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8c7b225d9eec3a10461b551ba0a18a12dc1b7c128ad9e748bf670d6f943e98dc | 2026-01-01T00:00:00-05:00 | Towards autonomous time-calibration of large quantum-dot devices: Detection, real-time feedback, and noise spectroscopy | arXiv:2512.24894v1 Announce Type: cross Abstract: The performance and scalability of semiconductor quantum-dot (QD) qubits are limited by electrostatic drift and charge noise that shift operating points and destabilize qubit parameters. As systems expand to large one- and two-dimensional arrays, manual recalibration be... | https://arxiv.org/abs/2512.24894 | Academic Papers | svg |
b699a5c83021dad2ccd2a994503b9f7a88f281f1c264553ad1c4211e5fb2a280 | 2026-01-01T00:00:00-05:00 | Adaptive Resource Orchestration for Distributed Quantum Computing Systems | arXiv:2512.24902v1 Announce Type: cross Abstract: Scaling quantum computing beyond a single device requires networking many quantum processing units (QPUs) into a coherent quantum-HPC system. We propose the Modular Entanglement Hub (ModEn-Hub) architecture: a hub-and-spoke photonic interconnect paired with a real-time ... | https://arxiv.org/abs/2512.24902 | Academic Papers | svg |
f34204e8fa7028350f61ad2563a302af0c67960b35c8ffe793475b1415bc7441 | 2026-01-01T00:00:00-05:00 | No Vision, No Wearables: 5G-based 2D Human Pose Recognition with Integrated Sensing and Communications | arXiv:2512.24923v1 Announce Type: cross Abstract: With the increasing maturity of contactless human pose recognition (HPR) technology, indoor interactive applications have raised higher demands for natural, controller-free interaction methods. However, current mainstream HPR solutions relying on vision or radio-frequen... | https://arxiv.org/abs/2512.24923 | Academic Papers | svg |
3eab905a9c44d215354e955f42ed56e10b15db0e442f018f240ab51b801f0d4e | 2026-01-01T00:00:00-05:00 | Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach | arXiv:2512.24927v1 Announce Type: cross Abstract: Higher-order ODE solvers have become a standard tool for accelerating diffusion probabilistic model (DPM) sampling, motivating the widespread view that first-order methods are inherently slower and that increasing discretization order is the primary path to faster gener... | https://arxiv.org/abs/2512.24927 | Academic Papers | svg |
99f47dfd81d8f3dc9fd21e9a73e66d6e0d76b34bed4605933bbb73f34d0536f7 | 2026-01-01T00:00:00-05:00 | Geometric characterisation of structural and regular equivalences in undirected (hyper)graphs | arXiv:2512.24961v1 Announce Type: cross Abstract: Similarity notions between vertices in a graph, such as structural and regular equivalence, are one of the main ingredients in clustering tools in complex network science. We generalise structural and regular equivalences for undirected hypergraphs and provide a charact... | https://arxiv.org/abs/2512.24961 | Academic Papers | svg |
1e51c27160d67891650fe3fe850d1969c93e15c4c4c556c36f9e14bfb9128471 | 2026-01-01T00:00:00-05:00 | The Impact of LLMs on Online News Consumption and Production | arXiv:2512.24968v1 Announce Type: cross Abstract: Large language models (LLMs) change how consumers acquire information online; their bots also crawl news publishers' websites for training data and to answer consumer queries; and they provide tools that can lower the cost of content creation. These changes lead to pred... | https://arxiv.org/abs/2512.24968 | Academic Papers | svg |
259e2f4ae8f7140fec7bb57b63e7dbcbc64eaa8342d6d0f3c4d65d9ea40cbd02 | 2026-01-01T00:00:00-05:00 | Large language models and the entropy of English | arXiv:2512.24969v1 Announce Type: cross Abstract: We use large language models (LLMs) to uncover long-ranged structure in English texts from a variety of sources. The conditional entropy or code length in many cases continues to decrease with context length at least to $N\sim 10^4$ characters, implying that there are d... | https://arxiv.org/abs/2512.24969 | Academic Papers | svg |
a07c7b889a2cc72e40cd0dedfe6dd295d0e5bd483b42b7885d6d292a78470991 | 2026-01-01T00:00:00-05:00 | SymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets | arXiv:2512.24977v1 Announce Type: cross Abstract: Sequential structure is a key feature of multiple domains of natural cognition and behavior, such as language, movement and decision-making. Likewise, it is also a central property of tasks to which we would like to apply artificial intelligence. It is therefore of grea... | https://arxiv.org/abs/2512.24977 | Academic Papers | svg |
6d5b8a5733fa9bd7a6d698c13bcb8b173ab47568692d43900cbee0449f7ba4fc | 2026-01-01T00:00:00-05:00 | Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis | arXiv:2512.24999v1 Announce Type: cross Abstract: We introduce \textit{basic inequalities} for first-order iterative optimization algorithms, forming a simple and versatile framework that connects implicit and explicit regularization. While related inequalities appear in the literature, we isolate and highlight a speci... | https://arxiv.org/abs/2512.24999 | Academic Papers | svg |
f09b6404ba30d4cf660ac76bc12763241cc0eb60cb7c5abc2d1f328b802818f8 | 2026-01-01T00:00:00-05:00 | Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions | arXiv:2010.11450v2 Announce Type: replace Abstract: A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input. Our goal is to identify the optimal approximation-sm... | https://arxiv.org/abs/2010.11450 | Academic Papers | svg |
8bc0089a9924909af2b255208df1561460841b0d5ed5f5052f535a39f0e0a655 | 2026-01-01T00:00:00-05:00 | Pointwise Distance Distributions for detecting near-duplicates in large materials databases | arXiv:2108.04798v4 Announce Type: replace Abstract: Many real objects are modeled as discrete sets of points, such as corners or other salient features. For our main applications in chemistry, points represent atomic centers in a molecule or a solid material. We study the problem of classifying discrete (finite and per... | https://arxiv.org/abs/2108.04798 | Academic Papers | svg |
2f1674d31488aac08d508f11768a8d212ed3f378905c7f5a1ad8857d986c60b6 | 2026-01-01T00:00:00-05:00 | To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints Online | arXiv:2203.17259v4 Announce Type: replace Abstract: Double-blind conferences have engaged in debates over whether to allow authors to post their papers online on arXiv or elsewhere during the review process. Independently, some authors of research papers face the dilemma of whether to put their papers on arXiv due to i... | https://arxiv.org/abs/2203.17259 | Academic Papers | svg |
71d817e54b4e592a4ad2a3dc236b20f0b43adb65ddbd0af8335ed49724bb965a | 2026-01-01T00:00:00-05:00 | A Nonparametric Framework for Online Stochastic Matching with Correlated Arrivals | arXiv:2208.02229v5 Announce Type: replace Abstract: The design of online algorithms for matching markets and revenue management settings is usually bound by the assumption that the demand process is formed by a fixed-length sequence of queries with unknown types, each drawn independently. This notion of serial independ... | https://arxiv.org/abs/2208.02229 | Academic Papers | svg |
3a40d0eb21ad4d0848573827c3d959bb8f8a4d41385e5c907b61bcd8dc432f7f | 2026-01-01T00:00:00-05:00 | Active Learning with Neural Networks: Insights from Nonparametric Statistics | arXiv:2210.08367v2 Announce Type: replace Abstract: Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly reduce the amount of labeled training data. Empirical successes of deep active learning h... | https://arxiv.org/abs/2210.08367 | Academic Papers | svg |
007b0ed9a809c32fbbc460ce3a761f34893467fea0c87f5070e2758a519f6423 | 2026-01-01T00:00:00-05:00 | The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing | arXiv:2302.01186v4 Announce Type: replace Abstract: We propose $\textsf{ScaledGD($\lambda$)}$, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and when the matrix is possibly ill-conditioned. Using overparametrized factor representations, $\textsf{Sc... | https://arxiv.org/abs/2302.01186 | Academic Papers | svg |
e74ce723cea6c774a4caac67f1cfc51153898c7b25e1a511887759bae542be84 | 2026-01-01T00:00:00-05:00 | Maximum Independent Set when excluding an induced minor: $K_1 + tK_2$ and $tC_3 \uplus C_4$ | arXiv:2302.08182v2 Announce Type: replace Abstract: Dallard, Milani\v{c}, and \v{S}torgel [arXiv '22] ask if for every class excluding a fixed planar graph $H$ as an induced minor, Maximum Independent Set can be solved in polynomial time, and show that this is indeed the case when $H$ is any planar complete bipartite g... | https://arxiv.org/abs/2302.08182 | Academic Papers | svg |
7338b01ebb6a379701770f941236bf6b5598087381706e5d6d53cbfc744e436d | 2026-01-01T00:00:00-05:00 | SymX: Energy-based Simulation from Symbolic Expressions | arXiv:2303.02156v2 Announce Type: replace Abstract: Optimization time integrators are effective at solving complex multi-physics problems including deformable solids with non-linear material models, contact with friction, strain limiting, etc. For challenging problems, Newton-type optimizers are often used, which neces... | https://arxiv.org/abs/2303.02156 | Academic Papers | svg |
d510c6b26e7495a16b12697c87e13f622d15f8df4f7681a01e70d76a9d62a86d | 2026-01-01T00:00:00-05:00 | CascadeNS: Confidence-Cascaded Neurosymbolic Model for Sarcasm Detection | arXiv:2304.01424v2 Announce Type: replace Abstract: Sarcasm detection in product reviews requires balancing domain-specific symbolic pattern recognition with deep semantic understanding. Symbolic representations capture explicit linguistic phenomena that are often decisive for sarcasm detection. Existing work either fa... | https://arxiv.org/abs/2304.01424 | Academic Papers | svg |
eaa4aa90527d265ce4e779717d5661f28e8631d085a2061b802a1453ada926a4 | 2026-01-01T00:00:00-05:00 | HiGen: Hierarchical Graph Generative Networks | arXiv:2305.19337v3 Announce Type: replace Abstract: Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. To address this limitation, we propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates ... | https://arxiv.org/abs/2305.19337 | Academic Papers | svg |
80319e6b7082641310257fe8816b8f0a9564a09ab1203445d505b5edaf6f3708 | 2026-01-01T00:00:00-05:00 | HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising | arXiv:2306.17797v2 Announce Type: replace Abstract: Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, existing deep learning (DL)-based approaches only restore one clean HSI from the given noisy HSI with a deterministic mapping, thus ignori... | https://arxiv.org/abs/2306.17797 | Academic Papers | svg |
5af93af60f6ddbbc809762d2d7240817cca2b004ef62c56bda650f2bb47c86d1 | 2026-01-01T00:00:00-05:00 | An analysis on stochastic Lanczos quadrature with asymmetric quadrature nodes | arXiv:2307.00847v3 Announce Type: replace Abstract: The stochastic Lanczos quadrature method has garnered significant attention recently. Upon examination of the error analyses given by Ubaru, Chen and Saad and Cortinovis and Kressner, certain notable inconsistencies arise. It turns out that the former's results are va... | https://arxiv.org/abs/2307.00847 | Academic Papers | svg |
3863fd71950427b77b6f23ebfe15ec2909fd201a77079f53b65618abd0e5873b | 2026-01-01T00:00:00-05:00 | Almost perfect nonlinear power functions with exponents expressed as fractions | arXiv:2307.15657v2 Announce Type: replace Abstract: Let $F$ be a finite field, let $f$ be a function from $F$ to $F$, and let $a$ be a nonzero element of $F$. The discrete derivative of $f$ in direction $a$ is $\Delta_a f \colon F \to F$ with $(\Delta_a f)(x)=f(x+a)-f(x)$. The differential spectrum of $f$ is the multis... | https://arxiv.org/abs/2307.15657 | Academic Papers | svg |
b5c7ee77ffcbbf9baef69971a62d467325d6768f614f4e887f7a224625d371ec | 2026-01-01T00:00:00-05:00 | Content-based Recommendation Engine for Video Streaming Platform | arXiv:2308.08406v2 Announce Type: replace Abstract: Recommendation engines suggest content, products, or services to the user by using machine learning algorithms. This paper proposes a content-based recommendation engine that provides personalized video suggestions based on users' previous interactions and preferences... | https://arxiv.org/abs/2308.08406 | Academic Papers | svg |
b63a49ef766c0e4baf28eb825a53840e3a4714a3d8a29479ed6dbeaf0d861e98 | 2026-01-01T00:00:00-05:00 | Enumeration and updates for conjunctive linear algebra queries through expressibility | arXiv:2310.04118v5 Announce Type: replace Abstract: Due to the importance of linear algebra and matrix operations in data analytics, there is significant interest in using relational query optimization and processing techniques for evaluating (sparse) linear algebra programs. In particular, in recent years close connec... | https://arxiv.org/abs/2310.04118 | Academic Papers | svg |
70569819d161e9e4a9530b4915aee42e078cc87b97fa37660333973ed1a08652 | 2026-01-01T00:00:00-05:00 | Multi-fidelity Bayesian Optimization: A Review | arXiv:2311.13050v3 Announce Type: replace Abstract: Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings o... | https://arxiv.org/abs/2311.13050 | Academic Papers | svg |
cb67c812ece0f353503ed6058954f1a4f55761943346259cf138930cb396dbcd | 2026-01-01T00:00:00-05:00 | Ricci-Notation Tensor Framework for Model-based Approaches to Imaging | arXiv:2312.04018v4 Announce Type: replace Abstract: Model-based approaches to imaging, like specialized image enhancements in astronomy, facilitate explanations of relationships between observed inputs and computed outputs. These models may be expressed with extended matrix-vector (EMV) algebra, especially when they in... | https://arxiv.org/abs/2312.04018 | Academic Papers | svg |
68842da6becaf25f9047d33314939c2085cec9e36ded5567ff501d508766e848 | 2026-01-01T00:00:00-05:00 | ODIN: Object Density Aware Index for CkNN Queries over Moving Objects on Road Networks | arXiv:2312.12688v2 Announce Type: replace Abstract: We study the problem of processing continuous k nearest neighbor (CkNN) queries over moving objects on road networks, which is an essential operation in a variety of applications. We are particularly concerned with scenarios where the object densities in different par... | https://arxiv.org/abs/2312.12688 | Academic Papers | svg |
ebc4f40dda575b614a41a07ec1814fc11f52d0ab0da6154e8fcfc92aabf61789 | 2026-01-01T00:00:00-05:00 | Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling | arXiv:2402.18508v3 Announce Type: replace Abstract: In the rapidly evolving field of deep learning, the demand for models that are both expressive and computationally efficient has never been more critical. This paper introduces Orchid, a novel architecture designed to address the quadratic complexity of traditional at... | https://arxiv.org/abs/2402.18508 | Academic Papers | svg |
be53b8136f865ddacdf39e0ada4d3fa7acff6b4025898ea2967cf8563668c87f | 2026-01-01T00:00:00-05:00 | Subsequence Matching and LCS under Cartesian-Tree Equivalence | arXiv:2402.19146v4 Announce Type: replace Abstract: Two strings of the same length are said to Cartesian-tree match (CT-match) if their Cartesian-trees are isomorphic [Park et al., TCS 2020]. Cartesian-tree matching is a natural model that allows for capturing similarities of numerical sequences. Oizumi et al. [CPM 202... | https://arxiv.org/abs/2402.19146 | Academic Papers | svg |
5497db96e6e473f2a2a8e867cde0be7f60c2fc8ad4407c952072722c6dad2bab | 2026-01-01T00:00:00-05:00 | Structuring Concept Space with the Musical Circle of Fifths by Utilizing Music Grammar Based Activations | arXiv:2403.00790v4 Announce Type: replace Abstract: We propose a neural coding framework harmonic toroidal codes in which abstract cognitive operations are implemented through dynamical activity on manifolds derived from music theoretic structures. | https://arxiv.org/abs/2403.00790 | Academic Papers | svg |
430beec081b82f5b40f5f0a40e2254db3dc44fa8054f50d499654b941777d630 | 2026-01-01T00:00:00-05:00 | Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons | arXiv:2403.07688v2 Announce Type: replace Abstract: When training neural networks, dying neurons -- units becoming inactive or saturated -- are traditionally seen as harmful. This paper sheds new light on this phenomenon. By exploring the impact of various hyperparameter configurations on dying neurons during training,... | https://arxiv.org/abs/2403.07688 | Academic Papers | svg |
1f3c978526061083dfad227dad0ae4d405d7df2c6960cb7e16a498d76cd51750 | 2026-01-01T00:00:00-05:00 | Matching Semantically Similar Non-Identical Objects | arXiv:2403.08227v4 Announce Type: replace Abstract: Not identical but similar objects are ubiquitous in our world, ranging from four-legged animals such as dogs and cats to cars of different models and flowers of various colors. This study addresses a novel task of matching such non-identical objects at the pixel level... | https://arxiv.org/abs/2403.08227 | Academic Papers | svg |
a5a2c9b93171d89d78c0cd1a4a2d381680f20ba03d28a0834910f30ae0f9dabf | 2026-01-01T00:00:00-05:00 | Reconstructing Hand-Held Objects in 3D from Images and Videos | arXiv:2404.06507v4 Announce Type: replace Abstract: Objects manipulated by the hand (i.e., manipulanda) are particularly challenging to reconstruct from Internet videos. Not only does the hand occlude much of the object, but also the object is often only visible in a small number of image pixels. At the same time, two ... | https://arxiv.org/abs/2404.06507 | Academic Papers | svg |
fdb1e08749c00f2eb45cf0d20fef12fa974c10587d01982dd14a3b0eccf73ce0 | 2026-01-01T00:00:00-05:00 | FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol | arXiv:2404.15834v2 Announce Type: replace Abstract: The NOSTR is a communication protocol for the social web, based on the w3c websockets standard. Although it is still in its infancy, it is well known as a social media protocol, with thousands of trusted users and multiple user interfaces, offering a unique experience... | https://arxiv.org/abs/2404.15834 | Academic Papers | svg |
87d6317d88f7ce8d615b05b164a61795d6e6f3857ec9b48ef7d17f06c431950e | 2026-01-01T00:00:00-05:00 | Myopically Verifiable Probabilistic Certificates for Safe Control and Learning | arXiv:2404.16883v2 Announce Type: replace Abstract: This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk events in infinitesi... | https://arxiv.org/abs/2404.16883 | Academic Papers | svg |
f246f5ffceaf8297df10a207e859940823572df0b7aac892692a622aa286e2bb | 2026-01-01T00:00:00-05:00 | Are Biological Systems More Intelligent Than Artificial Intelligence? | arXiv:2405.02325v5 Announce Type: replace Abstract: Are biological self-organising systems more `intelligent' than artificial intelligence (AI)? If so, why? I explore this through a mathematical lens which frames intelligence in terms of adaptability. I model systems as stacks of abstraction layers (\emph{Stack Theory}... | https://arxiv.org/abs/2405.02325 | Academic Papers | svg |
239e33f158493986c48c594887ecbf53ee5ea8eb99106f9ea4a6ff29ac82ba5c | 2026-01-01T00:00:00-05:00 | Finding Diverse Solutions Parameterized by Cliquewidth | arXiv:2405.20931v2 Announce Type: replace Abstract: Finding a few solutions for a given problem that are diverse, as opposed to finding a single best solution to solve the problem, has recently become a notable topic in theoretical computer science. Recently, Baste, Fellows, Jaffke, Masa\v{r}\'ik, Oliveira, Philip, and... | https://arxiv.org/abs/2405.20931 | Academic Papers | svg |
2d4d74cf2cd311729a1aea7479fc3d39161b0d45bd3a05fb4a7f626d9dbd4d03 | 2026-01-01T00:00:00-05:00 | Jacobian-Enhanced Neural Networks | arXiv:2406.09132v3 Announce Type: replace Abstract: Jacobian-Enhanced Neural Networks (JENN) are densely connected multi-layer perceptrons, whose training process is modified to predict partial derivatives accurately. Their main benefit is better accuracy with fewer training points compared to standard neural networks.... | https://arxiv.org/abs/2406.09132 | Academic Papers | svg |
871f16ddaf481ae3d32439d9c3666705f441236534233081da80abaa416b184b | 2026-01-01T00:00:00-05:00 | ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts | arXiv:2406.10973v5 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) techniques such as low-rank adaptation (LoRA) can effectively adapt large pre-trained foundation models to downstream tasks using only a small fraction (0.1%-10%) of the original trainable weights. An under-explored question of P... | https://arxiv.org/abs/2406.10973 | Academic Papers | svg |
65131e988370f27c4f73c31f082693051696ad2ca5fdfaaab8634fd154e30043 | 2026-01-01T00:00:00-05:00 | MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs | arXiv:2406.17126v2 Announce Type: replace Abstract: Spurious bias, a tendency to exploit spurious correlations between superficial input attributes and prediction targets, has revealed a severe robustness pitfall in classical machine learning problems. Multimodal Large Language Models (MLLMs), which leverage pretrained... | https://arxiv.org/abs/2406.17126 | Academic Papers | svg |
933fcdd772bf9e962f122816301de6d148cc96b44f029d9abf02dc742f2e872a | 2026-01-01T00:00:00-05:00 | DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models | arXiv:2407.01519v5 Announce Type: replace Abstract: We present DiffIR2VR-Zero, a zero-shot framework that enables any pre-trained image restoration diffusion model to perform high-quality video restoration without additional training. While image diffusion models have shown remarkable restoration capabilities, their di... | https://arxiv.org/abs/2407.01519 | Academic Papers | svg |
86bf6237e9b91ef36baefa4d9c36afeb785faf0b45e74d42ef69247ce30ba6c4 | 2026-01-01T00:00:00-05:00 | The Fr\'echet Distance Unleashed: Approximating a Dog with a Frog | arXiv:2407.03101v4 Announce Type: replace Abstract: We show that a variant of the continuous Frechet distance between polygonal curves can be computed using essentially the same algorithm used to solve the discrete version. The new variant is not necessarily monotone, but this shortcoming can be easily handled via refi... | https://arxiv.org/abs/2407.03101 | Academic Papers | svg |
0d5717c78f469a15ef63b585e8d77df99042a4b05fba535c539a17642add05de | 2026-01-01T00:00:00-05:00 | LTLBench: Towards Benchmarks for Evaluating Temporal Logic Reasoning in Large Language Models | arXiv:2407.05434v2 Announce Type: replace Abstract: Temporal Reasoning (TR) is a critical ability for LLMs to understand and reason over temporal information and relationships between events. To study the TR ability in LLMs, prior works provide different ways for evaluating various aspects of TR ability. In this work, ... | https://arxiv.org/abs/2407.05434 | Academic Papers | svg |
e1d059837282863b065e9e7c99671086b331fbf43ca444269973258336a90788 | 2026-01-01T00:00:00-05:00 | UnPaSt: unsupervised patient stratification by biclustering of omics data | arXiv:2408.00200v2 Announce Type: replace Abstract: Unsupervised patient stratification is essential for disease subtype discovery, yet, despite growing evidence of molecular heterogeneity of non-oncological diseases, popular methods are benchmarked primarily using cancers with mutually exclusive molecular subtypes wel... | https://arxiv.org/abs/2408.00200 | Academic Papers | svg |
0adc4e75a44502fa664cdebbf236924a7da655815cbcf1ff5cf1883d16c6779f | 2026-01-01T00:00:00-05:00 | Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems | arXiv:2408.05992v3 Announce Type: replace Abstract: This paper presents a novel online transfer learning approach in state-based potential games (TL-SbPGs) for distributed self-optimization in manufacturing systems. The approach targets practical industrial scenarios where knowledge sharing among similar players enhanc... | https://arxiv.org/abs/2408.05992 | Academic Papers | svg |
ea40e6f79278d0af28535f77d198a00cca4cfb161d10c9efd7bbe40b1c61522e | 2026-01-01T00:00:00-05:00 | [Draft] High-order estimation-based properties and high-order observers for labeled finite-state automata | arXiv:2408.06141v3 Announce Type: replace Abstract: In this paper, we consider labeled finite-state automata (LFSAs), extend some state estimation-based properties from a single agent to a finite ordered set of agents. We also extend the notion of observer to \emph{high-order observer} using our \emph{concurrent compos... | https://arxiv.org/abs/2408.06141 | Academic Papers | svg |
b170bce0fee7c92e7e200fec4fa589babdb7f810440521b9e139cd51df20c261 | 2026-01-01T00:00:00-05:00 | Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities | arXiv:2408.07666v5 Announce Type: replace Abstract: Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive computation. As model merging becomes increasingly prevalent across various fields, it is cru... | https://arxiv.org/abs/2408.07666 | Academic Papers | svg |
3e4119d4255094c1da573b4fa11be7de4929eb7194cd6c3d86c1a75b88c59124 | 2026-01-01T00:00:00-05:00 | Stock Price Responses to Firm-Level News in Supply Chain Networks | arXiv:2409.06255v4 Announce Type: replace Abstract: This study examines how positive and negative news about firms are associated with stock prices and whether these associations extend to suppliers and clients linked via supply chain relationships, using large samples of publicly listed firms worldwide and in Japan. N... | https://arxiv.org/abs/2409.06255 | Academic Papers | svg |
3cb58777b1ac233990445918bc4fe739c2d41d19e5ce08815277aaa7b2397958 | 2026-01-01T00:00:00-05:00 | Proactive Recommendation in Social Networks: Steering User Interest with Causal Inference | arXiv:2409.08934v2 Announce Type: replace Abstract: Recommending items that solely cater to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by directly adjusting items exposed to them. However, the recommended items for direct ste... | https://arxiv.org/abs/2409.08934 | Academic Papers | svg |
acb9fea492dbab0b344c085118ed3ee7c0572b2eac9ec3afb900556c187bc5bd | 2026-01-01T00:00:00-05:00 | Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering | arXiv:2410.00414v4 Announce Type: replace Abstract: Semantic parsers convert natural language to logical forms, which can be evaluated on knowledge bases (KBs) to produce denotations. Recent semantic parsers have been developed with sequence-to-sequence (seq2seq) pre-trained language models (PLMs) or large language mod... | https://arxiv.org/abs/2410.00414 | Academic Papers | svg |
9933a124569bafd0d2cb9c21e4d1f99bf9178b16f64c3642e652ee00921f9664 | 2026-01-01T00:00:00-05:00 | Beyond Firms and Industries: Shock Propagation through Establishment- and Product-Level Supply Chains | arXiv:2410.05595v4 Announce Type: replace Abstract: This paper investigates how the granularity of supply-chain data affects the propagation of economic shocks through production networks. Using newly constructed establishment-level supply chains with product-level information links for Japan, we simulate disruption dy... | https://arxiv.org/abs/2410.05595 | Academic Papers | svg |
d32a23746ef3bcf4fb95888a539163d73a6c2ea85acbf451744cb4d63fa2bfeb | 2026-01-01T00:00:00-05:00 | Faster and Simpler Online Computation of String Net Frequency | arXiv:2410.06837v3 Announce Type: replace Abstract: An occurrence of a repeated substring $u$ in a string $S$ is called a net occurrence if extending the occurrence to the left or to the right decreases the number of occurrences to 1. The net frequency (NF) of a repeated substring $u$ in a string $S$ is the number of n... | https://arxiv.org/abs/2410.06837 | Academic Papers | svg |
b1184135899d267a86d210ae77384d2053f608044f9fa111c75eab06cd061283 | 2026-01-01T00:00:00-05:00 | SwitchFS: Asynchronous Metadata Updates for Distributed Filesystems with In-Network Coordination | arXiv:2410.08618v3 Announce Type: replace Abstract: Distributed filesystem metadata updates are typically synchronous. This creates inherent challenges for access efficiency, load balancing, and directory contention, especially under dynamic and skewed workloads. This paper argues that synchronous updates are overly co... | https://arxiv.org/abs/2410.08618 | Academic Papers | svg |
a151e84e11fdabb33b01a7a51b170b944e6f9216d84f607e783034cddda01073 | 2026-01-01T00:00:00-05:00 | A Systematic Survey on Large Language Models for Algorithm Design | arXiv:2410.14716v4 Announce Type: replace Abstract: Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions. In just a few years, t... | https://arxiv.org/abs/2410.14716 | Academic Papers | svg |
fd17f74906a6344576e3bc199b189ac646967c5991c2b8e3de967b627a037864 | 2026-01-01T00:00:00-05:00 | Automatic identification of diagnosis from hospital discharge letters via weakly-supervised Natural Language Processing | arXiv:2410.15051v2 Announce Type: replace Abstract: Identifying patient diagnoses from discharge letters is essential to enable large-scale cohort selection and epidemiological research, but traditional supervised approaches rely on extensive manual annotation, which is often impractical for large textual datasets. In ... | https://arxiv.org/abs/2410.15051 | Academic Papers | svg |
5ccc53020acf504d4fc9624638adef947481094d3f8df8b1b7df1c9bfcebf63e | 2026-01-01T00:00:00-05:00 | EON: A practical energy-preserving rough diffuse BRDF | arXiv:2410.18026v3 Announce Type: replace Abstract: We introduce the "Energy-preserving Oren--Nayar" (EON) model for reflection from rough surfaces. Unlike the popular qualitative Oren--Nayar model (QON) and its variants, our model is energy-preserving via analytical energy compensation. We include self-contained GLSL ... | https://arxiv.org/abs/2410.18026 | Academic Papers | svg |
453c88a538039425a9fe66e9562d627e35ee20d6dac94cb5c279fa55aabc125a | 2026-01-01T00:00:00-05:00 | Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation | arXiv:2410.18565v2 Announce Type: replace Abstract: We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques. These include Weighted Instruct... | https://arxiv.org/abs/2410.18565 | Academic Papers | svg |
d43048b24b9c32190e7c2c052a8fb027c0c0722bf4d62f3c4276a5379b6e98eb | 2026-01-01T00:00:00-05:00 | Computing the bridge length: the key ingredient in a continuous isometry classification of periodic point sets | arXiv:2410.23288v2 Announce Type: replace Abstract: The fundamental model of any periodic crystal is a periodic set of points at all atomic centres. Since crystal structures are determined in a rigid form, their strongest equivalence is rigid motion (composition of translations and rotations) or isometry (also includin... | https://arxiv.org/abs/2410.23288 | Academic Papers | svg |
cb386362c2e31b2401990e411a3abd3706548c9ef52ca32c147ab8b9e7818b53 | 2026-01-01T00:00:00-05:00 | Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching | arXiv:2411.00759v3 Announce Type: replace Abstract: Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unlike continuous flow matching, the rectification strategy cannot be applied due to the stochasticity of discrete paths, n... | https://arxiv.org/abs/2411.00759 | Academic Papers | svg |
43e4679417e62f21f06b5b2cd636d1e04b8f0112d8f41a360133f31858b165eb | 2026-01-01T00:00:00-05:00 | Two-Stage Robust Optimal Operation of Distribution Networks Considering Renewable Energy and Demand Asymmetric Uncertainties | arXiv:2411.10166v3 Announce Type: replace Abstract: This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networks, aiming at deriving an optimal operational scheme capable of addressing asymmetric un... | https://arxiv.org/abs/2411.10166 | Academic Papers | svg |
7c46bf67c3dfbb9b62ffa80c2a5c604a271666ba771af9b790096665a28d852f | 2026-01-01T00:00:00-05:00 | The Generalization Error of Supervised Machine Learning Algorithms | arXiv:2411.12030v2 Announce Type: replace Abstract: In this paper, the method of gaps, a technique for deriving closed-form expressions in terms of information measures for the generalization error of supervised machine learning algorithms is introduced. The method relies on the notion of \emph{gaps}, which characteriz... | https://arxiv.org/abs/2411.12030 | Academic Papers | svg |
e4ac5737fb6b015e80ab879cea5df37e8701a9f38870b35757d61cbd5b4d2d97 | 2026-01-01T00:00:00-05:00 | Strong Linearizability without Compare&Swap: The Case of Bags | arXiv:2411.19365v3 Announce Type: replace Abstract: Because strongly-linearizable objects provide stronger guarantees than linearizability, they serve as valuable building blocks for the design of concurrent data structures. Yet, many objects that have linearizable implementations from base objects weaker than compare&... | https://arxiv.org/abs/2411.19365 | Academic Papers | svg |
138b93ae1bdb2cc0b1a84ea66ac50a44e10dd03d7c646afd09bfcd84ccad9b8b | 2026-01-01T00:00:00-05:00 | Explaining Object Detectors via Collective Contribution of Pixels | arXiv:2412.00666v3 Announce Type: replace Abstract: Visual explanations for object detectors are crucial for enhancing their reliability. Object detectors identify and localize instances by assessing multiple visual features collectively. When generating explanations, overlooking these collective influences in detectio... | https://arxiv.org/abs/2412.00666 | Academic Papers | svg |
d4a2ff19f5cd7cf5e6e7d74bea0fa403809c7b028ed9a754c447079a7376bcfc | 2026-01-01T00:00:00-05:00 | Private Linear Regression with Differential Privacy and PAC Privacy | arXiv:2412.02578v2 Announce Type: replace Abstract: Linear regression is a fundamental tool for statistical analysis, which has motivated the development of linear regression methods that satisfy provable privacy guarantees so that the learned model reveals little about any one data point used to construct it. Most exi... | https://arxiv.org/abs/2412.02578 | Academic Papers | svg |
b9619b06315956f76c8779fdc072b06832c6e67762276b34b21d458f7920dc23 | 2026-01-01T00:00:00-05:00 | SoundnessBench: A Soundness Benchmark for Neural Network Verifiers | arXiv:2412.03154v3 Announce Type: replace Abstract: Neural network (NN) verification aims to formally verify properties of NNs, which is crucial for ensuring the behavior of NN-based models in safety-critical applications. In recent years, the community has developed many NN verifiers and benchmarks to evaluate them. H... | https://arxiv.org/abs/2412.03154 | Academic Papers | svg |
d26dde2a8b01266ac202e5a00040c447e0c53283bc3a2599ac3328b5a6b8c22b | 2026-01-01T00:00:00-05:00 | INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning | arXiv:2412.03565v2 Announce Type: replace Abstract: Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a holistic level, they still struggle with instance-level understanding that requires a mo... | https://arxiv.org/abs/2412.03565 | Academic Papers | svg |
fd5c5c91841b32b385ae721659e070dba539b64700f0817294483c32532b8dee | 2026-01-01T00:00:00-05:00 | Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots | arXiv:2412.04235v3 Announce Type: replace Abstract: I combine detection and mitigation techniques to addresses hallucinations in Large Language Models (LLMs). Mitigation is achieved in a question-answering Retrieval-Augmented Generation (RAG) framework while detection is obtained by introducing the Negative Missing Inf... | https://arxiv.org/abs/2412.04235 | Academic Papers | svg |
e761772c709f49153acad35364544ba9025245eb8f65dc05a26e0d9b07040f0c | 2026-01-01T00:00:00-05:00 | The Oracle Complexity of Simplex-based Matrix Games: Linear Separability and Nash Equilibria | arXiv:2412.06990v3 Announce Type: replace Abstract: We study the problem of solving matrix games of the form $\max_{\mathbf{w}\in\mathcal{W}}\min_{\mathbf{p}\in\Delta}\mathbf{p}^{\top}A\mathbf{w}$, where $A$ is some matrix and $\Delta$ is the probability simplex. This problem encapsulates canonical tasks such as findin... | https://arxiv.org/abs/2412.06990 | Academic Papers | svg |
c56f668805b853c452608a288d329d214f6a79b44cd79aa363c0e3d1cc529ebb | 2026-01-01T00:00:00-05:00 | Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning | arXiv:2412.07454v3 Announce Type: replace Abstract: Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gradient inversion and model poisoning by malicious clients, remain unresolved. Existing so... | https://arxiv.org/abs/2412.07454 | Academic Papers | svg |
2093d2da7d158e50c1e2075a2f3e5f21d7db05990a25ae758ed508c925e9cb92 | 2026-01-01T00:00:00-05:00 | Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction | arXiv:2412.08243v2 Announce Type: replace Abstract: Camera-based 3D Semantic Occupancy Prediction (SOP) is crucial for understanding complex 3D scenes from limited 2D image observations. Existing SOP methods typically aggregate contextual features to assist the occupancy representation learning, alleviating issues like... | https://arxiv.org/abs/2412.08243 | Academic Papers | svg |
6394aa18c66555d5aa1994ae4523bd3ce53d72612daabf07974b36584c7d05b7 | 2026-01-01T00:00:00-05:00 | Lagrangian Index Policy for Restless Bandits with Average Reward | arXiv:2412.12641v3 Announce Type: replace Abstract: We study the Lagrangian Index Policy (LIP) for restless multi-armed bandits with long-run average reward. In particular, we compare the performance of LIP with the performance of the Whittle Index Policy (WIP), both heuristic policies known to be asymptotically optima... | https://arxiv.org/abs/2412.12641 | Academic Papers | svg |
39cf67ed2e5c1e47ad3513ca3bbd1e38e28555e8343173b694ebec62e98ad74a | 2026-01-01T00:00:00-05:00 | OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization | arXiv:2412.15159v2 Announce Type: replace Abstract: Video diffusion models (VDMs) have demonstrated remarkable capabilities in text-to-video (T2V) generation. Despite their success, VDMs still suffer from degraded image quality and flickering artifacts. To address these issues, some approaches have introduced preferenc... | https://arxiv.org/abs/2412.15159 | Academic Papers | svg |
81f88e37a743a95eea586c91d368bfc247d63f7c3c66d13600cb78cb6222852b | 2026-01-01T00:00:00-05:00 | Quantifying Positional Biases in Text Embedding Models | arXiv:2412.15241v4 Announce Type: replace Abstract: Embedding models are crucial for tasks in Information Retrieval (IR) and semantic similarity measurement, yet their handling of longer texts and associated positional biases remains underexplored. In this study, we investigate the impact of content position and input ... | https://arxiv.org/abs/2412.15241 | Academic Papers | svg |
cf4b4ebad6f3ed90d8d8aa4db4cb48ed0dfdde415f570b5f2ed99a9c29ce804d | 2026-01-01T00:00:00-05:00 | Quantum $(r,\delta)$-locally recoverable codes | arXiv:2412.16590v3 Announce Type: replace Abstract: Classical $(r,\delta)$-locally recoverable codes are designed for avoiding loss of information in large scale distributed and cloud storage systems. We introduce the quantum counterpart of those codes by defining quantum $(r,\delta)$-locally recoverable codes which ar... | https://arxiv.org/abs/2412.16590 | Academic Papers | svg |
845de599922816df0fe347e0242c620c0fda01818145f3ce029e302a93f03306 | 2026-01-01T00:00:00-05:00 | A Gas-Kinetic Scheme for Maxwell Equations | arXiv:2412.16845v2 Announce Type: replace Abstract: The Gas-Kinetic Scheme (GKS), widely used in computational fluid dynamics for simulating hypersonic and other complicated flow phenomena, is extended in this work to electromagnetic problems by solving Maxwell's equations. In contrast to the classical GKS formulation,... | https://arxiv.org/abs/2412.16845 | Academic Papers | svg |
6d3e538a8be6e80d52a9f71499c0129719fc00a4bf9515e74393054426f08d04 | 2026-01-01T00:00:00-05:00 | Distributed Graph Algorithms with Predictions | arXiv:2501.05267v2 Announce Type: replace Abstract: We initiate the study of deterministic distributed graph algorithms with predictions in synchronous message passing systems. The process at each node in the graph is given a prediction, which is some extra information about the problem instance that may be incorrect. ... | https://arxiv.org/abs/2501.05267 | Academic Papers | svg |
c1c1a718968c4d43bdedf38bccdb8a35b0cfaae9a143aba22c90595a3b296564 | 2026-01-01T00:00:00-05:00 | EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model | arXiv:2501.05710v3 Announce Type: replace Abstract: Recent research shows that emotions can enhance users' cognition and influence information communication. While research on visual emotion analysis is extensive, limited work has been done on helping users generate emotionally rich image content. Existing work on emot... | https://arxiv.org/abs/2501.05710 | Academic Papers | svg |
aca8967c5753a0ed1608f2507ee74f53bd4c2acf25e5842d4748951f0a97d596 | 2026-01-01T00:00:00-05:00 | Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models | arXiv:2501.07033v3 Announce Type: replace Abstract: This study explores the use of Generative Adversarial Networks (GANs) to detect AI deepfakes and fraudulent activities in online payment systems. With the growing prevalence of deepfake technology, which can manipulate facial features in images and videos, the potenti... | https://arxiv.org/abs/2501.07033 | Academic Papers | svg |
624e8e3ca6d02cd6199f97663be6c832145c49e8fc5c25feaedc48d608aefab6 | 2026-01-01T00:00:00-05:00 | Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone | arXiv:2501.12073v4 Announce Type: replace Abstract: Drones are increasingly used in forestry to capture high-resolution remote sensing data, supporting enhanced monitoring, assessment, and decision-making processes. While operations above the forest canopy are already highly automated, flying inside forests remains cha... | https://arxiv.org/abs/2501.12073 | Academic Papers | svg |
79156ea53e59d6926c31c45fee44544a70d3fa07e0baf8441ea3d6fb1fb2cb88 | 2026-01-01T00:00:00-05:00 | Knowledge-Driven Federated Graph Learning on Model Heterogeneity | arXiv:2501.12624v4 Announce Type: replace Abstract: Federated graph learning (FGL) has emerged as a promising paradigm for collaborative graph representation learning, enabling multiple parties to jointly train models while preserving data privacy. However, most existing approaches assume homogeneous client models and ... | https://arxiv.org/abs/2501.12624 | Academic Papers | svg |
31c3e00823e8c281a1d2e2cf6296e7ba08418374552bc9a000996795792e772b | 2026-01-01T00:00:00-05:00 | Illusions of Relevance: Arbitrary Content Injection Attacks Deceive Retrievers, Rerankers, and LLM Judges | arXiv:2501.18536v2 Announce Type: replace Abstract: This work considers a black-box threat model in which adversaries attempt to propagate arbitrary non-relevant content in search. We show that retrievers, rerankers, and LLM relevance judges are all highly vulnerable to attacks that enable arbitrary content to be promo... | https://arxiv.org/abs/2501.18536 | Academic Papers | svg |
0afc40fb5285a2b9b4b08a29f8303802b08f5f23b48753c228e392ef81893ebb | 2026-01-01T00:00:00-05:00 | MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding | arXiv:2502.03183v3 Announce Type: replace Abstract: Modern Video Large Language Models (VLLMs) often rely on uniform frame sampling for video understanding, but this approach frequently fails to capture critical information due to frame redundancy and variations in video content. We propose MaxInfo, the first training-... | https://arxiv.org/abs/2502.03183 | Academic Papers | svg |
50e30f4e86a7caeb8e17322568066dbcc633efb7d1fc9fe4a52d26e6daa91865 | 2026-01-01T00:00:00-05:00 | An Empirical Study of Methods for Small Object Detection from Satellite Imagery | arXiv:2502.03674v2 Announce Type: replace Abstract: This paper reviews object detection methods for finding small objects from remote sensing imagery and provides an empirical evaluation of four state-of-the-art methods to gain insights into method performance and technical challenges. In particular, we use car detecti... | https://arxiv.org/abs/2502.03674 | Academic Papers | svg |
ad99b3c0887cabaa3d058d8d6bc01a23a0b341bfef7a93e2bb71c0ea1f2b4f93 | 2026-01-01T00:00:00-05:00 | Large Multimodal Models for Low-Resource Languages: A Survey | arXiv:2502.05568v2 Announce Type: replace Abstract: In this survey, we systematically analyze techniques used to adapt large multimodal models (LMMs) for low-resource (LR) languages, examining approaches ranging from visual enhancement and data creation to cross-modal transfer and fusion strategies. Through a comprehen... | https://arxiv.org/abs/2502.05568 | Academic Papers | svg |
2f5c632eb1e43dc20dc6a1da0593a0c363f2f16c0dfc8b4b4a5cadb77d359103 | 2026-01-01T00:00:00-05:00 | Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task, Multi-Dialogue Settings | arXiv:2502.11007v4 Announce Type: replace Abstract: Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multiple dialogues and multi-modal data sources. These unique characteristics of LLMs, together with their large model size, make t... | https://arxiv.org/abs/2502.11007 | Academic Papers | svg |
31fea3e78d7a96d10cc3e31791dee92ec240e488236859063fcd7a4b864a8017 | 2026-01-01T00:00:00-05:00 | Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification | arXiv:2502.14433v2 Announce Type: replace Abstract: Many real-world applications rely on land surface temperature (LST) data at high spatiotemporal resolution. In complex urban areas, LST exhibits significant variations, fluctuating dramatically within and across city blocks. Landsat provides high spatial resolution da... | https://arxiv.org/abs/2502.14433 | Academic Papers | svg |
f9a906429766f90c7e7e6686cace4a98835b2a1cc3fdef93b0cf716ff5b621a4 | 2026-01-01T00:00:00-05:00 | ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting | arXiv:2502.14780v2 Announce Type: replace Abstract: Efficient and privacy-preserving multimodal interaction is essential as AR, VR, and modern smartphones with powerful cameras become primary interfaces for human-computer communication. Existing powerful large vision-language models (VLMs) enabling multimodal interacti... | https://arxiv.org/abs/2502.14780 | Academic Papers | svg |
0adbbc69c694d323568db0fa8f0c96981475496d7f198fec55f12532c5a7682e | 2026-01-01T00:00:00-05:00 | CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems | arXiv:2502.17821v3 Announce Type: replace Abstract: Multi-modal learning has emerged as a key technique for improving performance across domains such as autonomous driving, robotics, and reasoning. However, in certain scenarios, particularly in resource-constrained environments, some modalities available during trainin... | https://arxiv.org/abs/2502.17821 | Academic Papers | svg |
60a74768c6ecd5696f34a11631d14a7d04f72badffb3c0910a28dd7380b22bab | 2026-01-01T00:00:00-05:00 | SciceVPR: Stable Cross-Image Correlation Enhanced Model for Visual Place Recognition | arXiv:2502.20676v2 Announce Type: replace Abstract: Visual Place Recognition (VPR) is a major challenge for robotics and autonomous systems, with the goal of predicting the location of an image based solely on its visual features. State-of-the-art (SOTA) models extract global descriptors using the powerful foundation m... | https://arxiv.org/abs/2502.20676 | Academic Papers | svg |
e5f43cf93b8072ca2645d72be6ae227f7a9b0a2c59c021b59ac65997d299f12b | 2026-01-01T00:00:00-05:00 | Recent Advances in Numerical Solutions for Hamilton-Jacobi PDEs | arXiv:2502.20833v2 Announce Type: replace Abstract: Hamilton-Jacobi partial differential equations (HJ PDEs) play a central role in many applications such as economics, physics, and engineering. These equations describe the evolution of a value function which encodes valuable information about the system, such as actio... | https://arxiv.org/abs/2502.20833 | Academic Papers | svg |
4f1e853eb08ae5977868c6f3666f758520ff1e05ffb86685ba7ea8f1e4029316 | 2026-01-01T00:00:00-05:00 | OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction | arXiv:2503.03734v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions. Existing approaches require fine-tuning pre-trained visionlanguage models (VLMs) as visual and language features are independently fed into downs... | https://arxiv.org/abs/2503.03734 | Academic Papers | svg |
3bcbe76b1f9705b4eb7c4b7881fd2d3eadf3bf4b23bc653216d92ac66fe58d37 | 2026-01-01T00:00:00-05:00 | Simple Self Organizing Map with Visual Transformer | arXiv:2503.04121v2 Announce Type: replace Abstract: Vision Transformers (ViTs) have demonstrated exceptional performance in various vision tasks. However, they tend to underperform on smaller datasets due to their inherent lack of inductive biases. Current approaches address this limitation implicitly-often by pairing ... | https://arxiv.org/abs/2503.04121 | Academic Papers | svg |
04bd8f45f4981107dca21e52f34950387ff3b84163d13aa3e05782a370483d38 | 2026-01-01T00:00:00-05:00 | Illuminating Darkness: Learning to Enhance Low-light Images In-the-Wild | arXiv:2503.06898v3 Announce Type: replace Abstract: Single-shot low-light image enhancement (SLLIE) remains challenging due to the limited availability of diverse, real-world paired datasets. To bridge this gap, we introduce the Low-Light Smartphone Dataset (LSD), a large-scale, high-resolution (4K+) dataset collected ... | https://arxiv.org/abs/2503.06898 | Academic Papers | svg |
fcff1f62a7a128271714ffaff1390bb522dbbe373815accd09a14999f458ee12 | 2026-01-01T00:00:00-05:00 | Effective and Efficient Jailbreaks of Black-Box LLMs with Cross-Behavior Attacks | arXiv:2503.08990v2 Announce Type: replace Abstract: Despite recent advancements in Large Language Models (LLMs) and their alignment, they can still be jailbroken, i.e., harmful and toxic content can be elicited from them. While existing red-teaming methods have shown promise in uncovering such vulnerabilities, these me... | https://arxiv.org/abs/2503.08990 | Academic Papers | svg |
7ce605256ee1e48699ee125b667c8d6c19fda2d7aaf71bf059eafa9c17b1646a | 2026-01-01T00:00:00-05:00 | Revisiting Agnostic Boosting | arXiv:2503.09384v3 Announce Type: replace Abstract: Boosting is a key method in statistical learning, allowing for converting weak learners into strong ones. While well studied in the realizable case, the statistical properties of weak-to-strong learning remain less understood in the agnostic setting, where there are n... | https://arxiv.org/abs/2503.09384 | Academic Papers | svg |
870dc69c5fa2b51e0ed164b1350fc8eb40be0ff95bd1689b5bdf0feb2988cdd1 | 2026-01-01T00:00:00-05:00 | Adjusted Count Quantification Learning on Graphs | arXiv:2503.09395v2 Announce Type: replace Abstract: Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances are vertices. Previously, this problem has only been addressed via node clustering method... | https://arxiv.org/abs/2503.09395 | Academic Papers | svg |
b4c81ace253c61bd8ed11d58042aaae37dc27c0144fec221290629a88dfe9c8c | 2026-01-01T00:00:00-05:00 | Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data Distributions | arXiv:2503.14553v4 Announce Type: replace Abstract: Federated Learning (FL) has emerged as one of the prominent paradigms for distributed machine learning (ML). However, it is well-established that its performance can degrade significantly under non-IID (non-independent and identically distributed) data distributions a... | https://arxiv.org/abs/2503.14553 | Academic Papers | svg |
a36bc8fb8b8a4439f3844e02945843aa6169ed6e115d449881ec5ec75b583260 | 2026-01-01T00:00:00-05:00 | A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond | arXiv:2503.21614v2 Announce Type: replace Abstract: Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. However, a growing concern lies in their tendency to produce excessively ... | https://arxiv.org/abs/2503.21614 | Academic Papers | svg |
44d99b3b44b4b96f52f66d36c661b5315a8344c81e3f57e52a7ccbf226ec58e9 | 2026-01-01T00:00:00-05:00 | AINav: Large Language Model-Based Adaptive Interactive Navigation | arXiv:2503.22942v2 Announce Type: replace Abstract: Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster... | https://arxiv.org/abs/2503.22942 | Academic Papers | svg |
100891f251769823402b1e39d7b0299380e5829c50c8ec4a797fabbb70536f71 | 2026-01-01T00:00:00-05:00 | Lattice: Learning to Efficiently Compress the Memory | arXiv:2504.05646v2 Announce Type: replace Abstract: Attention mechanisms have revolutionized sequence learning but suffer from quadratic computational complexity. This paper introduces \model, a novel recurrent neural network (RNN) mechanism that leverages the inherent low-rank structure of K-V matrices to efficiently ... | https://arxiv.org/abs/2504.05646 | Academic Papers | svg |
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