Retrieving molecular structures from tandem mass spectra is a crucial step in rapid compound identification. Existing retrieval methods, such as traditional mass spectral library matching, suffer from limited spectral library coverage, while recent cross-modal representation learning frameworks often encounter modality misalignment, resulting in suboptimal retrieval accuracy and generalization. To address these limitations, we propose GLMR, a Generative Language Model-based Retrieval framework that mitigates the cross-modal misalignment through a two-stage process. In the pre-retrieval stage, a contrastive learning-based model identifies top candidate molecules as contextual priors for the input mass spectrum. In the generative retrieval stage, these candidate molecules are integrated with the input mass spectrum to guide a generative model in producing refined molecular structures, which are then used to re-rank the candidates based on molecular similarity. Experiments on both MassSpecGym and the proposed MassRET-20k dataset demonstrate that GLMR significantly outperforms existing methods, achieving over 40% improvement in top-1 accuracy and exhibiting strong generalizability.

User interface (UI) development requires translating design mockups into functional code, a process that remains repetitive and labor-intensive. While recent Vision-Language Models (VLMs) automate UI-to-Code generation, they generate only static HTML/CSS/JavaScript layouts lacking interactivity. To address this, we propose WebVIA, the first agentic framework for interactive UI-to-Code generation and validation. The framework comprises three components: 1) an exploration agent to capture multi-state UI screenshots; 2) a UI2Code model that generates executable interactive code; 3) a validation module that verifies the interactivity. Experiments demonstrate that WebVIA-Agent achieves more stable and accurate UI exploration than general-purpose agents (e.g., Gemini-2.5-Pro). In addition, our fine-tuned WebVIA-UI2Code models exhibit substantial improvements in generating executable and interactive HTML/CSS/JavaScript code, outperforming their base counterparts across both interactive and static UI2Code benchmarks. Our code and models are available at \href{https://zheny2751-dotcom.github.io/webvia.github.io/}{\texttt{https://webvia.github.io}}.

With the growing demand for high-quality image generation on resource-constrained devices, efficient diffusion models have received increasing attention. However, such models suffer from approximation errors introduced by efficiency techniques, which significantly degrade generation quality. Once deployed, these errors are difficult to correct, as modifying the model is typically infeasible in deployment environments. Through an analysis of error propagation across diffusion timesteps, we reveal that these approximation errors can accumulate exponentially, severely impairing output quality. Motivated by this insight, we propose Iterative Error Correction (IEC), a novel test-time method that mitigates inference-time errors by iteratively refining the model's output. IEC is theoretically proven to reduce error propagation from exponential to linear growth, without requiring any retraining or architectural changes. IEC can seamlessly integrate into the inference process of existing diffusion models, enabling a flexible trade-off between performance and efficiency. Extensive experiments show that IEC consistently improves generation quality across various datasets, efficiency techniques, and model architectures, establishing it as a practical and generalizable solution for test-time enhancement of efficient diffusion models.

Adapting Large Language Models (LLMs) to a continuous stream of tasks is a critical yet challenging endeavor. While Parameter-Efficient Fine-Tuning (PEFT) methods have become a standard for this, they face a fundamental dilemma in continual learning. Reusing a single set of PEFT parameters for new tasks often leads to catastrophic forgetting of prior knowledge. Conversely, allocating distinct parameters for each task prevents forgetting but results in a linear growth of the model's size and fails to facilitate knowledge transfer between related tasks. To overcome these limitations, we propose a novel adaptive PEFT method referred to as \textit{Mixtures of SubExperts (MoSEs)}, a novel continual learning framework designed for minimal forgetting and efficient scalability. MoSEs integrate a sparse Mixture of SubExperts into the transformer layers, governed by a task-specific routing mechanism. This architecture allows the model to isolate and protect knowledge within dedicated SubExperts, thereby minimizing parameter interference and catastrophic forgetting. Crucially, the router can adaptively select and combine previously learned sparse parameters for new tasks, enabling effective knowledge transfer while ensuring that the model's capacity grows sublinearly. We evaluate MoSEs on the comprehensive TRACE benchmark datasets. Our experiments demonstrate that MoSEs significantly outperform conventional continual learning approaches in both knowledge retention and scalability to new tasks, achieving state-of-the-art performance with substantial memory and computational savings.

Human emotions fundamentally shape decision-making, social interactions, and cognitive processes. Modern human-computer interaction (HCI) systems, however, remain largely oblivious to users' affective states, relying exclusively on explicit inputs such as touch, speech, or gaze. The proliferation of consumer wearables such as smartwatches, fitness trackers, and health monitors has democratized access to continuous physiological data, creating unprecedented opportunities for emotionally intelligent computing [1, 2]. Physiological signals offer several advantages over traditional modalities (facial expressions, voice) for emotion recognition: they are continuous, difficult to consciously manipulate, and unaffected by environmental factors such as lighting or occlusion [3]. Among these signals, heart rate variability (HR V), the temporal variation between consecutive heartbeats, has emerged as a robust biomarker of autonomic nervous system activity and emotional states [4, 5]. Despite significant research advances in affective computing, most emotion recognition systems exhibit two critical limitations: 1. Privacy vulnerabilities: Cloud-based inference requires transmitting sensitive bio-metric data to external servers, exposing users to data breaches, surveillance, and loss of autonomy [6].

Discharge medication recommendation plays a critical role in ensuring treatment continuity, preventing readmission, and improving long - term management for patients with chronic metabolic diseases. This paper present an overview of the CHIP 2025 Shared Task 2 competition, which aimed to develop state - of - the - art approaches for automatic ally recommend ing appropriate discharge medications using real - world Chinese EHR data . For this task, w e constructed CDrugRed, a high - quality dataset consisting of 5,894 de - identified hospitalization records from 3,190 patients in China . This task is challenging due to multi - label nature of medication recommendation, heterogeneous clinical text, and patient - specific variability in treatment plans . A total of 526 teams registered, with 167 and 95 teams submitting valid results to the Phase A and Phase B leader-boards, respect ively. The top - performing team achieved the highest overall performance on the final test set, with a Jaccard score of 0.5102, F1 score of 0.6267, demonstrating the potential of advanced large language model (LLM) - based ensemble system s .

Abstract--This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations. The fundamental challenge of the DODE problem in microscopic simulations stems from the complex temporal dynamics and inherent uncertainty of individual vehicle dynamics. This makes it highly challenging to precisely determine which vehicle traverses which link at any given moment, resulting in intricate and often ambiguous relationships between origin-destination (OD) matrices and their contributions to resultant link flows. This phenomenon constitutes the credit assignment problem, a central challenge addressed in this study . We formulate the DODE problem as a Markov Decision Process (MDP) and propose a novel framework that applies model-free deep reinforcement learning (DRL). Within our proposed framework, the agent learns an optimal policy to sequentially generate OD matrices, refining its strategy through direct interaction with the simulation environment. The proposed method is validated on the Nguyen-Dupuis network using SUMO, where its performance is evaluated against ground-truth link flows aggregated at 5-minute intervals over a 30-minute horizon. Experimental results demonstrate that our approach achieves a 43.2% reduction in mean squared error (MSE) compared to the best-performing conventional baseline. By reframing DODE as a sequential decision-making problem, our approach addresses the credit assignment challenge through its learned policy, thereby overcoming the limitations of conventional methods and proposing a novel framework for calibration of microscopic traffic simulations. Modern strategies, such as speed harmonization, lane-changing control, and adaptive traffic signal control, are now predominantly designed and evaluated at the microscopic level, marking a significant shift in traffic management paradigms [2], [3], [4].

Unit tests often lack concise summaries that convey test intent, especially in auto-generated or poorly documented codebases. Large Language Models (LLMs) offer a promising solution, but their effectiveness depends heavily on how they are prompted. Unlike generic code summarization, test-code summarization poses distinct challenges because test methods validate expected behavior through assertions rather than implementing functionality. This paper presents a new benchmark of 91 real-world Java test cases paired with developer-written summaries and conducts a controlled ablation study to investigate how test code-related components-such as the method under test (MUT), assertion messages, and assertion semantics-affect the performance of LLM-generated test summaries. We evaluate four code LLMs (Codex, Codestral, DeepSeek, and Qwen-Coder) across seven prompt configurations using n-gram metrics (BLEU, ROUGE-L, METEOR), semantic similarity (BERTScore), and LLM-based evaluation. Results show that prompting with assertion semantics improves summary quality by an average of 0.10 points (2.3%) over full MUT context (4.45 vs. 4.35) while requiring fewer input tokens. Codex and Qwen-Coder achieve the highest alignment with human-written summaries, while DeepSeek underperforms despite high lexical overlap. The replication package is publicly available at https://doi.org/10. 5281/zenodo.17067550

Accurate accident anticipation is essential for enhancing the safety of autonomous vehicles (A Vs). However, existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data imperfections, which can significantly degrade prediction accuracy. Additionally, previous models have not adequately addressed the considerable variability in driver behavior and accident rates across different vehicle types. To overcome these limitations, this study introduces ROAR, a novel approach for accident detection and prediction. ROAR combines Discrete Wavelet Transform (DWT), a self-adaptive object-aware module, and dynamic focal loss to tackle these challenges. The DWT effectively extracts features from noisy and incomplete data, while the object-aware module improves accident prediction by focusing on high-risk vehicles and modeling the spatial-temporal relationships among traffic agents. Evaluated on three widely used datasets--Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D)--our model consistently outperforms existing baselines in key metrics such as Average Precision (AP) and mean Time-to-Accident (mTT A). These results demonstrate the model's robustness in real-world conditions, particularly in handling sensor degradation, environmental noise, and imbalanced data distributions. This work offers a promising solution for reliable and accurate accident anticipation in complex traffic environments. INTRODUCTION Traffic accidents are a persistent global issue, causing significant harm to both individuals and society. With the rise of autonomous driving, the need to proactively address this challenge has never been more pressing [1].

In high-stakes scenarios-such as self-harm, legal, or medical queries-LLMs must be both trustworthy and helpful. However, these goals often conflict. We propose priority alignment, a new alignment paradigm that enforces a strict "trustworthy-before-helpful" ordering: optimization of helpfulness is conditioned on first meeting trustworthy thresholds (e.g., harmlessness or honesty). To realize this, we introduce Self-Priority Alignment (SPA)-a fully unsupervised framework that generates diverse responses, self-evaluates them and refines them by the model itself, and applies dual-criterion denoising to remove inconsistency and control variance. From this, SPA constructs lexicographically ordered preference pairs and fine-tunes the model using an uncertainty-weighted alignment loss that emphasizes high-confidence, high-gap decisions. Experiments across multiple benchmarks show that SPA improves helpfulness without compromising safety, outperforming strong baselines while preserving general capabilities. Our results demonstrate that SPA provides a scalable and interpretable alignment strategy for critical LLM applications.