communication have become critical. Radio map reconstruction is essential for enabling advanced applications, yet challenges such as complex signal propagation and sparse observational data hinder accurate reconstruction in practical scenarios. Existing methods often fail to align physical constraints with data-driven features, particularly under sparse measurement conditions. To address these issues, we propose Physics-Aligned Radio Map Diffusion Model (PhyRMDM), a novel framework that establishes cross-domain representation alignment between physical principles and neural network features through dual learning pathways. The proposed model integrates Physics-Informed Neural Networks (PINNs) with a representation alignment mechanism that explicitly enforces consistency between Helmholtz equation constraints and environmental propagation patterns. Our architecture employs two synergistic U-Nets: the first ensures physical consistency by minimizing PDE residuals and boundary conditions through latent space alignment, while the second refines predictions via diffusion-based denoising with attention-guided feature fusion. This dual alignment strategy enables simultaneous satisfaction of wave propagation laws and data distribution characteristics. Experimental results demonstrate significant improvements over state-of-the-art methods, achieving NMSE of 0.0031 and RMSE of 0.0125 under Static Radio Map (SRM) conditions, and NMSE of 0.0047 with RMSE of 0.0146 in Dynamic Radio Map (DRM) scenarios. The proposed representation alignment paradigm provides 37.2% accuracy enhancement in ultra-sparse cases (1% sampling rate), confirming its effectiveness in bridging physics-based modeling and deep learning for radio map reconstruction. These advancements establish a new framework for sparse signal environment characterization, with direct applications in 5G/6G network optimization and intelligent spectrum management. The code can be found on the website: https://github.com/Hxxxz0/RMDM

Concept Bottleneck Models (CBMs) enhance the interpretability of AI systems, particularly by bridging visual input with human-understandable concepts, effectively acting as a form of multimodal interpretability model. However, existing CBMs typically assume static datasets, which fundamentally limits their adaptability to real-world, continuously evolving multimodal data streams. To address this, we define a novel continual learning task for CBMs: simultaneously handling concept-incremental and class-incremental learning. This task requires models to continuously acquire new concepts (often representing cross-modal attributes) and classes while robustly preserving previously learned knowledge. To tackle this challenging problem, we propose CONceptual Continual Incremental Learning (CONCIL), a novel framework that fundamentally re-imagines concept and decision layer updates as linear regression problems. This reformulation eliminates the need for gradient-based optimization, thereby effectively preventing catastrophic forgetting. Crucially, CONCIL relies solely on recursive matrix operations, rendering it highly computationally efficient and well-suited for real-time and large-scale multimodal data applications. Experimental results compellingly demonstrate that CONCIL achieves ''absolute knowledge memory'' and significantly surpasses the performance of traditional CBM methods in both concept- and class-incremental settings, thus establishing a new paradigm for continual learning in CBMs, particularly valuable for dynamic multimodal understanding.

Recent advancements in Audio Language Models (ALMs) have led to significant improvements in speech-related tasks. However, their capacity for profound metaphorical reasoning, especially when derived from audio-specific cues, has yet to be thoroughly investigated. To address this gap, we introduce Unspoken, a bilingual (Chinese-English) question answering benchmark designed to assess ALMs' comprehension of non-literal, metaphor-rich audio. Unlike prior text-centric evaluations, Unspoken emphasizes prosody, phonetic ambiguity, emotional inflection, and other nuanced acoustic features critical to metaphor understanding but often lost in transcription. We construct a high-quality dataset of 2,764 manually curated and validated QA pairs, spanning three reasoning dimensions: semantic, acoustic, and contextual, and covering six common types of metaphors. Evaluation across 23 mainstream ALMs reveals a substantial performance gap: the best model achieves only 69.5% accuracy, significantly below the human average of 81.1%. By analyzing the error patterns, we identify five key failure modes that reveal fundamental limitations in current models' reasoning capabilities. Unspoken not only sets a new standard for evaluating metaphorical reasoning in audio but also pioneers a novel research direction that moves beyond transcription-based assessments. Grounding metaphor understanding in authentic human communication scenarios offers deep insight for developing more cognitively capable ALMs. The data and codes are available at https://github.com/Hongru0306/UNSPOKEN.

Is the Text to Motion model robust? Recent advancements in Text to Motion models primarily stem from more accurate predictions of specific actions. However, the text modality typically relies solely on pre-trained Contrastive Language-Image Pretraining (CLIP) models. Our research has uncovered a significant issue with the text-tomotion model: its predictions often exhibit inconsistent outputs, resulting in vastly different or even incorrect poses when presented with semantically similar or identical text inputs. In this paper, we undertake an analysis to elucidate the underlying causes of this instability, establishing a clear link between the unpredictability of model outputs and the erratic attention patterns of the text encoder module. Consequently, we introduce a formal framework aimed at addressing this issue, which we term the Stable Text-to-Motion Framework (SATO). SATO consists of three modules, each dedicated to stable attention, stable prediction, and maintaining a balance between accuracy and robustness trade-off. We present a methodology for constructing an SATO that satisfies the stability of attention and prediction. To verify the stability of the model, we introduced a new textual synonym perturbation dataset based on HumanML3D and KIT-ML. Results show that SATO is significantly more stable against synonyms and other slight perturbations while keeping its high accuracy performance. Codes and models are released at

The lack of interpretability in the field of medical image analysis has significant ethical and legal implications. Existing interpretable methods in this domain encounter several challenges, including dependency on specific models, difficulties in understanding and visualization, and issues related to efficiency. To address these limitations, we propose a novel framework called Med-MICN (Medical Multi-dimensional Interpretable Concept Network). Med-MICN provides interpretability alignment for various angles, including neural symbolic reasoning, concept semantics, and saliency maps, which are superior to current interpretable methods. Its advantages include high prediction accuracy, interpretability across multiple dimensions, and automation through an end-to-end concept labeling process that reduces the need for extensive human training effort when working with new datasets. To demonstrate the effectiveness and interpretability of Med-MICN, we apply it to four benchmark datasets and compare it with baselines. The results clearly demonstrate the superior performance and interpretability of our Med-MICN.