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cbd1615a232943b902b271ef46e83e9b79b26e616b5cf54ccbd4cd65000a6001 | 2026-01-23T00:00:00-05:00 | Emergence and Evolution of Interpretable Concepts in Diffusion Models | arXiv:2504.15473v2 Announce Type: replace Abstract: Diffusion models have become the go-to method for text-to-image generation, producing high-quality images from pure noise. However, the inner workings of diffusion models is still largely a mystery due to their black-box nature and complex, multi-step generation proce... | https://arxiv.org/abs/2504.15473 | Academic Papers | svg |
f08714de489259f5220ca1142d9dfb21341bd2d0ba2f382fdfbf05868ba8a7a5 | 2026-01-23T00:00:00-05:00 | Boosting Generative Image Modeling via Joint Image-Feature Synthesis | arXiv:2504.16064v3 Announce Type: replace Abstract: Latent diffusion models (LDMs) dominate high-quality image generation, yet integrating representation learning with generative modeling remains a challenge. We introduce a novel generative image modeling framework that seamlessly bridges this gap by leveraging a diffu... | https://arxiv.org/abs/2504.16064 | Academic Papers | svg |
44aaab291b49f53a6a4977d3f6ad0567a3bac245b4a64e33eec057bd753ba918 | 2026-01-23T00:00:00-05:00 | Who Is Responsible? Self-Adaptation Under Multiple Concurrent Uncertainties With Unknown Sources in Complex ROS-Based Systems | arXiv:2504.20477v2 Announce Type: replace Abstract: Robotic systems increasingly operate in dynamic, unpredictable environments, where tightly coupled sensors and software modules increase the probability of a single fault cascading across components and admitting multiple plausible strategies to resolve the underlying... | https://arxiv.org/abs/2504.20477 | Academic Papers | svg |
6322f63737bc5bb448b8aad79bb1072dd6c200258561f2435f03d415b487d77a | 2026-01-23T00:00:00-05:00 | Passing the Buck to AI: How Individuals' Decision-Making Patterns Affect Reliance on AI | arXiv:2505.01537v2 Announce Type: replace Abstract: Psychological research has identified different patterns individuals have while making decisions, such as vigilance (making decisions after thorough information gathering), hypervigilance (rushed and anxious decision-making), and buckpassing (deferring decisions to ot... | https://arxiv.org/abs/2505.01537 | Academic Papers | svg |
db4aa719500bfad944cea44342c618efc4011c4d1c11690b75ae437c40953334 | 2026-01-23T00:00:00-05:00 | Adaptively Point-weighting Curriculum Learning | arXiv:2505.01665v2 Announce Type: replace Abstract: Curriculum learning (CL) mimics human learning, in which easy samples are learned first, followed by harder samples, and has become an effective method for training deep networks. However, many existing automatic CL methods maintain a preference for easy samples durin... | https://arxiv.org/abs/2505.01665 | Academic Papers | svg |
f7fa3888c1c33a65785fa91e50c065e7084b6082933b2589f492aaeb32aac7f7 | 2026-01-23T00:00:00-05:00 | Semantics-Aware Unified Terrestrial Non-Terrestrial 6G Networks | arXiv:2505.01796v2 Announce Type: replace Abstract: The integration of Terrestrial and Non-Terrestrial Networks (TN-NTNs), introduced in 5G, is advancing toward a unified and seamless network of networks in Sixth-Generation (6G). This evolution markedly increases the volume of generated and exchanged data, imposing str... | https://arxiv.org/abs/2505.01796 | Academic Papers | svg |
7dbd25589b5df1f67d89744c0bd22a7ba659d5f1ecaba22217d7006cd8974c12 | 2026-01-23T00:00:00-05:00 | RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale | arXiv:2505.03005v4 Announce Type: replace Abstract: We present Rapid Attention Distillation to Linear Attention Decoders at Scale (RADLADS), a protocol for rapidly converting softmax attention transformers into linear attention decoder models, along with two new RWKV-variant architectures, and models converted from pop... | https://arxiv.org/abs/2505.03005 | Academic Papers | svg |
bd03987f77f48d8b450e2fb017239c9be65d1b30d2e014a3addb089eaa38450f | 2026-01-23T00:00:00-05:00 | Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View | arXiv:2505.03336v2 Announce Type: replace Abstract: Recommender systems based on Large Language Models (LLMs) are often plagued by hallucinations of out-of-domain (OOD) items. To address this, we propose RecLM, a unified framework that bridges the gap between retrieval and generation by instantiating three grounding pa... | https://arxiv.org/abs/2505.03336 | Academic Papers | svg |
a603140519e449ba041c3c06e42c5823c362fa881888a11c1a462b624de8b809 | 2026-01-23T00:00:00-05:00 | PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models | arXiv:2505.05577v2 Announce Type: replace Abstract: Existing biomedical benchmarks do not provide end-to-end infrastructure for training, evaluation, and inference of models that integrate multimodal biological data and a broad range of machine learning tasks in therapeutics. We present PyTDC, an open-source machine-le... | https://arxiv.org/abs/2505.05577 | Academic Papers | svg |
27e173753a94adce8f6e72cd3c083daf4d225ff894e7bdffcec40c3dfb600230 | 2026-01-23T00:00:00-05:00 | Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis | arXiv:2505.05855v2 Announce Type: replace Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, paired low- and high-resolution (LR/HR) training datasets, which are scarce, and are trained for fixed upsampling scales. W... | https://arxiv.org/abs/2505.05855 | Academic Papers | svg |
b2058441fb7a5d77ff4ec91d690c336b3a309bd195e5744fc70b341e1c8026c1 | 2026-01-23T00:00:00-05:00 | A large-scale evaluation of commonsense knowledge in humans and large language models | arXiv:2505.10309v3 Announce Type: replace Abstract: Commonsense knowledge, a major constituent of artificial intelligence (AI), is primarily evaluated in practice by human-prescribed ground-truth labels. An important, albeit implicit, assumption of these labels is that they accurately capture what any human would think... | https://arxiv.org/abs/2505.10309 | Academic Papers | svg |
40aab95452a71d79f9b6f82558735c8efdabd8fc4fa2cc66387c5511f3524ac0 | 2026-01-23T00:00:00-05:00 | Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Reasoning | arXiv:2505.13353v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed for understanding large codebases, but whether they understand operational semantics of long code context or rely on pattern matching shortcuts remains unclear. We distinguish between lexical recall (retrieving co... | https://arxiv.org/abs/2505.13353 | Academic Papers | svg |
e5fa97e74061d428797ee3a0bc9753593b274b7ffcfe9fe3e92bb9d367cdd641 | 2026-01-23T00:00:00-05:00 | Multi-View Projection for Unsupervised Domain Adaptation in 3D Semantic Segmentation | arXiv:2505.15545v3 Announce Type: replace Abstract: 3D semantic segmentation plays a pivotal role in autonomous driving and road infrastructure analysis, yet state-of-the-art 3D models are prone to severe domain shift when deployed across different datasets. In this paper, we propose an Unsupervised Domain Adaptation a... | https://arxiv.org/abs/2505.15545 | Academic Papers | svg |
3e86c4d08a47146566f1bc11ef9db1885e79fb7b5533f23fa096092c063bb793 | 2026-01-23T00:00:00-05:00 | CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis | arXiv:2505.17590v3 Announce Type: replace Abstract: Recently, 3D GANs based on 3D Gaussian splatting have been proposed for high quality synthesis of human heads. However, existing methods stabilize training and enhance rendering quality from steep viewpoints by conditioning the random latent vector on the current came... | https://arxiv.org/abs/2505.17590 | Academic Papers | svg |
8b928e98b9b2b85a76955501ad438e5e14fef8ec2ddfeed3cea11c74d773edf1 | 2026-01-23T00:00:00-05:00 | GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning | arXiv:2505.18763v4 Announce Type: replace Abstract: Recent advances in reinforcement learning (RL) have demonstrated the powerful exploration capabilities and multimodality of generative diffusion-based policies. While substantial progress has been made in offline RL and off-policy RL settings, integrating diffusion po... | https://arxiv.org/abs/2505.18763 | Academic Papers | svg |
5996efa4a3b2ca9e0c337cd184ad2c1cd7388aa142ebc60c1ffe0aba23e16bc5 | 2026-01-23T00:00:00-05:00 | BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change | arXiv:2505.19328v3 Announce Type: replace Abstract: Ambivalence and hesitancy (A/H), a closely related construct, is the primary reasons why individuals delay, avoid, or abandon health behaviour changes. It is a subtle and conflicting emotion that sets a person in a state between positive and negative orientations, or ... | https://arxiv.org/abs/2505.19328 | Academic Papers | svg |
d98062fca5fbee376bcfc45c2e28adef3ad9b97e1a68ad94c31ac4a3ebc00747 | 2026-01-23T00:00:00-05:00 | OccLE: Label-Efficient 3D Semantic Occupancy Prediction | arXiv:2505.20617v4 Announce Type: replace Abstract: 3D semantic occupancy prediction offers an intuitive and efficient scene understanding and has attracted significant interest in autonomous driving perception. Existing approaches either rely on full supervision, which demands costly voxel-level annotations, or on sel... | https://arxiv.org/abs/2505.20617 | Academic Papers | svg |
5f0461c6081eda84e00e7132fc672ed88549c539ecbc892fcb96124491a17b1e | 2026-01-23T00:00:00-05:00 | NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment | arXiv:2505.22327v2 Announce Type: replace Abstract: Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social Good" (NLP4SG) across nine domains relevant to global development and risk agendas, summa... | https://arxiv.org/abs/2505.22327 | Academic Papers | svg |
3dc197c0f3af1011c35af0e3c6d41ee3e787a4e158ddc845c45e4aaf40380cf3 | 2026-01-23T00:00:00-05:00 | MEDAL: A Framework for Benchmarking LLMs as Multilingual Open-Domain Dialogue Evaluators | arXiv:2505.22777v5 Announce Type: replace Abstract: Evaluating the quality of open-domain chatbots has become increasingly reliant on LLMs acting as automatic judges. However, existing meta-evaluation benchmarks are static, outdated, and lacking in multilingual coverage, limiting their ability to fully capture subtle w... | https://arxiv.org/abs/2505.22777 | Academic Papers | svg |
b825fd06b98c8a460e3aa90100c4031ceda70022bef0cdf16c4f2d3f9d4b338b | 2026-01-23T00:00:00-05:00 | Skin Lesion Phenotyping via Nested Multi-modal Contrastive Learning | arXiv:2505.23709v2 Announce Type: replace Abstract: We introduce SLIMP (Skin Lesion Image-Metadata Pre-training) for learning rich representations of skin lesions through a novel nested contrastive learning approach that captures complex relationships between images and metadata. Melanoma detection and skin lesion clas... | https://arxiv.org/abs/2505.23709 | Academic Papers | svg |
c7cf510beecf589b142bc4b2f0fc08bd44501ca8ea016ed18ed0634e84769664 | 2026-01-23T00:00:00-05:00 | R-KV: Redundancy-aware KV Cache Compression for Reasoning Models | arXiv:2505.24133v4 Announce Type: replace Abstract: Reasoning models have demonstrated impressive performance in self-reflection and chain-of-thought reasoning. However, they often produce excessively long outputs, leading to prohibitively large key-value (KV) caches during inference. While chain-of-thought inference s... | https://arxiv.org/abs/2505.24133 | Academic Papers | svg |
3194a2841aa53f2e041d3d7f05ceeb15bf20d87684c5dc917aa682a2f9827a86 | 2026-01-23T00:00:00-05:00 | MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark | arXiv:2506.04779v2 Announce Type: replace Abstract: Speech inherently contains rich acoustic information that extends far beyond the textual language. In real-world spoken language understanding, effective interpretation often requires integrating semantic meaning (e.g., content), paralinguistic features (e.g., emotion... | https://arxiv.org/abs/2506.04779 | Academic Papers | svg |
b02145fdb782c07ab510e87aa1e36fbe050d1abf1f485da0d4c6d051edb0cf79 | 2026-01-23T00:00:00-05:00 | How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare | arXiv:2506.06299v4 Announce Type: replace Abstract: Advances in AI offer the prospect of manipulating beliefs and behaviors on a population-wide level. Large language models and autonomous agents now let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without s... | https://arxiv.org/abs/2506.06299 | Academic Papers | svg |
f0acaa2587f540d37dc52c27988684132f0febd47096a46581dd9fae67b5e83a | 2026-01-23T00:00:00-05:00 | The PML method for calculating the propagative wave numbers of electromagnetic wave in periodic structures | arXiv:2506.07084v2 Announce Type: replace Abstract: When the electromagnetic wave is incident on the periodic structures, in addition to the scattering field, some guided modes that are traveling in the periodic medium could be generated. In the present paper, we study the calculation of guided modes. We formulate the ... | https://arxiv.org/abs/2506.07084 | Academic Papers | svg |
e031fa87d9c50dd78f83b0c82c7d901424e7a9c280001e2a96c5eb1022236bdb | 2026-01-23T00:00:00-05:00 | VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning | arXiv:2506.09049v3 Announce Type: replace Abstract: Coordinating multiple embodied agents in dynamic environments remains a core challenge in artificial intelligence, requiring both perception-driven reasoning and scalable cooperation strategies. While recent works have leveraged large language models (LLMs) for multi-... | https://arxiv.org/abs/2506.09049 | Academic Papers | svg |
5efbfc07f195f9a62ac460d29250e7b52091fb04bb40d088bf8bc6327de850b9 | 2026-01-23T00:00:00-05:00 | EmbedAgent: Benchmarking Large Language Models in Embedded System Development | arXiv:2506.11003v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown promise in various tasks, yet few benchmarks assess their capabilities in embedded system development.In this paper, we introduce EmbedAgent, a paradigm designed to simulate real-world roles in embedded system development, such ... | https://arxiv.org/abs/2506.11003 | Academic Papers | svg |
0ca85beb6220d4d6ad5f8d2d8b2b858b53878532939492ce5893aab20ae16e4a | 2026-01-23T00:00:00-05:00 | Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics | arXiv:2506.12365v3 Announce Type: replace Abstract: This survey paper outlines the key developments in the field of Large Language Models (LLMs), including enhancements to their reasoning skills, adaptability to various tasks, increased computational efficiency, and the ability to make ethical decisions. The techniques... | https://arxiv.org/abs/2506.12365 | Academic Papers | svg |
89121eec2ec9f16642c26b1a774ec35b609aefbef0ee0c27e2502dd8ba302516 | 2026-01-23T00:00:00-05:00 | Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting | arXiv:2506.12787v3 Announce Type: replace Abstract: Modeling the wireless radiance field (WRF) is fundamental to modern communication systems, enabling key tasks such as localization, sensing, and channel estimation. Traditional approaches, which rely on empirical formulas or physical simulations, often suffer from lim... | https://arxiv.org/abs/2506.12787 | Academic Papers | svg |
45c5c9d06f3adba96b88fdf3d4b649649cbd338e6c7bb9f527b2e38d38a6e9c8 | 2026-01-23T00:00:00-05:00 | KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction | arXiv:2506.13196v4 Announce Type: replace Abstract: Accurate prediction of protein-ligand binding affinity is critical for drug discovery. While recent deep learning approaches have demonstrated promising results, they often rely solely on structural features of proteins and ligands, overlooking their valuable biochemi... | https://arxiv.org/abs/2506.13196 | Academic Papers | svg |
259ce59e489a3ac1d210f02861e23c9041a83b6c454a1a5e69698d019492170e | 2026-01-23T00:00:00-05:00 | DAGs for the Masses | arXiv:2506.13998v3 Announce Type: replace Abstract: A recent approach to building consensus protocols on top of Directed Acyclic Graphs (DAGs) shows much promise due to its simplicity and stable throughput. However, as each node in the DAG typically includes a linear number of references to the nodes in the previous ro... | https://arxiv.org/abs/2506.13998 | Academic Papers | svg |
67d2aab040dd2cc2995c2f896f97c9c8f2e7678f1b38a2e761e2dd24febf2539 | 2026-01-23T00:00:00-05:00 | FormGym: Doing Paperwork with Agents | arXiv:2506.14079v3 Announce Type: replace Abstract: Completing paperwork is a challenging and time-consuming problem. Form filling is especially challenging in the pure-image domain without access to OCR, typeset PDF text, or a DOM. For computer agents, it requires multiple abilities, including multi-modal understandin... | https://arxiv.org/abs/2506.14079 | Academic Papers | svg |
b939b615f3d6404fbb198f95126475c5b61d8779c69517ad3617481a5f732658 | 2026-01-23T00:00:00-05:00 | Dynamic Exploration on Segment-Proposal Graphs for Tubular Centerline Tracking | arXiv:2506.18930v2 Announce Type: replace Abstract: Optimal curve methods provide a fundamental framework for tubular centerline tracking. Point-wise approaches, such as minimal paths, are theoretically elegant but often suffer from shortcut and short-branch combination problems in complex scenarios. Nonlocal segment-w... | https://arxiv.org/abs/2506.18930 | Academic Papers | svg |
28941c1d3bf6f2bec901ec152120ee9049fe53c9960d40141532848ad05a00ed | 2026-01-23T00:00:00-05:00 | MultiHuman-Testbench: Benchmarking Image Generation for Multiple Humans | arXiv:2506.20879v4 Announce Type: replace Abstract: Generation of images containing multiple humans, performing complex actions, while preserving their facial identities, is a significant challenge. A major factor contributing to this is the lack of a dedicated benchmark. To address this, we introduce MultiHuman-Testbe... | https://arxiv.org/abs/2506.20879 | Academic Papers | svg |
f7f47a5c8be8e2c15eaa925b27c9eeae0280bd2820952fe9e1bb7eb913ccba8c | 2026-01-23T00:00:00-05:00 | SciArena: An Open Evaluation Platform for Non-Verifiable Scientific Literature-Grounded Tasks | arXiv:2507.01001v2 Announce Type: replace Abstract: We present SciArena, an open and collaborative platform for evaluating foundation models on scientific literature-grounded tasks. Unlike traditional benchmarks for scientific literature understanding and synthesis, SciArena engages the research community directly, fol... | https://arxiv.org/abs/2507.01001 | Academic Papers | svg |
a739b7268bc1abd3a61e61af1ca76c28f71fc3efb393ed1764f4f6bfd87a836f | 2026-01-23T00:00:00-05:00 | Training-Free Geospatial Place Representation Learning from Large-Scale Point-of-Interest Graph Data | arXiv:2507.02921v3 Announce Type: replace Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries. Existing geospatial representation learning approaches typically aggregate Points of Interest(POI) into pre-defined administrative reg... | https://arxiv.org/abs/2507.02921 | Academic Papers | svg |
07fb3f4975a39435e43be2918d0e421b5d88d0a4f0f44186b7dd1ce7bd8aefcd | 2026-01-23T00:00:00-05:00 | Toward Efficient Speech Emotion Recognition via Spectral Learning and Attention | arXiv:2507.03251v3 Announce Type: replace Abstract: Speech Emotion Recognition (SER) traditionally relies on auditory data analysis for emotion classification. Several studies have adopted different methods for SER. However, existing SER methods often struggle to capture subtle emotional variations and generalize acros... | https://arxiv.org/abs/2507.03251 | Academic Papers | svg |
6ab0dfd572516220f1e9e5c68389b51883a701261f4f1822f70250880e2a5397 | 2026-01-23T00:00:00-05:00 | You May Use the Same Channel Knowledge Map for Environment-Aware NLoS Sensing and Communication | arXiv:2507.03589v2 Announce Type: replace Abstract: As one of the key usage scenarios for the sixth generation (6G) wireless networks, integrated sensing and communication (ISAC) provides an efficient framework to achieve simultaneous wireless sensing and communication. However, traditional wireless sensing techniques ... | https://arxiv.org/abs/2507.03589 | Academic Papers | svg |
ba63e093a5752026d1c2b1e5e889dc61c2ed54a1cf54456bab82d1fb080fa261 | 2026-01-23T00:00:00-05:00 | Skipper: Maximal Matching with a Single Pass over Edges | arXiv:2507.04420v4 Announce Type: replace Abstract: Maximal Matching (MM) is a fundamental graph problem with diverse applications. While state-of-the-art parallel MM algorithms have a total expected work linear in number of edges, they require randomization, iterative graph processing, and contraction after each itera... | https://arxiv.org/abs/2507.04420 | Academic Papers | svg |
367b59008ca77339ef5b9b09adc30fbd022907d424a701cee6b2502fec12ebb2 | 2026-01-23T00:00:00-05:00 | Stability, Complexity and Data-Dependent Worst-Case Generalization Bounds | arXiv:2507.06775v2 Announce Type: replace Abstract: Providing generalization guarantees for stochastic optimization algorithms remains a key challenge in learning theory. Recently, numerous works demonstrated the impact of the geometric properties of optimization trajectories on generalization performance. These works ... | https://arxiv.org/abs/2507.06775 | Academic Papers | svg |
e0ff450d5fb1bbf8e0f8dbfabe769a33d558f5a51b0ceabcb8a5f9bee20c4416 | 2026-01-23T00:00:00-05:00 | DocPolarBERT: A Pre-trained Model for Document Understanding with Relative Polar Coordinate Encoding of Layout Structures | arXiv:2507.08606v4 Announce Type: replace Abstract: We introduce DocPolarBERT, a layout-aware BERT model for document understanding that eliminates the need for absolute 2D positional embeddings. We extend self-attention to take into account text block positions in relative polar coordinate system rather than the Carte... | https://arxiv.org/abs/2507.08606 | Academic Papers | svg |
926d0267c11e87fb99acb39d60ece6f36b02b495cca6a0302bcab8ca8524928c | 2026-01-23T00:00:00-05:00 | A Unified Framework for Efficient Kernel and Polynomial Interpolation | arXiv:2507.12629v3 Announce Type: replace Abstract: We present a unified interpolation scheme that combines compactly-supported positive-definite kernels and multivariate polynomials. This unified framework generalizes interpolation with compactly-supported kernels and also classical polynomial least squares approximat... | https://arxiv.org/abs/2507.12629 | Academic Papers | svg |
c693a3497f13671fbed24c5069e2b9d9c306f6342447012473a72d783f99dc6d | 2026-01-23T00:00:00-05:00 | VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning | arXiv:2507.14625v2 Announce Type: replace Abstract: Vertical federated learning (VFL) enables multiple parties with disjoint features to collaboratively train models without sharing raw data. While privacy vulnerabilities of VFL are extensively-studied, its security threats-particularly targeted label attacks-remain un... | https://arxiv.org/abs/2507.14625 | Academic Papers | svg |
320b900b703c0da43b27e0eea557d3ef43b84a780cd15234581c9c61e5f5ca26 | 2026-01-23T00:00:00-05:00 | VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Masking | arXiv:2507.14629v2 Announce Type: replace Abstract: Though vertical federated learning (VFL) is generally considered to be privacy-preserving, recent studies have shown that VFL system is vulnerable to label inference attacks originating from various attack surfaces. Among these attacks, the model completion (MC) attac... | https://arxiv.org/abs/2507.14629 | Academic Papers | svg |
1e04ec379d4cb01082d4e0e9e9bd086e747ffe009948cd6e2c321c7bcda53c7f | 2026-01-23T00:00:00-05:00 | Can Language Models Discover Scaling Laws? | arXiv:2507.21184v5 Announce Type: replace Abstract: Discovering scaling laws for predicting model performance at scale is a fundamental and open-ended challenge, mostly reliant on slow, case specific human experimentation. To investigate the potential for LLMs to automate this process, we collect over 5,000 experiments... | https://arxiv.org/abs/2507.21184 | Academic Papers | svg |
5bb98a9e71923947ea01f67ca7eb192d56a725942f24aae42b4d04b72f0a0b1f | 2026-01-23T00:00:00-05:00 | SURE-Med: Systematic Uncertainty Reduction for Enhanced Reliability in Medical Report Generation | arXiv:2508.01693v2 Announce Type: replace Abstract: Automated medical report generation (MRG) holds great promise for reducing the heavy workload of radiologists. However, its clinical deployment is hindered by three major sources of uncertainty. First, visual uncertainty, caused by noisy or incorrect view annotations,... | https://arxiv.org/abs/2508.01693 | Academic Papers | svg |
b126e7c49904fb5a43a1616ae3b411f4e3980785baa93db6023dd6fbcf86798d | 2026-01-23T00:00:00-05:00 | Evolving in Tasks: Empowering the Multi-modality Large Language Model as the Computer Use Agent | arXiv:2508.04037v2 Announce Type: replace Abstract: Computer use agents represent an emerging area in artificial intelligence, aiming to operate computers autonomously to fulfill user tasks, attracting significant attention from both industry and academia. However, the performance of existing agents remains insufficien... | https://arxiv.org/abs/2508.04037 | Academic Papers | svg |
48128bf868517f186bd70980ab88be3a9792fb08cfb3009712ef844725ae1eb7 | 2026-01-23T00:00:00-05:00 | Cohesive Group Discovery in Interaction Graphs under Explicit Density Constraints | arXiv:2508.04174v3 Announce Type: replace Abstract: Discovering cohesive groups is a fundamental primitive in graph-based recommender systems, underpinning tasks such as social recommendation, bundle discovery, and community-aware modeling. In interaction graphs, cohesion is often modeled as the $\gamma$-quasi-clique, ... | https://arxiv.org/abs/2508.04174 | Academic Papers | svg |
a53b191bf6ac7eff8fe924ece2340a7c2ef4323240156f4db5d0dbb5b541501c | 2026-01-23T00:00:00-05:00 | ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline | arXiv:2508.06094v3 Announce Type: replace Abstract: Constructed languages (conlangs) such as Esperanto and Quenya have played diverse roles in art, philosophy, and international communication. Meanwhile, foundation models have revolutionized creative generation in text, images, and beyond. In this work, we leverage mod... | https://arxiv.org/abs/2508.06094 | Academic Papers | svg |
83a29b4c7d47fe3fce5650b07e70b1af895a55ccc0fedb8651fdd832034195ef | 2026-01-23T00:00:00-05:00 | A Segmentation-driven Editing Method for Bolt Defect Augmentation and Detection | arXiv:2508.10509v3 Announce Type: replace Abstract: Bolt defect detection is critical to ensure the safety of transmission lines. However, the scarcity of defect images and imbalanced data distributions significantly limit detection performance. To address this problem, we propose a segmentationdriven bolt defect editi... | https://arxiv.org/abs/2508.10509 | Academic Papers | svg |
5f84c13e9dce3b49a51345815aba54cab5067e55c6705f9a413499c26579fdf0 | 2026-01-23T00:00:00-05:00 | Mantis: A Foundation Model for Mechanistic Disease Forecasting | arXiv:2508.12260v4 Announce Type: replace Abstract: Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To help address t... | https://arxiv.org/abs/2508.12260 | Academic Papers | svg |
35b67ac2766921e8c44ff0b7785950b502dcc090911c973b05068646c4c1fab2 | 2026-01-23T00:00:00-05:00 | Graph-Based Deterministic Polynomial Algorithm for NP Problems | arXiv:2508.13166v4 Announce Type: replace Abstract: The P versus NP problem asks whether every problem in NP, whose membership can be verified in polynomial time given a suitable certificate, can be decided by a deterministic Turing machine in polynomial time. In this paper, we present a proof that P = NP by constructi... | https://arxiv.org/abs/2508.13166 | Academic Papers | svg |
238dda7be71150a2bcd399be3a7466ecc4d60ec1cf84888ed5502e98d4a61c79 | 2026-01-23T00:00:00-05:00 | Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation | arXiv:2508.14082v2 Announce Type: replace Abstract: Semi-supervised regression (SSR), which aims to predict continuous scores of samples while reducing reliance on a large amount of labeled data, has recently received considerable attention across various applications, including computer vision, natural language proces... | https://arxiv.org/abs/2508.14082 | Academic Papers | svg |
056ed0924c8bbb6eaed12d502edce9d298b83b77e821ffed51b8cc92094b5fe4 | 2026-01-23T00:00:00-05:00 | Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks | arXiv:2508.16150v4 Announce Type: replace Abstract: Membership Inference Attacks (MIAs) pose a significant privacy risk by enabling adversaries to determine if a specific data point was part of a model's training set. This work empirically investigates whether MU algorithms can function as a targeted, active defense me... | https://arxiv.org/abs/2508.16150 | Academic Papers | svg |
3012e5817c2a19cb4a5d5300ca9af86087327f0295a65156866a931a0a3955f2 | 2026-01-23T00:00:00-05:00 | Being Kind Isn't Always Being Safe: Diagnosing Affective Hallucination in LLMs | arXiv:2508.16921v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly engaged in emotionally vulnerable conversations that extend beyond information seeking to moments of personal distress. As they adopt affective tones and simulate empathy, they risk creating the illusion of genuine relatio... | https://arxiv.org/abs/2508.16921 | Academic Papers | svg |
f76a7d4201f30d334fea7bbe698ce2cbb1430ba65a1549596e54e9e56e25c994 | 2026-01-23T00:00:00-05:00 | Evaluating Compiler Optimization Impacts on zkVM Performance | arXiv:2508.17518v2 Announce Type: replace Abstract: Zero-knowledge proofs (ZKPs) are the cornerstone of programmable cryptography. They enable (1) privacy-preserving and verifiable computation across blockchains, and (2) an expanding range of off-chain applications such as credential schemes. Zero-knowledge virtual mac... | https://arxiv.org/abs/2508.17518 | Academic Papers | svg |
bc8546cce1d6937c43c3b935493e0c84ad41c0b6d784313fb3b9185250c8554c | 2026-01-23T00:00:00-05:00 | Membership Inference Attacks on LLM-based Recommender Systems | arXiv:2508.18665v5 Announce Type: replace Abstract: Large language models (LLMs) based recommender systems (RecSys) can adapt to different domains flexibly. It utilizes in-context learning (ICL), i.e., prompts, to customize the recommendation functions, which include sensitive historical user-specific item interactions... | https://arxiv.org/abs/2508.18665 | Academic Papers | svg |
b9b3d0af414b3c9d64ca77062a4fdaf3feba734135f5a04ff3ea8168e04bd35a | 2026-01-23T00:00:00-05:00 | Attacks on Approximate Caches in Text-to-Image Diffusion Models | arXiv:2508.20424v3 Announce Type: replace Abstract: Diffusion models are a powerful class of generative models that produce images and other content from user prompts, but they are computationally intensive. To mitigate this cost, recent academic and industry work has adopted approximate caching, which reuses intermedi... | https://arxiv.org/abs/2508.20424 | Academic Papers | svg |
472fb12c7c1c462a4c707eae9207fc53e334d49415cfda5071af756844b8fd36 | 2026-01-23T00:00:00-05:00 | The Percept-V Challenge: Can Multimodal LLMs Crack Simple Perception Problems? | arXiv:2508.21143v3 Announce Type: replace Abstract: Cognitive science research treats visual perception, the ability to understand and make sense of a visual input, as one of the early developmental signs of intelligence. Its TVPS-4 framework categorizes and tests human perception into seven skills such as visual discr... | https://arxiv.org/abs/2508.21143 | Academic Papers | svg |
86d5f689a03c282b9742c438917223b9211b605a98f69964ac8e8d4541b8d77a | 2026-01-23T00:00:00-05:00 | Is this chart lying to me? Automating the detection of misleading visualizations | arXiv:2508.21675v2 Announce Type: replace Abstract: Misleading visualizations are a potent driver of misinformation on social media and the web. By violating chart design principles, they distort data and lead readers to draw inaccurate conclusions. Prior work has shown that both humans and multimodal large language mo... | https://arxiv.org/abs/2508.21675 | Academic Papers | svg |
932e67ea5930937bd822ff88088c3ee1e75c7711f37382bca907e6817af712e0 | 2026-01-23T00:00:00-05:00 | StoxLSTM: A Stochastic Extended Long Short-Term Memory Network for Time Series Forecasting | arXiv:2509.01187v2 Announce Type: replace Abstract: The Extended Long Short-Term Memory (xLSTM) network has demonstrated strong capability in modeling complex long-term dependencies in time series data. Despite its success, the deterministic architecture of xLSTM limits its representational capacity and forecasting per... | https://arxiv.org/abs/2509.01187 | Academic Papers | svg |
3a281235cb496322ec378f75b502723688784eeda895ca7758f62aab78f5ab1e | 2026-01-23T00:00:00-05:00 | Disentangling trust from cooperation: Trust as reduced monitoring across social dilemmas | arXiv:2509.04143v3 Announce Type: replace Abstract: It is commonly assumed that trust increases cooperation. However, game-theoretic models often fail to distinguish between cooperative actions and trust, making it difficult to independently measure trust and determine how its effects vary in different social dilemmas.... | https://arxiv.org/abs/2509.04143 | Academic Papers | svg |
85ff9f7e2d3782fb2a1587a82ee2dbf9ab95e41061acf93e1817da8fa0855f8e | 2026-01-23T00:00:00-05:00 | Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model | arXiv:2509.04548v2 Announce Type: replace Abstract: Recent advances in multimodal models have demonstrated impressive capabilities in unified image generation and editing. However, many prominent open-source models prioritize scaling model parameters over optimizing training strategies, limiting their efficiency and pe... | https://arxiv.org/abs/2509.04548 | Academic Papers | svg |
17dc726e7ac8f68e1bf8ccade10fac932adc9faa2c3e1efcc71acdcb63ecaf81 | 2026-01-23T00:00:00-05:00 | Collaborate, Deliberate, Evaluate: How LLM Alignment Affects Coordinated Multi-Agent Outcomes | arXiv:2509.05882v2 Announce Type: replace Abstract: As Large Language Models (LLMs) get integrated into diverse workflows, they are increasingly being regarded as "collaborators" with humans, and required to work in coordination with other AI systems. If such AI collaborators are to reliably coordinate their actions an... | https://arxiv.org/abs/2509.05882 | Academic Papers | svg |
812665d487806521ca9f4b6c027f60b33f85f3c5169edc80cfd85dc9a51e2969 | 2026-01-23T00:00:00-05:00 | Xi+: Uncertainty Supervision for Robust Speaker Embedding | arXiv:2509.05993v4 Announce Type: replace Abstract: There are various factors that can influence the performance of speaker recognition systems, such as emotion, language and other speaker-related or context-related variations. Since individual speech frames do not contribute equally to the utterance-level representati... | https://arxiv.org/abs/2509.05993 | Academic Papers | svg |
7c5b962a734594edd2b725251f760ca9f3a1fd1a695966c3a412c2ca35934f5e | 2026-01-23T00:00:00-05:00 | Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data | arXiv:2509.07526v3 Announce Type: replace Abstract: Large language models (LLMs) have transformed NLP, yet their integration with audio remains underexplored despite audio's centrality to human communication. We introduce Falcon3-Audio, a family of Audio-Language Models (ALMs) built on instruction-tuned LLMs and Whispe... | https://arxiv.org/abs/2509.07526 | Academic Papers | svg |
47988681943adb9d1493e0994077740217cf776c6a87ad68b5026c34e813d2da | 2026-01-23T00:00:00-05:00 | Behind the Scenes: Mechanistic Interpretability of LoRA-adapted Whisper for Speech Emotion Recognition | arXiv:2509.08454v3 Announce Type: replace Abstract: Large pre-trained speech models such as Whisper offer strong generalization but pose significant challenges for resource-efficient adaptation. Low-Rank Adaptation (LoRA) has become a popular parameter-efficient fine-tuning method, yet its underlying mechanisms in spee... | https://arxiv.org/abs/2509.08454 | Academic Papers | svg |
7a89ae2408e6493c78d491bc952b465c63259b1bd54e51493c01366f92f43b07 | 2026-01-23T00:00:00-05:00 | LLMs Homogenize Values in Constructive Arguments on Value-Laden Topics | arXiv:2509.10637v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to promote prosocial and constructive discourse online. Yet little is known about how these models negotiate and shape underlying values when reframing people's arguments on value-laden topics. We conducted experiment... | https://arxiv.org/abs/2509.10637 | Academic Papers | svg |
70270400749cef7127b4b90ebcfbcb441fb576c095ac9cd8de90618f325b859c | 2026-01-23T00:00:00-05:00 | No Mesh, No Problem: Estimating Coral Volume and Surface from Sparse Multi-View Images | arXiv:2509.11164v3 Announce Type: replace Abstract: Effective reef monitoring requires the quantification of coral growth via accurate volumetric and surface area estimates, which is a challenging task due to the complex morphology of corals. We propose a novel, lightweight, and scalable learning framework that address... | https://arxiv.org/abs/2509.11164 | Academic Papers | svg |
9a452ed3d5377f2c33ef14357289acf44de48007dd11446e31acb18250ef3148 | 2026-01-23T00:00:00-05:00 | DF-LLaVA: Unlocking MLLM's potential for Synthetic Image Detection via Prompt-Guided Knowledge Injection | arXiv:2509.14957v2 Announce Type: replace Abstract: With the increasing prevalence of synthetic images, evaluating image authenticity and locating forgeries accurately while maintaining human interpretability remains a challenging task. Existing detection models primarily focus on simple authenticity classification, ul... | https://arxiv.org/abs/2509.14957 | Academic Papers | svg |
8c5444abeb9d5b2541eeed149f51f6c1ebedfc56c3abe405934a4efd12ac712b | 2026-01-23T00:00:00-05:00 | From Canopy to Ground via ForestGen3D: Learning Cross-Domain Generation of 3D Forest Structure from Aerial-to-Terrestrial LiDAR | arXiv:2509.16346v2 Announce Type: replace Abstract: The 3D structure of living and non-living components in ecosystems plays a critical role in determining ecological processes and feedbacks from both natural and human-driven disturbances. Anticipating the effects of wildfire, drought, disease, or atmospheric depositio... | https://arxiv.org/abs/2509.16346 | Academic Papers | svg |
22aaad587b2266efe3c77fd9fdf08a8fd3f8e9b1edb09ef28b08a7353ec5cef6 | 2026-01-23T00:00:00-05:00 | TextCrafter: Optimization-Calibrated Noise for Defending Against Text Embedding Inversion | arXiv:2509.17302v5 Announce Type: replace Abstract: Text embedding inversion attacks reconstruct original sentences from latent representations, posing severe privacy threats in collaborative inference and edge computing. We propose TextCrafter, an optimization-based adversarial perturbation mechanism that combines RL ... | https://arxiv.org/abs/2509.17302 | Academic Papers | svg |
e5b5282007516cba829693cfa790e726b21fd4df2666c9ac1f2c4f59c04429c8 | 2026-01-23T00:00:00-05:00 | VideoPro: Adaptive Program Reasoning for Long Video Understanding | arXiv:2509.17743v3 Announce Type: replace Abstract: Large language models (LLMs) have shown promise in generating program workflows for visual tasks. However, previous approaches often rely on closed-source models, lack systematic reasoning, and struggle with long-form video question answering (videoQA). To address the... | https://arxiv.org/abs/2509.17743 | Academic Papers | svg |
94a246981d11440f4b4b2bcb0a46c4a5decd2e7b1b72da1e5d40dd3e49d0aa2d | 2026-01-23T00:00:00-05:00 | FedIA: Towards Domain-Robust Aggregation in Federated Graph Learning | arXiv:2509.18171v3 Announce Type: replace Abstract: Federated Graph Learning (FGL) enables a central server to coordinate model training across distributed clients without local graph data being shared. However, FGL significantly suffers from cross-silo domain shifts, where each "silo" (domain) contains a limited numbe... | https://arxiv.org/abs/2509.18171 | Academic Papers | svg |
fe706675ad14361fb46e5180f9795a0244a22bc900a4ea24bace3a767049e515 | 2026-01-23T00:00:00-05:00 | MCGrad: Multicalibration at Web Scale | arXiv:2509.19884v3 Announce Type: replace Abstract: We propose MCGrad, a novel and scalable multicalibration algorithm. Multicalibration - calibration in subgroups of the data - is an important property for the performance of machine learning-based systems. Existing multicalibration methods have thus far received limit... | https://arxiv.org/abs/2509.19884 | Academic Papers | svg |
fd2d7ccc3f9239d7a1313d4c1ec25a5a4e1aa2a7bfd971de9716faf43d3b17f8 | 2026-01-23T00:00:00-05:00 | Real-Time Object Detection Meets DINOv3 | arXiv:2509.20787v3 Announce Type: replace Abstract: Benefiting from the simplicity and effectiveness of Dense O2O and MAL, DEIM has become the mainstream training framework for real-time DETRs, significantly outperforming the YOLO series. In this work, we extend it with DINOv3 features, resulting in DEIMv2. DEIMv2 span... | https://arxiv.org/abs/2509.20787 | Academic Papers | svg |
6d6d57f575496e02301cec1845669a850cbee747a23b964a8c814370846d9d0c | 2026-01-23T00:00:00-05:00 | PhishLumos: An Adaptive Multi-Agent System for Proactive Phishing Campaign Mitigation | arXiv:2509.21772v2 Announce Type: replace Abstract: Phishing attacks are a significant societal threat, disproportionately harming vulnerable populations and eroding trust in essential digital services. Current defenses are often reactive, failing against modern evasive tactics like cloaking that conceal malicious cont... | https://arxiv.org/abs/2509.21772 | Academic Papers | svg |
3e153487198c6006513d8d0d3bf6dd806babc91cd6b6f5a3c65d691b938071c2 | 2026-01-23T00:00:00-05:00 | VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference | arXiv:2509.24257v4 Announce Type: replace Abstract: Decentralized inference provides a scalable and resilient paradigm for serving large language models (LLMs), enabling fragmented global resource utilization and reducing reliance on centralized providers. However, in a permissionless environment without trusted nodes,... | https://arxiv.org/abs/2509.24257 | Academic Papers | svg |
b82e2211ce74864c32d0da42d43ebd41e58f31983723a52ece15dc0b246d872d | 2026-01-23T00:00:00-05:00 | BPMN Assistant: An LLM-Based Approach to Business Process Modeling | arXiv:2509.24592v2 Announce Type: replace Abstract: This paper presents BPMN Assistant, a tool that leverages Large Language Models for natural language-based creation and editing of BPMN diagrams. While direct XML generation is common, it is verbose, slow, and prone to syntax errors during complex modifications. We in... | https://arxiv.org/abs/2509.24592 | Academic Papers | svg |
cdd71dc7016d1c694208b04d4ffb3bbab0d513141531c8876db5296409f394c6 | 2026-01-23T00:00:00-05:00 | PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detection | arXiv:2509.25856v2 Announce Type: replace Abstract: Industrial anomaly detection is increasingly relying on foundation models, aiming for strong out-of-distribution generalization and rapid adaptation in real-world deployments. Notably, past studies have primarily focused on textual prompt tuning, leaving the intrinsic... | https://arxiv.org/abs/2509.25856 | Academic Papers | svg |
9adbe5ba7b9d4c28aabf297a0173cdce572a8123edee05cc2fc08b4fb2f28808 | 2026-01-23T00:00:00-05:00 | Signature-Informed Transformer for Asset Allocation | arXiv:2510.03129v3 Announce Type: replace Abstract: Modern deep learning for asset allocation typically separates forecasting from optimization. We argue this creates a fundamental mismatch where minimizing prediction errors fails to yield robust portfolios. We propose the Signature Informed Transformer to address this... | https://arxiv.org/abs/2510.03129 | Academic Papers | svg |
8afd93c9a56c5e1bdcb668a6359e9cbd523a7d15659080f614c9e73b88c6d5a9 | 2026-01-23T00:00:00-05:00 | DECOR: Deep Embedding Clustering with Orientation Robustness | arXiv:2510.03328v2 Announce Type: replace Abstract: In semiconductor manufacturing, early detection of wafer defects is critical for product yield optimization. However, raw wafer data from wafer quality tests are often complex, unlabeled, imbalanced and can contain multiple defects on a single wafer, making it crucial... | https://arxiv.org/abs/2510.03328 | Academic Papers | svg |
f8fb36a82aa02ec5953776c270c2f07f54a9876bb13357ba19fb57b3a4118a08 | 2026-01-23T00:00:00-05:00 | PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization | arXiv:2510.04436v2 Announce Type: replace Abstract: Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasibility, remains a great challeng... | https://arxiv.org/abs/2510.04436 | Academic Papers | svg |
0ead608fc3860c72e8f58067b849d2db9cc75c3ab97766b0a3a309483460198b | 2026-01-23T00:00:00-05:00 | New Insights into Involutory and Orthogonal MDS Matrices | arXiv:2510.05766v2 Announce Type: replace Abstract: MDS matrices play a critical role in the design of diffusion layers for block ciphers and hash functions due to their optimal branch number. Involutory and orthogonal MDS matrices offer additional benefits by allowing identical or nearly identical circuitry for both e... | https://arxiv.org/abs/2510.05766 | Academic Papers | svg |
487a41a91c837e1f4fa5b2ada8daa802ac5dacbb610e294c4a9c7aac13ee98fe | 2026-01-23T00:00:00-05:00 | Does LLM Focus on the Right Words? Mitigating Context Bias in LLM-based Recommenders | arXiv:2510.10978v2 Announce Type: replace Abstract: Large language models (LLMs), owing to their extensive open-domain knowledge and semantic reasoning capabilities, have been increasingly integrated into recommender systems (RS). However, a substantial gap remains between the pre-training objectives of LLMs and the sp... | https://arxiv.org/abs/2510.10978 | Academic Papers | svg |
f053dcaa4f1e4df3176924248665a072a8d161cb57c767b33935a650d0bbca96 | 2026-01-23T00:00:00-05:00 | CoSPED: Consistent Soft Prompt Targeted Data Extraction and Defense | arXiv:2510.11137v3 Announce Type: replace Abstract: Large language models have gained widespread attention recently, but their potential security vulnerabilities, especially privacy leakage, are also becoming apparent. To test and evaluate for data extraction risks in LLM, we proposed CoSPED, short for Consistent Soft ... | https://arxiv.org/abs/2510.11137 | Academic Papers | svg |
38ef27f0111bb36507a21fc582f9692285239ac79c3d51005898bf1408f8548d | 2026-01-23T00:00:00-05:00 | Community Engagement and the Lifespan of Open-Source Software Projects | arXiv:2510.15408v2 Announce Type: replace Abstract: Open-source software (OSS) projects depend on community engagement (CE) for longevity. However, CE's quantifiable impact on project dynamics and lifespan is underexplored. Objectives: This study defines CE in OSS, identifies key metrics, and evaluates their influence ... | https://arxiv.org/abs/2510.15408 | Academic Papers | svg |
2b023f5bbcf01f2cc6a744b0555e3ad14cb23254f43d371daac49abb8af1f8ae | 2026-01-23T00:00:00-05:00 | Enhanced Fish Freshness Classification with Incremental Handcrafted Feature Fusion | arXiv:2510.17145v2 Announce Type: replace Abstract: Accurate assessment of fish freshness remains a major challenge in the food industry, with direct consequences for product quality, market value, and consumer health. Conventional sensory evaluation is inherently subjective, inconsistent, and difficult to standardize ... | https://arxiv.org/abs/2510.17145 | Academic Papers | svg |
76ff4ed00d3faac49e8e565b5d6d20bcf9387fd713a6c77c17b24100c6d0c226 | 2026-01-23T00:00:00-05:00 | Auditing and Mitigating Bias in Gender Classification Algorithms: A Data-Centric Approach | arXiv:2510.17873v2 Announce Type: replace Abstract: Gender classification systems often inherit and amplify demographic imbalances in their training data. We first audit five widely used gender classification datasets, revealing that all suffer from significant intersectional underrepresentation. To measure the downstr... | https://arxiv.org/abs/2510.17873 | Academic Papers | svg |
16164898ce64e01961a903453206226699898880c8ebf937967dad682f011fde | 2026-01-23T00:00:00-05:00 | Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets | arXiv:2510.19950v2 Announce Type: replace Abstract: In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices. However, during deployment, these agents trade in live markets where their own transactions can shift asset prices, a ph... | https://arxiv.org/abs/2510.19950 | Academic Papers | svg |
19360dca539da04efc8be8f5c7c4bd0ceb61dd0049f86c0d6d514a2300803424 | 2026-01-23T00:00:00-05:00 | Yesnt: Are Diffusion Relighting Models Ready for Capture Stage Compositing? A Hybrid Alternative to Bridge the Gap | arXiv:2510.23494v2 Announce Type: replace Abstract: Volumetric video relighting is essential for bringing captured performances into virtual worlds, but current approaches struggle to deliver temporally stable, production-ready results. Diffusion-based intrinsic decomposition methods show promise for single frames, yet... | https://arxiv.org/abs/2510.23494 | Academic Papers | svg |
b1d6b83cda39402451cda5b2b18b601180e3c14c9988c5e18de301d38bbc3e49 | 2026-01-23T00:00:00-05:00 | TDFlow: Agentic Workflows for Test Driven Development | arXiv:2510.23761v2 Announce Type: replace Abstract: We introduce TDFlow, a novel test-driven agentic workflow that frames repository-scale software engineering as a test-resolution task, specifically designed to solve human-written tests. Given a set of tests, TDFlow repeatedly proposes, revises, and debugs repository-... | https://arxiv.org/abs/2510.23761 | Academic Papers | svg |
9d17b9d8a870906c7a9f7823744d03420067840edd2b49f5403e90785a19d1ad | 2026-01-23T00:00:00-05:00 | Understanding Reader Perception Shifts upon Disclosure of AI Authorship | arXiv:2510.24011v2 Announce Type: replace Abstract: As AI writing support becomes ubiquitous, how disclosing its use affects reader perception remains a critical, underexplored question. We conducted a study with 261 participants to examine how revealing varying levels of AI involvement shifts author impressions across... | https://arxiv.org/abs/2510.24011 | Academic Papers | svg |
ebc661795910ec4430850c1cbb11be66b268a85f28f2e4f923aab054728196ac | 2026-01-23T00:00:00-05:00 | PaTaRM: Bridging Pairwise and Pointwise Signals via Preference-Aware Task-Adaptive Reward Modeling | arXiv:2510.24235v2 Announce Type: replace Abstract: Reward models (RMs) are central to reinforcement learning from human feedback (RLHF), providing the critical supervision signals that align large language models (LLMs) with human preferences. Generative reward models (GRMs) provide greater interpretability than tradi... | https://arxiv.org/abs/2510.24235 | Academic Papers | svg |
724902e2a2a682cc49657482f6b091a9c9de1f460f85a619fccda5bd4aada2e7 | 2026-01-23T00:00:00-05:00 | Systems of Graph Formulas and their Equivalence to Alternating Graph Automata | arXiv:2510.25260v2 Announce Type: replace Abstract: Graph-based modeling plays a fundamental role in many areas of computer science. In this paper, we introduce systems of graph formulas with variables for specifying graph properties; this notion generalizes the graph formulas introduced in earlier work by incorporatin... | https://arxiv.org/abs/2510.25260 | Academic Papers | svg |
c69c41feea549e2268feadb812a3e922dc6773dd8785df10fdf072d71a501018 | 2026-01-23T00:00:00-05:00 | Data-Enabled Predictive Control and Guidance for Autonomous Underwater Vehicles | arXiv:2510.25309v2 Announce Type: replace Abstract: This paper presents a fully data-driven control framework for autonomous underwater vehicles (AUVs) based on Data-Enabled Predictive Control (DeePC). The approach eliminates the need for explicit hydrodynamic modeling by exploiting measured input-output data to predic... | https://arxiv.org/abs/2510.25309 | Academic Papers | svg |
634c3b5d267c38edbc1b953571bd3b4a361fbd558c15ff05b3b689a7bd9e37cc | 2026-01-23T00:00:00-05:00 | Analyzing the Impact of Demand Response on Short-Circuit Current via a Unit Commitment Model | arXiv:2511.00296v2 Announce Type: replace Abstract: In low-carbon grids, system flexibility can be enhanced through mechanisms such as Demand Response (DR), enabling the efficient utilization of renewable energy. However, as Synchronous Generators (SGs) are being replaced by renewable energy sources characterized by In... | https://arxiv.org/abs/2511.00296 | Academic Papers | svg |
f5ad4aa3a43dbfd1fe33533dca8e5761791b3ff2cb3ad81696d491e74a4af5d4 | 2026-01-23T00:00:00-05:00 | Subtree Mode and Applications | arXiv:2511.01376v2 Announce Type: replace Abstract: The mode of a collection of values (i.e., the most frequent value in the collection) is a key summary statistic. Finding the mode in a given range of an array of values is thus of great importance, and constructing a data structure to solve this problem is in fact the... | https://arxiv.org/abs/2511.01376 | Academic Papers | svg |
0dab709a0ecaeffc8c35c9647f4f2ad8e3d3b0420171c25113266db6dbfd0db0 | 2026-01-23T00:00:00-05:00 | Fast Ramsey Quantifier Elimination in LIRA (with applications to liveness checking) | arXiv:2511.05323v2 Announce Type: replace Abstract: Ramsey quantifiers have recently been proposed as a unified framework for handling properties of interests in program verification involving proofs in the form of infinite cliques, which are not expressible in first-order logic. Among others, these include liveness ve... | https://arxiv.org/abs/2511.05323 | Academic Papers | svg |
cf30dd621b64b94d0d9aacd01c61a8323fed3e5f523a7ca1c3b146bc5873e855 | 2026-01-23T00:00:00-05:00 | Can LLM Infer Risk Information From MCP Server System Logs? | arXiv:2511.05867v3 Announce Type: replace Abstract: Large Language Models (LLMs) demonstrate strong capabilities in solving complex tasks when integrated with external tools. The Model Context Protocol (MCP) has become a standard interface for enabling such tool-based interactions. However, these interactions introduce... | https://arxiv.org/abs/2511.05867 | Academic Papers | svg |
09482726199e4cfcb3c5868a67a35c2bf8cbe8a9a907725cd7100e796db285fc | 2026-01-23T00:00:00-05:00 | PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data | arXiv:2511.06943v2 Announce Type: replace Abstract: Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the carbon and energy cycles of the Earth system. However, existing trait maps remain limited by the high cost and sparse geographi... | https://arxiv.org/abs/2511.06943 | Academic Papers | svg |
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