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795105cf7e30e4a7c888dfed247490e5860077957c62924f253bb7a36053b9bc
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2026-01-01T00:00:00-05:00
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Bayesian Subspace Identification in the MIMO Case
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arXiv:2512.24435v1 Announce Type: new Abstract: This report investigates the extension of the Bayesian Subspace System Identification method proposed in our previous work to the Multiple-Input Multiple-Output (MIMO) case. We derive new equivariant priors and posterior distributions specifically suited for the MIMO framework. Numerical results utilizing the DAISY dataset are reported to validate the approach.
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https://arxiv.org/abs/2512.24435
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Academic Papers
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df51774ee9dc0f2062a275bd279c81329523768c1fa482f7043cf5f9dde1d963
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2026-01-01T00:00:00-05:00
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Exploring Compositionality in Vision Transformers using Wavelet Representations
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arXiv:2512.24438v1 Announce Type: new Abstract: While insights into the workings of the transformer model have largely emerged by analysing their behaviour on language tasks, this work investigates the representations learnt by the Vision Transformer (ViT) encoder through the lens of compositionality. We introduce a framework, analogous to prior work on measuring compositionality in representation learning, to test for compositionality in the ViT encoder. Crucial to drawing this analogy is the Discrete Wavelet Transform (DWT), which is a simple yet effective tool for obtaining input-dependent primitives in the vision setting. By examining the ability of composed representations to reproduce original image representations, we empirically test the extent to which compositionality is respected in the representation space. Our findings show that primitives from a one-level DWT decomposition produce encoder representations that approximately compose in latent space, offering a new perspective on how ViTs structure information.
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https://arxiv.org/abs/2512.24438
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Academic Papers
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a2d0fbeba3670d2c755ca3367afcfe63701b7e9afd2b554116f7e21043bb6e4a
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2026-01-01T00:00:00-05:00
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Sparse classification with positive-confidence data in high dimensions
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arXiv:2512.24443v1 Announce Type: new Abstract: High-dimensional learning problems, where the number of features exceeds the sample size, often require sparse regularization for effective prediction and variable selection. While established for fully supervised data, these techniques remain underexplored in weak-supervision settings such as Positive-Confidence (Pconf) classification. Pconf learning utilizes only positive samples equipped with confidence scores, thereby avoiding the need for negative data. However, existing Pconf methods are ill-suited for high-dimensional regimes. This paper proposes a novel sparse-penalization framework for high-dimensional Pconf classification. We introduce estimators using convex (Lasso) and non-convex (SCAD, MCP) penalties to address shrinkage bias and improve feature recovery. Theoretically, we establish estimation and prediction error bounds for the L1-regularized Pconf estimator, proving it achieves near minimax-optimal sparse recovery rates under Restricted Strong Convexity condition. To solve the resulting composite objective, we develop an efficient proximal gradient algorithm. Extensive simulations demonstrate that our proposed methods achieve predictive performance and variable selection accuracy comparable to fully supervised approaches, effectively bridging the gap between weak supervision and high-dimensional statistics.
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https://arxiv.org/abs/2512.24443
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Academic Papers
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819d85bd4dd72f4e1f6b1ab34ae7d67a2f5580c3ca920acdbd53aefa96c36dec
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2026-01-01T00:00:00-05:00
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Adaptive Learning Guided by Bias-Noise-Alignment Diagnostics
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arXiv:2512.24445v1 Announce Type: new Abstract: Learning systems deployed in nonstationary and safety-critical environments often suffer from instability, slow convergence, or brittle adaptation when learning dynamics evolve over time. While modern optimization, reinforcement learning, and meta-learning methods adapt to gradient statistics, they largely ignore the temporal structure of the error signal itself. This paper proposes a diagnostic-driven adaptive learning framework that explicitly models error evolution through a principled decomposition into bias, capturing persistent drift; noise, capturing stochastic variability; and alignment, capturing repeated directional excitation leading to overshoot. These diagnostics are computed online from lightweight statistics of loss or temporal-difference error trajectories and are independent of model architecture or task domain. We show that the proposed bias-noise-alignment decomposition provides a unifying control backbone for supervised optimization, actor-critic reinforcement learning, and learned optimizers. Building on this framework, we derive diagnostic-driven instantiations including a stabilized supervised optimizer, a diagnostic-regulated actor-critic scheme, and a diagnostic-conditioned learned optimizer. Under standard smoothness assumptions, we establish bounded effective updates and stability properties for all cases. Representative diagnostic illustrations in actor-critic learning highlight how the proposed signals modulate adaptation in response to temporal-difference error structure. Overall, this work elevates error evolution to a first-class object in adaptive learning and provides an interpretable, lightweight foundation for reliable learning in dynamic environments.
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https://arxiv.org/abs/2512.24445
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Academic Papers
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65f26b5ed715e3bc5f282c6f1a43d70e423152daf78018285dbb295964470c52
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2026-01-01T00:00:00-05:00
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Generative forecasting with joint probability models
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arXiv:2512.24446v1 Announce Type: new Abstract: Chaotic dynamical systems exhibit strong sensitivity to initial conditions and often contain unresolved multiscale processes, making deterministic forecasting fundamentally limited. Generative models offer an appealing alternative by learning distributions over plausible system evolutions; yet, most existing approaches focus on next-step conditional prediction rather than the structure of the underlying dynamics. In this work, we reframe forecasting as a fully generative problem by learning the joint probability distribution of lagged system states over short temporal windows and obtaining forecasts through marginalization. This new perspective allows the model to capture nonlinear temporal dependencies, represent multistep trajectory segments, and produce next-step predictions consistent with the learned joint distribution. We also introduce a general, model-agnostic training and inference framework for joint generative forecasting and show how it enables assessment of forecast robustness and reliability using three complementary uncertainty quantification metrics (ensemble variance, short-horizon autocorrelation, and cumulative Wasserstein drift), without access to ground truth. We evaluate the performance of the proposed method on two canonical chaotic dynamical systems, the Lorenz-63 system and the Kuramoto-Sivashinsky equation, and show that joint generative models yield improved short-term predictive skill, preserve attractor geometry, and achieve substantially more accurate long-range statistical behaviour than conventional conditional next-step models.
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https://arxiv.org/abs/2512.24446
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Academic Papers
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4de9354bf20d6cc354ac20942ba5855f04452032fdc96194a3ab1d040b970411
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2026-01-01T00:00:00-05:00
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PackKV: Reducing KV Cache Memory Footprint through LLM-Aware Lossy Compression
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arXiv:2512.24449v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) have demonstrated remarkable potential across a wide range of practical applications. However, long-context inference remains a significant challenge due to the substantial memory requirements of the key-value (KV) cache, which can scale to several gigabytes as sequence length and batch size increase. In this paper, we present \textbf{PackKV}, a generic and efficient KV cache management framework optimized for long-context generation. %, which synergistically supports both latency-critical and throughput-critical inference scenarios. PackKV introduces novel lossy compression techniques specifically tailored to the characteristics of KV cache data, featuring a careful co-design of compression algorithms and system architecture. Our approach is compatible with the dynamically growing nature of the KV cache while preserving high computational efficiency. Experimental results show that, under the same and minimum accuracy drop as state-of-the-art quantization methods, PackKV achieves, on average, \textbf{153.2}\% higher memory reduction rate for the K cache and \textbf{179.6}\% for the V cache. Furthermore, PackKV delivers extremely high execution throughput, effectively eliminating decompression overhead and accelerating the matrix-vector multiplication operation. Specifically, PackKV achieves an average throughput improvement of \textbf{75.7}\% for K and \textbf{171.7}\% for V across A100 and RTX Pro 6000 GPUs, compared to cuBLAS matrix-vector multiplication kernels, while demanding less GPU memory bandwidth. Code available on https://github.com/BoJiang03/PackKV
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https://arxiv.org/abs/2512.24449
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Academic Papers
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dd6494572f2985ab5b54f68f7e1f0a9f41278fce9008e809e21b86f752324c20
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2026-01-01T00:00:00-05:00
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Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations
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arXiv:2512.24452v1 Announce Type: new Abstract: Semantic communications conveys task-relevant meaning rather than focusing solely on message reconstruction, improving bandwidth efficiency and robustness for next-generation wireless systems. However, learned semantic representations can still leak sensitive information to unintended receivers (eavesdroppers). This paper presents a deep learning-based semantic communication framework that jointly supports multiple receiver tasks while explicitly limiting semantic leakage to an eavesdropper. The legitimate link employs a learned encoder at the transmitter, while the receiver trains decoders for semantic inference and data reconstruction. The security problem is formulated via an iterative min-max optimization in which an eavesdropper is trained to improve its semantic inference, while the legitimate transmitter-receiver pair is trained to preserve task performance while reducing the eavesdropper's success. We also introduce an auxiliary layer that superimposes a cooperative, adversarially crafted perturbation on the transmitted waveform to degrade semantic leakage to an eavesdropper. Performance is evaluated over Rayleigh fading channels with additive white Gaussian noise using MNIST and CIFAR-10 datasets. Semantic accuracy and reconstruction quality improve with increasing latent dimension, while the min-max mechanism reduces the eavesdropper's inference performance significantly without degrading the legitimate receiver. The perturbation layer is successful in reducing semantic leakage even when the legitimate link is trained only for its own task. This comprehensive framework motivates semantic communication designs with tunable, end-to-end privacy against adaptive adversaries in realistic wireless settings.
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https://arxiv.org/abs/2512.24452
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Academic Papers
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613248a3379c1833908439b595b55fa1f0bb914c790465ebca4d5c0c4a1d22ba
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2026-01-01T00:00:00-05:00
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Multipliers for forced Lurye systems with slope-restricted nonlinearities
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arXiv:2512.24453v1 Announce Type: new Abstract: Dynamic multipliers can be used to guarantee the stability of Lurye systems with slope-restricted nonlinearities, but give no guarantee that the closed-loop system has finite incremental gain. We show that multipliers guarantee the closed-loop power gain to be bounded and quantifiable. Power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee such Lurye systems have low sensitivity to noise, provided other exogenous signals have constant steady state. For periodic excitation, the closed-loop response can apparently have a subharmonic or chaotic response. We revisit a class of multipliers that can guarantee a unique, attractive and period-preserving solution. We show the multipliers can be derived using classical tools and reconsider assumptions required for their application. Their phase limitations are inherited from those of discrete-time multipliers. The multipliers cannot be used at all frequencies unless the circle criterion can also be applied; this is consistent with known results about dynamic multipliers and incremental stability.
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https://arxiv.org/abs/2512.24453
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Academic Papers
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216132b927fe60fe0f6f61b1d6a5e82d3744744d52892fae42ba988abce5bc4a
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2026-01-01T00:00:00-05:00
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Fast high-order spectral solvers for PDEs on triangulated surfaces with applications to deforming surfaces
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arXiv:2512.24456v1 Announce Type: new Abstract: In this paper, we extend the classical quadrilateral based hierarchical Poincar\'e-Steklov (HPS) framework to triangulated geometries. Traditionally, the HPS method takes as input an unstructured, high-order quadrilateral mesh and relies on tensor-product spectral discretizations on each element. To overcome this restriction, we introduce two complementary high-order strategies for triangular elements: a reduced quadrilateralization approach which is straightforward to implement, and triangle based spectral element method based on Dubiner polynomials. We show numerically that these extensions preserve the spectral accuracy, efficiency, and fast direct-solver structure of the HPS framework. The method is further extended to time dependent and evolving surfaces, and its performance is demonstrated through numerical experiments on reaction-diffusion systems, and geometry driven surface evolution.
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https://arxiv.org/abs/2512.24456
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Academic Papers
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a41d081020a22a5cd84334eda3df309270c6767cc8bda7b70ffa9908b80f44d2
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2026-01-01T00:00:00-05:00
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Document Data Matching for Blockchain-Supported Real Estate
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arXiv:2512.24457v1 Announce Type: new Abstract: The real estate sector remains highly dependent on manual document handling and verification, making processes inefficient and prone to fraud. This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiable credentials (VCs) to automate document extraction, verification, and management. The approach standardizes heterogeneous document formats into VCs and applies automated data matching to detect inconsistencies, while the blockchain provides a decentralized trust layer that reinforces transparency and integrity. A prototype was developed that comprises (i) an OCR-NLP extraction pipeline trained on synthetic datasets, (ii) a backend for credential issuance and management, and (iii) a frontend supporting issuer, holder, and verifier interactions. Experimental results show that the models achieve competitive accuracy across multiple document types and that the end-to-end pipeline reduces verification time while preserving reliability. The proposed framework demonstrates the potential to streamline real estate transactions, strengthen stakeholder trust, and enable scalable, secure digital processes.
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https://arxiv.org/abs/2512.24457
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Academic Papers
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c24b9ffabb60003dd58ba33e85b7e9d36cbb9beb4a6142ae3b1eced212d94136
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2026-01-01T00:00:00-05:00
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Cleaning English Abstracts of Scientific Publications
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arXiv:2512.24459v1 Announce Type: new Abstract: Scientific abstracts are often used as proxies for the content and thematic focus of research publications. However, a significant share of published abstracts contains extraneous information-such as publisher copyright statements, section headings, author notes, registrations, and bibliometric or bibliographic metadata-that can distort downstream analyses, particularly those involving document similarity or textual embeddings. We introduce an open-source, easy-to-integrate language model designed to clean English-language scientific abstracts by automatically identifying and removing such clutter. We demonstrate that our model is both conservative and precise, alters similarity rankings of cleaned abstracts and improves information content of standard-length embeddings.
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https://arxiv.org/abs/2512.24459
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Academic Papers
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82f1c4997bcbceae0e568947d36fb4553cd52567c35aa8e969dc4f9a69fe4471
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2026-01-01T00:00:00-05:00
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IELTS Writing Revision Platform with Automated Essay Scoring and Adaptive Feedback
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arXiv:2512.24460v1 Announce Type: new Abstract: This paper presents the design, development, and evaluation of a proposed revision platform assisting candidates for the International English Language Testing System (IELTS) writing exam. Traditional IELTS preparation methods lack personalised feedback, catered to the IELTS writing rubric. To address these shortcomings, the platform features an attractive user interface (UI), an Automated Essay Scoring system (AES), and targeted feedback tailored to candidates and the IELTS writing rubric. The platform architecture separates conversational guidance from a dedicated writing interface to reduce cognitive load and simulate exam conditions. Through iterative, Design-Based Research (DBR) cycles, the study progressed from rule-based to transformer-based with a regression head scoring, mounted with adaptive feedback. Early cycles (2-3) revealed fundamental limitations of rule-based approaches: mid-band compression, low accuracy, and negative $R^2$ values. DBR Cycle 4 implemented a DistilBERT transformer model with a regression head, yielding substantial improvements with MAE of 0.66 and positive $R^2$. This enabled Cycle 5's adaptive feedback implementation, which demonstrated statistically significant score improvements (mean +0.060 bands, p = 0.011, Cohen's d = 0.504), though effectiveness varied by revision strategy. Findings suggest automated feedback functions are most suited as a supplement to human instruction, with conservative surface-level corrections proving more reliable than aggressive structural interventions for IELTS preparation contexts. Challenges remain in assessing higher-band essays, and future work should incorporate longitudinal studies with real IELTS candidates and validation from official examiners.
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https://arxiv.org/abs/2512.24460
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Academic Papers
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d2bcdcf2db1def989fc64678117b09ad00e4a81176a6c70a8726da799cc860bc
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2026-01-01T00:00:00-05:00
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Align While Search: Belief-Guided Exploratory Inference for World-Grounded Embodied Agents
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arXiv:2512.24461v1 Announce Type: new Abstract: In this paper, we propose a test-time adaptive agent that performs exploratory inference through posterior-guided belief refinement without relying on gradient-based updates or additional training for LLM agent operating under partial observability. Our agent maintains an external structured belief over the environment state, iteratively updates it via action-conditioned observations, and selects actions by maximizing predicted information gain over the belief space. We estimate information gain using a lightweight LLM-based surrogate and assess world alignment through a novel reward that quantifies the consistency between posterior belief and ground-truth environment configuration. Experiments show that our method outperforms inference-time scaling baselines such as prompt-augmented or retrieval-enhanced LLMs, in aligning with latent world states with significantly lower integration overhead.
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https://arxiv.org/abs/2512.24461
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Academic Papers
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daa228bdb249f4842caf3266d1b2f619616b92657c9e60eeed8f95c59c207487
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2026-01-01T00:00:00-05:00
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"Game Changer" or "Overenthusiastic Drunk Acquaintance"? Generative AI Use by Blind and Low Vision Software Professionals in the Workplace
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arXiv:2512.24462v1 Announce Type: new Abstract: The software development workplace poses numerous technical and collaborative accessibility challenges for blind and low vision software professionals (BLVSPs). Though Generative AI (GenAI) is increasingly adopted within the software development industry and has been a rapidly growing topic of interest in research, to date, the unique perspectives of BLVSPs have yet to be consulted. We report on a qualitative study involving 39 semi-structured interviews with BLVSPs about what the introduction of GenAI has meant for their work. We found that BLVSPs used GenAI for many software development tasks, resulting in benefits such as increased productivity and accessibility. However, significant costs were also accompanied by GenAI use as they were more vulnerable to hallucinations than their sighted colleagues. Sometimes, organizational policies prevented use. Based on our findings, we discuss the higher-risks and higher-returns that BLVSPs had to carefully weigh when deciding whether and when to use GenAI tools for work.
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https://arxiv.org/abs/2512.24462
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Academic Papers
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df7a5eeb2f55508113daf543bc05ba0037f132cd23172ad5fafcf0abc09ff5f2
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2026-01-01T00:00:00-05:00
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Spectral and Spatial Graph Learning for Multispectral Solar Image Compression
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arXiv:2512.24463v1 Announce Type: new Abstract: High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations, leveraging two complementary modules: (1) the Inter-Spectral Windowed Graph Embedding (iSWGE), which explicitly models inter-band relationships by representing spectral channels as graph nodes with learned edge features; and (2) the Windowed Spatial Graph Attention and Convolutional Block Attention (WSGA-C), which combines sparse graph attention with convolutional attention to reduce spatial redundancy and emphasize fine-scale structures. Evaluations on the SDOML dataset across six extreme ultraviolet (EUV) channels show that our approach achieves a 20.15%reduction in Mean Spectral Information Divergence (MSID), up to 1.09% PSNR improvement, and a 1.62% log transformed MS-SSIM gain over strong learned baselines, delivering sharper and spectrally faithful reconstructions at comparable bits-per-pixel rates. The code is publicly available at https://github.com/agyat4/sgraph .
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https://arxiv.org/abs/2512.24463
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Academic Papers
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fd81498d9259ff34966f1bc315d2ffa9ecabf904403ecd9c452294b73d1413a6
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2026-01-01T00:00:00-05:00
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On the Difficulty of Measuring Divisiveness of Proposals under Ranked Preferences
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arXiv:2512.24467v1 Announce Type: new Abstract: Given the stated preferences of several people over a number of proposals regarding public policy initiatives, some of those proposals might be judged to be more ``divisive'' than others. When designing online participatory platforms to support digital democracy initiatives enabling citizens to deliberate over such proposals, we might wish to equip those platforms with the functionality to retrieve the most divisive proposals currently under discussion. Such a service would be useful for analysing the progress of deliberation and steering discussion towards issues that still require further debate. Guided by this use case, we explore possibilities for providing a clear definition of what it means to select a set of most divisive proposals on the basis of people's stated preferences over proposals. Then, employing the axiomatic method familiar from social choice theory, we show that the task of selecting the most divisive proposals in a manner that satisfies certain seemingly mild normative requirements faces a number of fundamental difficulties.
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https://arxiv.org/abs/2512.24467
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Academic Papers
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7b5dc3b003e856ed7cbbbf5d8cada288ee03ee25cfb20c87c539aa9e3dca69fc
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2026-01-01T00:00:00-05:00
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Infinite families of graphs and stable completion of arbitrary matrices, Part I
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arXiv:2512.24468v1 Announce Type: new Abstract: We study deterministic constructions of graphs for which the unique completion of low rank matrices is generically possible regardless of the values of the entries. We relate the completability to the presence of some patterns (particular unions of self-avoiding walks) in the subgraph of the lattice graph generated from the support of the bi-adjacency matrix. The construction makes it possible to design infinite families of graphs on which exact and stable completion is possible for every fixed rank matrix through the sum-of-squares hierarchy.
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https://arxiv.org/abs/2512.24468
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Academic Papers
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b85aa54a98a63e0ff65117f4c69408e1a4f0a9eb589bb6d9bcd808b4676499b0
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2026-01-01T00:00:00-05:00
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Foundation models on the bridge: Semantic hazard detection and safety maneuvers for maritime autonomy with vision-language models
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arXiv:2512.24470v1 Announce Type: new Abstract: The draft IMO MASS Code requires autonomous and remotely supervised maritime vessels to detect departures from their operational design domain, enter a predefined fallback that notifies the operator, permit immediate human override, and avoid changing the voyage plan without approval. Meeting these obligations in the alert-to-takeover gap calls for a short-horizon, human-overridable fallback maneuver. Classical maritime autonomy stacks struggle when the correct action depends on meaning (e.g., diver-down flag means people in the water, fire close by means hazard). We argue (i) that vision-language models (VLMs) provide semantic awareness for such out-of-distribution situations, and (ii) that a fast-slow anomaly pipeline with a short-horizon, human-overridable fallback maneuver makes this practical in the handover window. We introduce Semantic Lookout, a camera-only, candidate-constrained vision-language model (VLM) fallback maneuver selector that selects one cautious action (or station-keeping) from water-valid, world-anchored trajectories under continuous human authority. On 40 harbor scenes we measure per-call scene understanding and latency, alignment with human consensus (model majority-of-three voting), short-horizon risk-relief on fire hazard scenes, and an on-water alert->fallback maneuver->operator handover. Sub-10 s models retain most of the awareness of slower state-of-the-art models. The fallback maneuver selector outperforms geometry-only baselines and increases standoff distance on fire scenes. A field run verifies end-to-end operation. These results support VLMs as semantic fallback maneuver selectors compatible with the draft IMO MASS Code, within practical latency budgets, and motivate future work on domain-adapted, hybrid autonomy that pairs foundation-model semantics with multi-sensor bird's-eye-view perception and short-horizon replanning.
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https://arxiv.org/abs/2512.24470
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Academic Papers
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7f7758dff4156a9b038d93ba229b590086d1e0cf523aec628c681a95f7960d32
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2026-01-01T00:00:00-05:00
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F2IDiff: Real-world Image Super-resolution using Feature to Image Diffusion Foundation Model
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arXiv:2512.24473v1 Announce Type: new Abstract: With the advent of Generative AI, Single Image Super-Resolution (SISR) quality has seen substantial improvement, as the strong priors learned by Text-2-Image Diffusion (T2IDiff) Foundation Models (FM) can bridge the gap between High-Resolution (HR) and Low-Resolution (LR) images. However, flagship smartphone cameras have been slow to adopt generative models because strong generation can lead to undesirable hallucinations. For substantially degraded LR images, as seen in academia, strong generation is required and hallucinations are more tolerable because of the wide gap between LR and HR images. In contrast, in consumer photography, the LR image has substantially higher fidelity, requiring only minimal hallucination-free generation. We hypothesize that generation in SISR is controlled by the stringency and richness of the FM's conditioning feature. First, text features are high level features, which often cannot describe subtle textures in an image. Additionally, Smartphone LR images are at least $12MP$, whereas SISR networks built on T2IDiff FM are designed to perform inference on much smaller images ($<1MP$). As a result, SISR inference has to be performed on small patches, which often cannot be accurately described by text feature. To address these shortcomings, we introduce an SISR network built on a FM with lower-level feature conditioning, specifically DINOv2 features, which we call a Feature-to-Image Diffusion (F2IDiff) Foundation Model (FM). Lower level features provide stricter conditioning while being rich descriptors of even small patches.
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https://arxiv.org/abs/2512.24473
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Academic Papers
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3a4c5032776330f12a1577655ea36d1c8fa94dde79cd42e4725bcdd078a58a5e
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2026-01-01T00:00:00-05:00
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HOLOGRAPH: Active Causal Discovery via Sheaf-Theoretic Alignment of Large Language Model Priors
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arXiv:2512.24478v1 Announce Type: new Abstract: Causal discovery from observational data remains fundamentally limited by identifiability constraints. Recent work has explored leveraging Large Language Models (LLMs) as sources of prior causal knowledge, but existing approaches rely on heuristic integration that lacks theoretical grounding. We introduce HOLOGRAPH, a framework that formalizes LLM-guided causal discovery through sheaf theory--representing local causal beliefs as sections of a presheaf over variable subsets. Our key insight is that coherent global causal structure corresponds to the existence of a global section, while topological obstructions manifest as non-vanishing sheaf cohomology. We propose the Algebraic Latent Projection to handle hidden confounders and Natural Gradient Descent on the belief manifold for principled optimization. Experiments on synthetic and real-world benchmarks demonstrate that HOLOGRAPH provides rigorous mathematical foundations while achieving competitive performance on causal discovery tasks with 50-100 variables. Our sheaf-theoretic analysis reveals that while Identity, Transitivity, and Gluing axioms are satisfied to numerical precision (<10^{-6}), the Locality axiom fails for larger graphs, suggesting fundamental non-local coupling in latent variable projections. Code is available at [https://github.com/hyunjun1121/holograph](https://github.com/hyunjun1121/holograph).
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https://arxiv.org/abs/2512.24478
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Academic Papers
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4a409a65f1c821c567dbbdbf9d7e6b6d0e0e102c1862d1b39431364883ca5b91
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2026-01-01T00:00:00-05:00
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Design of Linear Residual Generators for Combined Fault Detection and Estimation in Nonlinear Systems
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arXiv:2512.24484v1 Announce Type: new Abstract: A systematic method for the design of linear residual generators for combined fault detection and estimation in nonlinear systems is developed. The proposed residual generator is a linear functional observer built for an extended system that incorporates the fault dynamics from a linear exo-system, and in addition possesses disturbance-decoupling properties. Necessary and sufficient conditions for the existence of such residual generators for nonlinear systems are derived. As long as these conditions are satisfied, we obtain explicit design formulas for the residual generator. The results are illustrated through a chemical reactor case study, which demonstrates the effectiveness of the proposed methodology.
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https://arxiv.org/abs/2512.24484
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Academic Papers
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450babaaa8c6a82713019df0f4da90de90273f7557cc1bedbc8a9e508c4a4b0f
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2026-01-01T00:00:00-05:00
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Networked Markets, Fragmented Data: Adaptive Graph Learning for Customer Risk Analytics and Policy Design
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arXiv:2512.24487v1 Announce Type: new Abstract: Financial institutions face escalating challenges in identifying high-risk customer behaviors within massive transaction networks, where fraudulent activities exploit market fragmentation and institutional boundaries. We address three fundamental problems in customer risk analytics: data silos preventing holistic relationship assessment, extreme behavioral class imbalance, and suboptimal customer intervention strategies that fail to balance compliance costs with relationship value. We develop an integrated customer intelligence framework combining federated learning, relational network analysis, and adaptive targeting policies. Our federated graph neural network enables collaborative behavior modeling across competing institutions without compromising proprietary customer data, using privacy-preserving embeddings to capture cross-market relational patterns. We introduce cross-bank Personalized PageRank to identify coordinated behavioral clusters providing interpretable customer network segmentation for risk managers. A hierarchical reinforcement learning mechanism optimizes dynamic intervention targeting, calibrating escalation policies to maximize prevention value while minimizing customer friction and operational costs. Analyzing 1.4 million customer transactions across seven markets, our approach reduces false positive and false negative rates to 4.64% and 11.07%, substantially outperforming single-institution models. The framework prevents 79.25% of potential losses versus 49.41% under fixed-rule policies, with optimal market-specific targeting thresholds reflecting heterogeneous customer base characteristics. These findings demonstrate that federated customer analytics materially improve both risk management effectiveness and customer relationship outcomes in networked competitive markets.
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https://arxiv.org/abs/2512.24487
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Academic Papers
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2478c195db16c1755bc085938f61cd0f0f107f4db8f4449028790d3a733c7149
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2026-01-01T00:00:00-05:00
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Energy-Aware Bayesian Control Barrier Functions for Physics-Informed Gaussian Process Dynamics
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arXiv:2512.24493v1 Announce Type: new Abstract: We study safe control for dynamical systems whose continuous-time dynamics are learned with Gaussian processes (GPs), focusing on mechanical and port-Hamiltonian systems where safety is naturally expressed via energy constraints. The availability of a GP Hamiltonian posterior naturally raises the question of how to systematically exploit this structure to design an energy-aware control barrier function with high-probability safety guarantees. We address this problem by developing a Bayesian-CBF framework and instantiating it with energy-aware Bayesian-CBFs (EB-CBFs) that construct conservative energy-based barriers directly from the Hamiltonian and vector-field posteriors, yielding safety filters that minimally modify a nominal controller while providing probabilistic energy safety guarantees. Numerical simulations on a mass-spring system demonstrate that the proposed EB-CBFs achieve high-probability safety under noisy sampled GP-learned dynamics.
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https://arxiv.org/abs/2512.24493
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Academic Papers
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222dbc7ebb72bae0743d71ddd093210f3a8ef69795d99554f9a9547973990544
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2026-01-01T00:00:00-05:00
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What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
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arXiv:2512.24497v1 Announce Type: new Abstract: A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and environments. A popular recent approach involves training a world model from state-action trajectories and subsequently use it with a planning algorithm to solve new tasks. Planning is commonly performed in the input space, but a recent family of methods has introduced planning algorithms that optimize in the learned representation space of the world model, with the promise that abstracting irrelevant details yields more efficient planning. In this work, we characterize models from this family as JEPA-WMs and investigate the technical choices that make algorithms from this class work. We propose a comprehensive study of several key components with the objective of finding the optimal approach within the family. We conducted experiments using both simulated environments and real-world robotic data, and studied how the model architecture, the training objective, and the planning algorithm affect planning success. We combine our findings to propose a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks. Code, data and checkpoints are available at https://github.com/facebookresearch/jepa-wms.
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https://arxiv.org/abs/2512.24497
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Academic Papers
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bd628a2aaa0d4ebbae4dff7388ee3ee1514377e4cf154a2ae7a5e14623194720
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2026-01-01T00:00:00-05:00
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Open Horn Type Theory
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arXiv:2512.24498v1 Announce Type: new Abstract: We introduce Open Horn Type Theory (OHTT), an extension of dependent type theory with two primitive judgment forms: coherence and gap, subject to a mutual exclusion law. Unlike classical or intuitionistic negation, gap is not defined via implication but is a primitive witness of non-coherence. Judgments may also be open -- neither coherent nor gapped -- yielding a trichotomy that generalizes the binary derivable/underivable distinction. The central construction is the transport horn: a configuration where a term and a path both cohere, but transport along the path is witnessed as gapped. This captures obstructions that Homotopy Type Theory (HoTT) cannot express, since HoTT's Kan condition guarantees all transport succeeds. We develop the semantics via ruptured simplicial sets -- simplicial sets equipped with coherence and gap structure -- and ruptured Kan complexes, which model types where some horns fill, some are gap-witnessed, and some remain open. We show that HoTT embeds as the coherent fragment of OHTT, recovered by imposing totality. Three classes of obstructions are developed in detail: topological (monodromy, holonomy, characteristic classes), semantic (polysemy, meaning fibrations), and logical (resource-sensitive derivability, substructural failure). In each case, the gap witness is positive structure -- not absence of proof, but certified obstruction.
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https://arxiv.org/abs/2512.24498
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Academic Papers
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63521164cf74e1d7dfa1eea800bad0354f6fa671c17d3825a77751f73bc21544
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2026-01-01T00:00:00-05:00
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Training-Free Color-Aware Adversarial Diffusion Sanitization for Diffusion Stegomalware Defense at Security Gateways
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arXiv:2512.24499v1 Announce Type: new Abstract: The rapid expansion of generative AI has normalized large-scale synthetic media creation, enabling new forms of covert communication. Recent generative steganography methods, particularly those based on diffusion models, can embed high-capacity payloads without fine-tuning or auxiliary decoders, creating significant challenges for detection and remediation. Coverless diffusion-based techniques are difficult to counter because they generate image carriers directly from secret data, enabling attackers to deliver stegomalware for command-and-control, payload staging, and data exfiltration while bypassing detectors that rely on cover-stego discrepancies. This work introduces Adversarial Diffusion Sanitization (ADS), a training-free defense for security gateways that neutralizes hidden payloads rather than detecting them. ADS employs an off-the-shelf pretrained denoiser as a differentiable proxy for diffusion-based decoders and incorporates a color-aware, quaternion-coupled update rule to reduce artifacts under strict distortion limits. Under a practical threat model and in evaluation against the state-of-the-art diffusion steganography method Pulsar, ADS drives decoder success rates to near zero with minimal perceptual impact. Results demonstrate that ADS provides a favorable security-utility trade-off compared to standard content transformations, offering an effective mitigation strategy against diffusion-driven steganography.
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https://arxiv.org/abs/2512.24499
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Academic Papers
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c5a4a039c357fb5e206e3d4202030ec656ba23a07a5289c7ada34c78a1717ee6
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2026-01-01T00:00:00-05:00
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Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice
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arXiv:2512.24503v1 Announce Type: new Abstract: Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scale training runs. However, the community has a limited understanding of whether and when conclusions drawn from small-scale experiments reliably transfer to full-scale model training. In this work, we uncover a subtle yet critical issue in the standard experimental protocol for data recipe assessment: the use of identical small-scale model training configurations across all data recipes in the name of "fair" comparison. We show that the experiment conclusions about data quality can flip with even minor adjustments to training hyperparameters, as the optimal training configuration is inherently data-dependent. Moreover, this fixed-configuration protocol diverges from full-scale model development pipelines, where hyperparameter optimization is a standard step. Consequently, we posit that the objective of data recipe assessment should be to identify the recipe that yields the best performance under data-specific tuning. To mitigate the high cost of hyperparameter tuning, we introduce a simple patch to the evaluation protocol: using reduced learning rates for proxy model training. We show that this approach yields relative performance that strongly correlates with that of fully tuned large-scale LLM pretraining runs. Theoretically, we prove that for random-feature models, this approach preserves the ordering of datasets according to their optimal achievable loss. Empirically, we validate this approach across 23 data recipes covering four critical dimensions of data curation, demonstrating dramatic improvements in the reliability of small-scale experiments.
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https://arxiv.org/abs/2512.24503
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Academic Papers
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e22a7330ffe1143f381e7b084323b692de74b77bcd26d6716b915514f7a94a75
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2026-01-01T00:00:00-05:00
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Thinking on Maps: How Foundation Model Agents Explore, Remember, and Reason Map Environments
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arXiv:2512.24504v1 Announce Type: new Abstract: Map environments provide a fundamental medium for representing spatial structure. Understanding how foundation model (FM) agents understand and act in such environments is therefore critical for enabling reliable map-based reasoning and applications. However, most existing evaluations of spatial ability in FMs rely on static map inputs or text-based queries, overlooking the interactive and experience-driven nature of spatial understanding.In this paper, we propose an interactive evaluation framework to analyze how FM agents explore, remember, and reason in symbolic map environments. Agents incrementally explore partially observable grid-based maps consisting of roads, intersections, and points of interest (POIs), receiving only local observations at each step. Spatial understanding is then evaluated using six kinds of spatial tasks. By systematically varying exploration strategies, memory representations, and reasoning schemes across multiple foundation models, we reveal distinct functional roles of these components. Exploration primarily affects experience acquisition but has a limited impact on final reasoning accuracy. In contrast, memory representation plays a central role in consolidating spatial experience, with structured memories particularly sequential and graph-based representations, substantially improving performance on structure-intensive tasks such as path planning. Reasoning schemes further shape how stored spatial knowledge is used, with advanced prompts supporting more effective multi-step inference. We further observe that spatial reasoning performance saturates across model versions and scales beyond a certain capability threshold, indicating that improvements in map-based spatial understanding require mechanisms tailored to spatial representation and reasoning rather than scaling alone.
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https://arxiv.org/abs/2512.24504
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Academic Papers
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814737511e9228e5aa79544452e411fcbb5a8c011d0e2c741a24ae1ccb597c99
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2026-01-01T00:00:00-05:00
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Evaluating the Reasoning Abilities of LLMs on Underrepresented Mathematics Competition Problems
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arXiv:2512.24505v1 Announce Type: new Abstract: Understanding the limitations of Large Language Models, or LLMs, in mathematical reasoning has been the focus of several recent studies. However, the majority of these studies use the same datasets for benchmarking, which limits the generalizability of their findings and may not fully capture the diverse challenges present in mathematical tasks. The purpose of the present study is to analyze the performance of LLMs on underrepresented mathematics competition problems. We prompted three leading LLMs, namely GPT-4o-mini, Gemini-2.0-Flash, and DeepSeek-V3, with the Missouri Collegiate Mathematics Competition problems in the areas of Calculus, Analytic Geometry, and Discrete Mathematics. The LLMs responses were then compared to the known correct solutions in order to determine the accuracy of the LLM for each problem domain. We also analyzed the LLMs reasoning to explore patterns in errors across problem types and models. DeepSeek-V3 has the best performance in all three categories of Calculus, Analytic Geometry, and Discrete Mathematics, both in reasoning and correct final answers. All three LLMs exhibited notably weak performance in Geometry. The majority of errors made by DeepSeek-V3 were attributed to computational and logical mistakes, whereas GPT-4o-mini frequently exhibited logical and approach-related errors. Gemini, on the other hand, tended to struggle with incomplete reasoning and drawing rushed conclusions. In conclusion, evaluating LLMs on underrepresented mathematics competition datasets can provide deeper insights into their distinct error patterns and highlight ongoing challenges in structured reasoning, particularly within the domain of Geometry.
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https://arxiv.org/abs/2512.24505
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Academic Papers
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6a8d72f85261bbe86e2270cad7bf3279024915b08a789d027c0a1a2d86f064f0
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2026-01-01T00:00:00-05:00
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Generalising E-prop to Deep Networks
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arXiv:2512.24506v1 Announce Type: new Abstract: Recurrent networks are typically trained with backpropagation through time (BPTT). However, BPTT requires storing the history of all states in the network and then replaying them sequentially backwards in time. This computation appears extremely implausible for the brain to implement. Real Time Recurrent Learning (RTRL) proposes an mathematically equivalent alternative where gradient information is propagated forwards in time locally alongside the regular forward pass, however it has significantly greater computational complexity than BPTT which renders it impractical for large networks. E-prop proposes an approximation of RTRL which reduces its complexity to the level of BPTT while maintaining a purely online forward update which can be implemented by an eligibility trace at each synapse. However, works on RTRL and E-prop ubiquitously investigate learning in a single layer with recurrent dynamics. However, learning in the brain spans multiple layers and consists of both hierarchal dynamics in depth as well as time. In this mathematical note, we extend the E-prop framework to handle arbitrarily deep networks, deriving a novel recursion relationship across depth which extends the eligibility traces of E-prop to deeper layers. Our results thus demonstrate an online learning algorithm can perform accurate credit assignment across both time and depth simultaneously, allowing the training of deep recurrent networks without backpropagation through time.
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https://arxiv.org/abs/2512.24506
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Academic Papers
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da0f397b9f9eb5e3a41faefd3ca68ed7c861140ea136a41d03de518973d927c3
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2026-01-01T00:00:00-05:00
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Understanding LLM Checkpoint/Restore I/O Strategies and Patterns
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arXiv:2512.24511v1 Announce Type: new Abstract: As LLMs and foundation models scale, checkpoint/restore has become a critical pattern for training and inference. With 3D parallelism (tensor, pipeline, data), checkpointing involves many processes, each managing numerous tensors of varying shapes and sizes, that must be persisted frequently to stable storage (e.g., parallel file systems). This turns checkpoint/restore into a big-data I/O problem characterized by volume, variety, and velocity. The workflow must traverse the full storage stack -- from GPU memory through host memory and local storage to external repositories -- whose tiers differ by orders of magnitude in performance, creating bottlenecks under concurrency even with asynchronous flush/prefetch. Kernel-accelerated I/O libraries such as \texttt{liburing} may mitigate these issues versus POSIX, but their effectiveness for LLM checkpointing remains underexplored. We develop microbenchmarks to quantify trade-offs when using \texttt{liburing}, evaluating how aggregation, alignment, and I/O coalescing interact under buffered and direct I/O. We find that uncoalesced small-buffer operations halve throughput relative to synthetic workloads, while file system-aware aggregation restores bandwidth and reduces metadata overhead. Compared to state-of-the-art LLM checkpointing engines, our approach achieves up to $3.9\times$ higher write throughput than DataStates-LLM and $7.6\times$ higher than TorchSnapshot. These results highlight the need for aggregation and coalescing strategies that align with modern file systems and I/O backends.
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https://arxiv.org/abs/2512.24511
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Academic Papers
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bf68d64356fadf0dc2814d4a17b7e6529107b26aa1ef49a5aa767fe7c9f23ae2
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2026-01-01T00:00:00-05:00
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From Static to Dynamic: Evaluating the Perceptual Impact of Dynamic Elements in Urban Scenes Using Generative Inpainting
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arXiv:2512.24513v1 Announce Type: new Abstract: Understanding urban perception from street view imagery has become a central topic in urban analytics and human centered urban design. However, most existing studies treat urban scenes as static and largely ignore the role of dynamic elements such as pedestrians and vehicles, raising concerns about potential bias in perception based urban analysis. To address this issue, we propose a controlled framework that isolates the perceptual effects of dynamic elements by constructing paired street view images with and without pedestrians and vehicles using semantic segmentation and MLLM guided generative inpainting. Based on 720 paired images from Dongguan, China, a perception experiment was conducted in which participants evaluated original and edited scenes across six perceptual dimensions. The results indicate that removing dynamic elements leads to a consistent 30.97% decrease in perceived vibrancy, whereas changes in other dimensions are more moderate and heterogeneous. To further explore the underlying mechanisms, we trained 11 machine learning models using multimodal visual features and identified that lighting conditions, human presence, and depth variation were key factors driving perceptual change. At the individual level, 65% of participants exhibited significant vibrancy changes, compared with 35-50% for other dimensions; gender further showed a marginal moderating effect on safety perception. Beyond controlled experiments, the trained model was extended to a city-scale dataset to predict vibrancy changes after the removal of dynamic elements. The city level results reveal that such perceptual changes are widespread and spatially structured, affecting 73.7% of locations and 32.1% of images, suggesting that urban perception assessments based solely on static imagery may substantially underestimate urban liveliness.
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https://arxiv.org/abs/2512.24513
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Academic Papers
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4eabb87866704503ab9c3b7dc1e9fd193f0d087bd00abac2dd044ceb1feef1fa
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2026-01-01T00:00:00-05:00
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Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech
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arXiv:2512.24517v1 Announce Type: new Abstract: Automatic speech transcripts are often delivered as unstructured word streams that impede readability and repurposing. We recast paragraph segmentation as the missing structuring step and fill three gaps at the intersection of speech processing and text segmentation. First, we establish TEDPara (human-annotated TED talks) and YTSegPara (YouTube videos with synthetic labels) as the first benchmarks for the paragraph segmentation task. The benchmarks focus on the underexplored speech domain, where paragraph segmentation has traditionally not been part of post-processing, while also contributing to the wider text segmentation field, which still lacks robust and naturalistic benchmarks. Second, we propose a constrained-decoding formulation that lets large language models insert paragraph breaks while preserving the original transcript, enabling faithful, sentence-aligned evaluation. Third, we show that a compact model (MiniSeg) attains state-of-the-art accuracy and, when extended hierarchically, jointly predicts chapters and paragraphs with minimal computational cost. Together, our resources and methods establish paragraph segmentation as a standardized, practical task in speech processing.
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https://arxiv.org/abs/2512.24517
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Academic Papers
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cd1273543e1b603a1ec2847542c994c881fc3ef284f2bbdbc103fd85dc3a2f14
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2026-01-01T00:00:00-05:00
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Using Large Language Models To Translate Machine Results To Human Results
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arXiv:2512.24518v1 Announce Type: new Abstract: Artificial intelligence (AI) has transformed medical imaging, with computer vision (CV) systems achieving state-of-the-art performance in classification and detection tasks. However, these systems typically output structured predictions, leaving radiologists responsible for translating results into full narrative reports. Recent advances in large language models (LLMs), such as GPT-4, offer new opportunities to bridge this gap by generating diagnostic narratives from structured findings. This study introduces a pipeline that integrates YOLOv5 and YOLOv8 for anomaly detection in chest X-ray images with a large language model (LLM) to generate natural-language radiology reports. The YOLO models produce bounding-box predictions and class labels, which are then passed to the LLM to generate descriptive findings and clinical summaries. YOLOv5 and YOLOv8 are compared in terms of detection accuracy, inference latency, and the quality of generated text, as measured by cosine similarity to ground-truth reports. Results show strong semantic similarity between AI and human reports, while human evaluation reveals GPT-4 excels in clarity (4.88/5) but exhibits lower scores for natural writing flow (2.81/5), indicating that current systems achieve clinical accuracy but remain stylistically distinguishable from radiologist-authored text.
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https://arxiv.org/abs/2512.24518
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Academic Papers
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6c329a9ee0f9d6c0314341fc552d8e631ef25ddaf47d1de6922f6b9388d2915c
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2026-01-01T00:00:00-05:00
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Analyzing Airline Alliances through Multi-Attribute Graph Partitioning to Maximize Competition and Market Penetration Capability
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arXiv:2512.24519v1 Announce Type: new Abstract: The air transportation market is highly competitive and dynamic. Airlines often form alliances to expand their network reach, improve operational efficiency, and enhance customer experience. However, the impact of these alliances on market competition and operational efficiency is not fully understood. In this paper, we propose a novel approach to analyze airline alliances using multi\mfabian{-}attribute graph partitioning. We develop metrics to quantify the competitiveness of flight segments and the market penetration capability of airlines based on their alliance memberships. We formulate a bi\mfabian{-}objective optimization problem to maximize both competition and market penetration simultaneously. We also propose algorithms to solve this optimization problem and demonstrate their effectiveness using real-world flight schedule data. Our results provide insights into the structure of airline alliances and their implications for market competition and operational efficiency.
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https://arxiv.org/abs/2512.24519
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Academic Papers
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161c0fbff1d743420828bdacc32d3ea2cf8eca5a4ae209856e0964e5b2a521b2
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2026-01-01T00:00:00-05:00
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Exponential Convergence of Deep Composite Polynomial Approximation for Cusp-Type Functions
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arXiv:2512.24523v1 Announce Type: new Abstract: We investigate deep composite polynomial approximations of continuous but non-differentiable functions with algebraic cusp singularities. The functions in focus consist of finitely many cusp terms of the form $|x-a_j|^{\alpha_j}$ with rational exponents $\alpha_j\in(0,1)$ on a real-analytic background. We propose a constructive approximation scheme that combines a division-free polynomial iteration for fractional powers with an outer layer for the analytic polynomial fitting. Our main result shows that this composite structure achieves exponential convergence in the the number of scalar coefficients in the inner and outer polynomial layers. Specifically, the $L^p([-1,1])$ approximation error, decays exponentially with respect to the parameter budget, in contrast to the algebraic rates obtained by classical single-layer polynomial approximation for cusp-type functions. Numerical experiments for both single and multiple cusp configurations confirm the theoretical rates and demonstrate the parameter efficiency of deep composite polynomial constructions.
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https://arxiv.org/abs/2512.24523
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Academic Papers
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87d43b53161810cf95bedcb0cef3a113ff7219552a89a3ddbb37bb79f604459e
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2026-01-01T00:00:00-05:00
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A Magnified View into Heterogeneous-ISA Thread Migration Performance without State Transformation
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arXiv:2512.24530v1 Announce Type: new Abstract: Heterogeneous-ISA processor designs have attracted considerable research interest. However, unlike their homogeneous-ISA counterparts, explicit software support for bridging ISA heterogeneity is required. The lack of a compilation toolchain ready to support heterogeneous-ISA targets has been a major factor hindering research in this exciting emerging area. For any such compiler, "getting right" the mechanics involved in state transformation upon migration and doing this efficiently is of critical importance. In particular, any runtime conversion of the current program stack from one architecture to another would be prohibitively expensive. In this paper, we design and develop Unifico, a new multi-ISA compiler that generates binaries that maintain the same stack layout during their execution on either architecture. Unifico avoids the need for runtime stack transformation, thus eliminating overheads associated with ISA migration. Additional responsibilities of the Unifico compiler backend include maintenance of a uniform ABI and virtual address space across ISAs. Unifico is implemented using the LLVM compiler infrastructure, and we are currently targeting the x86-64 and ARMv8 ISAs. We have evaluated Unifico across a range of compute-intensive NAS benchmarks and show its minimal impact on overall execution time, where less than 6% (10%) overhead is introduced on average for high-end (low-end) processors. We also analyze the performance impact of Unifico's key design features and demonstrate that they can be further optimized to mitigate this impact. When compared against the state-of-the-art Popcorn compiler, Unifico reduces binary size overhead from ~200% to ~10%, whilst eliminating the stack transformation overhead during ISA migration.
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https://arxiv.org/abs/2512.24530
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Academic Papers
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bfa8fc1bbe9741f4fa23d164aeb630ba58c7365445883c4c8422e652be7cea69
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2026-01-01T00:00:00-05:00
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Correctness of Extended RSA Public Key Cryptosystem
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arXiv:2512.24531v1 Announce Type: new Abstract: This paper proposes an alternative approach to formally establishing the correctness of the RSA public key cryptosystem. The methodology presented herein deviates slightly from conventional proofs found in existing literature. Specifically, this study explores the conditions under which the choice of the positive integer N, a fundamental component of RSA, can be extended beyond the standard selection criteria. We derive explicit conditions that determine when certain values of N are valid for the encryption scheme and explain why others may fail to satisfy the correctness requirements. The scope of this paper is limited to the mathematical proof of correctness for RSA-like schemes, deliberately omitting issues related to the cryptographic security of RSA.
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https://arxiv.org/abs/2512.24531
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Academic Papers
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99a2bb89f22207297822fa5a658017818693835d795cc8c3a93cc1a2cdb2a3a4
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2026-01-01T00:00:00-05:00
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From Building Blocks to Planning: Multi-Step Spatial Reasoning in LLMs with Reinforcement Learning
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arXiv:2512.24532v1 Announce Type: new Abstract: Spatial reasoning in large language models (LLMs) has gained increasing attention due to applications in navigation and planning. Despite strong general language capabilities, LLMs still struggle with spatial transformations and multi-step planning in structured environments. We propose a two-stage approach that decomposes spatial reasoning into atomic building blocks and their composition. First, we apply supervised fine-tuning on elementary spatial transformations, such as rotation, translation, and scaling, to equip the model with basic spatial physics. We then freeze this physics-aware model and train lightweight LoRA adapters within the GRPO framework to learn policies that compose these building blocks for multi-step planning in puzzle-based environments, in a closed-loop manner. To support this pipeline, we synthesize an ASCII-art dataset and construct a corresponding ASCII-based reinforcement learning environment. Our method consistently outperforms baselines, including the generic backbone, physics-aware model, and end-to-end RL models, under both Dynamic environments with explicit state updates and Static environments where the model must rely on its internal state across steps. In addition, the proposed approach converges faster and exhibits more stable training compared to end-to-end reinforcement learning from scratch. Finally, we analyze attention patterns to assess whether fine-tuning induces meaningful improvements in spatial understanding.
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https://arxiv.org/abs/2512.24532
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Academic Papers
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c88e82173021f5be9f60c403e9a71eb6fe2ed8e43b60fb51f14c92486507eec4
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2026-01-01T00:00:00-05:00
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A Graph Neural Network with Auxiliary Task Learning for Missing PMU Data Reconstruction
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arXiv:2512.24542v1 Announce Type: new Abstract: In wide-area measurement systems (WAMS), phasor measurement unit (PMU) measurement is prone to data missingness due to hardware failures, communication delays, and cyber-attacks. Existing data-driven methods are limited by inadaptability to concept drift in power systems, poor robustness under high missing rates, and reliance on the unrealistic assumption of full system observability. Thus, this paper proposes an auxiliary task learning (ATL) method for reconstructing missing PMU data. First, a K-hop graph neural network (GNN) is proposed to enable direct learning on the subgraph consisting of PMU nodes, overcoming the limitation of the incompletely observable system. Then, an auxiliary learning framework consisting of two complementary graph networks is designed for accurate reconstruction: a spatial-temporal GNN extracts spatial-temporal dependencies from PMU data to reconstruct missing values, and another auxiliary GNN utilizes the low-rank property of PMU data to achieve unsupervised online learning. In this way, the low-rank properties of the PMU data are dynamically leveraged across the architecture to ensure robustness and self-adaptation. Numerical results demonstrate the superior offline and online performance of the proposed method under high missing rates and incomplete observability.
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https://arxiv.org/abs/2512.24542
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Academic Papers
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0b50d5f3da78674c4ab2ad819a6fa0ac56e31eeacd42baf0b36dc6b9c88ebe6c
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2026-01-01T00:00:00-05:00
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More Than Bits: Multi-Envelope Double Binary Factorization for Extreme Quantization
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arXiv:2512.24545v1 Announce Type: new Abstract: For extreme low-bit quantization of large language models (LLMs), Double Binary Factorization (DBF) is attractive as it enables efficient inference without sacrificing accuracy. However, the scaling parameters of DBF are too restrictive; after factoring out signs, all rank components share the same magnitude profile, resulting in performance saturation. We propose Multi-envelope DBF (MDBF), which retains a shared pair of 1-bit sign bases but replaces the single envelope with a rank-$l$ envelope. By sharing sign matrices among envelope components, MDBF effectively maintains a binary carrier and utilizes the limited memory budget for magnitude expressiveness. We also introduce a closed-form initialization and an alternating refinement method to optimize MDBF. Across the LLaMA and Qwen families, MDBF enhances perplexity and zero-shot accuracy over previous binary formats at matched bits per weight while preserving the same deployment-friendly inference primitive.
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https://arxiv.org/abs/2512.24545
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Academic Papers
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df4bd1e2683c61f2df310a04afcc28c7137fd5b659d27a35726c8afee76fb844
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2026-01-01T00:00:00-05:00
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Hierarchical Vector-Quantized Latents for Perceptual Low-Resolution Video Compression
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arXiv:2512.24547v1 Announce Type: new Abstract: The exponential growth of video traffic has placed increasing demands on bandwidth and storage infrastructure, particularly for content delivery networks (CDNs) and edge devices. While traditional video codecs like H.264 and HEVC achieve high compression ratios, they are designed primarily for pixel-domain reconstruction and lack native support for machine learning-centric latent representations, limiting their integration into deep learning pipelines. In this work, we present a Multi-Scale Vector Quantized Variational Autoencoder (MS-VQ-VAE) designed to generate compact, high-fidelity latent representations of low-resolution video, suitable for efficient storage, transmission, and client-side decoding. Our architecture extends the VQ-VAE-2 framework to a spatiotemporal setting, introducing a two-level hierarchical latent structure built with 3D residual convolutions. The model is lightweight (approximately 18.5M parameters) and optimized for 64x64 resolution video clips, making it appropriate for deployment on edge devices with constrained compute and memory resources. To improve perceptual reconstruction quality, we incorporate a perceptual loss derived from a pre-trained VGG16 network. Trained on the UCF101 dataset using 2-second video clips (32 frames at 16 FPS), on the test set we achieve 25.96 dB PSNR and 0.8375 SSIM. On validation, our model improves over the single-scale baseline by 1.41 dB PSNR and 0.0248 SSIM. The proposed framework is well-suited for scalable video compression in bandwidth-sensitive scenarios, including real-time streaming, mobile video analytics, and CDN-level storage optimization.
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https://arxiv.org/abs/2512.24547
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Academic Papers
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c6cd9e252db1ca92c8615c6590e91e3508441a607efc94e9e08b62301d8a8deb
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2026-01-01T00:00:00-05:00
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DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of Mass
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arXiv:2512.24550v1 Announce Type: new Abstract: In this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface while overlooking the stability of contact point distribution, often resulting in unstable grasps due to inadequate contact configurations. To overcome this limitation, we propose a novel surface fitting algorithm that integrates contact stability while preserving geometric compatibility. Inspired by human grasping behavior, our method disentangles the grasp pose optimization into three sequential steps: (1) rotation optimization to align contact normals, (2) translation refinement to improve the alignment between the gripper frame origin and the object Center of Mass (CoM), and (3) gripper aperture adjustment to optimize contact point distribution. We validate our approach in simulation across 15 objects under both Known-shape (with clean CAD-derived dataset) and Observed-shape (with YCB object dataset) settings, including cross-platform grasp execution on three robot--gripper platforms. We further validate the method in real-world grasp experiments on a UR3e robot. Overall, DISF reduces CoM misalignment while maintaining geometric compatibility, translating into higher grasp success in both simulation and real-world execution compared to baselines. Additional videos and supplementary results are available on our project page: https://tomoya-yamanokuchi.github.io/disf-ras-project-page/
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https://arxiv.org/abs/2512.24550
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Academic Papers
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36e869e83d4ea3efd45d713c788bab41c51db64222aad8ee6704466dad3cb079
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2026-01-01T00:00:00-05:00
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PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation
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arXiv:2512.24551v1 Announce Type: new Abstract: Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly based on graphics or prompt extension struggle to generalize beyond simple simulated environments or learn implicit physical reasoning. The scarcity of training data with rich physics interactions and phenomena is also a problem. In this paper, we first introduce a Physics-Augmented video data construction Pipeline, PhyAugPipe, that leverages a vision-language model (VLM) with chain-of-thought reasoning to collect a large-scale training dataset, PhyVidGen-135K. Then we formulate a principled Physics-aware Groupwise Direct Preference Optimization, PhyGDPO, framework that builds upon the groupwise Plackett-Luce probabilistic model to capture holistic preferences beyond pairwise comparisons. In PhyGDPO, we design a Physics-Guided Rewarding (PGR) scheme that embeds VLM-based physics rewards to steer optimization toward physical consistency. We also propose a LoRA-Switch Reference (LoRA-SR) scheme that eliminates memory-heavy reference duplication for efficient training. Experiments show that our method significantly outperforms state-of-the-art open-source methods on PhyGenBench and VideoPhy2. Please check our project page at https://caiyuanhao1998.github.io/project/PhyGDPO for more video results. Our code, models, and data will be released at https://github.com/caiyuanhao1998/Open-PhyGDPO
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https://arxiv.org/abs/2512.24551
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Academic Papers
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56c8283edec61ff9fc9b93870ee3e59abeaf911932c635120f0394ea5bb4f60c
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2026-01-01T00:00:00-05:00
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OCP-LS: An Efficient Algorithm for Visual Localization
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arXiv:2512.24552v1 Announce Type: new Abstract: This paper proposes a novel second-order optimization algorithm. It aims to address large-scale optimization problems in deep learning because it incorporates the OCP method and appropriately approximating the diagonal elements of the Hessian matrix. Extensive experiments on multiple standard visual localization benchmarks demonstrate the significant superiority of the proposed method. Compared with conventional optimiza tion algorithms, our framework achieves competitive localization accuracy while exhibiting faster convergence, enhanced training stability, and improved robustness to noise interference.
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https://arxiv.org/abs/2512.24552
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00198359bad89623c0e296d573f7dd733ed56fca54ef76101854109e3a2ca926
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2026-01-01T00:00:00-05:00
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From Perception to Punchline: Empowering VLM with the Art of In-the-wild Meme
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arXiv:2512.24555v1 Announce Type: new Abstract: Generating humorous memes is a challenging multimodal task that moves beyond direct image-to-caption supervision. It requires a nuanced reasoning over visual content, contextual cues, and subjective humor. To bridge this gap between visual perception and humorous punchline creation, we propose HUMOR}, a novel framework that guides VLMs through hierarchical reasoning and aligns them with group-wise human preferences. First, HUMOR employs a hierarchical, multi-path Chain-of-Thought (CoT): the model begins by identifying a template-level intent, then explores diverse reasoning paths under different contexts, and finally anchors onto a high-quality, context-specific path. This CoT supervision, which traces back from ground-truth captions, enhances reasoning diversity. We further analyze that this multi-path exploration with anchoring maintains a high expected humor quality, under the practical condition that high-quality paths retain significant probability mass. Second, to capture subjective humor, we train a pairwise reward model that operates within groups of memes sharing the same template. Following established theory, this approach ensures a consistent and robust proxy for human preference, even with subjective and noisy labels. The reward model then enables a group-wise reinforcement learning optimization, guaranteeing providing a theoretical guarantee for monotonic improvement within the trust region. Extensive experiments show that HUMOR empowers various VLMs with superior reasoning diversity, more reliable preference alignment, and higher overall meme quality. Beyond memes, our work presents a general training paradigm for open-ended, human-aligned multimodal generation, where success is guided by comparative judgment within coherent output group.
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https://arxiv.org/abs/2512.24555
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076f371ba1ec986d3dff0881f4da0c5c6f439032c4c7cb5b028d0362581d7b4b
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2026-01-01T00:00:00-05:00
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Safe in the Future, Dangerous in the Past: Dissecting Temporal and Linguistic Vulnerabilities in LLMs
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arXiv:2512.24556v1 Announce Type: new Abstract: As Large Language Models (LLMs) integrate into critical global infrastructure, the assumption that safety alignment transfers zero-shot from English to other languages remains a dangerous blind spot. This study presents a systematic audit of three state of the art models (GPT-5.1, Gemini 3 Pro, and Claude 4.5 Opus) using HausaSafety, a novel adversarial dataset grounded in West African threat scenarios (e.g., Yahoo-Yahoo fraud, Dane gun manufacturing). Employing a 2 x 4 factorial design across 1,440 evaluations, we tested the non-linear interaction between language (English vs. Hausa) and temporal framing. Our results challenge the prevailing multilingual safety gap narrative. Instead of a simple degradation in low-resource settings, we identified a mechanism of Complex Interference where safety is determined by the intersection of variables. While models exhibited a Reverse Linguistic with Claude 4.5 Opus proving significantly safer in Hausa (45.0%) than in English (36.7%) due to uncertainty-driven refusal they suffered catastrophic failures in temporal reasoning. We report a profound Temporal Asymmetry, where past-tense framing bypassed defenses (15.6% safe) while future-tense scenarios triggered hyper-conservative refusals (57.2% safe). The magnitude of this volatility is illustrated by a 9.2x disparity between the safest and most vulnerable configurations, proving that safety is not a fixed property but a context-dependent state. We conclude that current models rely on superficial heuristics rather than robust semantic understanding, creating Safety Pockets that leave Global South users exposed to localized harms. We propose Invariant Alignment as a necessary paradigm shift to ensure safety stability across linguistic and temporal shifts.
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https://arxiv.org/abs/2512.24556
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0f3eda63aef4c32d4deddc803bf346a4d5e7937b0ec9a99495e33a4ae995889c
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2026-01-01T00:00:00-05:00
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Evolutionary Discovery of Sequence Acceleration Methods for Slab Geometry Neutron Transport
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arXiv:2512.24559v1 Announce Type: new Abstract: We present a genetic programming approach to automatically discover convergence acceleration methods for discrete ordinates solutions of neutron transport problems in slab geometry. Classical acceleration methods such as Aitken's delta-squared and Wynn epsilon assume specific convergence patterns and do not generalize well to the broad set of transport problems encountered in practice. We evolved mathematical formulas specifically tailored to SN convergence characteristics in this work. The discovered accelerator, featuring second differences and cross-product terms, achieved over 75 percent success rate in improving convergence compared to raw sequences - almost double that observed for classical techniques for the problem set considered. This work demonstrates the potential for discovering novel numerical methods in computational physics via genetic programming and attempts to honor Prof. Ganapol's legacy of advancing experimental mathematics applied to neutron transport.
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https://arxiv.org/abs/2512.24559
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30b3531c8cff7a312e13e08ff0e297b8c5225d407704cdabbf64e6c64a3f0b04
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2026-01-01T00:00:00-05:00
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Localized Calibrated Uncertainty in Code Language Models
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arXiv:2512.24560v1 Announce Type: new Abstract: Large Language models (LLMs) can generate complicated source code from natural language prompts. However, LLMs can generate output that deviates from what the user wants, requiring supervision and editing. To support this process, we offer techniques to localize where generations might be misaligned from user intent. We first create a dataset of "Minimal Intent Aligning Patches" of repaired LLM generated programs. Each program uses test cases to verify correctness. After creating a dataset of programs, we measure how well various techniques can assign a well-calibrated probability to indicate which parts of code will be edited in a minimal patch (i.e., give a probability that corresponds with empirical odds it is edited). We compare white-box probing (where we propose a technique for efficient arbitrary-span querying), against black-box reflective and self-consistency based approaches. We find probes with a small supervisor model can achieve low calibration error and Brier Skill Score of approx 0.2 estimating edited lines on code generated by models many orders of magnitude larger. We discuss the generalizability of the techniques, and the connections to AI oversight and control, finding a probe trained only on code shows some signs of generalizing to natural language errors if new probability scaling is allowed.
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https://arxiv.org/abs/2512.24560
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f5e2b519c2dd29d9453edb23aee4bca3896be951018a91300cb98fbd3a2e5c32
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2026-01-01T00:00:00-05:00
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RGBT-Ground Benchmark: Visual Grounding Beyond RGB in Complex Real-World Scenarios
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arXiv:2512.24561v1 Announce Type: new Abstract: Visual Grounding (VG) aims to localize specific objects in an image according to natural language expressions, serving as a fundamental task in vision-language understanding. However, existing VG benchmarks are mostly derived from datasets collected under clean environments, such as COCO, where scene diversity is limited. Consequently, they fail to reflect the complexity of real-world conditions, such as changes in illumination, weather, etc., that are critical to evaluating model robustness and generalization in safety-critical applications. To address these limitations, we present RGBT-Ground, the first large-scale visual grounding benchmark built for complex real-world scenarios. It consists of spatially aligned RGB and Thermal infrared (TIR) image pairs with high-quality referring expressions, corresponding object bounding boxes, and fine-grained annotations at the scene, environment, and object levels. This benchmark enables comprehensive evaluation and facilitates the study of robust grounding under diverse and challenging conditions. Furthermore, we establish a unified visual grounding framework that supports both uni-modal (RGB or TIR) and multi-modal (RGB-TIR) visual inputs. Based on it, we propose RGBT-VGNet, a simple yet effective baseline for fusing complementary visual modalities to achieve robust grounding. We conduct extensive adaptations to the existing methods on RGBT-Ground. Experimental results show that our proposed RGBT-VGNet significantly outperforms these adapted methods, particularly in nighttime and long-distance scenarios. All resources will be publicly released to promote future research on robust visual grounding in complex real-world environments.
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https://arxiv.org/abs/2512.24561
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9fa4ebac669931451cd7beca4de3e3b3898d26ae83b239db049b1091e186c836
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2026-01-01T00:00:00-05:00
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HaluNet: Multi-Granular Uncertainty Modeling for Efficient Hallucination Detection in LLM Question Answering
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arXiv:2512.24562v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at question answering (QA) but often generate hallucinations, including factual errors or fabricated content. Detecting hallucinations from internal uncertainty signals is attractive due to its scalability and independence from external resources. Existing methods often aim to accurately capture a single type of uncertainty while overlooking the complementarity among different sources, particularly between token-level probability uncertainty and the uncertainty conveyed by internal semantic representations, which provide complementary views on model reliability. We present \textbf{HaluNet}, a lightweight and trainable neural framework that integrates multi granular token level uncertainties by combining semantic embeddings with probabilistic confidence and distributional uncertainty. Its multi branch architecture adaptively fuses what the model knows with the uncertainty expressed in its outputs, enabling efficient one pass hallucination detection. Experiments on SQuAD, TriviaQA, and Natural Questions show that HaluNet delivers strong detection performance and favorable computational efficiency, with or without access to context, highlighting its potential for real time hallucination detection in LLM based QA systems.
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https://arxiv.org/abs/2512.24562
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a4649c4a1154908b8b2c5c40dc0a6ddf630070c0ca4b475d6b288ed9d332aab3
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2026-01-01T00:00:00-05:00
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CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts
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arXiv:2512.24564v1 Announce Type: new Abstract: Deep learning models for Electrocardiogram (ECG) diagnosis have achieved remarkable accuracy but exhibit fragility against adversarial perturbations, particularly Smooth Adversarial Perturbations (SAP) that mimic biological morphology. Existing defenses face a critical dilemma: Adversarial Training (AT) provides robustness but incurs a prohibitive computational burden, while certified methods like Randomized Smoothing (RS) introduce significant inference latency, rendering them impractical for real-time clinical monitoring. We posit that this vulnerability stems from the models' reliance on non-robust spurious correlations rather than invariant pathological features. To address this, we propose Causal Physiological Representation Learning (CPR). Unlike standard denoising approaches that operate without semantic constraints, CPR incorporates a Physiological Structural Prior within a causal disentanglement framework. By modeling ECG generation via a Structural Causal Model (SCM), CPR enforces a structural intervention that strictly separates invariant pathological morphology (P-QRS-T complex) from non-causal artifacts. Empirical results on PTB-XL demonstrate that CPR significantly outperforms standard clinical preprocessing methods. Specifically, under SAP attacks, CPR achieves an F1 score of 0.632, surpassing Median Smoothing (0.541 F1) by 9.1%. Crucially, CPR matches the certified robustness of Randomized Smoothing while maintaining single-pass inference efficiency, offering a superior trade-off between robustness, efficiency, and clinical interpretability.
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https://arxiv.org/abs/2512.24564
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7dd9979ec136b68eca8b0e8f370b3ca9dceba5c645e2cba9a731df183bad930b
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2026-01-01T00:00:00-05:00
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MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use
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arXiv:2512.24565v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (MCP) is considered a future trend. Current MCP evaluation sets suffer from issues such as reliance on external MCP services and a lack of difficulty awareness. To address these limitations, we propose MCPAgentBench, a benchmark based on real-world MCP definitions designed to evaluate the tool-use capabilities of agents. We construct a dataset containing authentic tasks and simulated MCP tools. The evaluation employs a dynamic sandbox environment that presents agents with candidate tool lists containing distractors, thereby testing their tool selection and discrimination abilities. Furthermore, we introduce comprehensive metrics to measure both task completion rates and execution efficiency. Experiments conducted on various latest mainstream Large Language Models reveal significant performance differences in handling complex, multi-step tool invocations. All code is open-source at Github.
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https://arxiv.org/abs/2512.24565
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16ef3197c1596b7199f70288dccb330eab59af351a91ad67fef269c7c0de9373
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2026-01-01T00:00:00-05:00
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Newton-Krylov Methods for Computing Steady States of Particle Timesteppers via Optimal Transport
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arXiv:2512.24567v1 Announce Type: new Abstract: Timesteppers constitute a powerful tool in modern computational science and engineering. Although they are typically used to advance the system forward in time, they can also be viewed as nonlinear mappings that implicitly encode steady states and stability information. In this work, we present an extension of the matrix-free framework for calculating, via timesteppers, steady states of deterministic systems to stochastic particle simulations, where intrinsic randomness prevents direct steady state extraction. By formulating stochastic timesteppers in the language of optimal transport, we reinterpret them as operators acting on probability measures rather than on individual particle trajectories. This perspective enables the construction of smooth cumulative- and inverse-cumulative-distribution-function ((I)CDF) timesteppers that evolve distributions rather than particles. Combined with matrix-free Newton-Krylov solvers, these smooth timesteppers allow efficient computation of steady-state distributions even under high stochastic noise. We perform an error analysis quantifying how noise affects finite-difference Jacobian action approximations, and demonstrate that convergence can be obtained even in high noise regimes. Finally, we introduce higher-dimensional generalizations based on smooth CDF-related representations of particles and validate their performance on a non-trivial two-dimensional distribution. Together, these developments establish a unified variational framework for computing meaningful steady states of both deterministic and stochastic timesteppers.
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https://arxiv.org/abs/2512.24567
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2ed4eebfc45cc47ca3bc2420d51dc1e783b5f5d1144c5fe1920b2f11631a44bf
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2026-01-01T00:00:00-05:00
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On the Effectiveness of Training Data Optimization for LLM-based Code Generation: An Empirical Study
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arXiv:2512.24570v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in code generation, largely driven by the availability of high-quality code datasets for effective training. To further improve data quality, numerous training data optimization techniques have been proposed; however, their overall effectiveness has not been systematically evaluated. To bridge this gap, we conduct the first large-scale empirical study, examining five widely-used training data optimization techniques and their pairwise combinations for LLM-based code generation across three benchmarks and four LLMs. Our results show that data synthesis is the most effective technique for improving functional correctness and reducing code smells, although it performs relatively worse on code maintainability compared to data refactoring, cleaning, and selection. Regarding combinations, we find that most combinations do not further improve functional correctness but can effectively enhance code quality (code smells and maintainability). Among all combinations, data synthesis combined with data refactoring achieves the strongest overall performance. Furthermore, our fine-grained analysis reinforces these findings and provides deeper insights into how individual techniques and their combinations influence code generation effectiveness. Overall, this work represents a first step toward a systematic understanding of training data optimization and combination strategies, offering practical guidance for future research and deployment in LLM-based code generation.
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https://arxiv.org/abs/2512.24570
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1a549207b8d7398a402959820f7c9f94ead122b6eaf355403477e50d92a459e6
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2026-01-01T00:00:00-05:00
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SynRAG: A Large Language Model Framework for Executable Query Generation in Heterogeneous SIEM System
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arXiv:2512.24571v1 Announce Type: new Abstract: Security Information and Event Management (SIEM) systems are essential for large enterprises to monitor their IT infrastructure by ingesting and analyzing millions of logs and events daily. Security Operations Center (SOC) analysts are tasked with monitoring and analyzing this vast data to identify potential threats and take preventive actions to protect enterprise assets. However, the diversity among SIEM platforms, such as Palo Alto Networks Qradar, Google SecOps, Splunk, Microsoft Sentinel and the Elastic Stack, poses significant challenges. As these systems differ in attributes, architecture, and query languages, making it difficult for analysts to effectively monitor multiple platforms without undergoing extensive training or forcing enterprises to expand their workforce. To address this issue, we introduce SynRAG, a unified framework that automatically generates threat detection or incident investigation queries for multiple SIEM platforms from a platform-agnostic specification. SynRAG can generate platformspecific queries from a single high-level specification written by analysts. Without SynRAG, analysts would need to manually write separate queries for each SIEM platform, since query languages vary significantly across systems. This framework enables seamless threat detection and incident investigation across heterogeneous SIEM environments, reducing the need for specialized training and manual query translation. We evaluate SynRAG against state-of-the-art language models, including GPT, Llama, DeepSeek, Gemma, and Claude, using Qradar and SecOps as representative SIEM systems. Our results demonstrate that SynRAG generates significantly better queries for crossSIEM threat detection and incident investigation compared to the state-of-the-art base models.
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https://arxiv.org/abs/2512.24571
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f78769e7e36ded98955e59b30a5899b26f10c4013883ab381251be8a6b0498ae
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2026-01-01T00:00:00-05:00
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Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs' Legal Reasoning Capabilities
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arXiv:2512.24572v1 Announce Type: new Abstract: We introduce the Korean Canonical Legal Benchmark (KCL), a benchmark designed to assess language models' legal reasoning capabilities independently of domain-specific knowledge. KCL provides question-level supporting precedents, enabling a more faithful disentanglement of reasoning ability from parameterized knowledge. KCL consists of two components: (1) KCL-MCQA, multiple-choice problems of 283 questions with 1,103 aligned precedents, and (2) KCL-Essay, open-ended generation problems of 169 questions with 550 aligned precedents and 2,739 instance-level rubrics for automated evaluation. Our systematic evaluation of 30+ models shows large remaining gaps, particularly in KCL-Essay, and that reasoning-specialized models consistently outperform their general-purpose counterparts. We release all resources, including the benchmark dataset and evaluation code, at https://github.com/lbox-kr/kcl.
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https://arxiv.org/abs/2512.24572
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a48f035a1eb7de74039b832fa0ffc73ef02d142b7c4f64946c7534fcc75f9234
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2026-01-01T00:00:00-05:00
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Understanding and Steering the Cognitive Behaviors of Reasoning Models at Test-Time
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arXiv:2512.24574v1 Announce Type: new Abstract: Large Language Models (LLMs) often rely on long chain-of-thought (CoT) reasoning to solve complex tasks. While effective, these trajectories are frequently inefficient, leading to high latency from excessive token generation, or unstable reasoning that alternates between underthinking (shallow, inconsistent steps) and overthinking (repetitive, verbose reasoning). In this work, we study the structure of reasoning trajectories and uncover specialized attention heads that correlate with distinct cognitive behaviors such as verification and backtracking. By lightly intervening on these heads at inference time, we can steer the model away from inefficient modes. Building on this insight, we propose CREST, a training-free method for Cognitive REasoning Steering at Test-time. CREST has two components: (1) an offline calibration step that identifies cognitive heads and derives head-specific steering vectors, and (2) an inference-time procedure that rotates hidden representations to suppress components along those vectors. CREST adaptively suppresses unproductive reasoning behaviors, yielding both higher accuracy and lower computational cost. Across diverse reasoning benchmarks and models, CREST improves accuracy by up to 17.5% while reducing token usage by 37.6%, offering a simple and effective pathway to faster, more reliable LLM reasoning.
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https://arxiv.org/abs/2512.24574
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a94ddd0cc8aebf9a3eb005e8c0a672fd163047d49ae4c87777a2b55915064b3e
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2026-01-01T00:00:00-05:00
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Improving Few-Shot Change Detection Visual Question Answering via Decision-Ambiguity-guided Reinforcement Fine-Tuning
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arXiv:2512.24591v1 Announce Type: new Abstract: Change detection visual question answering (CDVQA) requires answering text queries by reasoning about semantic changes in bi-temporal remote sensing images. A straightforward approach is to boost CDVQA performance with generic vision-language models via supervised fine-tuning (SFT). Despite recent progress, we observe that a significant portion of failures do not stem from clearly incorrect predictions, but from decision ambiguity, where the model assigns similar confidence to the correct answer and strong distractors. To formalize this challenge, we define Decision-Ambiguous Samples (DAS) as instances with a small probability margin between the ground-truth answer and the most competitive alternative. We argue that explicitly optimizing DAS is crucial for improving the discriminability and robustness of CDVQA models. To this end, we propose DARFT, a Decision-Ambiguity-guided Reinforcement Fine-Tuning framework that first mines DAS using an SFT-trained reference policy and then applies group-relative policy optimization on the mined subset. By leveraging multi-sample decoding and intra-group relative advantages, DARFT suppresses strong distractors and sharpens decision boundaries without additional supervision. Extensive experiments demonstrate consistent gains over SFT baselines, particularly under few-shot settings.
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https://arxiv.org/abs/2512.24591
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1db635657d8faaa6dff35a46f9d0f7d0317ab4898c109b011d824c3a76521926
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2026-01-01T00:00:00-05:00
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SliceLens: Fine-Grained and Grounded Error Slice Discovery for Multi-Instance Vision Tasks
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arXiv:2512.24592v1 Announce Type: new Abstract: Systematic failures of computer vision models on subsets with coherent visual patterns, known as error slices, pose a critical challenge for robust model evaluation. Existing slice discovery methods are primarily developed for image classification, limiting their applicability to multi-instance tasks such as detection, segmentation, and pose estimation. In real-world scenarios, error slices often arise from corner cases involving complex visual relationships, where existing instance-level approaches lacking fine-grained reasoning struggle to yield meaningful insights. Moreover, current benchmarks are typically tailored to specific algorithms or biased toward image classification, with artificial ground truth that fails to reflect real model failures. To address these limitations, we propose SliceLens, a hypothesis-driven framework that leverages LLMs and VLMs to generate and verify diverse failure hypotheses through grounded visual reasoning, enabling reliable identification of fine-grained and interpretable error slices. We further introduce FeSD (Fine-grained Slice Discovery), the first benchmark specifically designed for evaluating fine-grained error slice discovery across instance-level vision tasks, featuring expert-annotated and carefully refined ground-truth slices with precise grounding to local error regions. Extensive experiments on both existing benchmarks and FeSD demonstrate that SliceLens achieves state-of-the-art performance, improving Precision@10 by 0.42 (0.73 vs. 0.31) on FeSD, and identifies interpretable slices that facilitate actionable model improvements, as validated through model repair experiments.
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https://arxiv.org/abs/2512.24592
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cb129240a6e43cd306b0ad18178922124e826391bd53385ae95cb11b1b5cd01c
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2026-01-01T00:00:00-05:00
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3D Semantic Segmentation for Post-Disaster Assessment
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arXiv:2512.24593v1 Announce Type: new Abstract: The increasing frequency of natural disasters poses severe threats to human lives and leads to substantial economic losses. While 3D semantic segmentation is crucial for post-disaster assessment, existing deep learning models lack datasets specifically designed for post-disaster environments. To address this gap, we constructed a specialized 3D dataset using unmanned aerial vehicles (UAVs)-captured aerial footage of Hurricane Ian (2022) over affected areas, employing Structure-from-Motion (SfM) and Multi-View Stereo (MVS) techniques to reconstruct 3D point clouds. We evaluated the state-of-the-art (SOTA) 3D semantic segmentation models, Fast Point Transformer (FPT), Point Transformer v3 (PTv3), and OA-CNNs on this dataset, exposing significant limitations in existing methods for disaster-stricken regions. These findings underscore the urgent need for advancements in 3D segmentation techniques and the development of specialized 3D benchmark datasets to improve post-disaster scene understanding and response.
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https://arxiv.org/abs/2512.24593
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4981dd163360edfae935c43e9c3113e639c2744613f290485f0f22485f01f992
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2026-01-01T00:00:00-05:00
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A Tale of 1001 LoC: Potential Runtime Error-Guided Specification Synthesis for Verifying Large-Scale Programs
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arXiv:2512.24594v1 Announce Type: new Abstract: Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to long-context reasoning limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This paper presents Preguss -- a modular, fine-grained framework for automating the generation and refinement of formal specifications. Preguss synergizes between static analysis and deductive verification by steering two components in a divide-and-conquer fashion: (i) potential runtime error-guided construction and prioritization of verification units, and (ii) LLM-aided synthesis of interprocedural specifications at the unit level. We show that Preguss substantially outperforms state-of-the-art LLM-based approaches and, in particular, it enables highly automated RTE-freeness verification for real-world programs with over a thousand LoC, with a reduction of 80.6%~88.9% human verification effort.
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https://arxiv.org/abs/2512.24594
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f7ccb39fbcb0160c051292a27a759b4772fe6fd7e864d6d39a98cf729300f4e7
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2026-01-01T00:00:00-05:00
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Recursive Language Models
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arXiv:2512.24601v1 Announce Type: new Abstract: We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query.
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https://arxiv.org/abs/2512.24601
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21f3fc5c5b37663117ff3a8e4cfaa66b05bb30072e6dc251286e6d07c4ef76a6
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2026-01-01T00:00:00-05:00
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Secure Digital Semantic Communications: Fundamentals, Challenges, and Opportunities
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arXiv:2512.24602v1 Announce Type: new Abstract: Semantic communication (SemCom) has emerged as a promising paradigm for future wireless networks by prioritizing task-relevant meaning over raw data delivery, thereby reducing communication overhead and improving efficiency. However, shifting from bit-accurate transmission to task-oriented delivery introduces new security and privacy risks. These include semantic leakage, semantic manipulation, knowledge base vulnerabilities, model-related attacks, and threats to authenticity and availability. Most existing secure SemCom studies focus on analog SemCom, where semantic features are mapped to continuous channel inputs. In contrast, digital SemCom transmits semantic information through discrete bits or symbols within practical transceiver pipelines, offering stronger compatibility with realworld systems while exposing a distinct and underexplored attack surface. In particular, digital SemCom typically represents semantic information over a finite alphabet through explicit digital modulation, following two main routes: probabilistic modulation and deterministic modulation. These discrete mechanisms and practical transmission procedures introduce additional vulnerabilities affecting bit- or symbol-level semantic information, the modulation stage, and packet-based delivery and protocol operations. Motivated by these challenges and the lack of a systematic analysis of secure digital SemCom, this paper reviews SemCom fundamentals, clarifies the architectural differences between analog and digital SemCom and their security implications, organizes the threat landscape for digital SemCom, and discusses potential defenses. Finally, we outline open research directions toward secure and deployable digital SemCom systems.
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https://arxiv.org/abs/2512.24602
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Academic Papers
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0ea1bb32eed18b10000871d23a0ff830efb472c1afd98070c50f92616a64cc4a
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2026-01-01T00:00:00-05:00
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Collaborative Low-Rank Adaptation for Pre-Trained Vision Transformers
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arXiv:2512.24603v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has achieved remarkable success in fine-tuning pre-trained vision transformers for various downstream tasks. Existing studies mainly focus on exploring more parameter-efficient strategies or more effective representation learning schemes. However, these methods either sacrifice fine-tuning performance or introduce excessive trainable parameters, failing to strike a balance between learning performance and parameter efficiency. To address this problem, we propose a novel tuning method named collaborative low-rank adaptation (CLoRA) in this paper. CLoRA consists of base-space sharing and sample-agnostic diversity enhancement (SADE) components. To maintain parameter efficiency while expanding the learning capacity of low-rank modules (LRMs), base-space sharing allows all LRMs to share a set of down/up-projection spaces. In CLoRA, the low-rank matrices obtained from the shared spaces collaboratively construct each LRM. Since the representations extracted by these matrices may contain redundant information, SADE is employed to regularize the similarities among them to encourage diverse representations in the training process. We conduct extensive experiments on widely used image and point cloud datasets to evaluate the performance of CLoRA. Experimental results demonstrate that CLoRA strikes a better balance between learning performance and parameter efficiency, while requiring the fewest GFLOPs for point cloud analysis, compared with the state-of-the-art methods.
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https://arxiv.org/abs/2512.24603
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Academic Papers
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90deffcc32f85eab3a8bc1e07e3e3dfd3ecdb227c00f6cdc3f31b583d8decc0e
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2026-01-01T00:00:00-05:00
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MoniRefer: A Real-world Large-scale Multi-modal Dataset based on Roadside Infrastructure for 3D Visual Grounding
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arXiv:2512.24605v1 Announce Type: new Abstract: 3D visual grounding aims to localize the object in 3D point cloud scenes that semantically corresponds to given natural language sentences. It is very critical for roadside infrastructure system to interpret natural languages and localize relevant target objects in complex traffic environments. However, most existing datasets and approaches for 3D visual grounding focus on the indoor and outdoor driving scenes, outdoor monitoring scenarios remain unexplored due to scarcity of paired point cloud-text data captured by roadside infrastructure sensors. In this paper, we introduce a novel task of 3D Visual Grounding for Outdoor Monitoring Scenarios, which enables infrastructure-level understanding of traffic scenes beyond the ego-vehicle perspective. To support this task, we construct MoniRefer, the first real-world large-scale multi-modal dataset for roadside-level 3D visual grounding. The dataset consists of about 136,018 objects with 411,128 natural language expressions collected from multiple complex traffic intersections in the real-world environments. To ensure the quality and accuracy of the dataset, we manually verified all linguistic descriptions and 3D labels for objects. Additionally, we also propose a new end-to-end method, named Moni3DVG, which utilizes the rich appearance information provided by images and geometry and optical information from point cloud for multi-modal feature learning and 3D object localization. Extensive experiments and ablation studies on the proposed benchmarks demonstrate the superiority and effectiveness of our method. Our dataset and code will be released.
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https://arxiv.org/abs/2512.24605
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Academic Papers
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91fdc08957a453851c2f92228f75185cf49478251cfeacebf19b9b44116cbab4
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2026-01-01T00:00:00-05:00
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Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization
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arXiv:2512.24609v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well in language tasks but often lack collaborative awareness and struggle to optimize global performance in multi-agent settings. We present a reinforcement learning-augmented LLM agent framework that formulates cooperation as a decentralized partially observable Markov decision process (Dec-POMDP) and adopts centralized training with decentralized execution (CTDE). We introduce Group Relative Policy Optimization (GRPO) to jointly optimize agent policies with access to global signals during training, together with a simplified joint reward that balances task quality, speed, and coordination cost. On collaborative writing and coding benchmarks, our framework delivers a 3x increase in task processing speed over single-agent baselines, 98.7% structural/style consistency in writing, and a 74.6% test pass rate in coding. The approach consistently outperforms strong multi-agent LLM baselines and provides a practical path toward reliable collaboration in complex workflows.
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https://arxiv.org/abs/2512.24609
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Academic Papers
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023df41aea070a7dfa85c457bb6ac1890260cb384b653a0f1f0dfef52bafe9c2
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2026-01-01T00:00:00-05:00
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Group Deliberation Oriented Multi-Agent Conversational Model for Complex Reasoning
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arXiv:2512.24613v1 Announce Type: new Abstract: This paper proposes a group deliberation oriented multi-agent conversational model to address the limitations of single large language models in complex reasoning tasks. The model adopts a three-level role division architecture consisting of generation, verification, and integration. An opinion generation agent produces diverse reasoning perspectives, an evidence verification agent retrieves external knowledge and quantifies factual support, and a consistency arbitration agent integrates logically coherent conclusions. A self-game mechanism is introduced to expand multi-path reasoning trajectories, while a retrieval enhancement module dynamically supplements external knowledge. A composite reward function combining factual consistency and logical coherence is designed, and an improved proximal policy optimization strategy is applied for collaborative training. Experimental results show that the proposed model improves multi-hop reasoning accuracy by 16.8 percent on HotpotQA, 14.3 percent on 2WikiMultihopQA, and 19.2 percent on MeetingBank, while improving consistency by 21.5 percent. The model achieves higher reasoning efficiency than mainstream multi-agent approaches, providing an effective and stable solution for complex reasoning tasks.
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https://arxiv.org/abs/2512.24613
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Academic Papers
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11cf2af73b51acf60d81f2c2a587720a7b0e8971f4ef7510c7c5f36c02022b12
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2026-01-01T00:00:00-05:00
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Chat-Driven Optimal Management for Virtual Network Services
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arXiv:2512.24614v1 Announce Type: new Abstract: This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend on statistical language models to interpret user intent but cannot guarantee the feasibility of generated configurations. To overcome this, we develop a two-stage framework consisting of an Interpreter, which extracts intent from natural language prompts using NLP, and an Optimizer, which computes feasible virtual machine (VM) placement and routing via an integer linear programming. In particular, the Interpreter translates user chats into update directions, i.e., whether to increase, decrease, or maintain parameters such as CPU demand and latency bounds, thereby enabling iterative refinement of the network configuration. In this paper, two intent extractors, which are a Sentence-BERT model with support vector machine (SVM) classifiers and a large language model (LLM), are introduced. Experiments in single-user and multi-user settings show that the framework dynamically updates VM placement and routing while preserving feasibility. The LLM-based extractor achieves higher accuracy with fewer labeled samples, whereas the Sentence-BERT with SVM classifiers provides significantly lower latency suitable for real-time operation. These results underscore the effectiveness of combining NLP-driven intent extraction with optimization-based allocation for safe, interpretable, and user-friendly virtual network management.
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https://arxiv.org/abs/2512.24614
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Academic Papers
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5f03fc02d8be0cbc5f83d46acfcdf14291c40acd82649ee30e430a57db173f7e
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2026-01-01T00:00:00-05:00
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Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization
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arXiv:2512.24615v1 Announce Type: new Abstract: Existing Large Language Model (LLM) agent frameworks face two significant challenges: high configuration costs and static capabilities. Building a high-quality agent often requires extensive manual effort in tool integration and prompt engineering, while deployed agents struggle to adapt to dynamic environments without expensive fine-tuning. To address these issues, we propose \textbf{Youtu-Agent}, a modular framework designed for the automated generation and continuous evolution of LLM agents. Youtu-Agent features a structured configuration system that decouples execution environments, toolkits, and context management, enabling flexible reuse and automated synthesis. We introduce two generation paradigms: a \textbf{Workflow} mode for standard tasks and a \textbf{Meta-Agent} mode for complex, non-standard requirements, capable of automatically generating tool code, prompts, and configurations. Furthermore, Youtu-Agent establishes a hybrid policy optimization system: (1) an \textbf{Agent Practice} module that enables agents to accumulate experience and improve performance through in-context optimization without parameter updates; and (2) an \textbf{Agent RL} module that integrates with distributed training frameworks to enable scalable and stable reinforcement learning of any Youtu-Agents in an end-to-end, large-scale manner. Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47\%) and GAIA (72.8\%) using open-weight models. Our automated generation pipeline achieves over 81\% tool synthesis success rate, while the Practice module improves performance on AIME 2024/2025 by +2.7\% and +5.4\% respectively. Moreover, our Agent RL training achieves 40\% speedup with steady performance improvement on 7B LLMs, enhancing coding/reasoning and searching capabilities respectively up to 35\% and 21\% on Maths and general/multi-hop QA benchmarks.
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https://arxiv.org/abs/2512.24615
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Academic Papers
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f89aff2ec8721da0ea45682a4af409e8c1c78a41d87fdae987193a2d1bcce071
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2026-01-01T00:00:00-05:00
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Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
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arXiv:2512.24617v1 Announce Type: new Abstract: Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to semantically critical transitions. We propose $\textbf{Dynamic Large Concept Models (DLCM)}$, a hierarchical language modeling framework that learns semantic boundaries from latent representations and shifts computation from tokens to a compressed concept space where reasoning is more efficient. DLCM discovers variable-length concepts end-to-end without relying on predefined linguistic units. Hierarchical compression fundamentally changes scaling behavior. We introduce the first $\textbf{compression-aware scaling law}$, which disentangles token-level capacity, concept-level reasoning capacity, and compression ratio, enabling principled compute allocation under fixed FLOPs. To stably train this heterogeneous architecture, we further develop a $\textbf{decoupled $\mu$P parametrization}$ that supports zero-shot hyperparameter transfer across widths and compression regimes. At a practical setting ($R=4$, corresponding to an average of four tokens per concept), DLCM reallocates roughly one-third of inference compute into a higher-capacity reasoning backbone, achieving a $\textbf{+2.69$\%$ average improvement}$ across 12 zero-shot benchmarks under matched inference FLOPs.
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https://arxiv.org/abs/2512.24617
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Academic Papers
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83427afe1bbbfdd9071ec49bb3f48f0ed9022062cde665b2e6a1af52c277c28e
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2026-01-01T00:00:00-05:00
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Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models
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arXiv:2512.24618v1 Announce Type: new Abstract: We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that rely on distillation, Youtu-LLM (1.96B) is pre-trained from scratch to systematically cultivate reasoning and planning capabilities. The key technical advancements are as follows: (1) Compact Architecture with Long-Context Support: Built on a dense Multi-Latent Attention (MLA) architecture with a novel STEM-oriented vocabulary, Youtu-LLM supports a 128k context window. This design enables robust long-context reasoning and state tracking within a minimal memory footprint, making it ideal for long-horizon agent and reasoning tasks. (2) Principled "Commonsense-STEM-Agent" Curriculum: We curated a massive corpus of approximately 11T tokens and implemented a multi-stage training strategy. By progressively shifting the pre-training data distribution from general commonsense to complex STEM and agentic tasks, we ensure the model acquires deep cognitive abilities rather than superficial alignment. (3) Scalable Agentic Mid-training: Specifically for the agentic mid-training, we employ diverse data construction schemes to synthesize rich and varied trajectories across math, coding, and tool-use domains. This high-quality data enables the model to internalize planning and reflection behaviors effectively. Extensive evaluations show that Youtu-LLM sets a new state-of-the-art for sub-2B LLMs. On general benchmarks, it achieves competitive performance against larger models, while on agent-specific tasks, it significantly surpasses existing SOTA baselines, demonstrating that lightweight models can possess strong intrinsic agentic capabilities.
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https://arxiv.org/abs/2512.24618
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Academic Papers
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15e6441918a5b6815f88407e91bfe98a6f5a2d1e4f0f90b6c9da9d4800aecf6e
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2026-01-01T00:00:00-05:00
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Decentralized No-Regret Frequency-Time Scheduling for FMCW Radar Interference Avoidance
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arXiv:2512.24619v1 Announce Type: new Abstract: Automotive FMCW radars are indispensable to modern ADAS and autonomous-driving systems, but their increasing density has intensified the risk of mutual interference. Existing mitigation techniques, including reactive receiver-side suppression, proactive waveform design, and cooperative scheduling, often face limitations in scalability, reliance on side-channel communication, or degradation of range-Doppler resolution. Building on our earlier work on decentralized Frequency-Domain No-Regret hopping, this paper introduces a unified time-frequency game-theoretic framework that enables radars to adapt across both spectral and temporal resources. We formulate the interference-avoidance problem as a repeated anti-coordination game, in which each radar autonomously updates a mixed strategy over frequency subbands and chirp-level time offsets using regret-minimization dynamics. We show that the proposed Time-Frequency No-Regret Hopping algorithm achieves vanishing external and swap regret, and that the induced empirical play converges to an $\varepsilon$-coarse correlated equilibrium or a correlated equilibrium. Theoretical analysis provides regret bounds in the joint domain, revealing how temporal adaptation implicitly regularizes frequency selection and enhances robustness against asynchronous interference. Numerical experiments with multi-radar scenarios demonstrate substantial improvements in SINR, collision rate, and range-Doppler quality compared with time-frequency random hopping and centralized Nash-based benchmarks.
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https://arxiv.org/abs/2512.24619
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Academic Papers
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76ca9a5f61e231f3a23a579fdc6e3a097983de8752abe218902e7a0d2b4d0651
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2026-01-01T00:00:00-05:00
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LLHA-Net: A Hierarchical Attention Network for Two-View Correspondence Learning
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arXiv:2512.24620v1 Announce Type: new Abstract: Establishing the correct correspondence of feature points is a fundamental task in computer vision. However, the presence of numerous outliers among the feature points can significantly affect the matching results, reducing the accuracy and robustness of the process. Furthermore, a challenge arises when dealing with a large proportion of outliers: how to ensure the extraction of high-quality information while reducing errors caused by negative samples. To address these issues, in this paper, we propose a novel method called Layer-by-Layer Hierarchical Attention Network, which enhances the precision of feature point matching in computer vision by addressing the issue of outliers. Our method incorporates stage fusion, hierarchical extraction, and an attention mechanism to improve the network's representation capability by emphasizing the rich semantic information of feature points. Specifically, we introduce a layer-by-layer channel fusion module, which preserves the feature semantic information from each stage and achieves overall fusion, thereby enhancing the representation capability of the feature points. Additionally, we design a hierarchical attention module that adaptively captures and fuses global perception and structural semantic information using an attention mechanism. Finally, we propose two architectures to extract and integrate features, thereby improving the adaptability of our network. We conduct experiments on two public datasets, namely YFCC100M and SUN3D, and the results demonstrate that our proposed method outperforms several state-of-the-art techniques in both outlier removal and camera pose estimation. Source code is available at http://www.linshuyuan.com.
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https://arxiv.org/abs/2512.24620
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Academic Papers
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8893ec8a3787f8f8be1528f1f899e1a07524d66d800d3c2544a5f07ede9e0978
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2026-01-01T00:00:00-05:00
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FireRescue: A UAV-Based Dataset and Enhanced YOLO Model for Object Detection in Fire Rescue Scenes
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arXiv:2512.24622v1 Announce Type: new Abstract: Object detection in fire rescue scenarios is importance for command and decision-making in firefighting operations. However, existing research still suffers from two main limitations. First, current work predominantly focuses on environments such as mountainous or forest areas, while paying insufficient attention to urban rescue scenes, which are more frequent and structurally complex. Second, existing detection systems include a limited number of classes, such as flames and smoke, and lack a comprehensive system covering key targets crucial for command decisions, such as fire trucks and firefighters. To address the above issues, this paper first constructs a new dataset named "FireRescue" for rescue command, which covers multiple rescue scenarios, including urban, mountainous, forest, and water areas, and contains eight key categories such as fire trucks and firefighters, with a total of 15,980 images and 32,000 bounding boxes. Secondly, to tackle the problems of inter-class confusion and missed detection of small targets caused by chaotic scenes, diverse targets, and long-distance shooting, this paper proposes an improved model named FRS-YOLO. On the one hand, the model introduces a plug-and-play multidi-mensional collaborative enhancement attention module, which enhances the discriminative representation of easily confused categories (e.g., fire trucks vs. ordinary trucks) through cross-dimensional feature interaction. On the other hand, it integrates a dynamic feature sampler to strengthen high-response foreground features, thereby mitigating the effects of smoke occlusion and background interference. Experimental results demonstrate that object detection in fire rescue scenarios is highly challenging, and the proposed method effectively improves the detection performance of YOLO series models in this context.
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https://arxiv.org/abs/2512.24622
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Academic Papers
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536e0772c1a4bcd5720aa9f7ae7c4919f5d6a3843317af639f446fea0597359a
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2026-01-01T00:00:00-05:00
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AutoFed: Manual-Free Federated Traffic Prediction via Personalized Prompt
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arXiv:2512.24625v1 Announce Type: new Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management. However, due to significant privacy concerns surrounding traffic data, most existing methods rely on local training, resulting in data silos and limited knowledge sharing. Federated Learning (FL) offers an efficient solution through privacy-preserving collaborative training; however, standard FL struggles with the non-independent and identically distributed (non-IID) problem among clients. This challenge has led to the emergence of Personalized Federated Learning (PFL) as a promising paradigm. Nevertheless, current PFL frameworks require further adaptation for traffic prediction tasks, such as specialized graph feature engineering, data processing, and network architecture design. A notable limitation of many prior studies is their reliance on hyper-parameter optimization across datasets-information that is often unavailable in real-world scenarios-thus impeding practical deployment. To address this challenge, we propose AutoFed, a novel PFL framework for traffic prediction that eliminates the need for manual hyper-parameter tuning. Inspired by prompt learning, AutoFed introduces a federated representor that employs a client-aligned adapter to distill local data into a compact, globally shared prompt matrix. This prompt then conditions a personalized predictor, allowing each client to benefit from cross-client knowledge while maintaining local specificity. Extensive experiments on real-world datasets demonstrate that AutoFed consistently achieves superior performance across diverse scenarios. The code of this paper is provided at https://github.com/RS2002/AutoFed .
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https://arxiv.org/abs/2512.24625
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Academic Papers
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3587acb903becad477da857d76597456724d37175e8ccb5202eec4624fb7c5ff
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2026-01-01T00:00:00-05:00
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AI-Driven Acoustic Voice Biomarker-Based Hierarchical Classification of Benign Laryngeal Voice Disorders from Sustained Vowels
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arXiv:2512.24628v1 Announce Type: new Abstract: Benign laryngeal voice disorders affect nearly one in five individuals and often manifest as dysphonia, while also serving as non-invasive indicators of broader physiological dysfunction. We introduce a clinically inspired hierarchical machine learning framework for automated classification of eight benign voice disorders alongside healthy controls, using acoustic features extracted from short, sustained vowel phonations. Experiments utilized 15,132 recordings from 1,261 speakers in the Saarbruecken Voice Database, covering vowels /a/, /i/, and /u/ at neutral, high, low, and gliding pitches. Mirroring clinical triage workflows, the framework operates in three sequential stages: Stage 1 performs binary screening of pathological versus non-pathological voices by integrating convolutional neural network-derived mel-spectrogram features with 21 interpretable acoustic biomarkers; Stage 2 stratifies voices into Healthy, Functional or Psychogenic, and Structural or Inflammatory groups using a cubic support vector machine; Stage 3 achieves fine-grained classification by incorporating probabilistic outputs from prior stages, improving discrimination of structural and inflammatory disorders relative to functional conditions. The proposed system consistently outperformed flat multi-class classifiers and pre-trained self-supervised models, including META HuBERT and Google HeAR, whose generic objectives are not optimized for sustained clinical phonation. By combining deep spectral representations with interpretable acoustic features, the framework enhances transparency and clinical alignment. These results highlight the potential of quantitative voice biomarkers as scalable, non-invasive tools for early screening, diagnostic triage, and longitudinal monitoring of vocal health.
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https://arxiv.org/abs/2512.24628
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Academic Papers
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5945cdf76390b8207348d24e0f0b7777d2f3e3973ab5252ab4adc81a86d21870
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2026-01-01T00:00:00-05:00
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How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests
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arXiv:2512.24630v1 Announce Type: new Abstract: LLM-based software engineering is influencing modern software development. In addition to correctness, prior studies have also examined the performance of software artifacts generated by AI agents. However, it is unclear how exactly the agentic AI systems address performance concerns in practice. In this paper, we present an empirical study of performance-related pull requests generated by AI agents. Using LLM-assisted detection and BERTopic-based topic modeling, we identified 52 performance-related topics grouped into 10 higher-level categories. Our results show that AI agents apply performance optimizations across diverse layers of the software stack and that the type of optimization significantly affects pull request acceptance rates and review times. We also found that performance optimization by AI agents primarily occurs during the development phase, with less focus on the maintenance phase. Our findings provide empirical evidence that can support the evaluation and improvement of agentic AI systems with respect to their performance optimization behaviors and review outcomes.
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https://arxiv.org/abs/2512.24630
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Academic Papers
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e0c4279efaf1ffcdbcfb9c8e18bd895c442cc9914f8581de9385ba8df2894e48
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2026-01-01T00:00:00-05:00
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ReflecToMeet: An AI-Assisted Reflection Based System to Enhance Collaborative Preparedness
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arXiv:2512.24632v1 Announce Type: new Abstract: In collaborative settings, difficulties in sustaining a consistent pace and engagement often lead to task drift, reducing preparedness and overall effectiveness between meetings. To address this challenge, we conducted a formative study and developed ReflecToMeet, an AI assisted system that integrates theory driven reflective prompts with mechanisms for sharing teammates reflections. Informed by ten formative interviews, the system was evaluated in a mixed method study across three conditions: deeper reflection, regular reflection, and a control condition with unstructured reflection. Participants in the control condition demonstrated less deliberate thought and weaker collaboration, which led to stress and misalignment during team meetings. In contrast, structured reflection supported greater organization and steadier progress. The deeper reflection condition further facilitated confidence, teamwork, and idea generation, although it imposed a higher cognitive load. We conclude by discussing design implications for AI agents that facilitate reflection to enhance collaboration and broader considerations for AI assisted systems aimed at sustaining collaborative goals.
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https://arxiv.org/abs/2512.24632
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Academic Papers
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b3fc1a7f5176ff72d06243aa0768c24723bfcda1df901ebdd324727dbade3275
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2026-01-01T00:00:00-05:00
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DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information
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arXiv:2512.24635v1 Announce Type: new Abstract: Automated Program Repair (APR) aims to automatically generate correct patches for buggy programs. Recent approaches leveraging large language models (LLMs) have shown promise but face limitations. Most rely solely on static analysis, ignoring runtime behaviors. Some attempt to incorporate dynamic signals, but these are often restricted to training or fine-tuning, or injected only once into the repair prompt, without iterative use. This fails to fully capture program execution. Current iterative repair frameworks typically rely on coarse-grained feedback, such as pass/fail results or exception types, and do not leverage fine-grained execution-level information effectively. As a result, models struggle to simulate human stepwise debugging, limiting their effectiveness in multi-step reasoning and complex bug repair. To address these challenges, we propose DynaFix, an execution-level dynamic information-driven APR method that iteratively leverages runtime information to refine the repair process. In each repair round, DynaFix captures execution-level dynamic information such as variable states, control-flow paths, and call stacks, transforming them into structured prompts to guide LLMs in generating candidate patches. If a patch fails validation, DynaFix re-executes the modified program to collect new execution information for the next attempt. This iterative loop incrementally improves patches based on updated feedback, similar to the stepwise debugging practices of human developers. We evaluate DynaFix on the Defects4J v1.2 and v2.0 benchmarks. DynaFix repairs 186 single-function bugs, a 10% improvement over state-of-the-art baselines, including 38 bugs previously unrepaired. It achieves correct patches within at most 35 attempts, reducing the patch search space by 70% compared with existing methods, thereby demonstrating both effectiveness and efficiency in repairing complex bugs.
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https://arxiv.org/abs/2512.24635
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Academic Papers
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fed5e6c60b0d687d8b48ff0914e60e75be3f4baa0ba4bd9496bc48283a5da50a
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2026-01-01T00:00:00-05:00
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How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study
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arXiv:2512.24636v1 Announce Type: new Abstract: As Software Engineering enters its new era (SE 3.0), AI coding agents increasingly automate software development workflows. However, it remains unclear how exactly these agents recognize and address software energy concerns-an issue growing in importance due to large-scale data centers, energy-hungry language models, and battery-constrained devices. In this paper, we examined the energy awareness of agent-authored pull requests (PRs) using a publicly available dataset. We identified 216 energy-explicit PRs and conducted a thematic analysis, deriving a taxonomy of energy-aware work. Our further analysis of the applied optimization techniques shows that most align with established research recommendations. Although building and running these agents is highly energy intensive, encouragingly, the results indicate that they exhibit energy awareness when generating software artifacts. However, optimization-related PRs are accepted less frequently than others, largely due to their negative impact on maintainability.
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https://arxiv.org/abs/2512.24636
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Academic Papers
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aa66bf438289280857a6d39cb3f8ab834b1ab961480a79ceb8f36bb3b683b324
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2026-01-01T00:00:00-05:00
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MSched: GPU Multitasking via Proactive Memory Scheduling
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arXiv:2512.24637v1 Announce Type: new Abstract: The limited HBM capacity has become the primary bottleneck for hosting an increasing number of larger-scale GPU tasks. While demand paging extends capacity via host DRAM, it incurs up to 78x slowdown due to the massive working sets and poor locality of GPU workloads. We observe, however, that GPU memory access patterns are inherently predictable via kernel launch arguments and their asynchronous execution nature. Leveraging this, we propose MSched, an OS-level scheduler that extends GPU context switching to include proactive working set preparation, thereby coalescing fragmented, eventual, and expensive page faults into a single efficient migration. MSched employs a template-based approach to predict working sets with near-perfect accuracy and proposes a co-design between task scheduler and memory manager to enforce a globally optimal page placement policy. Evaluation demonstrates that MSched outperforms demand paging by up to 11.05x for scientific and deep learning workloads, and 57.88x for LLM under memory oversubscription.
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https://arxiv.org/abs/2512.24637
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Academic Papers
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2f450b49b2ec60ef04edf6d16d2e841062e13eae33b7b2c6307fd04f90c93319
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2026-01-01T00:00:00-05:00
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Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding
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arXiv:2512.24638v1 Announce Type: new Abstract: State ambiguity is common in robotic manipulation. Identical observations may correspond to multiple valid behavior trajectories. The visuomotor policy must correctly extract the appropriate types and levels of information from the history to identify the current task phase. However, naively extending the history window is computationally expensive and may cause severe overfitting. Inspired by the continuous nature of human reasoning and the recoding of working memory, we introduce PAM, a novel visuomotor Policy equipped with Adaptive working Memory. With minimal additional training cost in a two-stage manner, PAM supports a 300-frame history window while maintaining high inference speed. Specifically, a hierarchical frame feature extractor yields two distinct representations for motion primitives and temporal disambiguation. For compact representation, a context router with range-specific queries is employed to produce compact context features across multiple history lengths. And an auxiliary objective of reconstructing historical information is introduced to ensure that the context router acts as an effective bottleneck. We meticulously design 7 tasks and verify that PAM can handle multiple scenarios of state ambiguity simultaneously. With a history window of approximately 10 seconds, PAM still supports stable training and maintains inference speeds above 20Hz. Project website: https://tinda24.github.io/pam/
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https://arxiv.org/abs/2512.24638
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Academic Papers
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231fe03dc42f1c6949fd948a071464bc942bf88ccf5c4b4739c72e62eb9eaad7
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2026-01-01T00:00:00-05:00
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From Sequential to Spatial: Reordering Autoregression for Efficient Visual Generation
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arXiv:2512.24639v1 Announce Type: new Abstract: Inspired by the remarkable success of autoregressive models in language modeling, this paradigm has been widely adopted in visual generation. However, the sequential token-by-token decoding mechanism inherent in traditional autoregressive models leads to low inference efficiency.In this paper, we propose RadAR, an efficient and parallelizable framework designed to accelerate autoregressive visual generation while preserving its representational capacity. Our approach is motivated by the observation that visual tokens exhibit strong local dependencies and spatial correlations with their neighbors--a property not fully exploited in standard raster-scan decoding orders. Specifically, we organize the generation process around a radial topology: an initial token is selected as the starting point, and all other tokens are systematically grouped into multiple concentric rings according to their spatial distances from this center. Generation then proceeds in a ring-wise manner, from inner to outer regions, enabling the parallel prediction of all tokens within the same ring. This design not only preserves the structural locality and spatial coherence of visual scenes but also substantially increases parallelization. Furthermore, to address the risk of inconsistent predictions arising from simultaneous token generation with limited context, we introduce a nested attention mechanism. This mechanism dynamically refines implausible outputs during the forward pass, thereby mitigating error accumulation and preventing model collapse. By integrating radial parallel prediction with dynamic output correction, RadAR significantly improves generation efficiency.
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https://arxiv.org/abs/2512.24639
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Academic Papers
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d5343188f3bab2cc89558950cefd148ea0c9e3609dc24a25d94e85705ce01ead
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2026-01-01T00:00:00-05:00
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A Scalable Framework for logP Prediction: From Terabyte-Scale Data Integration to Interpretable Ensemble Modeling
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arXiv:2512.24643v1 Announce Type: new Abstract: This study presents a large-scale predictive modeling framework for logP prediction using 426850 bioactive compounds rigorously curated from the intersection of three authoritative chemical databases: PubChem, ChEMBL, and eMolecules. We developed a novel computational infrastructure to address the data integration challenge, reducing processing time from a projected over 100 days to 3.2 hours through byte-offset indexing architecture, a 740-fold improvement. Our comprehensive analysis revealed critical insights into the multivariate nature of lipophilicity: while molecular weight exhibited weak bivariate correlation with logP, SHAP analysis on ensemble models identified it as the single most important predictor globally. We systematically evaluated multiple modeling approaches, discovering that linear models suffered from inherent heteroskedasticity that classical remediation strategies, including weighted least squares and Box-Cox transformation, failed to address. Tree-based ensemble methods, including Random Forest and XGBoost, proved inherently robust to this violation, achieving an R-squared of 0.765 and RMSE of 0.731 logP units on the test set. Furthermore, a stratified modeling strategy, employing specialized models for drug-like molecules (91 percent of dataset) and extreme cases (nine percent), achieved optimal performance: an RMSE of 0.838 for the drug-like subset and an R-squared of 0.767 for extreme molecules, the highest of all evaluated approaches. These findings provide actionable guidance for molecular design, establish robust baselines for lipophilicity prediction using only 2D descriptors, and demonstrate that well-curated, descriptor-based ensemble models remain competitive with state-of-the-art graph neural network architectures.
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https://arxiv.org/abs/2512.24643
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Academic Papers
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a14553692902a00de3d025eac7cb08fb26e0e0f060e4ca22259bc0e8994802bc
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2026-01-01T00:00:00-05:00
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AudioFab: Building A General and Intelligent Audio Factory through Tool Learning
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arXiv:2512.24645v1 Announce Type: new Abstract: Currently, artificial intelligence is profoundly transforming the audio domain; however, numerous advanced algorithms and tools remain fragmented, lacking a unified and efficient framework to unlock their full potential. Existing audio agent frameworks often suffer from complex environment configurations and inefficient tool collaboration. To address these limitations, we introduce AudioFab, an open-source agent framework aimed at establishing an open and intelligent audio-processing ecosystem. Compared to existing solutions, AudioFab's modular design resolves dependency conflicts, simplifying tool integration and extension. It also optimizes tool learning through intelligent selection and few-shot learning, improving efficiency and accuracy in complex audio tasks. Furthermore, AudioFab provides a user-friendly natural language interface tailored for non-expert users. As a foundational framework, AudioFab's core contribution lies in offering a stable and extensible platform for future research and development in audio and multimodal AI. The code is available at https://github.com/SmileHnu/AudioFab.
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https://arxiv.org/abs/2512.24645
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Academic Papers
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ce2a77c70172128b23aee714212359673b6e52c8fdded48326ebacdc4ec68c78
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2026-01-01T00:00:00-05:00
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Solving the inverse Source Problems for wave equation with final time measurements by a data driven approach
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arXiv:2512.24647v1 Announce Type: new Abstract: This paper develops a discrete data-driven approach for solving the inverse source problem of the wave equation with final time measurements. Focusing on the $L^2$-Tikhonov regularization method, we analyze its convergence under two different noise models, using noisy discrete spatial observations. By exploiting the spectral decomposition of the forward operator and introducing a noise separation technique into the variational framework, we establish error bounds for the reconstructed solution $u$ and the source term $f$ without requiring classical source conditions. Moreover, an expected convergence rate for the source error is derived in a weaker topology. We also extend the analysis to the fully discrete case with finite element discretization, showing that the overall error depends only on the noise level, regularization parameter, time step size, and spatial mesh size. These estimates provide a basis for selecting the optimal regularization parameter in a data-driven manner, without a priori information. Numerical experiments validate the theoretical results and demonstrate the efficiency of the proposed algorithm.
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https://arxiv.org/abs/2512.24647
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Academic Papers
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4bfa46a0975640a0396d06ae475c9364c372255a5ad831c64e4a10104c47df1b
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2026-01-01T00:00:00-05:00
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Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation
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arXiv:2512.24651v1 Announce Type: new Abstract: Autonomous mobile robots operating in complex, dynamic environments face the dual challenge of navigating large-scale, structurally diverse spaces with static obstacles while safely interacting with various moving agents. Traditional graph-based planners excel at long-range pathfinding but lack reactivity, while Deep Reinforcement Learning (DRL) methods demonstrate strong collision avoidance but often fail to reach distant goals due to a lack of global context. We propose Hybrid Motion Planning with Deep Reinforcement Learning (HMP-DRL), a hybrid framework that bridges this gap. Our approach utilizes a graph-based global planner to generate a path, which is integrated into a local DRL policy via a sequence of checkpoints encoded in both the state space and reward function. To ensure social compliance, the local planner employs an entity-aware reward structure that dynamically adjusts safety margins and penalties based on the semantic type of surrounding agents. We validate the proposed method through extensive testing in a realistic simulation environment derived from real-world map data. Comprehensive experiments demonstrate that HMP-DRL consistently outperforms other methods, including state-of-the-art approaches, in terms of key metrics of robot navigation: success rate, collision rate, and time to reach the goal. Overall, these findings confirm that integrating long-term path guidance with semantically-aware local control significantly enhances both the safety and reliability of autonomous navigation in complex human-centric settings.
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https://arxiv.org/abs/2512.24651
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Academic Papers
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6a1350036962139db51aae84b8da780af720785feb26216c39ef89c93b9db7cc
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2026-01-01T00:00:00-05:00
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Practical Traceable Over-Threshold Multi-Party Private Set Intersection
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arXiv:2512.24652v1 Announce Type: new Abstract: Multi-Party Private Set Intersection (MP-PSI) with threshold enhances the flexibility of MP-PSI by disclosing elements present in at least $t$ participants' sets, rather than requiring elements to appear in all $n$ sets. In scenarios where each participant is responsible for its dataset, e.g., digital forensics, MP-PSI with threshold should disclose both intersection elements and corresponding holders such that elements are traceable and the reliability of intersection is guaranteed. We refer to MP-PSI with threshold supporting traceability as Traceable Over-Threshold MP-PSI (T-OT-MP-PSI). However, research on such protocols remains limited, and existing work tolerates at most $t-2$ semi-honest participants at considerable computational cost. We propose two novel Traceable OT-MP-PSI protocols. The first, Efficient Traceable OT-MP-PSI (ET-OT-MP-PSI), combines Shamir's secret sharing with an oblivious programmable pseudorandom function, achieving significantly improved efficiency with resistance to at most $t-2$ semi-honest participants. The second, Security-enhanced Traceable OT-MP-PSI (ST-OT-MP-PSI), achieves security against up to $n-1$ semi-honest participants by further leveraging the oblivious linear evaluation protocol. Compared to Mahdavi et al.'s protocol, ours eliminate the assumption that certain special parties do not collude. Experimental results demonstrate significant improvements: for $n=5$, $t=3$, and sets of size $2^{14}$, ET-OT-MP-PSI achieves $15056\times$ speedup and ST-OT-MP-PSI achieves $505\times$ speedup over Mahdavi et al.'s protocol.
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https://arxiv.org/abs/2512.24652
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Academic Papers
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5b7ba8523c88fcaaf906dc2c1530438619902db948846acb44b6d4e456361285
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2026-01-01T00:00:00-05:00
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RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence
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arXiv:2512.24653v1 Announce Type: new Abstract: While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstrations. Consequently, the ability of existing models to generalize across long-horizon bimanual tasks and mobile manipulation in unstructured environments remains limited. To bridge this gap, we present RoboMIND 2.0, a comprehensive real-world dataset comprising over 310K dual-arm manipulation trajectories collected across six distinct robot embodiments and 739 complex tasks. Crucially, to support research in contact-rich and spatially extended tasks, the dataset incorporates 12K tactile-enhanced episodes and 20K mobile manipulation trajectories. Complementing this physical data, we construct high-fidelity digital twins of our real-world environments, releasing an additional 20K-trajectory simulated dataset to facilitate robust sim-to-real transfer. To fully exploit the potential of RoboMIND 2.0, we propose MIND-2 system, a hierarchical dual-system frame-work optimized via offline reinforcement learning. MIND-2 integrates a high-level semantic planner (MIND-2-VLM) to decompose abstract natural language instructions into grounded subgoals, coupled with a low-level Vision-Language-Action executor (MIND-2-VLA), which generates precise, proprioception-aware motor actions.
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https://arxiv.org/abs/2512.24653
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Academic Papers
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819057c8beb51e2a51f0e58698b2d8d52611efe8d830f9db72f18967e4f95347
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2026-01-01T00:00:00-05:00
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Characterizing Bugs and Quality Attributes in Quantum Software: A Large-Scale Empirical Study
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arXiv:2512.24656v1 Announce Type: new Abstract: Quantum Software Engineering (QSE) is essential for ensuring the reliability and maintainability of hybrid quantum-classical systems, yet empirical evidence on how bugs emerge and affect quality in real-world quantum projects remains limited. This study presents the first ecosystem-scale longitudinal analysis of software defects across 123 open source quantum repositories from 2012 to 2024, spanning eight functional categories, including full-stack libraries, simulators, annealing, algorithms, compilers, assembly, cryptography, and experimental computing. Using a mixed method approach combining repository mining, static code analysis, issue metadata extraction, and a validated rule-based classification framework, we analyze 32,296 verified bug reports. Results show that full-stack libraries and compilers are the most defect-prone categories due to circuit, gate, and transpilation-related issues, while simulators are mainly affected by measurement and noise modeling errors. Classical bugs primarily impact usability and interoperability, whereas quantum-specific bugs disproportionately degrade performance, maintainability, and reliability. Longitudinal analysis indicates ecosystem maturation, with defect densities peaking between 2017 and 2021 and declining thereafter. High-severity defects cluster in cryptography, experimental computing, and compiler toolchains. Repositories employing automated testing detect more defects and resolve issues faster. A negative binomial regression further shows that automated testing is associated with an approximate 60 percent reduction in expected defect incidence. Overall, this work provides the first large-scale data-driven characterization of quantum software defects and offers empirical guidance for improving testing, documentation, and maintainability practices in QSE.
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https://arxiv.org/abs/2512.24656
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Academic Papers
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74ccb8112e3db456bf0f4376371f380ba44d3eb5bdc83eb9a1a2fa1240e85d41
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2026-01-01T00:00:00-05:00
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Antagonistic Bowden-Cable Actuation of a Lightweight Robotic Hand: Toward Dexterous Manipulation for Payload Constrained Humanoids
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arXiv:2512.24657v1 Announce Type: new Abstract: Humanoid robots toward human-level dexterity require robotic hands capable of simultaneously providing high grasping force, rapid actuation speeds, multiple degrees of freedom, and lightweight structures within human-like size constraints. Meeting these conflicting requirements remains challenging, as satisfying this combination typically necessitates heavier actuators and bulkier transmission systems, significantly restricting the payload capacity of robot arms. In this letter, we present a lightweight anthropomorphic hand actuated by Bowden cables, which uniquely combines rolling-contact joint optimization with antagonistic cable actuation, enabling single-motor-per-joint control with negligible cable-length deviation. By relocating the actuator module to the torso, the design substantially reduces distal mass while maintaining anthropomorphic scale and dexterity. Additionally, this antagonistic cable actuation eliminates the need for synchronization between motors. Using the proposed methods, the hand assembly with a distal mass of 236g (excluding remote actuators and Bowden sheaths) demonstrated reliable execution of dexterous tasks, exceeding 18N fingertip force and lifting payloads over one hundred times its own mass. Furthermore, robustness was validated through Cutkosky taxonomy grasps and trajectory consistency under perturbed actuator-hand transformations.
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https://arxiv.org/abs/2512.24657
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Academic Papers
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9bcb5eb41f87989bbf4648b80ee35b249909f099e15c518f2e275aaed5889892
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2026-01-01T00:00:00-05:00
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Taking Advantage of Rational Canonical Form for Faster Ring-LWE based Encrypted Controller with Recursive Multiplication
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arXiv:2512.24658v1 Announce Type: new Abstract: This paper aims to provide an efficient implementation of encrypted linear dynamic controllers that perform recursive multiplications on a Ring-Learning With Errors (Ring-LWE) based cryptosystem. By adopting a system-theoretical approach, we significantly reduce both time and space complexities, particularly the number of homomorphic operations required for recursive multiplications. Rather than encrypting the entire state matrix of a given controller, the state matrix is transformed into its rational canonical form, whose sparse and circulant structure enables that encryption and computation are required only on its nontrivial columns. Furthermore, we propose a novel method to ``pack'' each of the input and the output matrices into a single polynomial, thereby reducing the number of homomorphic operations. Simulation results demonstrate that the proposed design enables a remarkably fast implementation of encrypted controllers.
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https://arxiv.org/abs/2512.24658
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Academic Papers
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5219d350ee0982a3d5b6cf34995e4fe38911ce45dc77e90a3594a709ea98323d
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2026-01-01T00:00:00-05:00
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Hierarchical Online Optimization Approach for IRS-enabled Low-altitude MEC in Vehicular Networks
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arXiv:2512.24659v1 Announce Type: new Abstract: In this paper, we propose an intelligent reflecting surface (IRS)-enabled low-altitude multi-access edge computing (MEC) architecture, where an aerial MEC server cooperates with a terrestrial MEC server to provide computing services, while hybrid IRSs (i.e., building-installed and UAV-carried IRSs) are deployed to enhance the air-ground connectivity under blockage. Based on this architecture, we formulate a multi-objective optimization problem (MOOP) to minimize the task completion delay and energy consumption by jointly optimizing task offloading, UAV trajectory control, IRS phase-shift configuration, and computation resource allocation. The considered problem is NP-hard, and thus we propose a hierarchical online optimization approach (HOOA) to efficiently solve the problem. Specifically, we reformulate the MOOP as a Stackelberg game, where MEC servers collectively act as the leader to determine the system-level decisions, while the vehicles act as followers to make individual decisions. At the follower level, we present a many-to-one matching mechanism to generate feasible discrete decisions. At the leader level, we propose a generative diffusion model-enhanced twin delayed deep deterministic policy gradient (GDMTD3) algorithm integrated with a Karush-Kuhn-Tucker (KKT)-based method, which is a deep reinforcement learning (DRL)-based approach, to determine the continuous decisions. Simulation results demonstrate that the proposed HOOA achieves significant improvements, which reduces average task completion delay by 2.5% and average energy consumption by 3.1% compared with the best-performing benchmark approach and state-of-the-art DRL algorithm, respectively. Moreover, the proposed HOOA exhibits superior convergence stability while maintaining strong robustness and scalability in dynamic environments.
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https://arxiv.org/abs/2512.24659
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Academic Papers
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10276df3a1b7814b027be292f59fbfa8cf103665dcc7f0d36f11239797837600
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2026-01-01T00:00:00-05:00
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Do Large Language Models Know What They Are Capable Of?
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arXiv:2512.24661v1 Announce Type: new Abstract: We investigate whether large language models (LLMs) can predict whether they will succeed on a given task and whether their predictions improve as they progress through multi-step tasks. We also investigate whether LLMs can learn from in-context experiences to make better decisions about whether to pursue a task in scenarios where failure is costly. All LLMs we tested are overconfident, but most predict their success with better-than-random discriminatory power. We find that newer and larger LLMs generally do not have greater discriminatory power, though Claude models do show such a trend. On multi-step agentic tasks, the overconfidence of several frontier LLMs worsens as they progress through the tasks, and reasoning LLMs perform comparably to or worse than non-reasoning LLMs. With in-context experiences of failure, some but not all LLMs reduce their overconfidence leading to significantly improved decision making, while others do not. Interestingly, all LLMs' decisions are approximately rational given their estimated probabilities of success, yet their overly-optimistic estimates result in poor decision making. These results suggest that current LLM agents are hindered by their lack of awareness of their own capabilities. We discuss the implications of LLMs' awareness of their capabilities for AI misuse and misalignment risks.
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https://arxiv.org/abs/2512.24661
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Academic Papers
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1914e38e84e39ff633abc40bdcde459ee739d460a6e93a1dd59ef248cc780ba4
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2026-01-01T00:00:00-05:00
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Renormalization Group Guided Tensor Network Structure Search
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arXiv:2512.24663v1 Announce Type: new Abstract: Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional data representation. Despite recent advances, existing TN-SS methods face significant limitations in computational tractability, structure adaptivity, and optimization robustness across diverse tensor characteristics. They struggle with three key challenges: single-scale optimization missing multi-scale structures, discrete search spaces hindering smooth structure evolution, and separated structure-parameter optimization causing computational inefficiency. We propose RGTN (Renormalization Group guided Tensor Network search), a physics-inspired framework transforming TN-SS via multi-scale renormalization group flows. Unlike fixed-scale discrete search methods, RGTN uses dynamic scale-transformation for continuous structure evolution across resolutions. Its core innovation includes learnable edge gates for optimization-stage topology modification and intelligent proposals based on physical quantities like node tension measuring local stress and edge information flow quantifying connectivity importance. Starting from low-complexity coarse scales and refining to finer ones, RGTN finds compact structures while escaping local minima via scale-induced perturbations. Extensive experiments on light field data, high-order synthetic tensors, and video completion tasks show RGTN achieves state-of-the-art compression ratios and runs 4-600$\times$ faster than existing methods, validating the effectiveness of our physics-inspired approach.
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https://arxiv.org/abs/2512.24663
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Academic Papers
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svg
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d2ec5961ede3b419a20906e20f5b9a53161a0b0b7942487a9ba9fa0462238d0f
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2026-01-01T00:00:00-05:00
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HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs
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arXiv:2512.24665v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for heterogeneous node classification, where an adversary injects a small set of trigger nodes and connections during training to force specific victim nodes to be misclassified into an attacker-chosen label at test time while preserving clean performance. We propose HeteroHBA, a generative backdoor framework that selects influential auxiliary neighbors for trigger attachment via saliency-based screening and synthesizes diverse trigger features and connection patterns to better match the local heterogeneous context. To improve stealthiness, we combine Adaptive Instance Normalization (AdaIN) with a Maximum Mean Discrepancy (MMD) loss to align the trigger feature distribution with benign statistics, thereby reducing detectability, and we optimize the attack with a bilevel objective that jointly promotes attack success and maintains clean accuracy. Experiments on multiple real-world heterogeneous graphs with representative HGNN architectures show that HeteroHBA consistently achieves higher attack success than prior backdoor baselines with comparable or smaller impact on clean accuracy; moreover, the attack remains effective under our heterogeneity-aware structural defense, CSD. These results highlight practical backdoor risks in heterogeneous graph learning and motivate the development of stronger defenses.
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https://arxiv.org/abs/2512.24665
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Academic Papers
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cbe6fc938a7ef141f2006c27bb285891ff33f192f9eb62944c4b72319ff1f433
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2026-01-01T00:00:00-05:00
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Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients
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arXiv:2512.24667v1 Announce Type: new Abstract: With the development of large-scale models, traditional distributed bilevel optimization algorithms cannot be applied directly in low-resource clients. The key reason lies in the excessive computation involved in optimizing both the lower- and upper-level functions. Thus, we present the first resource-adaptive distributed bilevel optimization framework with a second-order free hypergradient estimator, which allows each client to optimize the submodels adapted to the available resources. Due to the coupled influence of partial outer parameters x and inner parameters y, it's challenging to theoretically analyze the upper bound regarding the globally averaged hypergradient for full model parameters. The error bound of inner parameter also needs to be reformulated since the local partial training. The provable theorems show that both RABO and RAFBO can achieve an asymptotically optimal convergence rate of $O(1/\sqrt{C_x^{\ast}Q})$, which is dominated by the minimum coverage of the outer parameter $C_x^{\ast}$. Extensive experiments on two different tasks demonstrate the effectiveness and computation efficiency of our proposed methods.
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https://arxiv.org/abs/2512.24667
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Academic Papers
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683256203196c442cab1934e3711514c51e248838186327f7b7ad9888e04a106
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2026-01-01T00:00:00-05:00
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VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots
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arXiv:2512.24673v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive action chunks have a profound impact on the overall performance of VLA models. Existing methods suffer from jitter, stalling, or even pauses in robotic action execution, which not only limits the achievable execution speed but also reduces the overall success rate of task completion. This paper introduces VLA-RAIL (A Real-Time Asynchronous Inference Linker), a novel framework designed to address these issues by conducting model inference and robot motion control asynchronously and guaranteeing smooth, continuous, and high-speed action execution. The core contributions of the paper are two fold: a Trajectory Smoother that effectively filters out the noise and jitter in the trajectory of one action chunk using polynomial fitting and a Chunk Fuser that seamlessly align the current executing trajectory and the newly arrived chunk, ensuring position, velocity, and acceleration continuity between two successive action chunks. We validate the effectiveness of VLA-RAIL on a benchmark of dynamic simulation tasks and several real-world manipulation tasks. Experimental results demonstrate that VLA-RAIL significantly reduces motion jitter, enhances execution speed, and improves task success rates, which will become a key infrastructure for the large-scale deployment of VLA models.
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https://arxiv.org/abs/2512.24673
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Academic Papers
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549ec5af6d106764c65c264e56f983218d24027bf77535e8b1f0746194e02f92
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2026-01-01T00:00:00-05:00
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Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions
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arXiv:2512.24679v1 Announce Type: new Abstract: Intelligent fault diagnosis has become an indispensable technique for ensuring machinery reliability. However, existing methods suffer significant performance decline in real-world scenarios where models are tested under unseen working conditions, while domain adaptation approaches are limited to their reliance on target domain samples. Moreover, most existing studies rely on single-modal sensing signals, overlooking the complementary nature of multi-modal information for improving model generalization. To address these limitations, this paper proposes a multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis. A dual disentanglement framework is developed to decouple modality-invariant and modality-specific features, as well as domain-invariant and domain-specific representations, enabling both comprehensive multi-modal representation learning and robust domain generalization. A cross-domain mixed fusion strategy is designed to randomly mix modality information across domains for modality and domain diversity augmentation. Furthermore, a triple-modal fusion mechanism is introduced to adaptively integrate multi-modal heterogeneous information. Extensive experiments are conducted on induction motor fault diagnosis under both unseen constant and time-varying working conditions. The results demonstrate that the proposed method consistently outperforms advanced methods and comprehensive ablation studies further verify the effectiveness of each proposed component and multi-modal fusion. The code is available at: https://github.com/xiapc1996/MMDG.
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https://arxiv.org/abs/2512.24679
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Academic Papers
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