Posterior Collapse as a Phase Transition in Variational Autoencoders

We investigate the phenomenon of posterior collapse in variational autoencoders (VAEs) from the perspective of statistical physics, and reveal that it constitutes a phase transition governed jointly by data structure and model hyper-parameters. By analyzing the stability of the trivial solution associated with posterior collapse, we identify a critical hyper-parameter threshold. This critical boundary, separating meaningful latent inference from collapse, is characterized by a discontinuity in the KL divergence between the approximate posterior and the prior distribution. We validate this critical behavior on both synthetic and real-world datasets, confirming the existence of a phase transition. Our results demonstrate that posterior collapse is not merely an optimization failure, but rather an emerging phase transition arising from the interplay between data structure and variational constraints. This perspective offers new insights into the trainability and representational capacity of deep generative models.

ASSESS: A Semantic and Structural Evaluation Framework for Statement Similarity

Statement autoformalization, the automated translation of statements from natural language into formal languages, has seen significant advancements, yet the development of automated evaluation metrics remains limited. Existing metrics for formal statement similarity often fail to balance semantic and structural information. String-based approaches capture syntactic structure but ignore semantic meaning, whereas proof-based methods validate semantic equivalence but disregard structural nuances and, critically, provide no graded similarity score in the event of proof failure. To address these issues, we introduce ASSESS (A Semantic and Structural Evaluation Framework for Statement Similarity), which comprehensively integrates semantic and structural information to provide a continuous similarity score. Our framework first transforms formal statements into Operator Trees to capture their syntactic structure and then computes a similarity score using our novel TransTED (Transformation Tree Edit Distance) Similarity metric, which enhances traditional Tree Edit Distance by incorporating semantic awareness through transformations. For rigorous validation, we present EPLA (Evaluating Provability and Likeness for Autoformalization), a new benchmark of 524 expert-annotated formal statement pairs derived from miniF2F and ProofNet, with labels for both semantic provability and structural likeness. Experiments on EPLA demonstrate that TransTED Similarity outperforms existing methods, achieving state-of-the-art accuracy and the highest Kappa coefficient. The benchmark, and implementation code will be made public soon.

NavMoE: Hybrid Model- and Learning-based Traversability Estimation for Local Navigation via Mixture of Experts

This paper explores traversability estimation for robot navigation. A key bottleneck in traversability estimation lies in efficiently achieving reliable and robust predictions while accurately encoding both geometric and semantic information across diverse environments. We introduce Navigation via Mixture of Experts (NAVMOE), a hierarchical and modular approach for traversability estimation and local navigation. NAVMOE combines multiple specialized models for specific terrain types, each of which can be either a classical model-based or a learning-based approach that predicts traversability for specific terrain types. NAVMOE dynamically weights the contributions of different models based on the input environment through a gating network. Overall, our approach offers three advantages: First, NAVMOE enables traversability estimation to adaptively leverage specialized approaches for different terrains, which enhances generalization across diverse and unseen environments. Second, our approach significantly improves efficiency with negligible cost of solution quality by introducing a training-free lazy gating mechanism, which is designed to minimize the number of activated experts during inference. Third, our approach uses a two-stage training strategy that enables the training for the gating networks within the hybrid MoE method that contains nondifferentiable modules. Extensive experiments show that NAVMOE delivers a better efficiency and performance balance than any individual expert or full ensemble across different domains, improving cross- domain generalization and reducing average computational cost by 81.2% via lazy gating, with less than a 2% loss in path quality.

GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design

Environment designers in the entertainment industry create imaginative 2D and 3D scenes for games, films, and television, requiring both fine-grained control of specific details and consistent global coherence. Designers have increasingly integrated generative AI into their workflows, often relying on large language models (LLMs) to expand user prompts for text-to-image generation, then iteratively refining those prompts and applying inpainting. However, our formative study with 10 designers surfaced two key challenges: (1) the lengthy LLM-generated prompts make it difficult to understand and isolate the keywords that must be revised for specific visual elements; and (2) while inpainting supports localized edits, it can struggle with global consistency and correctness. Based on these insights, we present GenTune, an approach that enhances human--AI collaboration by clarifying how AI-generated prompts map to image content. Our GenTune system lets designers select any element in a generated image, trace it back to the corresponding prompt labels, and revise those labels to guide precise yet globally consistent image refinement. In a summative study with 20 designers, GenTune significantly improved prompt--image comprehension, refinement quality, and efficiency, and overall satisfaction (all $p < .01$) compared to current practice. A follow-up field study with two studios further demonstrated its effectiveness in real-world settings.

A Semantic-Aware Framework for Safe and Intent-Integrative Assistance in Upper-Limb Exoskeletons

Upper-limb exoskeletons are primarily designed to provide assistive support by accurately interpreting and responding to human intentions. In home-care scenarios, exoskeletons are expected to adapt their assistive configurations based on the semantic information of the task, adjusting appropriately in accordance with the nature of the object being manipulated. However, existing solutions often lack the ability to understand task semantics or collaboratively plan actions with the user, limiting their generalizability. To address this challenge, this paper introduces a semantic-aware framework that integrates large language models into the task planning framework, enabling the delivery of safe and intent-integrative assistance. The proposed approach begins with the exoskeleton operating in transparent mode to capture the wearer's intent during object grasping. Once semantic information is extracted from the task description, the system automatically configures appropriate assistive parameters. In addition, a diffusion-based anomaly detector is used to continuously monitor the state of human-robot interaction and trigger real-time replanning in response to detected anomalies. During task execution, online trajectory refinement and impedance control are used to ensure safety and regulate human-robot interaction. Experimental results demonstrate that the proposed method effectively aligns with the wearer's cognition, adapts to semantically varying tasks, and responds reliably to anomalies.

Finite-Time Convergence Analysis of ODE-based Generative Models for Stochastic Interpolants

Stochastic interpolants offer a robust framework for continuously transforming samples between arbitrary data distributions, holding significant promise for generative modeling. Despite their potential, rigorous finite-time convergence guarantees for practical numerical schemes remain largely unexplored. In this work, we address the finite-time convergence analysis of numerical implementations for ordinary differential equations (ODEs) derived from stochastic interpolants. Specifically, we establish novel finite-time error bounds in total variation distance for two widely used numerical integrators: the first-order forward Euler method and the second-order Heun's method. Furthermore, our analysis on the iteration complexity of specific stochastic interpolant constructions provides optimized schedules to enhance computational efficiency. Our theoretical findings are corroborated by numerical experiments, which validate the derived error bounds and complexity analyses.

VP-SelDoA: Visual-prompted Selective DoA Estimation of Target Sound via Semantic-Spatial Matching

Audio-visual sound source localization (AV-SSL) identifies the position of a sound source by exploiting the complementary strengths of auditory and visual signals. However, existing AV-SSL methods encounter three major challenges: 1) inability to selectively isolate the target sound source in multi-source scenarios, 2) misalignment between semantic visual features and spatial acoustic features, and 3) overreliance on paired audio-visual data. To overcome these limitations, we introduce Cross-Instance Audio-Visual Localization (CI-AVL), a novel task that leverages images from different instances of the same sound event category to localize target sound sources, thereby reducing dependence on paired data while enhancing generalization capabilities. Our proposed VP-SelDoA tackles this challenging task through a semantic-level modality fusion and employs a Frequency-Temporal ConMamba architecture to generate target-selective masks for sound isolation. We further develop a Semantic-Spatial Matching mechanism that aligns the heterogeneous semantic and spatial features via integrated cross- and self-attention mechanisms. To facilitate the CI-AVL research, we construct a large-scale dataset named VGG-SSL, comprising 13,981 spatial audio clips across 296 sound event categories. Extensive experiments show that our proposed method outperforms state-of-the-art audio-visual localization methods, achieving a mean absolute error (MAE) of 12.04 and an accuracy (ACC) of 78.23%.

Generalized Tree Edit Distance (GTED): A Faithful Evaluation Metric for Statement Autoformalization

Statement autoformalization, the automated translation of statement from natural language into formal languages, has become a subject of extensive research, yet the development of robust automated evaluation metrics remains limited. Existing evaluation methods often lack semantic understanding, face challenges with high computational costs, and are constrained by the current progress of automated theorem proving. To address these issues, we propose GTED (Generalized Tree Edit Distance), a novel evaluation framework that first standardizes formal statements and converts them into operator trees, then determines the semantic similarity using the eponymous GTED metric. On the miniF2F and ProofNet benchmarks, GTED outperforms all baseline metrics by achieving the highest accuracy and Kappa scores, thus providing the community with a more faithful metric for automated evaluation. The code and experimental results are available at https://github.com/XiaoyangLiu-sjtu/GTED.

A Contemporary Survey on Fluid Antenna Systems: Fundamentals and Networking Perspectives

The explosive growth of teletraffic, fueled by the convergence of cyber-physical systems and data-intensive applications, such as the Internet of Things (IoT), autonomous systems, and immersive communications, demands a multidisciplinary suite of innovative solutions across the physical and network layers. Fluid antenna systems (FAS) represent a transformative advancement in antenna design, offering enhanced spatial degrees of freedom through dynamic reconfigurability. By exploiting spatial flexibility, FAS can adapt to varying channel conditions and optimize wireless performance, making it a highly promising candidate for next-generation communication networks. This paper provides a comprehensive survey of the state of the art in FAS research. We begin by examining key application scenarios in which FAS offers significant advantages. We then present the fundamental principles of FAS, covering channel measurement and modeling, single-user configurations, and the multi-user fluid antenna multiple access (FAMA) framework. Following this, we delve into key network-layer techniques such as quality-of-service (QoS) provisioning, power allocation, and content placement strategies. We conclude by identifying prevailing challenges and outlining future research directions to support the continued development of FAS in next-generation wireless networks.

DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation

Recently, large language models (LLMs) have been introduced into recommender systems (RSs), either to enhance traditional recommendation models (TRMs) or serve as recommendation backbones. However, existing LLM-based RSs often do not fully exploit the complementary advantages of LLMs (e.g., world knowledge and reasoning) and TRMs (e.g., recommendation-specific knowledge and efficiency) to fully explore the item space. To address this, we propose DeepRec, a novel LLM-based RS that enables autonomous multi-turn interactions between LLMs and TRMs for deep exploration of the item space. In each interaction turn, LLMs reason over user preferences and interact with TRMs to retrieve candidate items. After multi-turn interactions, LLMs rank the retrieved items to generate the final recommendations. We adopt reinforcement learning(RL) based optimization and propose novel designs from three aspects: recommendation model based data rollout, recommendation-oriented hierarchical rewards, and a two-stage RL training strategy. For data rollout, we introduce a preference-aware TRM, with which LLMs interact to construct trajectory data. For rewards, we design a hierarchical reward function that involves both process-level and outcome-level rewards to optimize the interaction process and recommendation performance, respectively. For RL training, we develop a two-stage training strategy, where the first stage aims to guide LLMs to interact with TRMs and the second stage focuses on performance improvement. Experiments on public datasets demonstrate that DeepRec significantly outperforms both traditional and LLM-based baselines, offering a new paradigm for deep exploration in recommendation systems.