Computer Vision
PERSIST: Persistent-State Discrimination for Shot Boundary Detection
Tingyu Lin, Christian Stippel, Armin Dadras et al. (+4 more)
Shot boundary detection (SBD) is widely treated as the localisation of local visual discontinuities, yet many false positives such as hand-held shake, illumination flicker, motion blur, occlusion, and damaged archival material produce equally sharp local change without introducing a new shot. We reformulate SBD as boundary semantic discrimination: a frame is favoured as a boundary only when its local change evidence is accompanied by a persistent update of the
video's latent temporal state, rather than a transient excursion that returns to the surrounding trend. This persistence test is operationalised with a continuous latent state from a FiLM-conditioned sinusoidal representation network and a structured discriminator that combines three semantic cues, local change, transient impulse, and return-to-trend, into a single interpretable per-frame signal over a dual-rate temporal backbone. The resulting framework, PERSIST, turns every decision into an inspectable one: the persistence criterion is trained into the classifier, its per-frame effect stays readable from the gate triple, and its learned latent state is measurably boundary-discriminative. On a 2,727-
video per-subtype diagnostic it removes 33-80% of flash, text-overlay, and archival false positives relative to an identically trained cue detector, and at matched true-transition recall it roughly halves TransNetV2's pseudo-event false positives on that diagnostic and cuts its false positives on ClipShots footage by about a quarter, while preserving recall. It does so while reaching parity with the strongest public detector across online, broadcast, short-form, and historical-archive transfer evaluations, under markedly stricter training: it learns from ClipShots real transitions only, whereas the anchor draws on additional corpora whose transitions are 85% synthetic. Code is available at https://github.com/linty5/PERSIST.
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All authors Tingyu Lin, Christian Stippel, Armin Dadras, Jakob Zenzmaier, Florian Kleber, Wolfgang Aigner, Robert Sablatnig
Artificial Intelligence
MMPCBench: Benchmarking Multimodal Large Language Models on Proactive Critique of Flawed Inputs
Jinzhe Li, Gengxu Li, Jinnan Li et al. (+2 more)
As
Multimodal Large
Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructions but also on validating them. We define Proactive Critique as the model's autonomous ability to identify, analyze and fix faulty user inputs without extra
prompts. However, evaluations mainly test models under ideal circumstances or simple refusal behaviors, largely ignoring active error processing. To fill this gap, we propose MMPCBench, a comprehensive framework for evaluating MLLMs' proactive critique competence. It features a fine-grained taxonomy of 4 primary error types spanning 12 subcategories, ranging from cross-modal contradictions to missing visual premises. We adopt a hierarchical evaluation protocol to measure models' error detection, diagnosis and resolution performance, and apply
alignment-aware metrics to assess the coherence between internal
reasoning and final responses. Tests on 14 mainstream MLLMs show obvious weaknesses in proactive critique, especially in dealing with subtle visual anomalies. Notably, we identify a pervasive "consistency gap":
reasoning models can often correctly identify and analyze errors during internal
reasoning yet suppress these valid insights in final outputs to prioritize response compliance. The code and data is available at https://github.com/ALIENS32/MMPCBench.
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All authors Jinzhe Li, Gengxu Li, Jinnan Li, Yuan Wu, Yi Chang
Computer Vision
3D-MRL: Nested Multimodal 3D Representations via Matryoshka Representation Learning
Márcus Lobo, Vitor Matias, Jeová Farias et al. (+1 more)
Vision-
Language Models align point clouds with image and text
embeddings, enabling zero-shot recognition,
retrieval, and open-vocabulary understanding of 3D shapes. Existing
multimodal 3D pre-training methods produce fixed-dimensional
embeddings, requiring separate models for different computational budgets. We propose 3D Matryoshka Representation Learning (3D-MRL), a
multimodal 3D pre-training framework based on Matryoshka Representation Learning. 3D-MRL learns nested 3D representations by aligning point clouds with frozen CLIP image and text
embeddings while applying contrastive supervision across multiple
embedding dimensions. The Matryoshka objective is applied only to the 3D encoder, allowing a single model to produce representations at different dimensionalities without retraining. Experiments on the Objaverse-LVIS, ModelNet40, and ScanNet
datasets show that 3D-MRL achieves competitive performance on zero-shot and few-shot 3D recognition tasks. In addition, the learned representations support
retrieval across different
embedding dimensions within a single model. On Objaverse-LVIS, 3D-MRL improves Top-1 accuracy from 46.8% to 50.9%.
Retrieval experiments further show that different
embedding dimensions yield varying levels of semantic and
geometric specificity.
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All authors Márcus Lobo, Vitor Matias, Jeová Farias, Moacir Ponti
Computation and Language
Cloud and On-Premises Deployment of Uzbek Legal RAG via Targeted Retriever Fine-Tuning
Tatul Danielyan, Mariam Avetisyan and Hrant Davtyan
Deploying large
language models for legal question answering raises challenges that general-purpose leaderboards do not capture, particularly for low-resource
languages and under hard operational constraints. We report on building and operating a
retrieval-augmented (RAG) legal assistant for Uzbek that must run in two regimes: a managed cloud service that maximizes answer quality within a per-
token cost ceiling, and an on-premises deployment for clients whose legal data may not leave their infrastructure, restricting us to open-weight models on limited local hardware under latency constraints. Because no evaluation existed for this setting, we build two domain
benchmarks: a
retrieval benchmark of 178 expert-annotated legal queries with gold provision spans, and an end-to-end
benchmark of 504 expert-curated question–answer pairs scored by an LLM judge whose ratings we validate against human judgments and against an independent-family judge. Applying these
benchmarks under each regime, we find the open-versus-proprietary gap is small and cheaply closed by fine-tuning. Therefore, we train UTE-1, which is a state-of-the-art text embedder among open models for Uzbek. We also demonstrate that closing the performance gap via fine-tuning is both impractical due to the intensive hardware demands of long-context legal Q&A and unnecessary, given that legal acts change frequently. We support this by reporting a negative result from a QLoRA experiment. We distill practical guidance for similar deployments, drawn from a system serving real users in production. We release our
benchmarks, evaluation code and the fine-tuned embedder (UTE-1) \href{https://metric-ai-lab.github.io/Uzbek-Legal-RAG/}{at this https URL} to support future work on low-resource legal NLP.
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Cryptography and Security
A Multi-Month Study of Git Commit Signing
Abubakar Sadiq Shittu, John Sadik and Scott Ruoti
Git commit signing, introduced in 2012, is one mechanism for establishing commit provenance in software supply chains, yet developer-controlled adoption remains rare and developers' experiences using it are understudied. To examine this experience, we conducted a three-month study with senior undergraduate and graduate computer science students (n = 22), whom we treat as proxies for junior developers. Participants configured commit signing independently, used it across four coursework projects, extended it to a second device, examined an external repository containing anomalous commits, and answered security-
reasoning prompts. We found that while almost all participants successfully signed every commit and rated routine signing positively, many faced friction during setup, multi-device configuration, and repository verification. Despite signing all semester, they struggled to spot anomalous commits during verification, with over a quarter finding none. Additionally, nearly half expressed at least one misconception regarding signing guarantees or key management. Without isolating whether these difficulties stemmed from tooling, education, or understanding, we conclude that making signing easier is not enough to ensure effective security use. Rather, secure adoption also requires tools and education that support signature verification against authorized identities, interpretation of missing signatures and unknown keys, and correct
reasoning about key-lifecycle operations.
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Computer Vision
Eduard Zamfir, Christian Reisswig, Zongwei Wu et al. (+2 more)
Natural images concentrate their detail in a small fraction of the frame, yet
diffusion models spend a full
token on every patch, in every layer and at every timestep. The waste is largest in pixel-space models, with no autoencoder to absorb low-level redundancy first. Probing a pretrained pixel text-to-image
transformer, we find its middle-block
tokens redundant wherever the image is flat. The redundancy occupies connected, content-shaped regions, and exploiting it requires
tokens with the same geometry. Cutting a
Hilbert ordering of the patches provides them. Consecutive positions are always image neighbours, so any contiguous run is a connected region whose size and shape follow the content, and grouping in two dimensions becomes a cut in one. Existing reductions each lose part of this. Similarity merging scatters its groups, latent bottlenecks discard position, and skipping deletes what it should summarize. We cut where the model's features change most and pool each run into one region
token. Our Region
Token Interface (\method{}) adapts a
diffusion model to these
tokens, with the region count drawn at random during fine-tuning so one checkpoint serves every budget. \method{} leads prior reduction methods at matched budgets, matches dense quality at $2.0\times$ the speed, and stays close at $2.6\times$. The code and models are open-sourced at https://eduardzamfir.github.io/rti
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All authors Eduard Zamfir, Christian Reisswig, Zongwei Wu, Yongqin Xian, Radu Timofte
Computer Vision
RAGDiffusion++: From Macro-Retrieval to Micro-Fidelity Alignment for Garment Generation
Yuhan Li, Xianfeng Tan, Fangao Zeng et al. (+6 more)
Standard clothing asset generation—restoring forward-facing flat-lay garment images from diverse real-world contexts—holds immense commercial value yet demands both macroscopic topological accuracy and microscopic physical fidelity. Although our previous work RAGDiffusion effectively eradicated large-scale structural
hallucinations via
retrieval-augmented macro-constraints, achieving industrial-grade micro-texture realism remains an unsolved bottleneck. We formally identify this limitation as High-Frequency Trajectory Collapse: supervised fine-tuning (SFT) converges to the conditional mean of the training distribution, which is dominated by smooth, low-frequency textures, causing high-frequency patterns (e.g., fabric weaves, intricate logos) to become nearly un-sampleable. Naively applying Reinforcement Learning (RL) post-training further triggers Artifact Hacking, where models exploit semantic biases in generic reward models by generating deceptive checkerboard noise. Our key insight is that RL can fundamentally reshape the sampling distribution of flow models—elevating the probability of high-fidelity trajectories under accurate reward guidance—while
adversarial regularization prevents exploitation of reward blind spots. Realizing this principle requires three prerequisites: (i)inherent capacity, established through a 27,725-pair high-complexity garment
dataset (STGarment-Plus) and a Dual-Image-Stream FLUX architecture upgrade; (ii)perceptive reward, provided by a novel attribute-aware reward model (Garment-RM) trained on 500K images via fine-grained contrastive learning, achieving 84.67% human preference accuracy; and (iii)hacking prevention, enforced by our
Adversarial-Regularized GRPO (AR-GRPO) strategy that integrates a dynamic discriminator into the RL sampling trajectory to penalize artifacts while enriching authentic high-frequency details.
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All authors Yuhan Li, Xianfeng Tan, Fangao Zeng, Wenxiang Shang, Pipei Huang, Hao Zhou, Zhiyu Jin, Wenjun Zhang, Bingbing Ni
Artificial Intelligence
Understanding Deep Learning via Entropy Space Theory
Li Li, Tong Zhang, Wentao Yu et al. (+1 more)
Deep learning is often criticized for its theoretical research lagging behind practice. To make deep learning easier to understand, the
entropy space theory is first introduced here. The
entropy space can cover all the possibilities of any deep learning model by topological structure. It is independent of network parameters. Through the designed fundamental operations and norm,
entropy space is proven to be a normed space within the formal axiomatic framework. Based on the theory, a unified coordinate system is proposed. It can coordinatize every state of a model and rank them by compression of the maximal value of information
entropy. The theory offers a novel priori framework for mathematical fundamentals of deep learning.
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All authors Li Li, Tong Zhang, Wentao Yu, Zuobin Wang
Computation and Language
Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning
Zhaolu Kang, Meixin Wu, Yu Xue et al. (+6 more)
Omni-modal large
language models are increasingly evaluated on clean text–
vision–audio inputs, where every channel is present, synchronized, and readily interpretable. Such scores are often taken as evidence of robust cross-modal fusion, but clean evaluation cannot tell whether success depends on stable cross-modal structure or on cues sufficient only in intact inputs. To address this gap, we define a modality fault line: a boundary at which model behavior becomes unstable when a modality remains present and human-interpretable, but its internal evidence structure is perturbed. We introduce SCEval (Structure-Corruption Evaluation) a diagnostic evaluation protocol that keeps the question, answer space, and modality channels fixed while applying controlled structural corruptions to text,
vision, and audio individually and jointly. Built from $273$ human-verified tri-modal examples from Social-IQ, OmniBench, and VALOR, SCEval evaluates $15$ proprietary and open-source omni-modal systems. The results show that structural corruption lowers clean accuracy, text–
vision damage forms the most stable shared fault line, and multi-modal degradation is non-additive rather than a simple function of the number of corrupted modalities. Clean omni-modal accuracy therefore does not establish that a model will remain reliable when cross-modal evidence becomes structurally unreliable.
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All authors Zhaolu Kang, Meixin Wu, Yu Xue, Yingjie He, Qiming Shi, Lei Wei, Yidi Wang, Richeng Xuan, Zhichao Hu
Data Structures and Algorithms
A Simplified Analysis of the Good-Bad $3/2$-Approximation Algorithm for Some Minimum-Cost Graph Problems
Shayan Ranjbarzadeh, David P. Williamson and Hannane Yaghoubizade
In this paper, we consider an easy greedy approximation
algorithm, the good-bad
algorithm, introduced by Couëtoux for finding a minimum-cost set of edges such that every connected component has at least $k$ vertices. Couëtoux proves that the good-bad
algorithm achieves a $3/2$-approximation for this problem. Davis and Williamson extend this result to the more general problem of finding a minimum-cost edge set that contains at least one edge from every cut $S\subseteq V$ satisfying $h(S) = 1$ where $h:2^V \rightarrow \{0,1\}$ is downward monotone; that is, $h(S) = 1$ implies $h(T) = 1$ for every nonempty subset $T \subseteq S$. The original problem corresponds to $h(S) =1$ when $|S|<k$. We give a simplified analysis of the good-bad
algorithm for downward monotone functions.
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Systems and Control
Stochastic Nonlinear Model Predictive Control with Gaussian Mixture Uncertainty Propagation
Konstantinos Prattis, Luca Laurenti and Azita Dabiri
We propose a novel
Stochastic Nonlinear Model Predictive Control (SNMPC) framework for nonlinear systems with additive noise. Building on recent advances in nonlinear uncertainty propagation, we show that the state distribution of the system can be tractably approximated over time by Gaussian mixture distributions, with formal error bounds in Wasserstein distance. This representation yields closed-form expressions for expected costs and chance constraints, which become exact for affine constraints and exact up to a constant for quadratic costs. Consequently, the resulting control problem can be solved efficiently via nonlinear programming, while providing formal open-loop guarantees of correctness and asymptotic optimality. Experiments on a set of
benchmarks demonstrate that the proposed approach compares favorably with existing methods in nonlinear settings with multi-modal disturbances, where standard approaches lead to poorly scaled solutions and unsafe or overly conservative control actions.
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Numerical Analysis · Cross-listed from math.OC
Nechvile-Transformed Spacecraft Dynamics and Propellant Computation in the 3-Body Problem
Michael J. Dixon and Isaac M. Ross
The uncontrolled equations of motion in the Nechvile frame for the restricted three-body problem have been well-known since at least the 1960s. It would seem that adding an external force to these equations is quite trivial: simply add an external force per mass term to the acceleration equations. Here we show that the last statement is not true. In fact, we show that the additive generic external force must be multiplied by the inverse of $(1 + e \cos\theta)^3$ where $e$ is the relative eccentricity of the primaries and $\theta$ is the true anomaly of the rotating frame located at the barycenter. Furthermore, when this result is combined with the mass flow rate equation, it generates several surprising results due to the mismatch between the resulting quadratic term and the cubic term in the equations of motion. This leads to a corresponding modification of the rocket equation itself. A Birkhoff-theoretic solution to an illustrative cislunar space mission problem shows propellent savings of 80% with the use of the correct cost functional. The popular quadratic cost utilizes more than $2X$ the minimum propellant consumption.
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Computation and Language
SHADOWBENCH: Toward Reliable Automatic Evaluation of Semantic Alignment in Autoformalization
Hojae Han, Jongyoon Kim, Sanghyeok Park et al. (+8 more)
Autoformalization translates informal mathematical
theorems into code for proof assistants such as Lean. A central challenge is that current evaluation metrics can accept type-correct but misaligned statements or reject correct statements written in a different formulation. Inspired by Pass@$k$, we propose SA-Pass (*Semantic
Alignment Pass*), which tests formal statements using auxiliary statements called *shadows* that characterize the intended statement. A generated statement receives full credit only when it compiles, implies each shadow (forward check), and is implied by their conjunction (backward check). We instantiate SA-Pass in ShadowBench, a Lean 4 full autoformalization
benchmark of 178 postgraduate- to research-level problems spanning eight mathematical areas. Claude Code (Opus 4.8) with Numina-Lean-
Agent reaches $61.8\%$ compile rate and $11.2\%$ SA-Pass. Across outputs generated by six agentic configurations, SA-Pass achieves $98.8\%$ binary agreement with expert judgments. An early version of ShadowBench served as the
benchmark for Track 4 of the ICML 2026 AI4Math Challenge.
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All authors Hojae Han, Jongyoon Kim, Sanghyeok Park, Dongwook Cheon, Yeachan Park, Myeong Jae Jeon, Sunjong Choe, Soonho Kong, Wonseok Hur, Seung-won Hwang, Donghoon Hyeon
Computer Vision
LightFuse: Relightable Interactive Gaussian Scene Reconstruction via Multi-Scan Fusion and 2D Gaussian Ray Tracing
Haonan Zhou, Gaoxiang Linghu, Youlin Jia et al. (+5 more)
Relightable interactive scene reconstruction aims to build an editable 3D model from scans of different object arrangements and render new layouts under novel illumination. Existing methods either bake lighting into appearance or recover material and illumination only for fixed scenes, leaving edited layouts with inconsistent shadows and indirect lighting. We present LightFuse, a 2D Gaussian framework that extends interactive scene reconstruction with explicit material-illumination decomposition and physically based relighting. LightFuse first fuses observations across states to reconstruct a shared background and movable objects. It then conducts ray-tracing-oriented geometry refinement to produce more complete and consistent surfaces. On the refined geometry, staged training with differentiable one-bounce ray tracing separates shared metallic–roughness material from state-specific environment lighting. The resulting scene supports object rearrangement, material editing, and relighting, while ray tracing recomputes appearance after each interaction. Experiments across synthetic scenes demonstrate state-of-the-art relighting quality, outperforming the strongest baseline by +9.74\,dB PSNR and +0.121 SSIM on average. Project page: https://zhn202.github.io/LightFuse/
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All authors Haonan Zhou, Gaoxiang Linghu, Youlin Jia, Hongyu Cui, Kewei Wei, Kaiyue Zhou, Bruce X.B. Yu, Gaoang Wang
Computer Vision
Learning to Ground Before Reading: Unified PCB Engineering Drawing Parsing with Compact Vision-Language Models
Jinghao Liu, Xingrun Liu, Gengchen Sun et al. (+3 more)
PCB engineering drawings mix sparse graphics, dense tables, and text whose meaning depends on page position. Localizing the regions and sending crops to specialized recognizers are determined as the methods for most parsers, so missed regions cannot be recovered downstream. We train a compact VLM to read the full page and get a sequence of region classes, normalized boxes, and text or HTML content. Bounding boxes are converted to coordinate
tokens for supervision. Inference uses no detector or crop parser. The joint target is difficult to optimize because class and box
tokens are sparse relative to the much longer content sequences. Our localization-first curriculum learns the class-box format before adding content targets with content-aware resampling. On the fixed validation split of the Engineering Drawing
Dataset (ED
dataset), Localization-First improves strict localization F1 by 0.0955 over joint training (paired image-bootstrap 95% interval: [0.0350, 0.1572]). G-Unified has the lowest NED, highest
cell F1, and only nonzero exact-match score. It provides a detector-free baseline for full-page PCB drawing parsing.
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All authors Jinghao Liu, Xingrun Liu, Gengchen Sun, Han Xiao, Xingyu Chen, Yuhui Deng
Artificial Intelligence · Cross-listed from physics.soc-ph
Measurement Validity in LLM Cultural Alignment
An Duy Nguyen and Muhammad Aurangzeb Ahmad
Researchers increasingly treat LLM survey responses as a proxy for human cultural values. This includes projecting model outputs onto instruments like the Inglehart-Welzel Cultural Map and drawing conclusions about which cultures a model resembles. While a model's answer to a value-laden questions may be interpreted as a cultural signal, it also carries sampling noise and, can be quite sensitive to question framing. In this paper, we separate survey responses, sampling noise and question framing for multiple LLMs. We decompose response variance from these models into variation across random seeds,
prompt rewordings. We employ noise-to-signal ratio (NSR) to test whether a model's apparent cultural position is distinguishable from noise. When applied across a dozen models from four geographic origins, calibrated against 88 Integrated Values Survey countries, the answer is often no. NSR exceeds 1.0 on 49 of 117 valid model-question pairs (42%), reaching 5.56 in the worst case. Two models even refuse to answer sufficient number of survey questions outright. Our results corroborate previous findings that LLMs cluster toward Western, English-speaking cultural positions. However, what does not hold up in this study is the precision with which anyone can currently interpret a specific model's coordinates:
prompt tone alone can shift a model by 2.4 map units, comparable to the distance between actual countries in the Inglehart-Welzel Cultural Map. These findings suggest that cultural attribution from LLM survey responses requires establishing the reliability of the underlying measurements before interpreting model coordinates as evidence of cultural representation.
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Machine Learning · Cross-listed from stat.CO
Signed random Fourier features for fast density estimation with indefinite kernels
Xie Wang, Nicolas Langrené and Wen Chen
Kernel density estimation (KDE) is one of the most fundamental statistical
estimators of density functions. Its direct implementation on a
dataset of $N$ points incurs an $\mathcal{O}(N^{2})$ computational cost, which is prohibitive for large-scale
datasets. Kernel approximation techniques can be applied to bring the computational cost down to $\mathcal{O}(N)$. The random
Fourier features (RFF) technique, based on sampling from the spectral density of the kernel function, has become popular to speed up kernel
estimators for machine learning applications. Unfortunately, it is restricted to positive definite kernels, while the majority of kernel functions popular in KDE, such as the
parabolic kernel, do not satisfy this property. To overcome this limitation, this article introduces the signed random
Fourier features (SRFF) technique. It is a generalization of RFF compatible with indefinite kernels whose inverse
Fourier transform is absolutely integrable. The motivation for introducing this method is to speed up KDE in the case of multivariate compact kernels, which are generally not positive definite. We detail how to implement SRFF for both product kernels and isotropic kernels. For the class of Kuttner-Golubov kernels $K(\boldsymbol{x}_{i},\boldsymbol{x}_{j})=(1-\left\Vert \boldsymbol{x}_{i}-\boldsymbol{x}_{j}\right\Vert ^{\alpha})^{\beta}\mathbf{1}_{\{\left\Vert \boldsymbol{x}_{i}-\boldsymbol{x}_{j}\right\Vert \leq1\}}$ where $\boldsymbol{x}_{i}\in\mathbb{R}^{d}$, $\boldsymbol{x}_{j}\in\mathbb{R}^{d}$, $\alpha>0$, $\beta>0$, which includes the triangular,
parabolic, biweight, triweight, and other kernel functions of interest for KDE as particular examples, we provide an explicit acceptance-rejection
algorithm to sample from its signed spectral density. Our numerical tests on a
dataset of one million points confirm the computational efficiency and accuracy of SRFF for large-scale KDE.
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Artificial Intelligence
EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation
Yuhan Liu, Zongwei Hong, Jinglun Li et al. (+3 more)
Diffusion-based visual generative models deliver strong image and
video synthesis quality but incur high inference costs because sequential samplers repeatedly evaluate large networks. Caching-based methods reduce inference latency by reusing intermediate computations across adjacent timesteps. However, existing cache controllers rely primarily on local temporal variation and overlook the trajectory-level consequences of cache reuse. We introduce Error-Propagation-Aware Cache (EpaCache), a training-free caching policy that adaptively allocates the reuse budget on timesteps with lower downstream impact. Experiments on image and
video synthesis models demonstrate that EpaCache consistently improves the latency–fidelity trade-off over existing caching methods. On FLUX.1-dev, EpaCache outperforms the prior state-of-the-art caching method in both latency and fidelity, reducing inference time from $11.7$ s to $11.3$ s while improving PSNR from $21.4$ to $22.8$. On HunyuanVideo, EpaCache achieves a $2.63\times$ speedup over uncached inference and improves SSIM from $0.891$ to $0.905$ over the prior state-of-the-art method at matched latency.
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All authors Yuhan Liu, Zongwei Hong, Jinglun Li, Linze Li, Shen Zhang, Yao Tang
Artificial Intelligence
RACER: Reinforced Agent Collaboration for Explainable Reasoning on Knowledge Graphs
Yuwei Lou, Hao Hu, Yuzhou Jiang et al. (+5 more)
Large
Language Models (LLMs) often suffer from
hallucination and struggle with complex
reasoning tasks requiring multi-hop domain knowledge. While integrating Knowledge Graphs (KGs) provides a structured and verifiable information source, current KG-enhanced LLM paradigms usually rely on single-
agent path extraction and fixed prompting, lacking adaptability and facing huge search spaces. To address these challenges, we propose RACER, a Reinforced
Agent Collaboration framework for Explainable
Reasoning on knowledge graphs. RACER employs a semantic-aware action
pruning and teacher-guided reinforcement learning mechanism to efficiently extract high-quality
reasoning pathways from large-scale KGs. Furthermore, to mitigate single-path generation pitfalls, we introduce a cross-task accumulated shared
memory graph paired with an
attention-driven multi-path knowledge refinement module. Finally, RACER orchestrates these components through a four-role multi-
agent collaboration system (GraphAgent, TemplateAgent, AnswerAgent, and CriticAgent) to dynamically refine
prompts and evaluate answers. Extensive experiments on CommonsenseQA and OpenBookQA
datasets demonstrate that RACER significantly outperforms state-of-the-art KG-enhanced LLM baselines with an average improvement of 5%, offering robust and highly interpretable
reasoning capabilities.
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All authors Yuwei Lou, Hao Hu, Yuzhou Jiang, Zongfei Zhang, Liang Wang, Jincai Liu, Jidong Ge, Xianping Tao
Machine Learning
Adaptive Multi-Branching for Shallow Decision Tree Induction
Hanul Park, Jeonghoon Choi, Juseong Kim et al. (+2 more)
Decision trees are attractive for tabular prediction tasks because each prediction follows an interpretable sequence of feature-threshold tests. Under a strict maximum-depth budget, however, conventional binary trees can be under-expressive, since each internal node makes only a single threshold decision. We study shallow-depth tree induction, where the goal is to improve accuracy while keeping root-to-leaf paths short. We propose the Multi-Branch Neural Decision Tree with Adaptive
Pruning (MBNDT), a single axis-aligned tree trained end-to-end with differentiable multi-way splits. Each internal node learns ordered thresholds over a selected feature and a branch mask that adapts its effective arity, and the trained model is converted to a deterministic single-path tree for inference. Across 21 OpenML binary-classification
benchmarks, MBNDT achieves the best average rank and mean balanced accuracy among depth-constrained single-tree baselines; a controlled ablation isolates multi-way splitting as the source of the gain. These gains come with an explicit trade-off: MBNDT realizes more leaves than the other single-tree baselines, making it best suited when accuracy under short, bounded decision paths is prioritized over minimal global tree size.
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All authors Hanul Park, Jeonghoon Choi, Juseong Kim, Sanghun Sel, Giltae Song
Computation and Language
Large Language Models Systematically Favor Popular Options: Evidence and Mitigation Across MCQs
Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani et al. (+2 more)
Multiple-choice questions (MCQs) are a standard format for evaluating large
language models (LLMs), yet the popularity of answer options can confound evaluation. Modern LLMs systematically prefer popular but incorrect options over less popular correct ones, a vulnerability we call popularity bias. This pattern aligns with confidence miscalibration: model confidence remains high even as accuracy collapses for popular options. To systematically isolate this phenomenon, we introduce PopMCQ, a
benchmark with six controlled strategies that vary option popularity while keeping the correct answer fixed. In our most
adversarial setting, where all distractors are more popular than the correct option, models choose popular but wrong answers 66% of the time. To mitigate this bias, we propose PopDebias, a lightweight inference-time correction that estimates and removes a popularity prior from model predictions. It requires no fine-tuning, is label-free at test time (using only a small calibration split for parameter fitting), and adds negligible computational cost. Experiments on 22 open-source LLMs (0.5B to 32B parameters) show consistent improvements, with accuracy gains up to 54.1 percentage points under strong popularity pressure. The code and data are available https://github.com/DataScienceUIBK/PopMCQ
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All authors Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani, Mahmoud Abdalla, Adam Jatowt
Networking
Sense Once, Serve Many: Common-Trace Factorized Constrained PPO for Online Sensing-Session Consolidation in Multi-Tenant ISAC Networks
Dang-Dung Vu
Integrated sensing and communication (
ISAC) networks can serve compatible requests through shared sensing sessions, but consolidation couples admission, reuse, profile selection, sensing service-level agreements (SLAs), communication quality of service (QoS), and future commitments. We formulate this problem as a constrained Markov decision process and propose Common-Trace Factorized Constrained Proximal Policy Optimization (CT-PPO). During training,
stochastic policy replicas share the same primitive workload trace; leave-one-out discounted Monte Carlo return contrasts provide reward credit to applicable actor factors, while constraint credit remains factor/prefix-specific. Across five training seeds and matched workloads, CT-PPO achieves the highest mean macro return, exceeding matched Joint-Credit PPO (JC-PPO) by 0.934 (95% confidence interval [0.702, 1.164]) and SLA-Aware Greedy by 1.847; versus JC-PPO, it reduces sensing-resource cost by 6.277 and raises accepted requests per created session by 0.0806. A four-way ablation shows that the factorized surrogate alone yields no detectable macro-return gain, whereas adding common-trace reward credit produces the dominant improvement. Without retraining, CT-PPO retains a return advantage at low, nominal, and high arrival loads, with the strongest gain under clustered arrivals. Deployment uses public observations and hard masks; CT-PPO's extra parameters are training-side, its actor footprint matches JC-PPO, and actor-only CPU latency is effectively unchanged.
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Networking
A-MADiff: Attention-Guided Multi-Agent DRL with Diffusion Policies for Memory-Aware Task Orchestration in Mobile AIGC Networks
Chongzhi Wu, Zhengtao Li, Jiawen Kang et al. (+4 more)
Artificial Intelligence-Generated Content (AIGC) services employ Generative AI (GenAI) models to automatically generate diverse content. Mobile AIGC networks host GenAI models on edge-located AIGC Service Providers (ASPs) to deliver low-latency and personalized AIGC services for mobile users. However, AIGC inference tasks typically occupy GPU
memory until task completion, causing GPU
memory exhaustion at serving ASPs and triggering out-of-
memory failures rather than merely increasing service latency. Existing studies on AIGC task orchestration have largely overlooked GPU
memory feasibility constraints. To address this issue, we develop a cooperative multi-
agent orchestration framework, in which each edge node is equipped with a scheduling
agent to route tasks to local ASPs or neighboring edge nodes. Since scheduling
agents make decisions based only on local observations, while peer offloading couples their resource states and long-term utilities, we formulate the orchestration process as a cooperative Decentralized Partially Observable Markov Decision Process (Dec-POMDP). To solve the Dec-POMDP, we propose an \underline{A}ttention-guided \underline{M}ulti-\underline{A}gent deep reinforcement learning
algorithm with \underline{Diff}usion policies (A-MADiff) under the centralized training with a decentralized execution paradigm. A-MADiff employs
diffusion-based decentralized actors to generate multi-modal preferences over feasible orchestration actions, and an
attention-guided centralized critic to estimate per-
agent values from cross-
agent states under GPU
memory heterogeneity. Numerical results demonstrate that A-MADiff significantly improves the cumulative reward over the state-of-the-art baseline.
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All authors Chongzhi Wu, Zhengtao Li, Jiawen Kang, Jinbo Wen, Xiaohuan Li, Maomao Zhang, Ekram Hossain
Computer Vision
QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation
Yaroslav Prytula, Anton Popov and Dmytro Fishman
Instance
segmentation of overlapping
cells in microscopy remains challenging due to semi-transparent structures that produce weak boundaries and mixed visual evidence in overlap regions. Existing methods address this through local regions of interest or shape priors but lack global
reasoning across overlapping objects. We present QCell, a novel query-based model that de-overlaps
cell instances in microscopy scenes. Our approach combines (i) an instance recombination module that decomposes and recombines query representations in latent space, enabling the model to reason about complete object structure under overlap, and (ii) a contrastive query
alignment objective that combines distinctive instance feature learning and separation of overlapping
cell queries. We additionally introduce a new Organoid
dataset benchmark for overlapping
cell segmentation. We show that QCell outperforms state-of-the-art methods across multiple
benchmarks, achieving +2.2 AP and +2.7 AJI on ISBI2014. Code is available at https://github.com/SlavkoPrytula/QCell
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Artificial Intelligence
Dynamic Important Example Mining for Reinforcement Finetuning
Haoru Tan, Sitong Wu, Yanfeng Chen et al. (+9 more)
Reinforcement fine-tuning (RFT) is increasingly used to strengthen the
reasoning abilities of large models, yet its effectiveness is bound by how training data are selected and used. Most data-centric RFT methods rely on static or heuristic sample selection, implicitly assuming a sample's value is fixed over training. This overlooks the non-stationary dynamics of policy learning and can lead to suboptimal updates. We propose Dynamic Important Example Mining (DIEM), a principled and fully automated framework that makes data utilization adaptive throughout RFT. DIEM integrates two components into each optimization step: (i) a gradient-
alignment importance
estimator that efficiently approximates each sample's marginal contribution to policy improvement; and (ii) a constrained batch reweighting scheme that maximizes aggregate utility while preserving the update's gradient magnitude to stabilize optimization. Across several
reasoning benchmarks, DIEM consistently outperforms strong static and dynamic baselines. The code will be released via https://github.com/hrtan/DIEM.
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All authors Haoru Tan, Sitong Wu, Yanfeng Chen, Shizhen Zhao, Yang-Tian Sun, Tianjia Liu, Chirui Chang, Shaofeng Zhang, Samm Sun, Xiuzhe Wu, Ruobing Xie, Xiaojuan Qi
Artificial Intelligence
GuardianAgent: Policy-Conditioned Risk-Adaptive Anonymization with Verified Adversarial Escalation
Ruiyi Yang, Gayathri Lihinikaduarachchi, Rahat Masood et al. (+2 more)
Privacy protection for live web traffic requires more than detecting private spans.
Agent-based
privacy protection systems must determine whether an outgoing action complies with the destination site's
privacy policy, then apply only the level of rewriting or sanitisation justified by the residual disclosure risk. We present GuardianAgent, a policy-conditioned anonymization framework that couples structured risk assessment with verified adaptive rewriting. GuardianAgent computes risk through AMRSF (Adaptive Multi-factor Risk Scoring Formula), an explicit controller that combines policy-violation likelihood with data sensitivity, recipient transmission, purpose legitimacy, contextual basis, and policy transparency, rather than relying on an LLM to assign risk directly. This risk score determines both the allow/transform/deny decision and the initial anonymization level. For efficiency, GuardianAgent uses an evidential fast path for low-uncertainty policy matches and invokes an LLM slow path only for uncertain cases. For rewriting, it applies a five-level hierarchy driven by a verified
adversarial guesser: guesses trigger escalation only when supported by the original text, preventing hallucinated attacker confidence from causing unnecessary over-anonymization. Experiments across three
benchmarks spanning legal text (TAB), Reddit posts (SynthPAI), and multi-format synthetic PII records (PII-Masking-300k) show that GuardianAgent achieves the strongest
privacy-utility trade-off among published baselines and is the only method to reach more than 0.90
privacy in all three domains, remaining robust under a backbone switch. Action-context stress tests further show that the same outgoing text receives different decisions and anonymization strengths under different recipients, purposes, action bases, and policy-transparency conditions.
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All authors Ruiyi Yang, Gayathri Lihinikaduarachchi, Rahat Masood, Flora D. Salim, Salil S. Kanhere
Artificial Intelligence
Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge
Saransh Kumar Gupta, Armaan Shah, Lipika Dey et al. (+2 more)
The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by LLMs. Although often convincing, such outputs may contain hallucinated ingredients, misrepresented quantities, or culturally implausible combinations, limiting their suitability for downstream applications and knowledge graph construction. In this paper, we present a semi-automated soundness assessment workflow for validating structured recipe data extracted from informal culinary sources and augmented by LLMs. Developed as part of FKG(.in), a knowledge graph of Indian food, the pipeline identifies and addresses common failure modes, including structural inconsistencies, semantic and logical incoherence, and deviations from the source text, through a multi-stage process combining formal grammars, vocabulary-based checks, statistical heuristics, Set
Transformer-based coherence modeling, and
retrieval-based verification. Although evaluated on Indian recipes, the proposed methods are applicable to broader
multilingual and multicultural culinary domains. We provide a practical, auditable, and application-agnostic approach to improving the reliability of machine-readable food knowledge infrastructures in the era of LLM-generated content.
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All authors Saransh Kumar Gupta, Armaan Shah, Lipika Dey, Partha Pratim Das, Ramesh Jain
Machine Learning
RL-FAT: Reinforcement Learning for Fair Adversarial Training
Tejaswini Medi, Levan Mikeladze and Margret Keuper
Deep neural networks remain highly vulnerable to
adversarial perturbations, and
adversarial training (AT) has become a widely used approach for improving robustness. However, improvements in average robust accuracy often mask substantial class-wise disparities: while some classes become more robust, others may remain disproportionately vulnerable under attack. This imbalance raises an important
adversarial fairness concern, particularly in
vision tasks where reliable robustness is expected across all categories. To address this challenge, we propose RL-FAT, a reinforcement-learning-inspired fair
adversarial training framework that uses policy-gradient based feedback from
adversarial predictions. RL-FAT interprets the prediction distribution as a policy and combines correctness-based prediction rewards with class-wise value estimates to compute class-specific advantages for policy-gradient optimization. This enables the model to adaptively focus on class-wise misclassification. Furthermore, we introduce a
fairness-emphasis
adversarial loss that assigns stronger training pressure to classes with high
adversarial loss, thereby mitigating class-wise robustness disparity. By combining reinforcement-driven adaptation with
fairness-emphasis regularization, RL-FAT improves
adversarial robustness while promoting a more balanced robustness distribution across classes. Extensive experiments demonstrate that our method achieves competitive robust accuracy and substantially reduces class-wise robustness imbalance compared with standard
adversarial training baselines.
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Computer Vision · Cross-listed from eess.IV
The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification
Olivier Bernard, William A. Romero R., Cyprien Bouton et al. (+24 more)
Late gadolinium enhancement (LGE) cardiac
magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Nowadays MI volume quantification is not performed routinely in clinical practice. Numerous deep learning (DL) methods have been developed to automate the
segmentation of the myocardium and infarct regions. However, most studies rely on relatively small
datasets which typically undergo pre-processing steps to standardize images and focus on a specific phase of myocardial infarction following reperfusion therapy. These limitations have impeded the development of models that are generalizable across diverse conditions and thus suitable for routine clinical use. To advance research and establish
benchmarks in generalizable learning for myocardial infarct quantification, this paper presents findings from the Myocardial
Segmentation with Automated Infarct Quantification (MYOSAIQ) challenge. The
dataset set up for the challenge combines 439 CMR volumes from two multicenter clinical trials, with representative data acquired in acute and chronic phases after acute MI. Data were acquired in 16 centers using MRI scanners from three different vendors. Six teams participated until the end of the challenge, employing various baseline models, data augmentation techniques, and confidence strategies. To enhance the significance of this study, we compare the challengers' results with those of fine-tuned foundation models. Our results indicate that well-designed UNet-based techniques outperform fully automatic foundation models for LGE MR
segmentation. While the best methods achieve high-quality and stable delineations of the left ventricle and myocardium under various conditions, they remain improvable in accurately segmenting infarct regions.
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All authors Olivier Bernard, William A. Romero R., Cyprien Bouton, Celia Goujat, Hang Jung Ling, Pierre-Marc Jodoin, Fumin Guo, Calder Sheagren, Graham Wright, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Hairui Wang, Xiaomei Wu, Franz Thaler, Gernot Plank, Martin Urschler, Ricardo M. Rosales, Esther Pueyo, Nicolas Duchateau, Frederic Cervenansky, Patrick Clarysse, Loic Belle, Thomas Bochaton, Nathan Mewton, Magalie Viallon, Pierre Croisille
Computer Vision
DARD: Zero-Shot Degradation-Aware Retinex-Guided Diffusion for Low-Light Image Enhancement
Wenjie Cai, Yuezhe Yang, Jianyang Xia et al. (+2 more)
Existing
diffusion-based enhancement methods provide strong generative capability for low-light image enhancement (LLIE), yet they either rely on paired supervision or lack reliable scene constraints in zero-shot settings, often leading to structural inconsistency and color drift. Motivated by conventional Retinex models, which offer physically interpretable priors that can serve as reliable scene constraints yet struggle with mixed degradations in real-world scenarios, we propose DARD, a zero-shot Degradation-Aware Retinex-guided
Diffusion framework for LLIE. DARD first extracts image-specific physical priors from the degraded input through a test-time degradation-aware Retinex decomposition, thereby providing reliable structural guidance for zero-shot restoration. It then injects these priors into reverse
diffusion through a timestep-adaptive frequency fusion strategy to balance structural anchoring and detail generation. Finally, a guided reverse refinement process with physical consistency and Contrastive
Language-Image Pre-training (CLIP)-based semantic guidance is introduced to suppress structural artifacts and semantic drift during sampling. Extensive experiments show that DARD achieves strong distortion and perceptual performance and consistently outperforms existing zero-shot baselines across multiple real-world low-light
benchmarks. To further validate the practical utility of our method for downstream applications, we evaluated its impact on semantic
segmentation. Experiments demonstrate that images enhanced by DARD achieve a 28.10% relative improvement in mIoU over AGLLDiff.
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All authors Wenjie Cai, Yuezhe Yang, Jianyang Xia, Xingbo Dong, Zhe Jin