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c6bc73078df6999ea91af4ab5a5804d21e0340e7116db72ef3163b6ffae540ad | 2026-01-16T00:00:00-05:00 | A New Construction Structure on Multi-access Coded Caching with Linear Subpacketization: Cyclic Multi-Access Non-Half-Sum Disjoint Packing | arXiv:2601.10510v1 Announce Type: new Abstract: We consider the $(K,L,M,N)$ multi-access coded caching system introduced by Hachem et al., which consists of a central server with $N$ files and $K$ cache nodes, each of memory size $M$, where each user can access $L$ cache nodes in a cyclic wrap-around fashion. At presen... | https://arxiv.org/abs/2601.10510 | Academic Papers | svg |
621c7b75d79795557a278774d001c1d18e2a0a00d00c5dac09dba1ec88d010fb | 2026-01-16T00:00:00-05:00 | Scalable Algorithms for Approximate DNF Model Counting | arXiv:2601.10511v1 Announce Type: new Abstract: Model counting of Disjunctive Normal Form (DNF) formulas is a critical problem in applications such as probabilistic inference and network reliability. For example, it is often used for query evaluation in probabilistic databases. Due to the computational intractability o... | https://arxiv.org/abs/2601.10511 | Academic Papers | svg |
845040a3a63defa52b3cee37f0a9553a2dd1db64ba4cebe5719c029351e387ea | 2026-01-16T00:00:00-05:00 | SatMap: Revisiting Satellite Maps as Prior for Online HD Map Construction | arXiv:2601.10512v1 Announce Type: new Abstract: Online high-definition (HD) map construction is an essential part of a safe and robust end-to-end autonomous driving (AD) pipeline. Onboard camera-based approaches suffer from limited depth perception and degraded accuracy due to occlusion. In this work, we propose SatMap... | https://arxiv.org/abs/2601.10512 | Academic Papers | svg |
a507f1b1504560c056b209b4455040485b1d477a5d35ccae2fad84718f6835f2 | 2026-01-16T00:00:00-05:00 | AEQ-Bench: Measuring Empathy of Omni-Modal Large Models | arXiv:2601.10513v1 Announce Type: new Abstract: While the automatic evaluation of omni-modal large models (OLMs) is essential, assessing empathy remains a significant challenge due to its inherent affectivity. To investigate this challenge, we introduce AEQ-Bench (Audio Empathy Quotient Benchmark), a novel benchmark to... | https://arxiv.org/abs/2601.10513 | Academic Papers | svg |
9eb6003d7355a19707d0a79d49e2187de0767391c21475742d63c1dda1d38cf8 | 2026-01-16T00:00:00-05:00 | Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Models | arXiv:2601.10519v1 Announce Type: new Abstract: Cognitive Radio (CR) systems, which dynamically adapt to changing spectrum environments, could benefit significantly from advancements in machine learning technologies. These systems can be enhanced in terms of spectral efficiency, robustness, and security through innovat... | https://arxiv.org/abs/2601.10519 | Academic Papers | svg |
ba80341674372e2acc8ca94e8d01c070e9f132e79f601f30430ea340ea44768f | 2026-01-16T00:00:00-05:00 | Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment | arXiv:2601.10520v1 Announce Type: new Abstract: As AI agents become increasingly autonomous, widely deployed in consequential contexts, and efficacious in bringing about real-world impacts, ensuring that their decisions are not only instrumentally effective but also normatively aligned has become critical. We introduce... | https://arxiv.org/abs/2601.10520 | Academic Papers | svg |
b99c0459746ba808944270f94afc7badac841806a94e7da9f610af44a2e54486 | 2026-01-16T00:00:00-05:00 | BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition | arXiv:2601.10521v1 Announce Type: new Abstract: Anticipating the intentions of Vulnerable Road Users (VRUs) is a critical challenge for safe autonomous driving (AD) and mobile robotics. While current research predominantly focuses on pedestrian crossing behaviors from a vehicle's perspective, interactions within dense ... | https://arxiv.org/abs/2601.10521 | Academic Papers | svg |
6352d76cd7b7c26ff4783c0e3f73d923d8bcf2df65628afbcbba87dc440c7e43 | 2026-01-16T00:00:00-05:00 | Diagnosing Generalization Failures in Fine-Tuned LLMs: A Cross-Architectural Study on Phishing Detection | arXiv:2601.10524v1 Announce Type: new Abstract: The practice of fine-tuning Large Language Models (LLMs) has achieved state-of-the-art performance on specialized tasks, yet diagnosing why these models become brittle and fail to generalize remains a critical open problem. To address this, we introduce and apply a multi-... | https://arxiv.org/abs/2601.10524 | Academic Papers | svg |
6348811c9aa2f8e303a1286ba4ce2d9332a63305a026d34e648c7e48099bf97b | 2026-01-16T00:00:00-05:00 | Learning from Brain Topography: A Hierarchical Local-Global Graph-Transformer Network for EEG Emotion Recognition | arXiv:2601.10525v1 Announce Type: new Abstract: Understanding how local neurophysiological patterns interact with global brain dynamics is essential for decoding human emotions from EEG signals. However, existing deep learning approaches often overlook the brain's intrinsic spatial organization, failing to simultaneous... | https://arxiv.org/abs/2601.10525 | Academic Papers | svg |
0c93b6d15b3d79c5605880da9811380b07b888168018329b90af1d5f4f36848e | 2026-01-16T00:00:00-05:00 | On the suboptimality of linear codes for binary distributed hypothesis testing | arXiv:2601.10526v1 Announce Type: new Abstract: We study a binary distributed hypothesis testing problem where two agents observe correlated binary vectors and communicate compressed information at the same rate to a central decision maker. In particular, we study linear compression schemes and show that simple truncat... | https://arxiv.org/abs/2601.10526 | Academic Papers | svg |
7695d19f07a9796ec3dfcefc9e10d3a0e698a8a97ed0fd22df53126f61962634 | 2026-01-16T00:00:00-05:00 | A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Doubao 1.8, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5 | arXiv:2601.10527v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has produced substantial gains in reasoning, perception, and generative capability across language and vision. However, whether these advances yield commensurate improvements ... | https://arxiv.org/abs/2601.10527 | Academic Papers | svg |
1f21bc5a8bc728f9ee7bf4b45af6c6c647763849e712c5e8e0f6b2d87813d50f | 2026-01-16T00:00:00-05:00 | PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models | arXiv:2601.10532v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in human-centric applications, yet they often fail to provide substantive emotional support. While Reinforcement Learning (RL) has been utilized to enhance empathy of LLMs, existing reward models typically evaluate em... | https://arxiv.org/abs/2601.10532 | Academic Papers | svg |
f9f534558fd7b1edda06c22d690c37083f0ce4406f5d3cfd4509e30c5afa0029 | 2026-01-16T00:00:00-05:00 | SVII-3D: Advancing Roadside Infrastructure Inventory with Decimeter-level 3D Localization and Comprehension from Sparse Street Imagery | arXiv:2601.10535v1 Announce Type: new Abstract: The automated creation of digital twins and precise asset inventories is a critical task in smart city construction and facility lifecycle management. However, utilizing cost-effective sparse imagery remains challenging due to limited robustness, inaccurate localization, ... | https://arxiv.org/abs/2601.10535 | Academic Papers | svg |
4496511796af9de5c7d9fe5baa85695ec849948439d935cdb42ad48b860bf4a9 | 2026-01-16T00:00:00-05:00 | CoGen: Creation of Reusable UI Components in Figma via Textual Commands | arXiv:2601.10536v1 Announce Type: new Abstract: The evolution of User Interface design has emphasized the need for efficient, reusable, and editable components to ensure an efficient design process. This research introduces CoGen, a system that uses machine learning techniques to generate reusable UI components directl... | https://arxiv.org/abs/2601.10536 | Academic Papers | svg |
879f17121271a56a1aa53e6f558c9ff09dbc6426a4cc275adbd21a95912956e6 | 2026-01-16T00:00:00-05:00 | Enhancing the quality of gauge images captured in smoke and haze scenes through deep learning | arXiv:2601.10537v1 Announce Type: new Abstract: Images captured in hazy and smoky environments suffer from reduced visibility, posing a challenge when monitoring infrastructures and hindering emergency services during critical situations. The proposed work investigates the use of the deep learning models to enhance the... | https://arxiv.org/abs/2601.10537 | Academic Papers | svg |
54573f9871fec63e5cec3ae1b3075520377473c46b8d8b12f0be7ebfb4685d74 | 2026-01-16T00:00:00-05:00 | Network Integrated Sensing and Communication | arXiv:2601.10538v1 Announce Type: new Abstract: Integrated sensing and communication (ISAC) is a cornerstone technology for 6G networks, offering unified support for high-rate communication and high-accuracy sensing. While existing literature extensively covers link-level designs, the transition toward large-scale depl... | https://arxiv.org/abs/2601.10538 | Academic Papers | svg |
19de3735c3dc43a8b0c10f7042e7d3eb44bfb18bad65767ade354a1048f4df9c | 2026-01-16T00:00:00-05:00 | Error-Correcting Codes for Two Bursts of t1-Deletion-t2-Insertion with Low Computational Complexity | arXiv:2601.10540v1 Announce Type: new Abstract: Burst errors involving simultaneous insertions, deletions, and substitutions occur in practical scenarios, including DNA data storage and document synchronization, motivating developments of channel codes that can correct such errors. In this paper, we address the problem... | https://arxiv.org/abs/2601.10540 | Academic Papers | svg |
a502c8b42d031ceed85ca44c9b0b132775c86efb8a4f527380d3f4f1fb818140 | 2026-01-16T00:00:00-05:00 | Mixtures of Transparent Local Models | arXiv:2601.10541v1 Announce Type: new Abstract: The predominance of machine learning models in many spheres of human activity has led to a growing demand for their transparency. The transparency of models makes it possible to discern some factors, such as security or non-discrimination. In this paper, we propose a mixt... | https://arxiv.org/abs/2601.10541 | Academic Papers | svg |
3b6c7c2e159b100df67c2ea31e9b1776836a9bb6761773471236cca6d7205187 | 2026-01-16T00:00:00-05:00 | Hybrid Encryption with Certified Deletion in Preprocessing Model | arXiv:2601.10542v1 Announce Type: new Abstract: Certified deletion allows Alice to outsource data to Bob and, at a later time, obtain a verifiable guarantee that the file has been irreversibly deleted at her request. The functionality, while impossible using classical information alone, can be achieved using quantum in... | https://arxiv.org/abs/2601.10542 | Academic Papers | svg |
6c68aed1226d5d11181c726dbfe4444c21626ba3e964173d3c438ebb4f935f6b | 2026-01-16T00:00:00-05:00 | Defending Large Language Models Against Jailbreak Attacks via In-Decoding Safety-Awareness Probing | arXiv:2601.10543v1 Announce Type: new Abstract: Large language models (LLMs) have achieved impressive performance across natural language tasks and are increasingly deployed in real-world applications. Despite extensive safety alignment efforts, recent studies show that such alignment is often shallow and remains vulne... | https://arxiv.org/abs/2601.10543 | Academic Papers | svg |
4b75e7ef2368851b7e951645fb3ace1706b332c9110eec841034b939de692d4f | 2026-01-16T00:00:00-05:00 | SDN-Driven Innovations in MANETs and IoT: A Path to Smarter Networks | arXiv:2601.10544v1 Announce Type: new Abstract: Mobile Ad Hoc Networks (MANETs) and Internet of Things (IoT) networks operate in decentralized and dynamic environments, making them ideal for scenarios lacking traditional infrastructure. However, these networks face challenges such as inefficient routing, limited scalab... | https://arxiv.org/abs/2601.10544 | Academic Papers | svg |
9a9b94934aa7a60cb74a7b3ce985fc9af22a5d1d423b3aa8e4dceb5f9d00fa4d | 2026-01-16T00:00:00-05:00 | HeartMuLa: A Family of Open Sourced Music Foundation Models | arXiv:2601.10547v1 Announce Type: new Abstract: We present a family of open-source Music Foundation Models designed to advance large-scale music understanding and generation across diverse tasks and modalities. Our framework consists of four major components: (1) HeartCLAP, an audio-text alignment model; (2) HeartTrans... | https://arxiv.org/abs/2601.10547 | Academic Papers | svg |
da8b535afc8d5392118f35d879a735756acf7cac81bcb6def40609e113abee44 | 2026-01-16T00:00:00-05:00 | Unleashing the Capabilities of Large Vision-Language Models for Intelligent Perception of Roadside Infrastructure | arXiv:2601.10551v1 Announce Type: new Abstract: Automated perception of urban roadside infrastructure is crucial for smart city management, yet general-purpose models often struggle to capture the necessary fine-grained attributes and domain rules. While Large Vision Language Models (VLMs) excel at open-world recogniti... | https://arxiv.org/abs/2601.10551 | Academic Papers | svg |
05d18db9f9a43caefa2f5dcc141c8128e502f95e65df3451d33143a03abc7a66 | 2026-01-16T00:00:00-05:00 | Inference-time Physics Alignment of Video Generative Models with Latent World Models | arXiv:2601.10553v1 Announce Type: new Abstract: State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility. While some attribute this deficiency to insufficient physics understanding from pre-training, we find that the shortfall in physic... | https://arxiv.org/abs/2601.10553 | Academic Papers | svg |
f0286b37fb94bc29712f58da1206671f5555ea65be391da903e3c87dafd90b89 | 2026-01-16T00:00:00-05:00 | DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery | arXiv:2601.10554v1 Announce Type: new Abstract: The efficacy of autonomous driving systems hinges critically on robust prediction and planning capabilities. However, current benchmarks are impeded by a notable scarcity of scenarios featuring dense traffic, which is essential for understanding and modeling complex inter... | https://arxiv.org/abs/2601.10554 | Academic Papers | svg |
07a498b52e6ce56a4e92156fa84fd9496a2dd61297f1dc73ca6132f00133dd27 | 2026-01-16T00:00:00-05:00 | Enhancing Mobile Ad Hoc Networks (MANETs) with Software-Defined Networking (SDN): A Balanced Approach | arXiv:2601.10556v1 Announce Type: new Abstract: Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks, characterized by their dynamic topologies and node mobility. In the era of cutting-edge technologies, integrating Software-Defined Networking (SDN) with MANETs offers a promising solution to manage these... | https://arxiv.org/abs/2601.10556 | Academic Papers | svg |
8b85bef76d9a56b4c69d51999f456677aa04ba05de621b62b68ed3181ca85a6f | 2026-01-16T00:00:00-05:00 | Chebyshev Accelerated Subspsace Eigensolver for Pseudo-hermitian Hamiltonians | arXiv:2601.10557v1 Announce Type: new Abstract: Studying the optoelectronic structure of materials can require the computation of up to several thousands of the smallest eigenpairs of a pseudo-hermitian Hamiltonian. Iterative eigensolvers may be preferred over direct methods for this task since their complexity is a fu... | https://arxiv.org/abs/2601.10557 | Academic Papers | svg |
7c9068a9899a0eb9dfb22a7a9191ed9b584ad40738e9fdac3b6e65b5e675067c | 2026-01-16T00:00:00-05:00 | Learning Latency-Aware Orchestration for Parallel Multi-Agent Systems | arXiv:2601.10560v1 Announce Type: new Abstract: Multi-agent systems (MAS) enable complex reasoning by coordinating multiple agents, but often incur high inference latency due to multi-step execution and repeated model invocations, severely limiting their scalability and usability in time-sensitive scenarios. Most exist... | https://arxiv.org/abs/2601.10560 | Academic Papers | svg |
0e9c27bc0abbcf88d93ae7cb039a97f753bff93c950140750f1041db42506c9e | 2026-01-16T00:00:00-05:00 | Process-Guided Concept Bottleneck Model | arXiv:2601.10562v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) improve the explainability of black-box Deep Learning (DL) by introducing intermediate semantic concepts. However, standard CBMs often overlook domain-specific relationships and causal mechanisms, and their dependence on complete concept l... | https://arxiv.org/abs/2601.10562 | Academic Papers | svg |
64b38d11efb515432e12809b23f0df3c2ffe64b6bad72c5e2af04eab1d7f931d | 2026-01-16T00:00:00-05:00 | Kolmogorov Arnold Networks and Multi-Layer Perceptrons: A Paradigm Shift in Neural Modelling | arXiv:2601.10563v1 Announce Type: new Abstract: The research undertakes a comprehensive comparative analysis of Kolmogorov-Arnold Networks (KAN) and Multi-Layer Perceptrons (MLP), highlighting their effectiveness in solving essential computational challenges like nonlinear function approximation, time-series prediction... | https://arxiv.org/abs/2601.10563 | Academic Papers | svg |
5eca1319955363d2f03915f18c177d9e0b63401c35e2b104260600c64e813aac | 2026-01-16T00:00:00-05:00 | Rewriting Systems on Arbitrary Monoids | arXiv:2601.10564v1 Announce Type: new Abstract: In this paper, we introduce monoidal rewriting systems (MRS), an abstraction of string rewriting in which reductions are defined over an arbitrary ambient monoid rather than a free monoid of words. This shift is partly motivated by logic: the class of free monoids is not ... | https://arxiv.org/abs/2601.10564 | Academic Papers | svg |
252bb2cef0b892868c71aa3fb99faa5234149213e8ec1c8e5997438ee0089eca | 2026-01-16T00:00:00-05:00 | Inferring signed social networks from contact patterns | arXiv:2601.10565v1 Announce Type: new Abstract: Social networks are typically inferred from indirect observations, such as proximity data; yet, most methods cannot distinguish between absent relationships and actual negative ties, as both can result in few or no interactions. We address the challenge of inferring signe... | https://arxiv.org/abs/2601.10565 | Academic Papers | svg |
29b27ceb2af4a6e9b60aa3d29619d84df64ed009c4419ae1abb748616baa25ff | 2026-01-16T00:00:00-05:00 | Representation-Aware Unlearning via Activation Signatures: From Suppression to Knowledge-Signature Erasure | arXiv:2601.10566v1 Announce Type: new Abstract: Selective knowledge erasure from LLMs is critical for GDPR compliance and model safety, yet current unlearning methods conflate behavioral suppression with true knowledge removal, allowing latent capabilities to persist beneath surface-level refusals. In this work, we add... | https://arxiv.org/abs/2601.10566 | Academic Papers | svg |
dd659ad7cb04658d6e598082b5f94f35a62d4886c8db66bb480f694c8392360c | 2026-01-16T00:00:00-05:00 | Generative AI collective behavior needs an interactionist paradigm | arXiv:2601.10567v1 Announce Type: new Abstract: In this article, we argue that understanding the collective behavior of agents based on large language models (LLMs) is an essential area of inquiry, with important implications in terms of risks and benefits, impacting us as a society at many levels. We claim that the di... | https://arxiv.org/abs/2601.10567 | Academic Papers | svg |
d213b6d1210532aaad4aacaf7a4543012399d9d26990b09cfac876411e99edd7 | 2026-01-16T00:00:00-05:00 | Sparse Signal Recovery from Random Measurements | arXiv:2601.10569v1 Announce Type: new Abstract: Given the compressed sensing measurements of an unknown vector $z \in \mathbb{R}^n$ using random matrices, we present a simple method to determine $z$ without solving any optimization problem or linear system. Our method uses $\Theta(\log n)$ random sensing matrices in $\... | https://arxiv.org/abs/2601.10569 | Academic Papers | svg |
23c68367866da1846fe1e5bed3205068c81a8ebdb53eea40ac4c869362ebfdf1 | 2026-01-16T00:00:00-05:00 | Long-term Monitoring of Kernel and Hardware Events to Understand Latency Variance | arXiv:2601.10572v1 Announce Type: new Abstract: This paper presents our experience to understand latency variance caused by kernel and hardware events, which are often invisible at the application level. For this purpose, we have built VarMRI, a tool chain to monitor and analyze those events in the long term. To mitiga... | https://arxiv.org/abs/2601.10572 | Academic Papers | svg |
5371cfd26918c7f24659a078b686174749d0ceaaa1828940665819ec8e5fb3de | 2026-01-16T00:00:00-05:00 | Jordan-Segmentable Masks: A Topology-Aware definition for characterizing Binary Image Segmentation | arXiv:2601.10577v1 Announce Type: new Abstract: Image segmentation plays a central role in computer vision. However, widely used evaluation metrics, whether pixel-wise, region-based, or boundary-focused, often struggle to capture the structural and topological coherence of a segmentation. In many practical scenarios, s... | https://arxiv.org/abs/2601.10577 | Academic Papers | svg |
21fddbf25483cf1e73fcb3235b2a7bb0d52c0e5ab67c4849ca7ba297ac3f23d3 | 2026-01-16T00:00:00-05:00 | Form and Meaning in Intrinsic Multilingual Evaluations | arXiv:2601.10580v1 Announce Type: new Abstract: Intrinsic evaluation metrics for conditional language models, such as perplexity or bits-per-character, are widely used in both mono- and multilingual settings. These metrics are rather straightforward to use and compare in monolingual setups, but rest on a number of assu... | https://arxiv.org/abs/2601.10580 | Academic Papers | svg |
3202d5939ca811e47d80185cd09b98e0145e013904757a6cb5a4cac30fd07b57 | 2026-01-16T00:00:00-05:00 | From Single to Multi-Agent Reasoning: Advancing GeneGPT for Genomics QA | arXiv:2601.10581v1 Announce Type: new Abstract: Comprehending genomic information is essential for biomedical research, yet extracting data from complex distributed databases remains challenging. Large language models (LLMs) offer potential for genomic Question Answering (QA) but face limitations due to restricted acce... | https://arxiv.org/abs/2601.10581 | Academic Papers | svg |
fb6d1fd4d02ef403198c419201ed8b97be6073921e7e3ab9540f1c61a65e5817 | 2026-01-16T00:00:00-05:00 | Mitigating GIL Bottlenecks in Edge AI Systems | arXiv:2601.10582v1 Announce Type: new Abstract: Deploying Python based AI agents on resource-constrained edge devices presents a runtime optimization challenge: high thread counts are needed to mask I/O latency, yet Python's Global Interpreter Lock (GIL) serializes execution. We demonstrate that naive thread-pool scali... | https://arxiv.org/abs/2601.10582 | Academic Papers | svg |
f0b688a7d088aba15a1d564ebce14281a6ba8f578993887f90431f6fe3619a1a | 2026-01-16T00:00:00-05:00 | Combinatorial Optimization Augmented Machine Learning | arXiv:2601.10583v1 Announce Type: new Abstract: Combinatorial optimization augmented machine learning (COAML) has recently emerged as a powerful paradigm for integrating predictive models with combinatorial decision-making. By embedding combinatorial optimization oracles into learning pipelines, COAML enables the const... | https://arxiv.org/abs/2601.10583 | Academic Papers | svg |
1f025e458f7f62dfbb9b6211f1bd74405f6263f6196b496e39b6ba16fb73b4cd | 2026-01-16T00:00:00-05:00 | Adversarial Evasion Attacks on Computer Vision using SHAP Values | arXiv:2601.10587v1 Announce Type: new Abstract: The paper introduces a white-box attack on computer vision models using SHAP values. It demonstrates how adversarial evasion attacks can compromise the performance of deep learning models by reducing output confidence or inducing misclassifications. Such attacks are parti... | https://arxiv.org/abs/2601.10587 | Academic Papers | svg |
0e50ee7bb714f1aae7f6b366c8beaa572ef9db6a39cf29d16d593b6c657b86e5 | 2026-01-16T00:00:00-05:00 | Be Your Own Red Teamer: Safety Alignment via Self-Play and Reflective Experience Replay | arXiv:2601.10589v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial ``jailbreak'' attacks designed to bypass safety guardrails. Current safety alignment methods depend heavily on static external red teaming, utilizing fixed defense prom... | https://arxiv.org/abs/2601.10589 | Academic Papers | svg |
d18697f792c81a4c3d8a66b885232f8200e92fafa3e359ab2a4a7e918c05111a | 2026-01-16T00:00:00-05:00 | ProbFM: Probabilistic Time Series Foundation Model with Uncertainty Decomposition | arXiv:2601.10591v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have emerged as a promising approach for zero-shot financial forecasting, demonstrating strong transferability and data efficiency gains. However, their adoption in financial applications is hindered by fundamental limitations in unce... | https://arxiv.org/abs/2601.10591 | Academic Papers | svg |
fa0b4b67f9b0fb9590c2125b85905cfda6beaed1987eaca04f7499755b0710dd | 2026-01-16T00:00:00-05:00 | Action100M: A Large-scale Video Action Dataset | arXiv:2601.10592v1 Announce Type: new Abstract: Inferring physical actions from visual observations is a fundamental capability for advancing machine intelligence in the physical world. Achieving this requires large-scale, open-vocabulary video action datasets that span broad domains. We introduce Action100M, a large-s... | https://arxiv.org/abs/2601.10592 | Academic Papers | svg |
9197e08dfa019f720d783e0c786bb9246937cbfd43387ef5fdb75c81a7fcd635 | 2026-01-16T00:00:00-05:00 | Improving Database Performance by Application-side Transaction Merging | arXiv:2601.10596v1 Announce Type: new Abstract: This paper explores a new opportunity to improve the performance of transaction processing at the application side by merging structurely similar statements or transactions. Concretely, we re-write transactions to 1) merge similar statements using specific SQL semantics; ... | https://arxiv.org/abs/2601.10596 | Academic Papers | svg |
89279757b347cb21f304d931d27c343f0a97df46819292e20e18f6edc879bfc1 | 2026-01-16T00:00:00-05:00 | Institutional AI: A Governance Framework for Distributional AGI Safety | arXiv:2601.10599v1 Announce Type: new Abstract: As LLM-based systems increasingly operate as agents embedded within human social and technical systems, alignment can no longer be treated as a property of an isolated model, but must be understood in relation to the environments in which these agents act. Even the most s... | https://arxiv.org/abs/2601.10599 | Academic Papers | svg |
1dd02c15a14b154b16534b038192005a866e61785f238a5446304a6b9b417063 | 2026-01-16T00:00:00-05:00 | Procedural Fairness in Multi-Agent Bandits | arXiv:2601.10600v1 Announce Type: new Abstract: In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or balancing utilities. However, evidence in psychology, economics, and Rawlsian theory suggests that fairness is also about process... | https://arxiv.org/abs/2601.10600 | Academic Papers | svg |
e86fbb8a8f78185f5ecf99f041d1a097634bdaf41213fe88a3926f0e1763ef7c | 2026-01-16T00:00:00-05:00 | Fundamental Limits of Multi-User Distributed Computing of Linearly Separable Functions | arXiv:2601.10603v1 Announce Type: new Abstract: This work establishes the fundamental limits of the classical problem of multi-user distributed computing of linearly separable functions. In particular, we consider a distributed computing setting involving $L$ users, each requesting a linearly separable function over $K... | https://arxiv.org/abs/2601.10603 | Academic Papers | svg |
73d2e7b3f00cd88bd83a2378df991251f1ec2f241ec41d2f30b95f7542b44661 | 2026-01-16T00:00:00-05:00 | Translating database mathematical schemes into relational database software applications with MatBase | arXiv:2601.10604v1 Announce Type: new Abstract: We present a pseudocode algorithm for translating our (Elementary) Mathematical Data Model schemes into relational ones and associated sets of non-relational constraints, used by MatBase, our intelligent database management system prototype. We prove that this algorithm i... | https://arxiv.org/abs/2601.10604 | Academic Papers | svg |
65fa3e9a3db62ab2b4a5c9edf91d2f1c541809933f49d55781febf0cd2827a26 | 2026-01-16T00:00:00-05:00 | A user subscription model in mobile radio access networks with network slicing | arXiv:2601.10605v1 Announce Type: new Abstract: Network slicing is an architectural enabling technology that logically decouples the current cellular networks into infrastructure providers (InPs) and Network Slice Tenants (NSTs). The network resources (e.g., radio access resources at each cell) are owned by the InP, an... | https://arxiv.org/abs/2601.10605 | Academic Papers | svg |
72bcc3ddbf5ddf9ce09528be1c297496405abd943bb150b07fa67edf77b5f3f7 | 2026-01-16T00:00:00-05:00 | RSATalker: Realistic Socially-Aware Talking Head Generation for Multi-Turn Conversation | arXiv:2601.10606v1 Announce Type: new Abstract: Talking head generation is increasingly important in virtual reality (VR), especially for social scenarios involving multi-turn conversation. Existing approaches face notable limitations: mesh-based 3D methods can model dual-person dialogue but lack realistic textures, wh... | https://arxiv.org/abs/2601.10606 | Academic Papers | svg |
dd461532021a969d32d60a44693361249c2bf70818c664cdf285ea0f9490f850 | 2026-01-16T00:00:00-05:00 | iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification | arXiv:2601.10609v1 Announce Type: new Abstract: Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored. To bridge this gap, we formally de... | https://arxiv.org/abs/2601.10609 | Academic Papers | svg |
9264a517d9a111d7d64abe36ec6dbb23b2d876c045fb85e427856bb458d84a0b | 2026-01-16T00:00:00-05:00 | Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding | arXiv:2601.10611v1 Announce Type: new Abstract: Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from them, or do not disclose their training data or recipe. As a result, the open-source commun... | https://arxiv.org/abs/2601.10611 | Academic Papers | svg |
251815a3210103f3027d5de988f284b3db72edeac44eb21aadd8c84ac8510388 | 2026-01-16T00:00:00-05:00 | Basis-Spline Assisted Coded Computing: Strategies and Error Bounds | arXiv:2601.10616v1 Announce Type: new Abstract: Coded computing has become a key framework for reliable distributed computation over decentralized networks, effectively mitigating the impact of stragglers. Although there exists a wide range of coded computing methods to handle both polynomial and non-polynomial functio... | https://arxiv.org/abs/2601.10616 | Academic Papers | svg |
6a414d3c75021b6c09c70e9614665a7715e16e17c847d44dfd7fadc5768233a9 | 2026-01-16T00:00:00-05:00 | Extrinsic Vector Field Processing | arXiv:2601.10621v1 Announce Type: new Abstract: We propose a novel discretization of tangent vector fields for triangle meshes. Starting with a Phong map continuously assigning normals to all points on the mesh, we define an extrinsic bases for continuous tangent vector fields by using the Rodrigues rotation to transpo... | https://arxiv.org/abs/2601.10621 | Academic Papers | svg |
6fce7c28e07f21b3c35f781bdc2cd0847b136d5f4bda72ba3a8eca2f2607e024 | 2026-01-16T00:00:00-05:00 | CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos | arXiv:2601.10632v1 Announce Type: new Abstract: In this paper, we find that the generation of 3D human motions and 2D human videos is intrinsically coupled. 3D motions provide the structural prior for plausibility and consistency in videos, while pre-trained video models offer strong generalization capabilities for mot... | https://arxiv.org/abs/2601.10632 | Academic Papers | svg |
d1941667fd5182abd2853b0b1376c00b1f1d003637b3d0218030d9813a93716b | 2026-01-16T00:00:00-05:00 | STEM: Scaling Transformers with Embedding Modules | arXiv:2601.10639v1 Announce Type: new Abstract: Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication overhead. We introduce STEM (Scaling Transformers with Embedding Modules), a static, token-inde... | https://arxiv.org/abs/2601.10639 | Academic Papers | svg |
41dee18ecb22e09d379fc04928394ded949b57c30f1b3124dd12ae93760abccc | 2026-01-16T00:00:00-05:00 | Converse Bounds for Sun-Jafar-type Weak Private Information Retrieval | arXiv:2601.10643v1 Announce Type: new Abstract: Building on the well-established capacity-achieving schemes of Sun-Jafar (for replicated storage) and the closely related scheme of Banawan-Ulukus (for MDS-coded setting), a recent work by Chandan et al. proposed new classes of weak private information retrieval (WPIR) sc... | https://arxiv.org/abs/2601.10643 | Academic Papers | svg |
e0b5dec71999696d0780c8697b38dd5ef14ef5a07cd0ab8d142fc0e0a08da39a | 2026-01-16T00:00:00-05:00 | RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generation | arXiv:2601.10644v1 Announce Type: new Abstract: Retrieval models are key components of Retrieval-Augmented Generation (RAG) systems, which generate search queries, process the documents returned, and generate a response. RAG systems are often dynamic and may involve multiple rounds of retrieval. While many state-of-the... | https://arxiv.org/abs/2601.10644 | Academic Papers | svg |
cb7e6f06dda279cf5b7c7a05c69c58a7640549a8ba7eeaa9e95319d119ebc731 | 2026-01-16T00:00:00-05:00 | Influential Training Data Retrieval for Explaining Verbalized Confidence of LLMs | arXiv:2601.10645v1 Announce Type: new Abstract: Large language models (LLMs) can increase users' perceived trust by verbalizing confidence in their outputs. However, prior work has shown that LLMs are often overconfident, making their stated confidence unreliable since it does not consistently align with factual accura... | https://arxiv.org/abs/2601.10645 | Academic Papers | svg |
31dca2de534d4de34acfa5602deb537ac67b8de185bca2ebce8fac8c6f3dc879 | 2026-01-16T00:00:00-05:00 | One-Shot Broadcast Joint Source-Channel Coding with Codebook Diversity | arXiv:2601.10648v1 Announce Type: new Abstract: We study a one-shot joint source-channel coding setting where the source is encoded once and broadcast to $K$ decoders through independent channels. Success is predicated on at least one decoder recovering the source within a maximum distortion constraint. We find that in... | https://arxiv.org/abs/2601.10648 | Academic Papers | svg |
f5106a12e2502f0cbb6d5642be0d8f81bbceb0d9ed3f1fe900aa7ef915d8f439 | 2026-01-16T00:00:00-05:00 | CURVE: A Benchmark for Cultural and Multilingual Long Video Reasoning | arXiv:2601.10649v1 Announce Type: new Abstract: Recent advancements in video models have shown tremendous progress, particularly in long video understanding. However, current benchmarks predominantly feature western-centric data and English as the dominant language, introducing significant biases in evaluation. To addr... | https://arxiv.org/abs/2601.10649 | Academic Papers | svg |
81844402bfacac18034ebfca6353ba76a4838e99024ac85b991514a98569897b | 2026-01-16T00:00:00-05:00 | Multi-Property Synthesis | arXiv:2601.10651v1 Announce Type: new Abstract: We study LTLf synthesis with multiple properties, where satisfying all properties may be impossible. Instead of enumerating subsets of properties, we compute in one fixed-point computation the relation between product-game states and the goal sets that are realizable from... | https://arxiv.org/abs/2601.10651 | Academic Papers | svg |
773abfe48d5fbbdadbb2dbdc040879cdc857fcded6bf06b6eeddf32cbb7d45b6 | 2026-01-16T00:00:00-05:00 | PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution | arXiv:2601.10657v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as powerful operators for evolutionary search, yet the design of efficient search scaffolds remains ad hoc. While promising, current LLM-in-the-loop systems lack a systematic approach to managing the evolutionary process. We ident... | https://arxiv.org/abs/2601.10657 | Academic Papers | svg |
31030d2f95a522939b89ad75653e2933f9c08d51eb6de24c8ccbe09cf18497fb | 2026-01-16T00:00:00-05:00 | Detecting Winning Arguments with Large Language Models and Persuasion Strategies | arXiv:2601.10660v1 Announce Type: new Abstract: Detecting persuasion in argumentative text is a challenging task with important implications for understanding human communication. This work investigates the role of persuasion strategies - such as Attack on reputation, Distraction, and Manipulative wording - in determin... | https://arxiv.org/abs/2601.10660 | Academic Papers | svg |
0f92c89363b57bb972a944d3ff891a17b058a5b0f49ca6e241a3a84a68d71f7e | 2026-01-16T00:00:00-05:00 | Stable evaluation of derivatives for barycentric and continued fraction representations of rational functions | arXiv:2601.10667v1 Announce Type: new Abstract: Fast algorithms for approximation by rational functions exist for both barycentric and Thiele continued fraction (TCF) representations. We present the first numerically stable methods for derivative evaluation in the barycentric representation, including an $O(n)$ algorit... | https://arxiv.org/abs/2601.10667 | Academic Papers | svg |
b3095b7dab57c49dad3919f44845062dc3dd9c7c8881943df93efb69b537a14c | 2026-01-16T00:00:00-05:00 | Safe Trajectory Gradient Flow Control of a Grid-Interfacing Inverter | arXiv:2601.10671v1 Announce Type: new Abstract: Grid-interfacing inverters serve as the interface between renewable energy resources and the electric power grid, offering fast, programmable control capabilities. However, their operation is constrained by hardware limitations, such as bounds on the current magnitude. Ex... | https://arxiv.org/abs/2601.10671 | Academic Papers | svg |
0599596872a1f970e0b86639b9d5558e1be5c40c4ac1124c08a8186711625612 | 2026-01-16T00:00:00-05:00 | Single-Stage Huffman Encoder for ML Compression | arXiv:2601.10673v1 Announce Type: new Abstract: Training and serving Large Language Models (LLMs) require partitioning data across multiple accelerators, where collective operations are frequently bottlenecked by network bandwidth. Lossless compression using Huffman codes is an effective way to alleviate the issue, how... | https://arxiv.org/abs/2601.10673 | Academic Papers | svg |
ad995ada375ab5429c8dff70fa8d5ea6ebde0f1777d031f948a4a651f370cb49 | 2026-01-16T00:00:00-05:00 | Breaking the Storage-Bandwidth Tradeoff in Distributed Storage with Quantum Entanglement | arXiv:2601.10676v1 Announce Type: new Abstract: This work investigates the use of quantum resources in distributed storage systems. Consider an $(n,k,d)$ distributed storage system in which a file is stored across $n$ nodes such that any $k$ nodes suffice to reconstruct the file. When a node fails, any $d$ helper nodes... | https://arxiv.org/abs/2601.10676 | Academic Papers | svg |
8b752f5d1de4d0fdc282e392d02a358e480389108e16c3b65427b6791ddbb9e0 | 2026-01-16T00:00:00-05:00 | Synchronizing Probabilities in Model-Driven Lossless Compression | arXiv:2601.10678v1 Announce Type: new Abstract: It is well-known in the field of lossless data compression that probabilistic next-symbol prediction can be used to compress sequences of symbols. Deep neural networks are able to capture rich dependencies in data, offering a powerful means of estimating these probabiliti... | https://arxiv.org/abs/2601.10678 | Academic Papers | svg |
446478b8fbb8e0993a1117aa0fd3ed014afe1f211cbdb8c059dbf2218e69aa24 | 2026-01-16T00:00:00-05:00 | Are Your Reasoning Models Reasoning or Guessing? A Mechanistic Analysis of Hierarchical Reasoning Models | arXiv:2601.10679v1 Announce Type: new Abstract: Hierarchical reasoning model (HRM) achieves extraordinary performance on various reasoning tasks, significantly outperforming large language model-based reasoners. To understand the strengths and potential failure modes of HRM, we conduct a mechanistic study on its reason... | https://arxiv.org/abs/2601.10679 | Academic Papers | svg |
156b27ce465d3b4240ae52b6ce2a7a2801e12da9230554fdec68d0eeef117f61 | 2026-01-16T00:00:00-05:00 | Structure and Diversity Aware Context Bubble Construction for Enterprise Retrieval Augmented Systems | arXiv:2601.10681v1 Announce Type: new Abstract: Large language model (LLM) contexts are typically constructed using retrieval-augmented generation (RAG), which involves ranking and selecting the top-k passages. The approach causes fragmentation in information graphs in document structures, over-retrieval, and duplicati... | https://arxiv.org/abs/2601.10681 | Academic Papers | svg |
18a846ae00f75b8285a0efc55cb49677f12d98b33d9fdb4ff2089e4e5419d788 | 2026-01-16T00:00:00-05:00 | Implementation of Oblivious Transfer over Binary-Input AWGN Channels by Polar Codes | arXiv:2601.10682v1 Announce Type: new Abstract: We develop a one-out-of-two-oblivious transfer protocol over the binary-input additive white Gaussian noise channel using polar codes. The scheme uses two decoder views linked by automorphisms of the polar transform and publicly draws the encoder at random from the corres... | https://arxiv.org/abs/2601.10682 | Academic Papers | svg |
e34f3e284f2c6eefac5ede965101ffd60a24f41ce8523ba40126f1afada7670f | 2026-01-16T00:00:00-05:00 | On the origin of neural scaling laws: from random graphs to natural language | arXiv:2601.10684v1 Announce Type: new Abstract: Scaling laws have played a major role in the modern AI revolution, providing practitioners predictive power over how the model performance will improve with increasing data, compute, and number of model parameters. This has spurred an intense interest in the origin of neu... | https://arxiv.org/abs/2601.10684 | Academic Papers | svg |
420ee97fbc9ea65a386b456e14c96623ae1d97bced759c9ad2962e9b9855df2a | 2026-01-16T00:00:00-05:00 | Improved Constructions of Reed-Solomon Codes with Optimal Repair Bandwidth | arXiv:2601.10685v1 Announce Type: new Abstract: Maximum-distance-separable (MDS) codes are widely used in distributed storage, yet naive repair of a single erasure in an $[n,k]$ MDS code downloads the entire contents of $k$ nodes. Minimum Storage Regenerating (MSR) codes (Dimakis et al., 2010) minimize repair bandwidth... | https://arxiv.org/abs/2601.10685 | Academic Papers | svg |
90b06a13a146a6297bfcdc9dc186967f0771a8503b5f5e840d3902495c17b127 | 2026-01-16T00:00:00-05:00 | A continental-scale dataset of ground beetles with high-resolution images and validated morphological trait measurements | arXiv:2601.10687v1 Announce Type: new Abstract: Despite the ecological significance of invertebrates, global trait databases remain heavily biased toward vertebrates and plants, limiting comprehensive ecological analyses of high-diversity groups like ground beetles. Ground beetles (Coleoptera: Carabidae) serve as criti... | https://arxiv.org/abs/2601.10687 | Academic Papers | svg |
3727d26b2633938aaf617249510dc950cfab25e0c142537745eb8b8cb8f760c1 | 2026-01-16T00:00:00-05:00 | An Extension-Based Accessibility Framework for Making Blockly Accessible to Blind and Low-Vision Users | arXiv:2601.10688v1 Announce Type: new Abstract: Block-based programming environments (BBPEs) such as Scratch and Code.org are now widely used in K-12 computer science classes, but they remain mostly inaccessible to blind or visually impaired (BVI) learners. A major problem is that prior accessibility solutions have rel... | https://arxiv.org/abs/2601.10688 | Academic Papers | svg |
175d4f060abb0c9b57c69ee6048364fbd5ef290e084140dc7995b32f7ef9ccf7 | 2026-01-16T00:00:00-05:00 | Data-driven stochastic reduced-order modeling of parametrized dynamical systems | arXiv:2601.10690v1 Announce Type: new Abstract: Modeling complex dynamical systems under varying conditions is computationally intensive, often rendering high-fidelity simulations intractable. Although reduced-order models (ROMs) offer a promising solution, current methods often struggle with stochastic dynamics and fa... | https://arxiv.org/abs/2601.10690 | Academic Papers | svg |
370fc6892cf3797c0b8b6c331134fa9521e28adf1d4ec750280e92984450f670 | 2026-01-16T00:00:00-05:00 | The Conversational Exam: A Scalable Assessment Design for the AI Era | arXiv:2601.10691v1 Announce Type: new Abstract: Traditional assessment methods collapse when students use generative AI to complete work without genuine engagement, creating an illusion of competence where they believe they're learning but aren't. This paper presents the conversational exam -- a scalable oral examinati... | https://arxiv.org/abs/2601.10691 | Academic Papers | svg |
68e5d179425279f6ec69220966572627fc990f347322cb6cfc3e6f5f4d28d2c8 | 2026-01-16T00:00:00-05:00 | The Impact of Generative AI on Architectural Conceptual Design: Performance, Creative Self-Efficacy and Cognitive Load | arXiv:2601.10696v1 Announce Type: new Abstract: Our study examines how generative AI (GenAI) influences performance, creative self-efficacy, and cognitive load in architectural conceptual design tasks. Thirty-six student participants from Architectural Engineering and other disciplines completed a two-phase architectur... | https://arxiv.org/abs/2601.10696 | Academic Papers | svg |
6037468ec5322425e90643242230dac280a0a8e71e3402d21c06482fc8f35322 | 2026-01-16T00:00:00-05:00 | Perfect Secret Key Generation for a class of Hypergraphical Sources | arXiv:2601.10697v1 Announce Type: new Abstract: Nitinawarat and Narayan proposed a perfect secret key generation scheme for the so-called \emph{pairwise independent network (PIN) model} by exploiting the combinatorial properties of the underlying graph, namely the spanning tree packing rate. This work considers a gener... | https://arxiv.org/abs/2601.10697 | Academic Papers | svg |
4a289e83b4d81b3c1a72940cf62cc9f63f1ba6d665d2a80237c82e68cf547b9c | 2026-01-16T00:00:00-05:00 | LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals | arXiv:2601.10700v1 Announce Type: new Abstract: Concept-based explanations quantify how high-level concepts (e.g., gender or experience) influence model behavior, which is crucial for decision-makers in high-stakes domains. Recent work evaluates the faithfulness of such explanations by comparing them to reference causa... | https://arxiv.org/abs/2601.10700 | Academic Papers | svg |
32009387efdcf28925a0f11013ae9f5bcd160074b04d8724a2b990dcb0617674 | 2026-01-16T00:00:00-05:00 | Communication-Efficient and Privacy-Adaptable Mechanism -- a Federated Learning Scheme with Convergence Analysis | arXiv:2601.10701v1 Announce Type: new Abstract: Federated learning enables multiple parties to jointly train learning models without sharing their own underlying data, offering a practical pathway to privacy-preserving collaboration under data-governance constraints. Continued study of federated learning is essential t... | https://arxiv.org/abs/2601.10701 | Academic Papers | svg |
56442b2ce1fcd353bd601d033c99d7569175c272ef90b5e996f327447390922e | 2026-01-16T00:00:00-05:00 | Grounding Agent Memory in Contextual Intent | arXiv:2601.10702v1 Announce Type: new Abstract: Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under different latent goals and constraints, causing memory systems to retrieve context-mismatched evidence. We propose STITCH (Struct... | https://arxiv.org/abs/2601.10702 | Academic Papers | svg |
51940bd8492dcd49f83b5c901e20ae878bc952930e80e3b18ff25745acab6313 | 2026-01-16T00:00:00-05:00 | Distributed Perceptron under Bounded Staleness, Partial Participation, and Noisy Communication | arXiv:2601.10705v1 Announce Type: new Abstract: We study a semi-asynchronous client-server perceptron trained via iterative parameter mixing (IPM-style averaging): clients run local perceptron updates and a server forms a global model by aggregating the updates that arrive in each communication round. The setting captu... | https://arxiv.org/abs/2601.10705 | Academic Papers | svg |
fca59275cbae34a4de0e5aa714439e01ccafe39a8a8cf01258247f4e4affa1b6 | 2026-01-16T00:00:00-05:00 | UFO Trees: Practical and Provably-Efficient Parallel Batch-Dynamic Trees | arXiv:2601.10706v1 Announce Type: new Abstract: The dynamic trees problem is to maintain a tree under edge updates while supporting queries like connectivity queries or path queries. Despite the first data structure for this fundamental problem -- the link-cut tree -- being invented 40 years ago, our experiments reveal... | https://arxiv.org/abs/2601.10706 | Academic Papers | svg |
f6b4cfafeecb1045f5abba28bdfefedc9522e90d67cad77503c8b599cf0519fa | 2026-01-16T00:00:00-05:00 | See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection | arXiv:2601.10707v1 Announce Type: new Abstract: Recent advances in end-to-end autonomous driving show that policies trained on patch-aligned features extracted from foundation models generalize better to Out-of-Distribution (OOD). We hypothesize that due to the self-attention mechanism, each patch feature implicitly em... | https://arxiv.org/abs/2601.10707 | Academic Papers | svg |
1d664fda0a36403f122302e477d88cd056295e3061261a09cf310610eeed3ccb | 2026-01-16T00:00:00-05:00 | High-accuracy and dimension-free sampling with diffusions | arXiv:2601.10708v1 Announce Type: new Abstract: Diffusion models have shown remarkable empirical success in sampling from rich multi-modal distributions. Their inference relies on numerically solving a certain differential equation. This differential equation cannot be solved in closed form, and its resolution via disc... | https://arxiv.org/abs/2601.10708 | Academic Papers | svg |
64647cd24110ed0e01c9a7a52c6e124042f97b600354d79868ffe237a7a2a89a | 2026-01-16T00:00:00-05:00 | From One-to-One to Many-to-Many: Dynamic Cross-Layer Injection for Deep Vision-Language Fusion | arXiv:2601.10710v1 Announce Type: new Abstract: Vision-Language Models (VLMs) create a severe visual feature bottleneck by using a crude, asymmetric connection that links only the output of the vision encoder to the input of the large language model (LLM). This static architecture fundamentally limits the ability of LL... | https://arxiv.org/abs/2601.10710 | Academic Papers | svg |
8f94e88fa6d636b69f9f973fb6742cd7250251ca79384cc29b9f670e698cf427 | 2026-01-16T00:00:00-05:00 | MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching | arXiv:2601.10712v1 Announce Type: new Abstract: Tool-Integrated Reasoning (TIR) empowers large language models (LLMs) to tackle complex tasks by interleaving reasoning steps with external tool interactions. However, existing reinforcement learning methods typically rely on outcome- or trajectory-level rewards, assignin... | https://arxiv.org/abs/2601.10712 | Academic Papers | svg |
b8d9a53db63637c53c5e69188c47ace5e0f00e9790ab4698cc5010721af78b1f | 2026-01-16T00:00:00-05:00 | Alterbute: Editing Intrinsic Attributes of Objects in Images | arXiv:2601.10714v1 Announce Type: new Abstract: We introduce Alterbute, a diffusion-based method for editing an object's intrinsic attributes in an image. We allow changing color, texture, material, and even the shape of an object, while preserving its perceived identity and scene context. Existing approaches either re... | https://arxiv.org/abs/2601.10714 | Academic Papers | svg |
e0c9c1ddcf5349e47026692cc6e508081bc73e7b41b5c33807ca76822c538e0d | 2026-01-16T00:00:00-05:00 | DInf-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids | arXiv:2601.10715v1 Announce Type: new Abstract: We present a novel differentiable grid-based representation for efficiently solving differential equations (DEs). Widely used architectures for neural solvers, such as sinusoidal neural networks, are coordinate-based MLPs that are both computationally intensive and slow t... | https://arxiv.org/abs/2601.10715 | Academic Papers | svg |
4b55752a7ab8d6db730c3c6fa6f7f64ce993adb7d8626c727640a774e8a0bdaa | 2026-01-16T00:00:00-05:00 | WildRayZer: Self-supervised Large View Synthesis in Dynamic Environments | arXiv:2601.10716v1 Announce Type: new Abstract: We present WildRayZer, a self-supervised framework for novel view synthesis (NVS) in dynamic environments where both the camera and objects move. Dynamic content breaks the multi-view consistency that static NVS models rely on, leading to ghosting, hallucinated geometry, ... | https://arxiv.org/abs/2601.10716 | Academic Papers | svg |
b367a0cb589f81dc01128d14c0611973df3db8757360c2fed95a9429ec016297 | 2026-01-16T00:00:00-05:00 | Multi-Level Embedding Conformer Framework for Bengali Automatic Speech Recognition | arXiv:2601.09710v1 Announce Type: cross Abstract: Bengali, spoken by over 300 million people, is a morphologically rich and lowresource language, posing challenges for automatic speech recognition (ASR). This research presents an end-to-end framework for Bengali ASR, building on a Conformer-CTC backbone with a multi-le... | https://arxiv.org/abs/2601.09710 | Academic Papers | svg |
3a7125d5763f96157428ed395d7a1ea182f1c92f14742cc3f44c5c3bf4b6bd6f | 2026-01-16T00:00:00-05:00 | From Ecological Connectivity to Outbreak Risk: A Heterogeneous Graph Network for Epidemiological Reasoning under Sparse Spatiotemporal Data | arXiv:2601.09738v1 Announce Type: cross Abstract: Estimating population-level prevalence and transmission dynamics of wildlife pathogens can be challenging, partly because surveillance data is sparse, detection-driven, and unevenly sequenced. Using highly pathogenic avian influenza A/H5 clade 2.3.4.4b as a case study, ... | https://arxiv.org/abs/2601.09738 | Academic Papers | svg |
d00a10f60c6d2a0aea832dd039daf7d5527c5c0641bd493ca7dd832ac8eb3b80 | 2026-01-16T00:00:00-05:00 | Limits of Rank Recovery in Bilinear Observation Problems | arXiv:2601.09754v1 Announce Type: cross Abstract: Bilinear observation problems arise in many physical and information-theoretic settings, where observables and states enter multiplicatively. Rank-based diagnostics are commonly used in such problems to assess the effective dimensionality accessible to observation, ofte... | https://arxiv.org/abs/2601.09754 | Academic Papers | svg |
ad9ae3871a26445413f94a48a424b80fbb1363f7fafd25f76c3ff3ce0bbf0e41 | 2026-01-16T00:00:00-05:00 | Detecting Batch Heterogeneity via Likelihood Clustering | arXiv:2601.09758v1 Announce Type: cross Abstract: Batch effects represent a major confounder in genomic diagnostics. In copy number variant (CNV) detection from NGS, many algorithms compare read depth between test samples and a reference sample, assuming they are process-matched. When this assumption is violated, with ... | https://arxiv.org/abs/2601.09758 | Academic Papers | svg |
8187c2cd370393419a9a4cf5f067cd155736ddff4d21c69e31d98c48ac66bbef | 2026-01-16T00:00:00-05:00 | CLiMB: A Domain-Informed Novelty Detection Clustering Framework for Scientific Discovery | arXiv:2601.09768v1 Announce Type: cross Abstract: In data-driven scientific discovery, a challenge lies in classifying well-characterized phenomena while identifying novel anomalies. Current semi-supervised clustering algorithms do not always fully address this duality, often assuming that supervisory signals are globa... | https://arxiv.org/abs/2601.09768 | Academic Papers | svg |
1bd0769cbc13bebf7403bb71e9547ae0149a7ee7f63ba6b7d36de9de77698a01 | 2026-01-16T00:00:00-05:00 | Zero-Error List Decoding for Classical-Quantum Channels | arXiv:2601.09786v1 Announce Type: cross Abstract: The aim of this work is to study the zero-error capacity of pure-state classical-quantum channels in the setting of list decoding. We provide an achievability bound for list-size two and a converse bound holding for every fixed list size. The two bounds coincide for cha... | https://arxiv.org/abs/2601.09786 | Academic Papers | svg |
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