# Research Papers — 2025-11 · bundle page 156 of 197

> Full-text markdown of papers #7751–7800 of 9830 published in 2025-11, newest first. AI-readable mirrors of the full 9830-paper 2025-11 archive (50 papers per file).

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### Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection

Authors: Alessio Arcudi, Alessandro Ferreri, Francesco Borsatti, Gian Antonio Susto
Published: 2025-11-08
Categories: cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2511.06054v1

Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability limitations and biases. This paper introduces the Function-based Isolation Forest (FuBIF), a generalization of IF that enables the use of real-valued functions for dataset branching, significantly enhancing the flexibility of evaluation tree construction. Complementing this, the FuBIF Feature Importance (FuBIFFI) algorithm extends the interpretability in IF-based approaches by providing feature importance scores across possible FuBIF models. This paper details the operational framework of FuBIF, evaluates its performance against established methods, and explores its theoretical contributions. An open-source implementation is provided to encourage further research and ensure reproducibility.

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### Efficient Hate Speech Detection: A Three-Layer LoRA-Tuned BERTweet Framework

Authors: Mahmoud El-Bahnasawi
Published: 2025-11-08
Categories: cs.CL
arXiv: https://arxiv.org/abs/2511.06051v1

This paper addresses the critical challenge of developing computationally efficient hate speech detection systems that maintain competitive performance while being practical for real-time deployment. We propose a novel three-layer framework that combines rule-based pre-filtering with a parameter-efficient LoRA-tuned BERTweet model and continuous learning capabilities. Our approach achieves 0.85 macro F1 score - representing 94% of the performance of state-of-the-art large language models like SafePhi (Phi-4 based) while using a base model that is 100x smaller (134M vs 14B parameters). Compared to traditional BERT-based approaches with similar computational requirements, our method demonstrates superior performance through strategic dataset unification and optimized fine-tuning. The system requires only 1.87M trainable parameters (1.37% of full fine-tuning) and trains in approximately 2 hours on a single T4 GPU, making robust hate speech detection accessible in resource-constrained environments while maintaining competitive accuracy for real-world deployment.

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### Visual Exploration of Feature Relationships in Sparse Autoencoders with Curated Concepts

Authors: Xinyuan Yan, Shusen Liu, Kowshik Thopalli, Bei Wang
Published: 2025-11-08
Categories: cs.CL, cs.LG
arXiv: https://arxiv.org/abs/2511.06048v1

Sparse autoencoders (SAEs) have emerged as a powerful tool for uncovering interpretable features in large language models (LLMs) through the sparse directions they learn. However, the sheer number of extracted directions makes comprehensive exploration intractable. While conventional embedding techniques such as UMAP can reveal global structure, they suffer from limitations including high-dimensional compression artifacts, overplotting, and misleading neighborhood distortions. In this work, we propose a focused exploration framework that prioritizes curated concepts and their corresponding SAE features over attempts to visualize all available features simultaneously. We present an interactive visualization system that combines topology-based visual encoding with dimensionality reduction to faithfully represent both local and global relationships among selected features. This hybrid approach enables users to investigate SAE behavior through targeted, interpretable subsets, facilitating deeper and more nuanced analysis of concept representation in latent space.

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### StreamSTGS: Streaming Spatial and Temporal Gaussian Grids for Real-Time Free-Viewpoint Video

Authors: Zhihui Ke, Yuyang Liu, Xiaobo Zhou, Tie Qiu
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06046v1

Streaming free-viewpoint video~(FVV) in real-time still faces significant challenges, particularly in training, rendering, and transmission efficiency. Harnessing superior performance of 3D Gaussian Splatting~(3DGS), recent 3DGS-based FVV methods have achieved notable breakthroughs in both training and rendering. However, the storage requirements of these methods can reach up to $10$MB per frame, making stream FVV in real-time impossible. To address this problem, we propose a novel FVV representation, dubbed StreamSTGS, designed for real-time streaming. StreamSTGS represents a dynamic scene using canonical 3D Gaussians, temporal features, and a deformation field. For high compression efficiency, we encode canonical Gaussian attributes as 2D images and temporal features as a video. This design not only enables real-time streaming, but also inherently supports adaptive bitrate control based on network condition without any extra training. Moreover, we propose a sliding window scheme to aggregate adjacent temporal features to learn local motions, and then introduce a transformer-guided auxiliary training module to learn global motions. On diverse FVV benchmarks, StreamSTGS demonstrates competitive performance on all metrics compared to state-of-the-art methods. Notably, StreamSTGS increases the PSNR by an average of $1$dB while reducing the average frame size to just $170$KB. The code is publicly available on https://github.com/kkkzh/StreamSTGS.

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### How Particle-System Random Batch Methods Enhance Graph Transformer: Memory Efficiency and Parallel Computing Strategy

Authors: Hanwen Liu, Yixuan Ma, Shi Jin, Yuguang Wang
Published: 2025-11-08
Categories: cs.LG, cs.AI, math.ST
arXiv: https://arxiv.org/abs/2511.06044v1

Attention mechanism is a significant part of Transformer models. It helps extract features from embedded vectors by adding global information and its expressivity has been proved to be powerful. Nevertheless, the quadratic complexity restricts its practicability. Although several researches have provided attention mechanism in sparse form, they are lack of theoretical analysis about the expressivity of their mechanism while reducing complexity. In this paper, we put forward Random Batch Attention (RBA), a linear self-attention mechanism, which has theoretical support of the ability to maintain its expressivity. Random Batch Attention has several significant strengths as follows: (1) Random Batch Attention has linear time complexity. Other than this, it can be implemented in parallel on a new dimension, which contributes to much memory saving. (2) Random Batch Attention mechanism can improve most of the existing models by replacing their attention mechanisms, even many previously improved attention mechanisms. (3) Random Batch Attention mechanism has theoretical explanation in convergence, as it comes from Random Batch Methods on computation mathematics. Experiments on large graphs have proved advantages mentioned above. Also, the theoretical modeling of self-attention mechanism is a new tool for future research on attention-mechanism analysis.

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### COFM: Consistent Optimal Transport Flow Matching via Partially Input Convex Neural Networks

Authors: Fanghui Song, Zhongjian Wang, Jiebao Sun
Published: 2025-11-08
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06042v2

Optimal transport (OT) provides a principled framework for learning mappings between probability distributions, and has found broad applications in generative modeling, inverse problems and scientific computing. Recently, flow matching methods have emerged as an efficient paradigm for learning continuous-time transport dynamics. However, existing OT-based flow matching methods often suffer from either high computational cost due to inner optimization or limited consistency. Moreover, it remains challenging to design neural architectures that can simultaneously guarantee convexity, stability, and efficient transport learning. In this paper, we propose a framework for consistent optimal transport flow matching. Specifically, we parameterize the transport potential using partially input convex neural networks (PICNN), and incorporate a Hamilton-Jacobi residual into the training objective to enforce dynamical consistency of the learned flow. This design enables a unified formulation that supports both one-step transport and multi-step ODE-based sampling, without requiring costly inner optimization. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves competitive performance compared with existing OT-based and flow matching approaches, while maintaining favorable computational efficiency. In particular, under the D=256 benchmark, COFM achieves more than a 2x reduction in L^2-UVP compared with state-of-the-art (SOTA) models, while requiring approximately 9x less computational time. These results suggest that combining convex potential structures with HJ-based dynamical regularization provides an effective framework for scalable and geometrically consistent transport learning.

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### Advancing Ocean State Estimation with efficient and scalable AI

Authors: Yanfei Xiang, Yuan Gao, Hao Wu, Quan Zhang, Ruiqi Shu, Xiao Zhou, Xi Wu, Xiaomeng Huang
Published: 2025-11-08
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2511.06041v1

Accurate and efficient global ocean state estimation remains a grand challenge for Earth system science, hindered by the dual bottlenecks of computational scalability and degraded data fidelity in traditional data assimilation (DA) and deep learning (DL) approaches. Here we present an AI-driven Data Assimilation Framework for Ocean (ADAF-Ocean) that directly assimilates multi-source and multi-scale observations, ranging from sparse in-situ measurements to 4 km satellite swaths, without any interpolation or data thinning. Inspired by Neural Processes, ADAF-Ocean learns a continuous mapping from heterogeneous inputs to ocean states, preserving native data fidelity. Through AI-driven super-resolution, it reconstructs 0.25$^\circ$ mesoscale dynamics from coarse 1$^\circ$ fields, which ensures both efficiency and scalability, with just 3.7\% more parameters than the 1$^\circ$ configuration. When coupled with a DL forecasting system, ADAF-Ocean extends global forecast skill by up to 20 days compared to baselines without assimilation. This framework establishes a computationally viable and scientifically rigorous pathway toward real-time, high-resolution Earth system monitoring.

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### The Algorithmic Phase Transition in Correlated Spiked Models

Authors: Zhangsong Li
Published: 2025-11-08
Categories: math.ST, cs.LG, math.PR, stat.ML
arXiv: https://arxiv.org/abs/2511.06040v4

We study the computational task of detecting and estimating correlated signals in a pair of spiked matrices $$ X=\tfracλ{\sqrt{n}} xu^{\top}+W, \quad Y=\tfracμ{\sqrt{n}} yv^{\top}+Z $$ where the spikes $x,y$ have correlation $ρ$. Specifically, we consider two fundamental models: (1) Correlated spiked Wigner model with signal-to-noise ratio $λ,μ$; (2) Correlated spiked $n*N$ Wishart (covariance) model with signal-to-noise ratio $\sqrtλ,\sqrtμ$. We propose an efficient detection and estimation algorithm based on counting a specific family of edge-decorated cycles. The algorithm's performance is governed by the function $$ F(λ,μ,ρ,γ)=\max\Big\{ \frac{ λ^2 }{ γ}, \frac{ μ^2 }{ γ}, \frac{ λ^2 ρ^2 }{ γ-λ^2+λ^2 ρ^2 } + \frac{ μ^2 ρ^2 }{ γ-μ^2+μ^2 ρ^2 } \Big\} \,. $$ We prove our algorithm succeeds for the correlated spiked Wigner model whenever $F(λ,μ,ρ,1)>1$, and succeeds for the correlated spiked Wishart model whenever $F(λ,μ,ρ,\tfrac{n}{N})>1$. Our result shows that an algorithm can leverage the correlation between the spikes to detect and estimate the signals even in regimes where efficiently recovering either $x$ from ${X}$ alone or $y$ from ${Y}$ alone is believed to be computationally infeasible. We complement our algorithmic results with evidence for a matching computational lower bound. In particular, we prove that when $F(λ,μ,ρ,1)<1$ for the correlated spiked Wigner model and when $F(λ,μ,ρ,\tfrac{n}{N})<1$ for the spiked Wishart model, all algorithms based on low-degree polynomials fails to distinguish $({X},{Y})$ with two independent noise matrices. This strongly suggests that $F=1$ is the precise computation threshold for our models.

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### S2ML: Spatio-Spectral Mutual Learning for Depth Completion

Authors: Zihui Zhao, Yifei Zhang, Zheng Wang, Yang Li, Kui Jiang, Zihan Geng, Chia-Wen Lin
Published: 2025-11-08
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2511.06033v1

The raw depth images captured by RGB-D cameras using Time-of-Flight (TOF) or structured light often suffer from incomplete depth values due to weak reflections, boundary shadows, and artifacts, which limit their applications in downstream vision tasks. Existing methods address this problem through depth completion in the image domain, but they overlook the physical characteristics of raw depth images. It has been observed that the presence of invalid depth areas alters the frequency distribution pattern. In this work, we propose a Spatio-Spectral Mutual Learning framework (S2ML) to harmonize the advantages of both spatial and frequency domains for depth completion. Specifically, we consider the distinct properties of amplitude and phase spectra and devise a dedicated spectral fusion module. Meanwhile, the local and global correlations between spatial-domain and frequency-domain features are calculated in a unified embedding space. The gradual mutual representation and refinement encourage the network to fully explore complementary physical characteristics and priors for more accurate depth completion. Extensive experiments demonstrate the effectiveness of our proposed S2ML method, outperforming the state-of-the-art method CFormer by 0.828 dB and 0.834 dB on the NYU-Depth V2 and SUN RGB-D datasets, respectively.

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### ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes

Authors: Wang-Tao Zhou, Zhao Kang, Ke Yan, Ling Tian
Published: 2025-11-08
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2511.06032v1

Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existing MTPP models rely on channel-mixing strategies that encode information from different event types into a single, fixed-size latent representation. This entanglement can obscure type-specific dynamics, leading to performance degradation and increased risk of overfitting. In this work, we introduce ITPP, a novel channel-independent architecture for MTPP modeling that decouples event type information using an encoder-decoder framework with an ODE-based backbone. Central to ITPP is a type-aware inverted self-attention mechanism, designed to explicitly model inter-channel correlations among heterogeneous event types. This architecture enhances effectiveness and robustness while reducing overfitting. Comprehensive experiments on multiple real-world and synthetic datasets demonstrate that ITPP consistently outperforms state-of-the-art MTPP models in both predictive accuracy and generalization.

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### Lethe: Layer- and Time-Adaptive KV Cache Pruning for Reasoning-Intensive LLM Serving

Authors: Hui Zeng, Daming Zhao, Pengfei Yang, WenXuan Hou, Tianyang Zheng, Hui Li, Weiye Ji, Jidong Zhai
Published: 2025-11-08
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06029v3

Generative reasoning with large language models (LLMs) often involves long decoding sequences, leading to substantial memory and latency overheads from accumulating key-value (KV) caches. While existing KV compression methods primarily focus on reducing prefill memory from long input sequences, they fall short in addressing the dynamic and layer-sensitive nature of long-form generation, which is central to reasoning tasks. We propose Lethe, a dynamic KV cache management framework that introduces adaptivity along both the spatial and temporal dimensions of decoding. Along the spatial dimension, Lethe performs layerwise sparsity-aware allocation, assigning token pruning budgets to each transformer layer based on estimated attention redundancy. Along the temporal dimension, Lethe conducts multi-round token pruning during generation, driven by a Recency-Aware Selective Retention} (RASR) mechanism. RASR extends traditional recency-based heuristics by also considering token relevance derived from evolving attention patterns, enabling informed decisions about which tokens to retain or evict. Empirical results demonstrate that Lethe achieves a favorable balance between efficiency and generation quality across diverse models and tasks, increases throughput by up to 2.56x.

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### Towards Implicit Aggregation: Robust Image Representation for Place Recognition in the Transformer Era

Authors: Feng Lu, Tong Jin, Canming Ye, Yunpeng Liu, Xiangyuan Lan, Chun Yuan
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06024v2

Visual place recognition (VPR) is typically regarded as a specific image retrieval task, whose core lies in representing images as global descriptors. Over the past decade, dominant VPR methods (e.g., NetVLAD) have followed a paradigm that first extracts the patch features/tokens of the input image using a backbone, and then aggregates these patch features into a global descriptor via an aggregator. This backbone-plus-aggregator paradigm has achieved overwhelming dominance in the CNN era and remains widely used in transformer-based models. In this paper, however, we argue that a dedicated aggregator is not necessary in the transformer era, that is, we can obtain robust global descriptors only with the backbone. Specifically, we introduce some learnable aggregation tokens, which are prepended to the patch tokens before a particular transformer block. All these tokens will be jointly processed and interact globally via the intrinsic self-attention mechanism, implicitly aggregating useful information within the patch tokens to the aggregation tokens. Finally, we only take these aggregation tokens from the last output tokens and concatenate them as the global representation. Although implicit aggregation can provide robust global descriptors in an extremely simple manner, where and how to insert additional tokens, as well as the initialization of tokens, remains an open issue worthy of further exploration. To this end, we also propose the optimal token insertion strategy and token initialization method derived from empirical studies. Experimental results show that our method outperforms state-of-the-art methods on several VPR datasets with higher efficiency and ranks 1st on the MSLS challenge leaderboard. The code is available at https://github.com/lu-feng/image.

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### Multi-Reward GRPO Fine-Tuning for De-biasing Large Language Models: A Study Based on Chinese-Context Discrimination Data

Authors: Deng Yixuan, Ji Xiaoqiang
Published: 2025-11-08
Categories: cs.CL
arXiv: https://arxiv.org/abs/2511.06023v1

Large Language Models (LLMs) often exhibit implicit biases and discriminatory tendencies that reflect underlying social stereotypes. While recent alignment techniques such as RLHF and DPO have mitigated some of these issues, they remain limited in addressing culturally specific and multi-dimensional forms of discrimination. This paper proposes a Multi-Reward Group Relative Policy Optimization (GRPO) framework to fine-tune LLMs toward ethical and bias-free behavior. Our approach constructs a synthetic English-language dataset derived from Chinese-context discrimination categories, including regional, ethnic, and occupational biases. Each instance is paired with both neutral and biased responses to train a reward model based on DeBERTa-v3, which provides multi-dimensional reward signals capturing fairness, neutrality, and linguistic quality. The trained reward model then guides GRPO fine-tuning to optimize model outputs along these ethical dimensions. Experimental results demonstrate significant reductions in bias intensity and improved alignment with non-discriminatory standards without compromising fluency or informativeness. This study highlights the effectiveness of GRPO-based multi-reward optimization for de-biasing LLMs and offers a replicable framework for cultural-contextual ethical alignment.

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### MiVID: Multi-Strategic Self-Supervision for Video Frame Interpolation using Diffusion Model

Authors: Priyansh Srivastava, Romit Chatterjee, Abir Sen, Aradhana Behura, Ratnakar Dash
Published: 2025-11-08
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2511.06019v1

Video Frame Interpolation (VFI) remains a cornerstone in video enhancement, enabling temporal upscaling for tasks like slow-motion rendering, frame rate conversion, and video restoration. While classical methods rely on optical flow and learning-based models assume access to dense ground-truth, both struggle with occlusions, domain shifts, and ambiguous motion. This article introduces MiVID, a lightweight, self-supervised, diffusion-based framework for video interpolation. Our model eliminates the need for explicit motion estimation by combining a 3D U-Net backbone with transformer-style temporal attention, trained under a hybrid masking regime that simulates occlusions and motion uncertainty. The use of cosine-based progressive masking and adaptive loss scheduling allows our network to learn robust spatiotemporal representations without any high-frame-rate supervision. Our framework is evaluated on UCF101-7 and DAVIS-7 datasets. MiVID is trained entirely on CPU using the datasets and 9-frame video segments, making it a low-resource yet highly effective pipeline. Despite these constraints, our model achieves optimal results at just 50 epochs, competitive with several supervised baselines.This work demonstrates the power of self-supervised diffusion priors for temporally coherent frame synthesis and provides a scalable path toward accessible and generalizable VFI systems.

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### One-Shot Knowledge Transfer for Scalable Person Re-Identification

Authors: Longhua Li, Lei Qi, Xin Geng
Published: 2025-11-08
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2511.06016v1

Edge computing in person re-identification (ReID) is crucial for reducing the load on central cloud servers and ensuring user privacy. Conventional compression methods for obtaining compact models require computations for each individual student model. When multiple models of varying sizes are needed to accommodate different resource conditions, this leads to repetitive and cumbersome computations. To address this challenge, we propose a novel knowledge inheritance approach named OSKT (One-Shot Knowledge Transfer), which consolidates the knowledge of the teacher model into an intermediate carrier called a weight chain. When a downstream scenario demands a model that meets specific resource constraints, this weight chain can be expanded to the target model size without additional computation. OSKT significantly outperforms state-of-the-art compression methods, with the added advantage of one-time knowledge transfer that eliminates the need for frequent computations for each target model.

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### MoSKA: Mixture of Shared KV Attention for Efficient Long-Sequence LLM Inference

Authors: Myunghyun Rhee, Sookyung Choi, Euiseok Kim, Joonseop Sim, Youngpyo Joo, Hoshik Kim
Published: 2025-11-08
Categories: cs.LG, cs.AI, cs.DC
arXiv: https://arxiv.org/abs/2511.06010v1

The escalating context length in Large Language Models (LLMs) creates a severe performance bottleneck around the Key-Value (KV) cache, whose memory-bound nature leads to significant GPU under-utilization. This paper introduces Mixture of Shared KV Attention (MoSKA), an architecture that addresses this challenge by exploiting the heterogeneity of context data. It differentiates between per-request unique and massively reused shared sequences. The core of MoSKA is a novel Shared KV Attention mechanism that transforms the attention on shared data from a series of memory-bound GEMV operations into a single, compute-bound GEMM by batching concurrent requests. This is supported by an MoE-inspired sparse attention strategy that prunes the search space and a tailored Disaggregated Infrastructure that specializes hardware for unique and shared data. This comprehensive approach demonstrates a throughput increase of up to 538.7x over baselines in workloads with high context sharing, offering a clear architectural path toward scalable LLM inference.

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### Distributed Deep Learning for Medical Image Denoising with Data Obfuscation

Authors: Sulaimon Oyeniyi Adebayo, Ayaz H. Khan
Published: 2025-11-08
Categories: cs.CV, cs.DC
arXiv: https://arxiv.org/abs/2511.06006v1

Medical image denoising is essential for improving image quality while minimizing the exposure of sensitive information, particularly when working with large-scale clinical datasets. This study explores distributed deep learning for denoising chest X-ray images from the NIH Chest X-ray14 dataset, using additive Gaussian noise as a lightweight obfuscation technique. We implement and evaluate U-Net and U-Net++ architectures under single-GPU, standard multi-GPU (DataParallel), and optimized multi-GPU training configurations using PyTorch's DistributedDataParallel (DDP) and Automatic Mixed Precision (AMP). Our results show that U-Net++ consistently delivers superior denoising performance, achieving competitive Peak Signal to Noise Ratio (PSNR) and Structured Similarity Index Method (SSIM) scores, though with less performance in Learned Perceptual Image Patch Similarity (LPIPS) compared to U-Net under low and moderate noise levels. This indicates U-Net++'s enhanced structural fidelity and low perceptual similarity. Meanwhile, our optimized training pipeline reduces training time by over 60% for both models compared to single-GPU training, and outperforms standard DataParallel by over 40%, with only a minor accuracy drop for both models (trading some accuracy for speed). These findings highlight the effectiveness of software-level optimization in distributed learning for medical imaging. This work demonstrates the practical viability of combining architectural design, lightweight obfuscation, and advanced distributed training strategies to accelerate and enhance medical image processing pipelines in real-world clinical and research environments. The full implementation is publicly available at: https://github.com/Suadey/medical-image-denoising-ddp.

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### How Reasoning Influences Intersectional Biases in Vision Language Models

Authors: Adit Desai, Sudipta Roy, Mohna Chakraborty
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06005v1

Vision Language Models (VLMs) are increasingly deployed across downstream tasks, yet their training data often encode social biases that surface in outputs. Unlike humans, who interpret images through contextual and social cues, VLMs process them through statistical associations, often leading to reasoning that diverges from human reasoning. By analyzing how a VLM reasons, we can understand how inherent biases are perpetuated and can adversely affect downstream performance. To examine this gap, we systematically analyze social biases in five open-source VLMs for an occupation prediction task, on the FairFace dataset. Across 32 occupations and three different prompting styles, we elicit both predictions and reasoning. Our findings reveal that the biased reasoning patterns systematically underlie intersectional disparities, highlighting the need to align VLM reasoning with human values prior to its downstream deployment.

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### MALeR: Improving Compositional Fidelity in Layout-Guided Generation

Authors: Shivank Saxena, Dhruv Srivastava, Makarand Tapaswi
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06002v1

Recent advances in text-to-image models have enabled a new era of creative and controllable image generation. However, generating compositional scenes with multiple subjects and attributes remains a significant challenge. To enhance user control over subject placement, several layout-guided methods have been proposed. However, these methods face numerous challenges, particularly in compositional scenes. Unintended subjects often appear outside the layouts, generated images can be out-of-distribution and contain unnatural artifacts, or attributes bleed across subjects, leading to incorrect visual outputs. In this work, we propose MALeR, a method that addresses each of these challenges. Given a text prompt and corresponding layouts, our method prevents subjects from appearing outside the given layouts while being in-distribution. Additionally, we propose a masked, attribute-aware binding mechanism that prevents attribute leakage, enabling accurate rendering of subjects with multiple attributes, even in complex compositional scenes. Qualitative and quantitative evaluation demonstrates that our method achieves superior performance in compositional accuracy, generation consistency, and attribute binding compared to previous work. MALeR is particularly adept at generating images of scenes with multiple subjects and multiple attributes per subject.

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### LLMs Do Not See Age: Assessing Demographic Bias in Automated Systematic Review Synthesis

Authors: Favour Yahdii Aghaebe, Tanefa Apekey, Elizabeth Williams, Nafise Sadat Moosavi
Published: 2025-11-08
Categories: cs.CL
arXiv: https://arxiv.org/abs/2511.06000v2

Clinical interventions often hinge on age: medications and procedures safe for adults may be harmful to children or ineffective for older adults. However, as language models are increasingly integrated into biomedical evidence synthesis workflows, it remains uncertain whether these systems preserve such crucial demographic distinctions. To address this gap, we evaluate how well state-of-the-art language models retain age-related information when generating abstractive summaries of biomedical studies. We construct DemogSummary, a novel age-stratified dataset of systematic review primary studies, covering child, adult, and older adult populations. We evaluate three prominent summarisation-capable LLMs, Qwen (open-source), Longformer (open-source) and GPT-4.1 Nano (proprietary), using both standard metrics and a newly proposed Demographic Salience Score (DSS), which quantifies age-related entity retention and hallucination. Our results reveal systematic disparities across models and age groups: demographic fidelity is lowest for adult-focused summaries, and under-represented populations are more prone to hallucinations. These findings highlight the limitations of current LLMs in faithful and bias-free summarisation and point to the need for fairness-aware evaluation frameworks and summarisation pipelines in biomedical NLP.

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### Exploring Category-level Articulated Object Pose Tracking on SE(3) Manifolds

Authors: Xianhui Meng, Yukang Huo, Li Zhang, Liu Liu, Haonan Jiang, Yan Zhong, Pingrui Zhang, Cewu Lu, Jun Liu
Published: 2025-11-08
Categories: cs.CV, cs.AI, cs.MM
arXiv: https://arxiv.org/abs/2511.05996v1

Articulated objects are prevalent in daily life and robotic manipulation tasks. However, compared to rigid objects, pose tracking for articulated objects remains an underexplored problem due to their inherent kinematic constraints. To address these challenges, this work proposes a novel point-pair-based pose tracking framework, termed \textbf{PPF-Tracker}. The proposed framework first performs quasi-canonicalization of point clouds in the SE(3) Lie group space, and then models articulated objects using Point Pair Features (PPF) to predict pose voting parameters by leveraging the invariance properties of SE(3). Finally, semantic information of joint axes is incorporated to impose unified kinematic constraints across all parts of the articulated object. PPF-Tracker is systematically evaluated on both synthetic datasets and real-world scenarios, demonstrating strong generalization across diverse and challenging environments. Experimental results highlight the effectiveness and robustness of PPF-Tracker in multi-frame pose tracking of articulated objects. We believe this work can foster advances in robotics, embodied intelligence, and augmented reality. Codes are available at https://github.com/mengxh20/PPFTracker.

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### Revisiting Entropy in Reinforcement Learning for Large Reasoning Models

Authors: Renren Jin, Pengzhi Gao, Yuqi Ren, Zhuowen Han, Tongxuan Zhang, Wuwei Huang, Wei Liu, Jian Luan, Deyi Xiong
Published: 2025-11-08
Categories: cs.CL, cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2511.05993v3

Reinforcement learning with verifiable rewards (RLVR) has emerged as a prominent paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, the entropy of LLMs usually collapses during RLVR training, leading to premature convergence to suboptimal local minima and hindering further performance improvement. Although various approaches have been proposed to mitigate entropy collapse, a comprehensive study of entropy in RLVR remains lacking. To bridge this gap, we conduct extensive experiments to investigate the entropy dynamics of LLMs trained with RLVR and analyze how model entropy correlates with response diversity, calibration, and performance across various benchmarks. Our results identify three key factors that influence entropy: the clipping thresholds in the optimization objective, the number of off-policy updates, and the diversity of the training data. Furthermore, through both theoretical analysis and empirical validation, we demonstrate that tokens with positive advantages are the primary drivers of entropy collapse. Motivated by this insight, we propose Positive-Advantage Reweighting, a simple yet effective approach that regulates model entropy by adjusting the loss weights assigned to tokens with positive advantages during RLVR training, while maintaining competitive performance.

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### Ontology Learning and Knowledge Graph Construction: A Comparison of Approaches and Their Impact on RAG Performance

Authors: Tiago da Cruz, Bernardo Tavares, Francisco Belo
Published: 2025-11-08
Categories: cs.IR, cs.AI
arXiv: https://arxiv.org/abs/2511.05991v1

Retrieval-Augmented Generation (RAG) systems combine Large Language Models (LLMs) with external knowledge, and their performance depends heavily on how that knowledge is represented. This study investigates how different Knowledge Graph (KG) construction strategies influence RAG performance. We compare a variety of approaches: standard vector-based RAG, GraphRAG, and retrieval over KGs built from ontologies derived either from relational databases or textual corpora. Results show that ontology-guided KGs incorporating chunk information achieve competitive performance with state-of-the-art frameworks, substantially outperforming vector retrieval baselines. Moreover, the findings reveal that ontology-guided KGs built from relational databases perform competitively to ones built with ontologies extracted from text, with the benefit of offering a dual advantage: they require a one-time-only ontology learning process, substantially reducing LLM usage costs; and avoid the complexity of ontology merging inherent to text-based approaches.

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### Learning solutions of parameterized stiff ODEs using Gaussian processes

Authors: Idoia Cortes Garcia, P. Förster, W. Schilders, S. Schöps
Published: 2025-11-08
Categories: math.NA, cs.CE, cs.LG
arXiv: https://arxiv.org/abs/2511.05990v1

Stiff ordinary differential equations (ODEs) play an important role in many scientific and engineering applications. Often, the dependence of the solution of the ODE on additional parameters is of interest, e.g.\ when dealing with uncertainty quantification or design optimization. Directly studying this dependence can quickly become too computationally expensive, such that cheaper surrogate models approximating the solution are of interest. One popular class of surrogate models are Gaussian processes (GPs). They perform well when approximating stationary functions, functions which have a similar level of variation along any given parameter direction, however solutions to stiff ODEs are often characterized by a mixture of regions of rapid and slow variation along the time axis and when dealing with such nonstationary functions, GP performance frequently degrades drastically. We therefore aim to reparameterize stiff ODE solutions based on the available data, to make them appear more stationary and hence recover good GP performance. This approach comes with minimal computational overhead and requires no internal changes to the GP implementation, as it can be seen as a separate preprocessing step. We illustrate the achieved benefits using multiple examples.

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### A Dual-Mode ViT-Conditioned Diffusion Framework with an Adaptive Conditioning Bridge for Breast Cancer Segmentation

Authors: Prateek Singh, Moumita Dholey, P. K. Vinod
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.05989v1

In breast ultrasound images, precise lesion segmentation is essential for early diagnosis; however, low contrast, speckle noise, and unclear boundaries make this difficult. Even though deep learning models have demonstrated potential, standard convolutional architectures frequently fall short in capturing enough global context, resulting in segmentations that are anatomically inconsistent. To overcome these drawbacks, we suggest a flexible, conditional Denoising Diffusion Model that combines an enhanced UNet-based generative decoder with a Vision Transformer (ViT) encoder for global feature extraction. We introduce three primary innovations: 1) an Adaptive Conditioning Bridge (ACB) for efficient, multi-scale fusion of semantic features; 2) a novel Topological Denoising Consistency (TDC) loss component that regularizes training by penalizing structural inconsistencies during denoising; and 3) a dual-head architecture that leverages the denoising objective as a powerful regularizer, enabling a lightweight auxiliary head to perform rapid and accurate inference on smaller datasets and a noise prediction head. Our framework establishes a new state-of-the-art on public breast ultrasound datasets, achieving Dice scores of 0.96 on BUSI, 0.90 on BrEaST and 0.97 on BUS-UCLM. Comprehensive ablation studies empirically validate that the model components are critical for achieving these results and for producing segmentations that are not only accurate but also anatomically plausible.

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### Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables

Authors: Theofanis Vergos, Polykarpos Vergos, Mehdi B. Tahoori, Georgios Zervakis
Published: 2025-11-08
Categories: cs.LG, cs.AR
arXiv: https://arxiv.org/abs/2511.05985v1

Flexible electronics offer unique advantages for conformable, lightweight, and disposable healthcare wearables. However, their limited gate count, large feature sizes, and high static power consumption make on-body machine learning classification highly challenging. While existing bendable RISC-V systems provide compact solutions, they lack the energy efficiency required. We present a mechanically flexible RISC-V that integrates a bespoke multiply-accumulate co-processor with fixed coefficients to maximize energy efficiency and minimize latency. Our approach formulates a constrained programming problem to jointly determine co-processor constants and optimally map Multi-Layer Perceptron (MLP) inference operations, enabling compact, model-specific hardware by leveraging the low fabrication and non-recurring engineering costs of flexible technologies. Post-layout results demonstrate near-real-time performance across several healthcare datasets, with our circuits operating within the power budget of existing flexible batteries and occupying only 2.42 mm^2, offering a promising path toward accessible, sustainable, and conformable healthcare wearables. Our microprocessors achieve an average 2.35x speedup and 2.15x lower energy consumption compared to the state of the art.

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### Benchmarking of Clustering Validity Measures Revisited

Authors: Connor Simpson, Ricardo J. G. B. Campello, Elizabeth Stojanovski
Published: 2025-11-08
Categories: stat.ML, cs.LG
arXiv: https://arxiv.org/abs/2511.05983v1

Validation plays a crucial role in the clustering process. Many different internal validity indexes exist for the purpose of determining the best clustering solution(s) from a given collection of candidates, e.g., as produced by different algorithms or different algorithm hyper-parameters. In this study, we present a comprehensive benchmark study of 26 internal validity indexes, which includes highly popular classic indexes as well as more recently developed ones. We adopted an enhanced revision of the methodology presented in Vendramin et al. (2010), developed here to address several shortcomings of this previous work. This overall new approach consists of three complementary custom-tailored evaluation sub-methodologies, each of which has been designed to assess specific aspects of an index's behaviour while preventing potential biases of the other sub-methodologies. Each sub-methodology features two complementary measures of performance, alongside mechanisms that allow for an in-depth investigation of more complex behaviours of the internal validity indexes under study. Additionally, a new collection of 16177 datasets has been produced, paired with eight widely-used clustering algorithms, for a wider applicability scope and representation of more diverse clustering scenarios.

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### Runtime Safety Monitoring of Deep Neural Networks for Perception: A Survey

Authors: Albert Schotschneider, Svetlana Pavlitska, J. Marius Zöllner
Published: 2025-11-08
Categories: cs.CV, cs.AI, cs.LG, cs.RO
arXiv: https://arxiv.org/abs/2511.05982v1

Deep neural networks (DNNs) are widely used in perception systems for safety-critical applications, such as autonomous driving and robotics. However, DNNs remain vulnerable to various safety concerns, including generalization errors, out-of-distribution (OOD) inputs, and adversarial attacks, which can lead to hazardous failures. This survey provides a comprehensive overview of runtime safety monitoring approaches, which operate in parallel to DNNs during inference to detect these safety concerns without modifying the DNN itself. We categorize existing methods into three main groups: Monitoring inputs, internal representations, and outputs. We analyze the state-of-the-art for each category, identify strengths and limitations, and map methods to the safety concerns they address. In addition, we highlight open challenges and future research directions.

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### Are Time-Indexed Foundation Models the Future of Time Series Imputation?

Authors: Etienne Le Naour, Tahar Nabil, Adrien Petralia, Ghislain Agoua
Published: 2025-11-08
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.05980v2

Foundation models for time series imputation remain largely unexplored. Recently, two such models, TabPFN-TS and MoTM, have emerged. These models share a common philosophy that places them within the family of time-indexed foundation models. This paper presents the first large-scale empirical study of these models for zero-shot imputation, which enables missing value recovery without retraining across a wide range of scenarios. We conduct extensive univariate experiments across 33 out-of-domain datasets (approximately 1.3M imputation windows) and evaluate their ability to integrate covariates at inference time to improve accuracy without fine-tuning. Our results demonstrate that time-indexed foundation models are a powerful and practical step toward achieving general-purpose, zero-shot imputation for real-world time series.

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### Kunlun Anomaly Troubleshooter: Enabling Kernel-Level Anomaly Detection and Causal Reasoning for Large Model Distributed Inference

Authors: Yuyang Liu, Jingjing Cai, Jiayi Ren, Peng Zhou, Danyang Zhang, Yin Du, Shijian Li
Published: 2025-11-08
Categories: cs.LG, cs.AI, cs.DC, cs.PF
arXiv: https://arxiv.org/abs/2511.05978v1

Anomaly troubleshooting for large model distributed inference (LMDI) remains a critical challenge. Resolving anomalies such as inference performance degradation or latency jitter in distributed system demands significant manual efforts from domain experts, resulting in extremely time-consuming diagnosis processes with relatively low accuracy. In this paper, we introduce Kunlun Anomaly Troubleshooter (KAT), the first anomaly troubleshooting framework tailored for LMDI. KAT addresses this problem through two core innovations. First, KAT exploits the synchronicity and consistency of GPU workers, innovatively leverages function trace data to precisely detect kernel-level anomalies and associated hardware components at nanosecond resolution. Second, KAT integrates these detection results into a domain-adapted LLM, delivering systematic causal reasoning and natural language interpretation of complex anomaly symptoms. Evaluations conducted in Alibaba Cloud Service production environment indicate that KAT achieves over 0.884 precision and 0.936 recall in anomaly detection, providing detail anomaly insights that significantly narrow down the diagnostic scope and improve both the efficiency and success rate of troubleshooting.

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### An Epistemic Perspective on Agent Awareness

Authors: Pavel Naumov, Alexandra Pavlova
Published: 2025-11-08
Categories: cs.AI, cs.LO, cs.MA
arXiv: https://arxiv.org/abs/2511.05977v1

The paper proposes to treat agent awareness as a form of knowledge, breaking the tradition in the existing literature on awareness. It distinguishes the de re and de dicto forms of such knowledge. The work introduces two modalities capturing these forms and formally specifies their meaning using a version of 2D-semantics. The main technical result is a sound and complete logical system describing the interplay between the two proposed modalities and the standard "knowledge of the fact" modality.

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### Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals

Authors: Alice Ragonesi, Stefania Fresca, Karli Gillette, Stefan Kurath-Koller, Gernot Plank, Elena Zappon
Published: 2025-11-08
Categories: cs.LG, math.NA, q-bio.TO
arXiv: https://arxiv.org/abs/2511.05973v1

Wolff-Parkinson-White (WPW) syndrome is a cardiac electrophysiology (EP) disorder caused by the presence of an accessory pathway (AP) that bypasses the atrioventricular node, faster ventricular activation rate, and provides a substrate for atrio-ventricular reentrant tachycardia (AVRT). Accurate localization of the AP is critical for planning and guiding catheter ablation procedures. While traditional diagnostic tree (DT) methods and more recent machine learning (ML) approaches have been proposed to predict AP location from surface electrocardiogram (ECG), they are often constrained by limited anatomical localization resolution, poor interpretability, and the use of small clinical datasets. In this study, we present a Deep Learning (DL) model for the localization of single manifest APs across 24 cardiac regions, trained on a large, physiologically realistic database of synthetic ECGs generated using a personalized virtual heart model. We also integrate eXplainable Artificial Intelligence (XAI) methods, Guided Backpropagation, Grad-CAM, and Guided Grad-CAM, into the pipeline. This enables interpretation of DL decision-making and addresses one of the main barriers to clinical adoption: lack of transparency in ML predictions. Our model achieves localization accuracy above 95%, with a sensitivity of 94.32% and specificity of 99.78%. XAI outputs are physiologically validated against known depolarization patterns, and a novel index is introduced to identify the most informative ECG leads for AP localization. Results highlight lead V2 as the most critical, followed by aVF, V1, and aVL. This work demonstrates the potential of combining cardiac digital twins with explainable DL to enable accurate, transparent, and non-invasive AP localization.

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### Interpretable Recognition of Cognitive Distortions in Natural Language Texts

Authors: Anton Kolonin, Anna Arinicheva
Published: 2025-11-08
Categories: cs.CL, cs.AI, cs.CY, cs.LG
arXiv: https://arxiv.org/abs/2511.05969v3

We propose a new approach to multi-factor classification of natural language texts based on weighted structured patterns such as N-grams, taking into account the heterarchical relationships between them, applied to solve such a socially impactful problem as the automation of detection of specific cognitive distortions in psychological care, relying on an interpretable, robust and transparent artificial intelligence model. The proposed recognition and learning algorithms improve the current state of the art in this field. The improvement is tested on two publicly available datasets, with significant improvements over literature-known F1 scores for the task, with optimal hyper-parameters determined, having code and models available for future use by the community.

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### DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities

Authors: Nagur Shareef Shaik, Teja Krishna Cherukuri, Adnan Masood, Dong Hye Ye
Published: 2025-11-08
Categories: cs.CV, cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2511.05968v1

The integration of medical images with clinical context is essential for generating accurate and clinically interpretable radiology reports. However, current automated methods often rely on resource-heavy Large Language Models (LLMs) or static knowledge graphs and struggle with two fundamental challenges in real-world clinical data: (1) missing modalities, such as incomplete clinical context , and (2) feature entanglement, where mixed modality-specific and shared information leads to suboptimal fusion and clinically unfaithful hallucinated findings. To address these challenges, we propose the DiA-gnostic VLVAE, which achieves robust radiology reporting through Disentangled Alignment. Our framework is designed to be resilient to missing modalities by disentangling shared and modality-specific features using a Mixture-of-Experts (MoE) based Vision-Language Variational Autoencoder (VLVAE). A constrained optimization objective enforces orthogonality and alignment between these latent representations to prevent suboptimal fusion. A compact LLaMA-X decoder then uses these disentangled representations to generate reports efficiently. On the IU X-Ray and MIMIC-CXR datasets, DiA has achieved competetive BLEU@4 scores of 0.266 and 0.134, respectively. Experimental results show that the proposed method significantly outperforms state-of-the-art models.

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### Adapted Foundation Models for Breast MRI Triaging in Contrast-Enhanced and Non-Contrast Enhanced Protocols

Authors: Tri-Thien Nguyen, Lorenz A. Kapsner, Tobias Hepp, Shirin Heidarikahkesh, Hannes Schreiter, Luise Brock, Dominika Skwierawska, Dominique Hadler, Julian Hossbach, Evelyn Wenkel, Sabine Ohlmeyer, Frederik B. Laun et al.
Published: 2025-11-08
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2511.05967v1

Background: Magnetic resonance imaging (MRI) has high sensitivity for breast cancer detection, but interpretation is time-consuming. Artificial intelligence may aid in pre-screening. Purpose: To evaluate the DINOv2-based Medical Slice Transformer (MST) for ruling out significant findings (Breast Imaging Reporting and Data System (BI-RADS) >=4) in contrast-enhanced and non-contrast-enhanced abbreviated breast MRI. Materials and Methods: This institutional review board approved retrospective study included 1,847 single-breast MRI examinations (377 BI-RADS >=4) from an in-house dataset and 924 from an external validation dataset (Duke). Four abbreviated protocols were tested: T1-weighted early subtraction (T1sub), diffusion-weighted imaging with b=1500 s/mm2 (DWI1500), DWI1500+T2-weighted (T2w), and T1sub+T2w. Performance was assessed at 90%, 95%, and 97.5% sensitivity using five-fold cross-validation and area under the receiver operating characteristic curve (AUC) analysis. AUC differences were compared with the DeLong test. False negatives were characterized, and attention maps of true positives were rated in the external dataset. Results: A total of 1,448 female patients (mean age, 49 +/- 12 years) were included. T1sub+T2w achieved an AUC of 0.77 +/- 0.04; DWI1500+T2w, 0.74 +/- 0.04 (p=0.15). At 97.5% sensitivity, T1sub+T2w had the highest specificity (19% +/- 7%), followed by DWI1500+T2w (17% +/- 11%). Missed lesions had a mean diameter <10 mm at 95% and 97.5% thresholds for both T1sub and DWI1500, predominantly non-mass enhancements. External validation yielded an AUC of 0.77, with 88% of attention maps rated good or moderate. Conclusion: At 97.5% sensitivity, the MST framework correctly triaged cases without BI-RADS >=4, achieving 19% specificity for contrast-enhanced and 17% for non-contrast-enhanced MRI. Further research is warranted before clinical implementation.

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### Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory

Authors: Yuxuan Lin, Hanjing Yan, Xuan Tong, Yang Chang, Huanzhen Wang, Ziheng Zhou, Shuyong Gao, Yan Wang, Wenqiang Zhang
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.05966v2

Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings, insufficient training samples often fail to cover the diverse patterns present in test samples. This challenge can be mitigated by extracting structural commonality from a small number of training samples. In this paper, we propose a novel few-shot unsupervised multimodal industrial anomaly detection method based on structural commonality, CIF (Commonality In Few). To extract intra-class structural information, we employ hypergraphs, which are capable of modeling higher-order correlations, to capture the structural commonality within training samples, and use a memory bank to store this intra-class structural prior. Firstly, we design a semantic-aware hypergraph construction module tailored for single-semantic industrial images, from which we extract common structures to guide the construction of the memory bank. Secondly, we use a training-free hypergraph message passing module to update the visual features of test samples, reducing the distribution gap between test features and features in the memory bank. We further propose a hyperedge-guided memory search module, which utilizes structural information to assist the memory search process and reduce the false positive rate. Experimental results on the MVTec 3D-AD dataset and the Eyecandies dataset show that our method outperforms the state-of-the-art (SOTA) methods in few-shot settings. Code is available at https://github.com/Sunny5250/CIF.

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### Adaptive Agent Selection and Interaction Network for Image-to-point cloud Registration

Authors: Zhixin Cheng, Xiaotian Yin, Jiacheng Deng, Bohao Liao, Yujia Chen, Xu Zhou, Baoqun Yin, Tianzhu Zhang
Published: 2025-11-08
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2511.05965v1

Typical detection-free methods for image-to-point cloud registration leverage transformer-based architectures to aggregate cross-modal features and establish correspondences. However, they often struggle under challenging conditions, where noise disrupts similarity computation and leads to incorrect correspondences. Moreover, without dedicated designs, it remains difficult to effectively select informative and correlated representations across modalities, thereby limiting the robustness and accuracy of registration. To address these challenges, we propose a novel cross-modal registration framework composed of two key modules: the Iterative Agents Selection (IAS) module and the Reliable Agents Interaction (RAI) module. IAS enhances structural feature awareness with phase maps and employs reinforcement learning principles to efficiently select reliable agents. RAI then leverages these selected agents to guide cross-modal interactions, effectively reducing mismatches and improving overall robustness. Extensive experiments on the RGB-D Scenes v2 and 7-Scenes benchmarks demonstrate that our method consistently achieves state-of-the-art performance.

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### Next-Latent Prediction Transformers Learn Compact World Models

Authors: Jayden Teoh, Manan Tomar, Kwangjun Ahn, Edward S. Hu, Tim Pearce, Pratyusha Sharma, Akshay Krishnamurthy, Riashat Islam, Alex Lamb, John Langford
Published: 2025-11-08
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.05963v4

Transformers replace recurrence with a memory that grows with sequence length and self-attention that enables ad-hoc lookups over past tokens. Consequently, they lack an inherent incentive to compress history into compact latent states with consistent transition rules. This often leads to learning solutions that generalize poorly. We introduce Next-Latent Prediction (NextLat), which extends standard next-token training with self-supervised predictions in the latent space. Specifically, NextLat trains a transformer to learn latent representations that are predictive of its next latent state given the next token. Theoretically, we show that these latents provably converge towards belief states, compressed information about the history necessary to predict the future. This simple auxiliary objective injects a recurrent inductive bias into transformers while leaving their architecture, parallel training efficiency, and inference unchanged. NextLat effectively encourages transformers to form compact internal world models with coherent belief states and transition dynamics -- crucial properties not guaranteed by standard next-token prediction alone. Empirically, across benchmarks in world modeling, reasoning, planning, and language modeling, NextLat demonstrates significant gains over standard next-token prediction and other baselines in downstream accuracy, representation compression, and lookahead planning. Furthermore, NextLat enables variable-length self-speculative decoding, accelerating inference by up to 3.3x in language modeling. NextLat offers a simple yet effective paradigm for learning compact, predictive representations in transformers that generalize better. Our code is available at https://github.com/JaydenTeoh/NextLat.

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### Deep Survival Analysis of Longitudinal EHR Data for Joint Prediction of Hospitalization and Death in COPD Patients

Authors: Enrico Manzini, Thomas Gonzalez Saito, Joan Escudero, Ana Génova, Cristina Caso, Tomas Perez-Porcuna, Alexandre Perera-Lluna
Published: 2025-11-08
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.05960v1

Patients with chronic obstructive pulmonary disease (COPD) have an increased risk of hospitalizations, strongly associated with decreased survival, yet predicting the timing of these events remains challenging and has received limited attention in the literature. In this study, we performed survival analysis to predict hospitalization and death in COPD patients using longitudinal electronic health records (EHRs), comparing statistical models, machine learning (ML), and deep learning (DL) approaches. We analyzed data from more than 150k patients from the SIDIAP database in Catalonia, Spain, from 2013 to 2017, modeling hospitalization as a first event and death as a semi-competing terminal event. Multiple models were evaluated, including Cox proportional hazards, SurvivalBoost, DeepPseudo, SurvTRACE, Dynamic Deep-Hit, and Deep Recurrent Survival Machine. Results showed that DL models utilizing recurrent architectures outperformed both ML and linear approaches in concordance and time-dependent AUC, especially for hospitalization, which proved to be the harder event to predict. This study is, to our knowledge, the first to apply deep survival analysis on longitudinal EHR data to jointly predict multiple time-to-event outcomes in COPD patients, highlighting the potential of DL approaches to capture temporal patterns and improve risk stratification.

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### CSGaze: Context-aware Social Gaze Prediction

Authors: Surbhi Madan, Shreya Ghosh, Ramanathan Subramanian, Abhinav Dhall, Tom Gedeon
Published: 2025-11-08
Categories: cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2511.05955v2

A person's gaze offers valuable insights into their focus of attention, level of social engagement, and confidence. In this work, we investigate how contextual cues combined with visual scene and facial information can be effectively utilized to predict and interpret social gaze patterns during conversational interactions. We introduce CSGaze, a context aware multimodal approach that leverages facial, scene information as complementary inputs to enhance social gaze pattern prediction from multi-person images. The model also incorporates a fine-grained attention mechanism centered on the principal speaker, which helps in better modeling social gaze dynamics. Experimental results show that CSGaze performs competitively with state-of-the-art methods on GP-Static, UCO-LAEO and AVA-LAEO. Our findings highlight the role of contextual cues in improving social gaze prediction. Additionally, we provide initial explainability through generated attention scores, offering insights into the model's decision-making process. We also demonstrate our model's generalizability by testing our model on open set datasets that demonstrating its robustness across diverse scenarios.

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### Pinching Visuo-haptic Display: Investigating Cross-Modal Effects of Visual Textures on Electrostatic Cloth Tactile Sensations

Authors: Takekazu Kitagishi, Chun-Wei Ooi, Yuichi Hiroi, Jun Rekimoto
Published: 2025-11-08
Categories: cs.HC, cs.CV, cs.MM
arXiv: https://arxiv.org/abs/2511.05952v1

This paper investigates how visual texture presentation influences tactile perception when interacting with electrostatic cloth displays. We propose a visuo-haptic system that allows users to pinch and rub virtual fabrics while feeling realistic frictional sensations modulated by electrostatic actuation. Through a user study, we examined the cross-modal effects between visual roughness and perceived tactile friction. The results demonstrate that visually rough textures amplify the perceived frictional force, even under identical electrostatic stimuli. These findings contribute to the understanding of multimodal texture perception and provide design insights for haptic feedback in virtual material interfaces.

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### Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling

Authors: Qi Wang, Hongzhi Zhang, Jia Fu, Kai Fu, Yahui Liu, Tinghai Zhang, Chenxi Sun, Gangwei Jiang, Jingyi Tang, Xingguang Ji, Yang Yue, Jingyuan Zhang et al.
Published: 2025-11-08
Categories: cs.AI
arXiv: https://arxiv.org/abs/2511.05951v1

Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In this study, we present a comprehensive and fully open-source pipeline for training a high-performance agentic model for interacting with external tools and environments, named Klear-Qwen3-AgentForge, starting from the Qwen3-8B base model. We design effective supervised fine-tuning (SFT) with synthetic data followed by multi-turn reinforcement learning (RL) to unlock the potential for multiple diverse agentic tasks. We perform exclusive experiments on various agentic benchmarks in both tool use and coding domains. Klear-Qwen3-AgentForge-8B achieves state-of-the-art performance among LLMs of similar size and remains competitive with significantly larger models.

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### Zero-Shot Polygon Matching with Pre-trained Models for Pose Estimation and Polygon Cloud from Challenging Stereo

Authors: Chang Li, Xingtao Peng
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.05949v2

While stereo matching has achieved maturity for 0D point and 1D line primitives, establishing correspondences for 2D polygons remains largely unexplored due to challenges including disparity discontinuity, scale variation, training dependency, and poor generalization, limiting downstream tasks such as pose estimation and 3D reconstruction. To address these issues, we are the first to propose a Zero-shot Polygon Matching paradigm with Pre-trained Models (i.e., Z(PM)2), which combines learned features and handcrafted geometric constraints through plug-and-play modules, extending matching from 0D/1D primitives to 2D polygons. The pipeline comprises three core stages: Firstly, detector leverages the pre-trained segment anything model to vectorize segmentation masks into graph-structured polygons integrating geometry and texture; Secondly, global matcher uses bidirectional-pyramid and multi-geometric constraints to handle viewpoint variation; Thirdly, local matcher leverages local-holistic bipartite graph optimization to resolve disparity discontinuity and topological inconsistency. Moreover, we develop polygon-matching-guided pose estimation using correspondences to obtain well-distributed, low-redundancy homologous points, and pioneer the polygon cloud concept with an optimal surface generation method, producing structurally complete and semantically rich 3D representations beyond point and line clouds. Since no polygon matching methods from stereo imagery are available for direct comparison, we selected state-of-the-art (SoTA) methods close to this task as baselines. Extensive experiments on five challenging datasets (ISPRS, KITTI, ScanNet, SceneFlow, DTU) show Z(PM)2 achieves a 68.60% matching area score, outperforming MESA by approximately 32% and ranking first in area-level pose estimation, with competitive speed and strong zero-shot generalization without any training requirement.

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### Reperio-rPPG: Relational Temporal Graph Neural Networks for Periodicity Learning in Remote Physiological Measurement

Authors: Ba-Thinh Nguyen, Thach-Ha Ngoc Pham, Hoang-Long Duc Nguyen, Thi-Duyen Ngo, Thanh-Ha Le
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.05946v1

Remote photoplethysmography (rPPG) is an emerging contactless physiological sensing technique that leverages subtle color variations in facial videos to estimate vital signs such as heart rate and respiratory rate. This non-invasive method has gained traction across diverse domains, including telemedicine, affective computing, driver fatigue detection, and health monitoring, owing to its scalability and convenience. Despite significant progress in remote physiological signal measurement, a crucial characteristic - the intrinsic periodicity - has often been underexplored or insufficiently modeled in previous approaches, limiting their ability to capture fine-grained temporal dynamics under real-world conditions. To bridge this gap, we propose Reperio-rPPG, a novel framework that strategically integrates Relational Convolutional Networks with a Graph Transformer to effectively capture the periodic structure inherent in physiological signals. Additionally, recognizing the limited diversity of existing rPPG datasets, we further introduce a tailored CutMix augmentation to enhance the model's generalizability. Extensive experiments conducted on three widely used benchmark datasets - PURE, UBFC-rPPG, and MMPD - demonstrate that Reperio-rPPG not only achieves state-of-the-art performance but also exhibits remarkable robustness under various motion (e.g., stationary, rotation, talking, walking) and illumination conditions (e.g., nature, low LED, high LED). The code is publicly available at https://github.com/deconasser/Reperio-rPPG.

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### Polymap: generating high definition map based on rasterized polygons

Authors: Shiyu Gao, Hao Jiang
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.05944v1

The perception of high-definition maps is an integral component of environmental perception in autonomous driving systems. Existing research have often focused on online construction of high-definition maps. For instance, the Maptr(9) series employ a detection-based method to output vectorized map instances parallelly in an end-to-end manner. However, despite their capability for real-time construction, detection-based methods are observed to lack robust generalizability(19), which hampers their applicability in auto-labeling systems. Therefore, aiming to improve the generalizability, we reinterpret road elements as rasterized polygons and design a concise framework based on instance segmentation. Initially, a segmentation-based transformer is employed to deliver instance masks in an end-to-end manner; succeeding this step, a Potrace-based(17) post-processing module is used to ultimately yield vectorized map elements. Quantitative results attained on the Nuscene(1) dataset substantiate the effectiveness and generaliz-ability of our method.

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### A PDE Perspective on Generative Diffusion Models

Authors: Kang Liu, Enrique Zuazua
Published: 2025-11-08
Categories: math.OC, cs.AI, math.AP
arXiv: https://arxiv.org/abs/2511.05940v3

Score-based diffusion models have emerged as a powerful class of generative methods, with successful applications across diverse domains. Despite their empirical success, their mathematical foundations remain only partially understood, particularly regarding the stability and consistency of the stochastic and partial differential equations underlying their dynamics. In this work, we develop a partial differential equation (PDE) framework for score-based diffusion processes associated with the heat flow of a compactly supported data measure. After establishing weak well-posedness on positive-time intervals, we derive a sharp one-sided divergence bound for the score and use it to obtain sharp uniform $L^p$-stability estimates for the generative Fokker--Planck equation. This bound is the Euclidean form of the Li--Yau differential inequality. Through entropy stability methods, we prove that the generated laws converge in the $2$-Wasserstein distance at rate $\sqrt t$ as $t\to0^+$ to a probability measure supported on the data support. This holds for both deterministic and stochastic generation, for positive terminal densities with finite absolute entropy and second moment. In the deterministic case, almost every trajectory converges to a point of the data support; for empirical data, we further obtain a trajectory-wise square-root rate. These results clarify how exact-score dynamics concentrate on the data support without necessarily recovering the data distribution. An explicitly solvable case identifies the limiting weights and their dependence on Gaussian initialization, while a relative-entropy estimate quantifies the effect of score approximation. Together, these findings connect the stability and concentration mechanisms with score learning and early stopping, providing a mathematical perspective on the balance between imitation fidelity and generative diversity.

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### Global Multiple Extraction Network for Low-Resolution Facial Expression Recognition

Authors: Jingyi Shi
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.05938v1

Facial expression recognition, as a vital computer vision task, is garnering significant attention and undergoing extensive research. Although facial expression recognition algorithms demonstrate impressive performance on high-resolution images, their effectiveness tends to degrade when confronted with low-resolution images. We find it is because: 1) low-resolution images lack detail information; 2) current methods complete weak global modeling, which make it difficult to extract discriminative features. To alleviate the above issues, we proposed a novel global multiple extraction network (GME-Net) for low-resolution facial expression recognition, which incorporates 1) a hybrid attention-based local feature extraction module with attention similarity knowledge distillation to learn image details from high-resolution network; 2) a multi-scale global feature extraction module with quasi-symmetric structure to mitigate the influence of local image noise and facilitate capturing global image features. As a result, our GME-Net is capable of extracting expression-related discriminative features. Extensive experiments conducted on several widely-used datasets demonstrate that the proposed GME-Net can better recognize low-resolution facial expression and obtain superior performance than existing solutions.

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### 10 Open Challenges Steering the Future of Vision-Language-Action Models

Authors: Soujanya Poria, Navonil Majumder, Chia-Yu Hung, Amir Ali Bagherzadeh, Chuan Li, Kenneth Kwok, Ziwei Wang, Cheston Tan, Jiajun Wu, David Hsu
Published: 2025-11-08
Categories: cs.RO, cs.AI
arXiv: https://arxiv.org/abs/2511.05936v1

Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly prevalent in the embodied AI arena, following the widespread success of their precursors -- LLMs and VLMs. In this paper, we discuss 10 principal milestones in the ongoing development of VLA models -- multimodality, reasoning, data, evaluation, cross-robot action generalization, efficiency, whole-body coordination, safety, agents, and coordination with humans. Furthermore, we discuss the emerging trends of using spatial understanding, modeling world dynamics, post training, and data synthesis -- all aiming to reach these milestones. Through these discussions, we hope to bring attention to the research avenues that may accelerate the development of VLA models into wider acceptability.

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### Interaction-Centric Knowledge Infusion and Transfer for Open-Vocabulary Scene Graph Generation

Authors: Lin Li, Chuhan Zhang, Dong Zhang, Chong Sun, Chen Li, Long Chen
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.05935v1

Open-vocabulary scene graph generation (OVSGG) extends traditional SGG by recognizing novel objects and relationships beyond predefined categories, leveraging the knowledge from pre-trained large-scale models. Existing OVSGG methods always adopt a two-stage pipeline: 1) \textit{Infusing knowledge} into large-scale models via pre-training on large datasets; 2) \textit{Transferring knowledge} from pre-trained models with fully annotated scene graphs during supervised fine-tuning. However, due to a lack of explicit interaction modeling, these methods struggle to distinguish between interacting and non-interacting instances of the same object category. This limitation induces critical issues in both stages of OVSGG: it generates noisy pseudo-supervision from mismatched objects during knowledge infusion, and causes ambiguous query matching during knowledge transfer. To this end, in this paper, we propose an inter\textbf{AC}tion-\textbf{C}entric end-to-end OVSGG framework (\textbf{ACC}) in an interaction-driven paradigm to minimize these mismatches. For \textit{interaction-centric knowledge infusion}, ACC employs a bidirectional interaction prompt for robust pseudo-supervision generation to enhance the model's interaction knowledge. For \textit{interaction-centric knowledge transfer}, ACC first adopts interaction-guided query selection that prioritizes pairing interacting objects to reduce interference from non-interacting ones. Then, it integrates interaction-consistent knowledge distillation to bolster robustness by pushing relational foreground away from the background while retaining general knowledge. Extensive experimental results on three benchmarks show that ACC achieves state-of-the-art performance, demonstrating the potential of interaction-centric paradigms for real-world applications.

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### AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders

Authors: Ayantika Das, Arunima Sarkar, Keerthi Ram, Mohanasankar Sivaprakasam
Published: 2025-11-08
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.05934v2

Generative modeling frameworks have emerged as an effective approach to capture high-dimensional image distributions from large datasets without requiring domain-specific knowledge, a capability essential for disease progression modeling. Recent generative approaches have attempted to capture progression by mapping images to a latent space and guiding representations to generate follow-up images from previous time points. However, these methods impose constraints on distribution learning, resulting in latent spaces with limited controllability for generating follow-up images without paired subject-specific longitudinal guidance. In order to enable controlled movements in the latent representational space and generate progression images from a previous time-point image without subject-specific guidance, we introduce a conditionable Diffusion Auto-encoder framework that forms a compact latent space capturing high-level semantics and providing means to control generation. Our approach leverages this latent space to condition and apply controlled shifts to the representations of previous time-point images by isolating progression and subject identity information for generating follow-up images. The shifts are implicitly guided by correlating with progression attributes and constraining to Alzheimer's disease specific regions, without paired longitudinal guidance. We validate the generations through image quality metrics, volumetric progression analysis, and downstream tasks in Alzheimer's disease datasets from different sources. This demonstrates the effectiveness of our approach for Alzheimer's progression modeling and longitudinal image generation.

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