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2d6288b61ad5e8148f836197a8745b628fd4bf8c2c55aaede7365ecc64f45acd | 2026-01-16T00:00:00-05:00 | Are Language Models Efficient Reasoners? A Perspective from Logic Programming | arXiv:2510.25626v2 Announce Type: replace Abstract: Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of reasoning: efficiency. In real-world reasoning scenarios, much of the available information is irrelevant, an... | https://arxiv.org/abs/2510.25626 | Academic Papers | svg |
2699c59324baa314baf6af9f1165169a9e976f3c82cae178ca190712c3114402 | 2026-01-16T00:00:00-05:00 | ZK-SenseLM: Verifiable Large-Model Wireless Sensing with Selective Abstention and Zero-Knowledge Attestation | arXiv:2510.25677v3 Announce Type: replace Abstract: ZK-SenseLM is a secure and auditable wireless sensing framework that pairs a large-model encoder for Wi-Fi channel state information (and optionally mmWave radar or RFID) with a policy-grounded decision layer and end-to-end zero-knowledge proofs of inference. The enco... | https://arxiv.org/abs/2510.25677 | Academic Papers | svg |
7c75f1da51b1c580f0929abe6ffc3e99940a18db4cb08632e42f2cb885d16edc | 2026-01-16T00:00:00-05:00 | JOGS: Joint Optimization of Pose Estimation and 3D Gaussian Splatting | arXiv:2510.26117v2 Announce Type: replace Abstract: Traditional novel view synthesis methods heavily rely on external camera pose estimation tools such as COLMAP, which often introduce computational bottlenecks and propagate errors. To address these challenges, we propose a unified framework that jointly optimizes 3D G... | https://arxiv.org/abs/2510.26117 | Academic Papers | svg |
36d2ad211115bc5f9e3eef61641efdc9432c21e5392f611436cba4e9cf986079 | 2026-01-16T00:00:00-05:00 | PlotCraft: Pushing the Limits of LLMs for Complex and Interactive Data Visualization | arXiv:2511.00010v2 Announce Type: replace Abstract: Recent Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation. However, their ability to create complex visualizations for scaled and structured data remains largely unevaluated and underdeveloped. To address this gap, we introduce Pl... | https://arxiv.org/abs/2511.00010 | Academic Papers | svg |
80d2aaaa828baf63a73c476c4115a938c49d923855734617a065c19f46a84fa4 | 2026-01-16T00:00:00-05:00 | Bootstrap Off-policy with World Model | arXiv:2511.00423v3 Announce Type: replace Abstract: Online planning has proven effective in reinforcement learning (RL) for improving sample efficiency and final performance. However, using planning for environment interaction inevitably introduces a divergence between the collected data and the policy's actual behavio... | https://arxiv.org/abs/2511.00423 | Academic Papers | svg |
cf1d3ebff7341746a96d4cd540fd624e5fcc64e4e8e8d30820e51229fcc6d77d | 2026-01-16T00:00:00-05:00 | Lightweight Diffusion-based Framework for Online Imagined Speech Decoding in Aphasia | arXiv:2511.07920v3 Announce Type: replace Abstract: Individuals with aphasia experience severe difficulty in real-time verbal communication, while most imagined speech decoding approaches remain limited to offline analysis or computationally demanding models. To address this limitation, we propose a two-session experim... | https://arxiv.org/abs/2511.07920 | Academic Papers | svg |
dc1ab2b53a27599ad6292fa5ab24e57c87ae22919a8ce931bdbcbc4af365eb8c | 2026-01-16T00:00:00-05:00 | Classification in Equilibrium: Structure of Optimal Decision Rules | arXiv:2511.08347v3 Announce Type: replace Abstract: This paper characterizes optimal classification when individuals adjust their behavior in response to the classification rule. We model the interaction between a designer and a population as a Stackelberg game: the designer selects a classification rule anticipating h... | https://arxiv.org/abs/2511.08347 | Academic Papers | svg |
8a8a8b5f1b30f71eda82cdf7ac85d8c06382ae6e58f9164931f5321156c8343f | 2026-01-16T00:00:00-05:00 | Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents | arXiv:2511.08378v2 Announce Type: replace Abstract: Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions. In the practical recommendation scenario, low-exposure items constitute the majority of interactions, creating a long-tail distribution that sever... | https://arxiv.org/abs/2511.08378 | Academic Papers | svg |
a5212cab5f5ebc4d5fabfd4d07d5dc36423336d68e8a54df39d9680f396a4664 | 2026-01-16T00:00:00-05:00 | Formal Verification of a Generic Algorithm for TDM Communication Over Inter Satellite Links | arXiv:2511.09485v2 Announce Type: replace Abstract: The Python Testbed for Federated Learning Algorithms is a simple FL framework targeting edge systems, which provides the three generic algorithms: the centralized federated learning, the decentralized federated learning, and the universal TDM communication in the curr... | https://arxiv.org/abs/2511.09485 | Academic Papers | svg |
bc50ec9508e775455e7994f2422c1fc1ee3dab7ea3c12e21ad05cd18e73077a7 | 2026-01-16T00:00:00-05:00 | Fine-grained MoE Load Balancing with Linear Programming | arXiv:2511.16947v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) has emerged as a promising approach to scale up deep learning models due to its significant reduction in computational resources. However, the dynamic nature of MoE leads to load imbalance among experts, severely impacting training efficiency.... | https://arxiv.org/abs/2511.16947 | Academic Papers | svg |
5f8a42e73bc02f555e2632b482516704ff8d4af3f8077bc2fb75b3df48be204c | 2026-01-16T00:00:00-05:00 | Modality-Balanced Collaborative Distillation for Multi-Modal Domain Generalization | arXiv:2511.20258v2 Announce Type: replace Abstract: Weight Averaging (WA) has emerged as a powerful technique for enhancing generalization by promoting convergence to a flat loss landscape, which correlates with stronger out-of-distribution performance. However, applying WA directly to multi-modal domain generalization... | https://arxiv.org/abs/2511.20258 | Academic Papers | svg |
f69bf445d0da904d9bb02f3dcfdf91901cc49ac2849ab3ea8d8a4d9a07019d51 | 2026-01-16T00:00:00-05:00 | Prototype-Guided Non-Exemplar Continual Learning for Cross-subject EEG Decoding | arXiv:2511.20696v2 Announce Type: replace Abstract: Due to the significant variability in electroencephalo-gram (EEG) signals across individuals, knowledge acquired from previous subjects is often overwritten as new subjects are introduced in continual EEG decoding tasks. Existing methods mainly rely on storing histori... | https://arxiv.org/abs/2511.20696 | Academic Papers | svg |
4c1987683f0931402d20256abec7bda4265f0046a779f183f9e43261e8979784 | 2026-01-16T00:00:00-05:00 | Sneak Path Current Modeling in Memristor Crossbar Arrays for Analog In-Memory Computing | arXiv:2511.21796v3 Announce Type: replace Abstract: Memristor crossbar arrays have emerged as a key component for next-generation non-volatile memories, artificial neural networks, and analog in-memory computing (IMC) systems. By minimizing data transfer between the processor and memory, they offer substantial energy s... | https://arxiv.org/abs/2511.21796 | Academic Papers | svg |
770bcbc7c79c60af66d758831fc2b8b5180a047a080b511d5350a6c306864a41 | 2026-01-16T00:00:00-05:00 | Generative Adversarial Gumbel MCTS for Abstract Visual Composition Generation | arXiv:2512.01242v2 Announce Type: replace Abstract: We study abstract visual composition, in which identity is primarily determined by the spatial configuration and relations among a small set of geometric primitives (e.g., parts, symmetry, topology). They are invariant primarily to texture and photorealistic detail. C... | https://arxiv.org/abs/2512.01242 | Academic Papers | svg |
9368fc3c206a6780081b197f48d7cc47d0916c910a768b029c213b0936f1fa7e | 2026-01-16T00:00:00-05:00 | PrivCode: When Code Generation Meets Differential Privacy | arXiv:2512.05459v3 Announce Type: replace Abstract: Large language models (LLMs) have presented outstanding performance in code generation and completion. However, fine-tuning these models on private datasets can raise privacy and proprietary concerns, such as the leakage of sensitive personal information. Differential... | https://arxiv.org/abs/2512.05459 | Academic Papers | svg |
dfe43f33710d4c38fefd7f89f8d7e7a5ebd099d07f0d08dd761aefd4ff263279 | 2026-01-16T00:00:00-05:00 | Mistake Notebook Learning: Batch-Clustered Failures for Training-Free Agent Adaptation | arXiv:2512.11485v2 Announce Type: replace Abstract: With the growing adoption of Large Language Model (LLM) agents in persistent, real-world roles, they naturally encounter continuous streams of tasks and inevitable failures. A key limitation, however, is their inability to systematically learn from these mistakes, for... | https://arxiv.org/abs/2512.11485 | Academic Papers | svg |
b94029c48fbcc8afe12a0599977855d95d1568a1c68cde2f56d55b04fa583ae6 | 2026-01-16T00:00:00-05:00 | Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data | arXiv:2512.12325v2 Announce Type: replace Abstract: We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural ``Ville event'' $E_\alpha$, this regret till time $T$ is bounded by $\ln^2(1/\alpha)/V_T +... | https://arxiv.org/abs/2512.12325 | Academic Papers | svg |
802f2a15e37cfb92b76221c5ba05f40a9ede6299c556802e7fd1c593e4e11376 | 2026-01-16T00:00:00-05:00 | Sharpen the Spec, Cut the Code: A Case for Generative File System with SYSSPEC | arXiv:2512.13047v3 Announce Type: replace Abstract: File systems are critical OS components that require constant evolution to support new hardware and emerging application needs. However, the traditional paradigm of developing features, fixing bugs, and maintaining the system incurs significant overhead, especially as... | https://arxiv.org/abs/2512.13047 | Academic Papers | svg |
d78d550dee3ba1f7c63a03431c013137beb5a668bfa29a75c27e88e2318a758c | 2026-01-16T00:00:00-05:00 | Can LLMs Understand What We Cannot Say? Measuring Multilevel Alignment Through Abortion Stigma Across Cognitive, Interpersonal, and Structural Levels | arXiv:2512.13142v4 Announce Type: replace Abstract: As Large Language Models (LLMs) increasingly mediate stigmatized health decisions, their capacity to understand complex psychological phenomena remains inadequately assessed. Can LLMs understand what we cannot say? We investigate whether LLMs coherently represent abor... | https://arxiv.org/abs/2512.13142 | Academic Papers | svg |
7db64e6515585925cf56ad34df236ab99c46022764c8d565e6f61399c8705eca | 2026-01-16T00:00:00-05:00 | Cost-Free Neutrality for the River Method | arXiv:2512.14409v2 Announce Type: replace Abstract: Recently, the River Method was introduced as novel refinement of the Split Cycle voting rule. The decision-making process of River is closely related to the well established Ranked Pairs Method. Both methods consider a margin graph computed from the voters' preference... | https://arxiv.org/abs/2512.14409 | Academic Papers | svg |
9dd8288c85a29217e9cb23203ac5c6e9932b0c732de0dabec00381946b3058fa | 2026-01-16T00:00:00-05:00 | UAV-enabled Computing Power Networks: Task Completion Probability Analysis | arXiv:2512.15173v2 Announce Type: replace Abstract: This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibility control via adaptive unmanned aerial vehicle (UAV) positioning, establishing UAV-en... | https://arxiv.org/abs/2512.15173 | Academic Papers | svg |
f80b854eb95fa71aa9e1f8f1a1363281728ad85d01a4c6f2ee5ab409966f0b45 | 2026-01-16T00:00:00-05:00 | TBC: A Target-Background Contrast Metric for Low-Altitude Infrared and Visible Image Fusion | arXiv:2512.15211v2 Announce Type: replace Abstract: Infrared and visible image fusion (IVIF) is a pivotal technology in low-altitude Unmanned Aerial Vehicle (UAV) reconnaissance missions, enabling robust target detection and tracking by integrating thermal saliency with environmental textures. However, traditional no-r... | https://arxiv.org/abs/2512.15211 | Academic Papers | svg |
2c8308c62a8f521089eeab44be055932ee7020fa742044f0ac094d1504703b58 | 2026-01-16T00:00:00-05:00 | Image Complexity-Aware Adaptive Retrieval for Efficient Vision-Language Models | arXiv:2512.15372v2 Announce Type: replace Abstract: Vision transformers in vision-language models typically use the same amount of compute for every image, regardless of whether it is simple or complex. We propose ICAR (Image Complexity-Aware Retrieval), an adaptive computation approach that enables vision transformers... | https://arxiv.org/abs/2512.15372 | Academic Papers | svg |
b5a757784d0b8bf38ec32691a2fb2a7c2b8ef171d32527c6804cd9add48951f0 | 2026-01-16T00:00:00-05:00 | Granular Ball Guided Masking: Structure-aware Data Augmentation | arXiv:2512.21011v2 Announce Type: replace Abstract: Deep learning models have achieved remarkable success in computer vision but still rely heavily on large-scale labeled data and tend to overfit when data is limited or distributions shift. Data augmentation -- particularly mask-based information dropping -- can enhanc... | https://arxiv.org/abs/2512.21011 | Academic Papers | svg |
8aec66275acb28cc243d885bca0292bf5bfdc612a982d0d6e0f5f55bbec406d2 | 2026-01-16T00:00:00-05:00 | Five Years of SciCap: What We Learned and Future Directions for Scientific Figure Captioning | arXiv:2512.21789v2 Announce Type: replace Abstract: Between 2021 and 2025, the SciCap project grew from a small seed-funded idea at The Pennsylvania State University (Penn State) into one of the central efforts shaping the scientific figure-captioning landscape. Supported by a Penn State seed grant, Adobe, and the Alfr... | https://arxiv.org/abs/2512.21789 | Academic Papers | svg |
3f19f28d3411039600a1fa566ff9caa071f52acdead420fad407e7825ad55f71 | 2026-01-16T00:00:00-05:00 | Beg to Differ: Understanding Reasoning-Answer Misalignment Across Languages | arXiv:2512.22712v2 Announce Type: replace Abstract: Large language models demonstrate strong reasoning capabilities through chain-of-thought prompting, but whether this reasoning quality transfers across languages remains underexplored. We introduce a human-validated framework to evaluate whether model-generated reason... | https://arxiv.org/abs/2512.22712 | Academic Papers | svg |
6a9295a335b2120d044e65f6772f9da14a2025b05d369f8adeb996a9e6adfe69 | 2026-01-16T00:00:00-05:00 | Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection | arXiv:2512.22972v2 Announce Type: replace Abstract: 4D millimeter-wave (mmWave) radar has been widely adopted in autonomous driving and robot perception due to its low cost and all-weather robustness. However, point-cloud-based radar representations suffer from information loss due to multi-stage signal processing, whi... | https://arxiv.org/abs/2512.22972 | Academic Papers | svg |
65155ce27015fe0893065b2cc487e15f19c02b85e5c04c47f478af8b70c04a99 | 2026-01-16T00:00:00-05:00 | RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance | arXiv:2512.22974v3 Announce Type: replace Abstract: Camouflaged image generation (CIG) has recently emerged as an efficient alternative for acquiring high-quality training data for camouflaged object detection (COD). However, existing CIG methods still suffer from a substantial gap to real camouflaged imagery: generate... | https://arxiv.org/abs/2512.22974 | Academic Papers | svg |
1ac7d9ca15ab5b4d0d5a483d4fef77cb1269946bd12da3934d6318513e447f64 | 2026-01-16T00:00:00-05:00 | Explicit Abstention Knobs for Predictable Reliability in Video Question Answering | arXiv:2601.00138v2 Announce Type: replace Abstract: High-stakes deployment of vision-language models (VLMs) requires selective prediction, where systems abstain when uncertain rather than risk costly errors. We investigate whether confidence-based abstention provides reliable control over error rates in video question ... | https://arxiv.org/abs/2601.00138 | Academic Papers | svg |
7eb77d46b11465477c01abde12061a6c6081cf3bd0c50335875eb0a19aa88a3e | 2026-01-16T00:00:00-05:00 | Entropy Production in Machine Learning Under Fokker-Planck Probability Flow | arXiv:2601.00554v2 Announce Type: replace Abstract: Machine learning models deployed in nonstationary environments inevitably experience performance degradation due to data drift. While numerous drift detection heuristics exist, most lack a dynamical interpretation and provide limited guidance on how retraining decisio... | https://arxiv.org/abs/2601.00554 | Academic Papers | svg |
83b5a4eb11681caf5e5737513dce935611b5943e2ef7ec2d28e1c9cea649d914 | 2026-01-16T00:00:00-05:00 | RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization | arXiv:2601.00705v3 Announce Type: replace Abstract: We introduce RGS-SLAM, a robust Gaussian-splatting SLAM framework that replaces the residual-driven densification stage of GS-SLAM with a training-free correspondence-to-Gaussian initialization. Instead of progressively adding Gaussians as residuals reveal missing geo... | https://arxiv.org/abs/2601.00705 | Academic Papers | svg |
fa138e7bb8c4a31931800609c3f84a5d883352e6e70a618b9273e4b415fb0586 | 2026-01-16T00:00:00-05:00 | Robust and Efficient Zeroth-Order LLM Fine-Tuning via Adaptive Bayesian Subspace Optimizer | arXiv:2601.01452v3 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) with zeroth-order (ZO) optimization reduces memory by approximating gradients through function evaluations. However, existing methods essentially perform updates in a one-dimensional space, and suffer from collapse or substanti... | https://arxiv.org/abs/2601.01452 | Academic Papers | svg |
7285d2aec0cb9fdb9022bf4ebde8a329d6c827de87229f942175e45e65c007da | 2026-01-16T00:00:00-05:00 | Bridging the gap: A comparative exploration of Speech-LLM and end-to-end architecture for multilingual conversational ASR | arXiv:2601.01461v2 Announce Type: replace Abstract: The INTERSPEECH 2025 Challenge on Multilingual Conversational Speech Language Models (MLC-SLM) promotes multilingual conversational ASR with large language models (LLMs). Our previous SHNU-mASR system adopted a competitive parallel-speech-encoder architecture that int... | https://arxiv.org/abs/2601.01461 | Academic Papers | svg |
5f8fb7787dfbb808498b2d6c98a3d657c26924c22edc11856fcb1a9a1590ed1d | 2026-01-16T00:00:00-05:00 | Physics-Constrained Learning of Energy-Preserving Stencils for Maxwell's Equations | arXiv:2601.01902v3 Announce Type: replace Abstract: We study data-driven construction of spatial discretizations for the one-dimensional Maxwell system. Using high-fidelity training data from a spectral discretization, we learn a \emph{linear convolution stencil} that approximates the spatial derivative operator in Max... | https://arxiv.org/abs/2601.01902 | Academic Papers | svg |
75822135dc2b44982964712dd11efbd1a0be2b1d062036e804837d3c7b818f5d | 2026-01-16T00:00:00-05:00 | Bayesian Monocular Depth Refinement via Neural Radiance Fields | arXiv:2601.03869v2 Announce Type: replace Abstract: Monocular depth estimation has applications in many fields, such as autonomous navigation and extended reality, making it an essential computer vision task. However, current methods often produce smooth depth maps that lack the fine geometric detail needed for accurat... | https://arxiv.org/abs/2601.03869 | Academic Papers | svg |
7bb99b0f415043c15d0da211d1f9eea9ae11f030da9f5530ac2b2b877c76c8b2 | 2026-01-16T00:00:00-05:00 | Constrained dynamics for searching saddle points on general Riemannian manifolds | arXiv:2601.03931v2 Announce Type: replace Abstract: Finding constrained saddle points on Riemannian manifolds is significant for analyzing energy landscapes arising in physics and chemistry. Existing works have been limited to special manifolds that admit global regular level-set representations, excluding applications... | https://arxiv.org/abs/2601.03931 | Academic Papers | svg |
3f6f097c07466a59c9e05f2d702bdc1c8a6c64398ec34e80d2916aa49856ee36 | 2026-01-16T00:00:00-05:00 | Disco-RAG: Discourse-Aware Retrieval-Augmented Generation | arXiv:2601.04377v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most existing RAG strategies treat retrieved passages in a flat and unstructured way, which preve... | https://arxiv.org/abs/2601.04377 | Academic Papers | svg |
d2bab7349d25c377bf1b7387f671cfe6816f6b84689ff27201c60e69ad63cae1 | 2026-01-16T00:00:00-05:00 | Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams | arXiv:2601.04741v2 Announce Type: replace Abstract: Given real-time sensor data streams obtained from machines, how can we continuously predict when a machine failure will occur? This work aims to continuously forecast the timing of future events by analyzing multi-sensor data streams. A key characteristic of real-worl... | https://arxiv.org/abs/2601.04741 | Academic Papers | svg |
678c333ef8468e27c548a1177b825a0dcf56a49abc0cf3d8b5c93ddaf3baa9ab | 2026-01-16T00:00:00-05:00 | Challenges and Research Directions for Large Language Model Inference Hardware | arXiv:2601.05047v2 Announce Type: replace Abstract: Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI trends, the primary challenges are memory and interconnect rather than... | https://arxiv.org/abs/2601.05047 | Academic Papers | svg |
e232b73771cf6857c669416562260e27b0a1536e0467a7f1099cafefea2ef65c | 2026-01-16T00:00:00-05:00 | DAVOS: An Autonomous Vehicle Operating System in the Vehicle Computing Era | arXiv:2601.05072v2 Announce Type: replace Abstract: Vehicle computing represents a fundamental shift in how autonomous vehicles are designed and deployed, transforming them from isolated transportation systems into mobile computing platforms that support both safety-critical, real-time driving and data-centric services... | https://arxiv.org/abs/2601.05072 | Academic Papers | svg |
2ac709c01daf234d502187acf2b22b754817923f57e0a47dd145c08032e62719 | 2026-01-16T00:00:00-05:00 | $PC^2$: Politically Controversial Content Generation via Jailbreaking Attacks on GPT-based Text-to-Image Models | arXiv:2601.05150v2 Announce Type: replace Abstract: The rapid evolution of text-to-image (T2I) models has enabled high-fidelity visual synthesis on a global scale. However, these advancements have introduced significant security risks, particularly regarding the generation of harmful content. Politically harmful conten... | https://arxiv.org/abs/2601.05150 | Academic Papers | svg |
dfab402f9354d39fdfbf1708ffa2218f553607a298d5aa066b8a7abb8d5b7447 | 2026-01-16T00:00:00-05:00 | Stock Market Price Prediction using Neural Prophet with Deep Neural Network | arXiv:2601.05202v2 Announce Type: replace Abstract: Stock market price prediction is a significant interdisciplinary research domain that depends at the intersection of finance, statistics, and economics. Forecasting Accurately predicting stock prices has always been a focal point for various researchers. However, exis... | https://arxiv.org/abs/2601.05202 | Academic Papers | svg |
3286395af2bb96ae1ef22fb37aa53572d43319f2926ce67341c682623f91ff62 | 2026-01-16T00:00:00-05:00 | STELP: Secure Transpilation and Execution of LLM-Generated Programs | arXiv:2601.05467v3 Announce Type: replace Abstract: Rapid evolution of Large Language Models (LLMs) has achieved major advances in reasoning, planning, and function-calling capabilities. Multi-agentic collaborative frameworks using such LLMs place them at the center of solving software development-related tasks such as... | https://arxiv.org/abs/2601.05467 | Academic Papers | svg |
952a955f6effd090ebc79d138c763a50008e7a46522e00fac5c3422b581f45d0 | 2026-01-16T00:00:00-05:00 | Safety Not Found (404): Hidden Risks of LLM-Based Robotics Decision Making | arXiv:2601.05529v2 Announce Type: replace Abstract: One mistake by an AI system in a safety-critical setting can cost lives. As Large Language Models (LLMs) become integral to robotics decision-making, the physical dimension of risk grows; a single wrong instruction can directly endanger human safety. This paper addres... | https://arxiv.org/abs/2601.05529 | Academic Papers | svg |
7adf919fad3d8d219964072782bdeccb69a5b28cee8a6cc20d85d49eeacf207d | 2026-01-16T00:00:00-05:00 | HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation | arXiv:2601.05656v2 Announce Type: replace Abstract: High-fidelity agent initialization is crucial for credible Agent-Based Modeling across diverse domains. A robust framework should be Topic-Adaptive, capturing macro-level joint distributions while ensuring micro-level individual rationality. Existing approaches fall i... | https://arxiv.org/abs/2601.05656 | Academic Papers | svg |
0a22418046ef10c1818a1d1339f0333707e99d0bc386bd62ed54a2e46da6d75a | 2026-01-16T00:00:00-05:00 | Moonworks Lunara Aesthetic Dataset | arXiv:2601.07941v2 Announce Type: replace Abstract: The dataset spans diverse artistic styles, including regionally grounded aesthetics from the Middle East, Northern Europe, East Asia, and South Asia, alongside general categories such as sketch and oil painting. All images are generated using the Moonworks Lunara mode... | https://arxiv.org/abs/2601.07941 | Academic Papers | svg |
658d7b25746328d2bd9afdc26429272019f153b41a524b24a03d5cff87fbbdd6 | 2026-01-16T00:00:00-05:00 | Cost and accuracy of long-term memory in Distributed Multi-Agent Systems based on Large Language Models | arXiv:2601.07978v2 Announce Type: replace Abstract: Distributed multi-agent systems (DMAS) based on large language models (LLMs) enable collaborative intelligence while preserving data privacy. However, systematic evaluations of long-term memory under network constraints are limited. This study introduces a flexible te... | https://arxiv.org/abs/2601.07978 | Academic Papers | svg |
c78974e52e58e316bf9bb6fe851e42153620543aa3a46b9425213a4df16418d7 | 2026-01-16T00:00:00-05:00 | Human-inspired Global-to-Parallel Multi-scale Encoding for Lightweight Vision Models | arXiv:2601.08190v2 Announce Type: replace Abstract: Lightweight vision networks have witnessed remarkable progress in recent years, yet achieving a satisfactory balance among parameter scale, computational overhead, and task performance remains difficult. Although many existing lightweight models manage to reduce compu... | https://arxiv.org/abs/2601.08190 | Academic Papers | svg |
dfa526a5ea9740f0d6e45151444ef087c93c91fbcbfe1d4b8a21a2b0c2fd9ab8 | 2026-01-16T00:00:00-05:00 | Semantic Misalignment in Vision-Language Models under Perceptual Degradation | arXiv:2601.08355v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for safe semantic reasoning and decision-making. While recent VLMs demonstrate strong performance on multimodal benchmarks, the... | https://arxiv.org/abs/2601.08355 | Academic Papers | svg |
3bf46539c1a62f660d4669e8cf945d0b93aac56ba15b37fd8c9a0609089f089d | 2026-01-16T00:00:00-05:00 | SPARK: Scalable Real-Time Point Cloud Aggregation with Multi-View Self-Calibration | arXiv:2601.08414v2 Announce Type: replace Abstract: Real-time multi-camera 3D reconstruction is crucial for 3D perception, immersive interaction, and robotics. Existing methods struggle with multi-view fusion, camera extrinsic uncertainty, and scalability for large camera setups. We propose SPARK, a self-calibrating re... | https://arxiv.org/abs/2601.08414 | Academic Papers | svg |
d4319a88610153d6c8dc5957a03a060b4d179455e99e013ab55b04d01cdc0a40 | 2026-01-16T00:00:00-05:00 | EfficientFSL: Enhancing Few-Shot Classification via Query-Only Tuning in Vision Transformers | arXiv:2601.08499v2 Announce Type: replace Abstract: Large models such as Vision Transformers (ViTs) have demonstrated remarkable superiority over smaller architectures like ResNet in few-shot classification, owing to their powerful representational capacity. However, fine-tuning such large models demands extensive GPU ... | https://arxiv.org/abs/2601.08499 | Academic Papers | svg |
43df37e7b97f3e01a93300d000979470464365bc71218741db6849821fa31a5b | 2026-01-16T00:00:00-05:00 | Efficient Maintenance of Leiden Communities in Large Dynamic Graphs | arXiv:2601.08554v2 Announce Type: replace Abstract: As a well-known community detection algorithm, Leiden has been widely used in various scenarios such as large language model generation (e.g., Graph-RAG), anomaly detection, and biological analysis. In these scenarios, the graphs are often large and dynamic, where ver... | https://arxiv.org/abs/2601.08554 | Academic Papers | svg |
26062f187d961de9df95ff99cb04f3c5443b85f25ffdf99c8f5620a1adebb287 | 2026-01-16T00:00:00-05:00 | Provably Safe Reinforcement Learning for Stochastic Reach-Avoid Problems with Entropy Regularization | arXiv:2601.08646v2 Announce Type: replace Abstract: We consider the problem of learning the optimal policy for Markov decision processes with safety constraints. We formulate the problem in a reach-avoid setup. Our goal is to design online reinforcement learning algorithms that ensure safety constraints with arbitraril... | https://arxiv.org/abs/2601.08646 | Academic Papers | svg |
801b5426da65b0901cbb6df297fb32c01aec8dede4e912724ce7714e32a1a247 | 2026-01-16T00:00:00-05:00 | RMBRec: Robust Multi-Behavior Recommendation towards Target Behaviors | arXiv:2601.08705v2 Announce Type: replace Abstract: Multi-behavior recommendation faces a critical challenge in practice: auxiliary behaviors (e.g., clicks, carts) are often noisy, weakly correlated, or semantically misaligned with the target behavior (e.g., purchase), which leads to biased preference learning and subo... | https://arxiv.org/abs/2601.08705 | Academic Papers | svg |
c0d6dd7537378d216e0b912fcddfc89b8f188f7ed43de6a34e4b4167a6701931 | 2026-01-16T00:00:00-05:00 | Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs | arXiv:2601.08763v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from exploration collapse: policies prematurely concentrate on a small set of dominant reasoning pa... | https://arxiv.org/abs/2601.08763 | Academic Papers | svg |
49bb9083f169f8c5c7329e8975298681494574af0aae02bb0acf78c033e5810a | 2026-01-16T00:00:00-05:00 | Scalable and Reliable Evaluation of AI Knowledge Retrieval Systems: RIKER and the Coherent Simulated Universe | arXiv:2601.08847v2 Announce Type: replace Abstract: Evaluating knowledge systems (LLMs, RAG, knowledge graphs, etc) faces fundamental challenges: static benchmarks are vulnerable to contamination, LLM-based judges exhibit systematic biases, and ground truth extraction requires expensive human annotation. We present RIK... | https://arxiv.org/abs/2601.08847 | Academic Papers | svg |
bf2fa9e7441a9a693de4867f12b788c1492a40ebfe8e7519078dd3d5da0ea111 | 2026-01-16T00:00:00-05:00 | TranslateGemma Technical Report | arXiv:2601.09012v2 Announce Type: replace Abstract: We present TranslateGemma, a suite of open machine translation models based on the Gemma 3 foundation models. To enhance the inherent multilingual capabilities of Gemma 3 for the translation task, we employ a two-stage fine-tuning process. First, supervised fine-tunin... | https://arxiv.org/abs/2601.09012 | Academic Papers | svg |
b59c4473b3ce72ddbe52ac4298249b34c07283af6dc391902885270e368a92d7 | 2026-01-16T00:00:00-05:00 | How Many Human Judgments Are Enough? Feasibility Limits of Human Preference Evaluation | arXiv:2601.09084v2 Announce Type: replace Abstract: Human preference evaluations are widely used to compare generative models, yet it remains unclear how many judgments are required to reliably detect small improvements. We show that when preference signal is diffuse across prompts (i.e., all prompt types are similarly... | https://arxiv.org/abs/2601.09084 | Academic Papers | svg |
3085fce2805a67c13c6ebcde2467f01a09ca2084296f19d12d6f7de6a817511e | 2026-01-16T00:00:00-05:00 | DScheLLM: Enabling Dynamic Scheduling through a Fine-Tuned Dual-System Large language Model | arXiv:2601.09100v2 Announce Type: replace Abstract: Production scheduling is highly susceptible to dynamic disruptions, such as variations in processing times, machine availability, and unexpected task insertions. Conventional approaches typically rely on event-specific models and explicit analytical formulations, whic... | https://arxiv.org/abs/2601.09100 | Academic Papers | svg |
1fee52038c35d3daa76f23a0e52441c35c33d6af5970a66458e330da8b4ca0c4 | 2026-01-16T00:00:00-05:00 | Discrete Solution Operator Learning for Geometry-Dependent PDEs | arXiv:2601.09143v2 Announce Type: replace Abstract: Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry induces discrete structural changes, including topological changes, abrupt changes in bounda... | https://arxiv.org/abs/2601.09143 | Academic Papers | svg |
829743cb70b2bf89ddc278564d2d3d34ce497cb0bfc073e0358e6e5ba0425c61 | 2026-01-16T00:00:00-05:00 | A.X K1 Technical Report | arXiv:2601.09200v2 Announce Type: replace Abstract: We introduce A.X K1, a 519B-parameter Mixture-of-Experts (MoE) language model trained from scratch. Our design leverages scaling laws to optimize training configurations and vocabulary size under fixed computational budgets. A.X K1 is pre-trained on a corpus of approx... | https://arxiv.org/abs/2601.09200 | Academic Papers | svg |
49403c7b62e2236ac10f15d0efda287c53fc95f944a5cf7878fe9bc25b22bfb3 | 2026-01-16T00:00:00-05:00 | Reward Learning through Ranking Mean Squared Error | arXiv:2601.09236v2 Announce Type: replace Abstract: Reward design remains a significant bottleneck in applying reinforcement learning (RL) to real-world problems. A popular alternative is reward learning, where reward functions are inferred from human feedback rather than manually specified. Recent work has proposed le... | https://arxiv.org/abs/2601.09236 | Academic Papers | svg |
438830a474434ea9c64962d1fec373c74cb48df4db451c7845d56c093eb39ff1 | 2026-01-16T00:00:00-05:00 | DSA-Tokenizer: Disentangled Semantic-Acoustic Tokenization via Flow Matching-based Hierarchical Fusion | arXiv:2601.09239v2 Announce Type: replace Abstract: Speech tokenizers serve as the cornerstone of discrete Speech Large Language Models (Speech LLMs). Existing tokenizers either prioritize semantic encoding, fuse semantic content with acoustic style inseparably, or achieve incomplete semantic-acoustic disentanglement. ... | https://arxiv.org/abs/2601.09239 | Academic Papers | svg |
5408cacd731a16b845f704eec69a8a9b57a757ecae02ba27d6368c0998319788 | 2026-01-16T00:00:00-05:00 | Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training Debiasing | arXiv:2601.09421v2 Announce Type: replace Abstract: Pre-trained language models (LMs) have, over the last few years, grown substantially in both societal adoption and training costs. This rapid growth in size has constrained progress in understanding and mitigating their biases. Since re-training LMs is prohibitively e... | https://arxiv.org/abs/2601.09421 | Academic Papers | svg |
65857a399c1900101d009802f054f24918f09c8c421e9858df7aca23cbffe0d9 | 2026-01-16T00:00:00-05:00 | Bridging Semantic Understanding and Popularity Bias with LLMs | arXiv:2601.09478v2 Announce Type: replace Abstract: Semantic understanding of popularity bias is a crucial yet underexplored challenge in recommender systems, where popular items are often favored at the expense of niche content. Most existing debiasing methods treat the semantic understanding of popularity bias as a m... | https://arxiv.org/abs/2601.09478 | Academic Papers | svg |
0e1d8ea13b6aaf4d0eb73e8b6b7a43fc88038438503f65a7d8ed04c6e4963a65 | 2026-01-16T00:00:00-05:00 | UAV-enabled Computing Power Networks: Design and Performance Analysis under Energy Constraints | arXiv:2601.09493v2 Announce Type: replace Abstract: This paper presents an innovative framework that boosts computing power by utilizing ubiquitous computing power distribution and enabling higher computing node accessibility via adaptive UAV positioning, establishing a UAV-enabled Computing Power Network (UAV-CPN). In... | https://arxiv.org/abs/2601.09493 | Academic Papers | svg |
9e55336b442c6d727c69f15297467901798369e9513fcb994d655dda25fd85b7 | 2026-01-16T00:00:00-05:00 | SiliconHealth: A Complete Low-Cost Blockchain Healthcare Infrastructure for Resource-Constrained Regions Using Repurposed Bitcoin Mining ASICs | arXiv:2601.09557v2 Announce Type: replace Abstract: This paper presents SiliconHealth, a comprehensive blockchain-based healthcare infrastructure designed for resource-constrained regions, particularly sub-Saharan Africa. We demonstrate that obsolete Bitcoin mining Application-Specific Integrated Circuits (ASICs) can b... | https://arxiv.org/abs/2601.09557 | Academic Papers | svg |
9ac69903471c3d3d5a399b96814896aa9e97638d92b9297d0ef4e6ed82717ac7 | 2026-01-16T00:00:00-05:00 | MM-BRIGHT: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval | arXiv:2601.09562v2 Announce Type: replace Abstract: Existing retrieval benchmarks primarily consist of text-based queries where keyword or semantic matching is usually sufficient. Many real-world queries contain multimodal elements, particularly, images such as diagrams, charts, and screenshots that require intensive r... | https://arxiv.org/abs/2601.09562 | Academic Papers | svg |
d494ef71f0661b575f3e5d0b1103befa2bbe7cf3ee4aa0895c3f4c30edca8640 | 2026-01-16T00:00:00-05:00 | Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning | arXiv:2601.09667v2 Announce Type: replace Abstract: Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, multi-agent RL (MARL) training is resource-intensive and unstable: co-adapting teammates induce non-stationar... | https://arxiv.org/abs/2601.09667 | Academic Papers | svg |
60671afba18433be9377f8ec81208eacf68fdaec45980738308c274dda4e300a | 2026-01-16T00:00:00-05:00 | STEP3-VL-10B Technical Report | arXiv:2601.09668v2 Announce Type: replace Abstract: We present STEP3-VL-10B, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. STEP3-VL-10B is realized through two strategic shifts: first, a unified, fully unfrozen pre-tr... | https://arxiv.org/abs/2601.09668 | Academic Papers | svg |
f57d79465f2675c16b737bc813fe3bf1e207187fe6438a78eb344cb4c60f60a5 | 2026-01-16T00:00:00-05:00 | Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising | arXiv:2310.09126v3 Announce Type: replace-cross Abstract: Recently, the mainstream practice for training low-light raw image denoising methods has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing the noise distribution of real-world sensors, profoundly influences the effectivene... | https://arxiv.org/abs/2310.09126 | Academic Papers | svg |
410769c70406e7722e6bdcffcd5f26d27f48252d2fb0e21ba1636293b697df1a | 2026-01-16T00:00:00-05:00 | Reuniting $\chi$-boundedness with polynomial $\chi$-boundedness | arXiv:2310.11167v4 Announce Type: replace-cross Abstract: A class $\mathcal{F}$ of graphs is $\chi$-bounded if there is a function $f$ such that $\chi(H)\le f(\omega(H))$ for all induced subgraphs $H$ of a graph in $\mathcal{F}$. If $f$ can be chosen to be a polynomial, we say that $\mathcal{F}$ is polynomially $\chi$-... | https://arxiv.org/abs/2310.11167 | Academic Papers | svg |
4b46a6949de53c2a489884edba0fa2b55de246a967e982160414014270c85651 | 2026-01-16T00:00:00-05:00 | Arbitrary Polynomial Separations in Trainable Quantum Machine Learning | arXiv:2402.08606v4 Announce Type: replace-cross Abstract: Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in expressive p... | https://arxiv.org/abs/2402.08606 | Academic Papers | svg |
46d183570ec6932a3c0628050de99f7260a6e49a3c2535bb4e75d33f5c5feea2 | 2026-01-16T00:00:00-05:00 | From higher-order rewriting systems to higher-order categorial algebras and higher-order Curry-Howard isomorphisms | arXiv:2402.12051v2 Announce Type: replace-cross Abstract: This ongoing project aims to define and investigate, from the standpoint of category theory, order theory and universal algebra, the notions of higher-order many-sorted rewriting system and of higher-order many-sorted categorial algebra and their relationships, ... | https://arxiv.org/abs/2402.12051 | Academic Papers | svg |
1cd3076b295df51bfd88267a74e408198c2ee7a34068a2a2e960cb6e3b206378 | 2026-01-16T00:00:00-05:00 | Instance-level quantitative saliency in multiple sclerosis lesion segmentation | arXiv:2406.09335v3 Announce Type: replace-cross Abstract: Explainable artificial intelligence (XAI) methods have been proposed to interpret model decisions in classification and, more recently, in semantic segmentation. However, instance-level XAI for semantic segmentation, namely explanations focused on a single objec... | https://arxiv.org/abs/2406.09335 | Academic Papers | svg |
d269414d73d95531a5c475e1ce1ffd80b3e9b3b46bcfa11ac9fd237eb0c343e3 | 2026-01-16T00:00:00-05:00 | HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction | arXiv:2407.06703v2 Announce Type: replace-cross Abstract: Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets acr... | https://arxiv.org/abs/2407.06703 | Academic Papers | svg |
ad70e5559e86d9cbd5c20fe2cd4e33e0a61badf0cd4362d08cbaafa21fa0c725 | 2026-01-16T00:00:00-05:00 | Persistent Homology via Ellipsoids | arXiv:2408.11450v3 Announce Type: replace-cross Abstract: Persistent homology is one of the most popular methods in topological data analysis. An initial step in its use involves constructing a nested sequence of simplicial complexes. There is an abundance of different complexes to choose from, with \v{C}ech, Rips, alp... | https://arxiv.org/abs/2408.11450 | Academic Papers | svg |
e7947d8a07a5df1431491bf4893d8fd7b6eaa11d6fc0316cb40d301ef38c33b2 | 2026-01-16T00:00:00-05:00 | A response-adaptive multi-arm design for continuous endpoints based on a weighted information measure | arXiv:2409.04970v2 Announce Type: replace-cross Abstract: Multi-arm trials are gaining interest in practice given the statistical and logistical advantages they can offer. The standard approach uses a fixed allocation ratio, but there is a call for making it adaptive and skewing the allocation of patients towards bette... | https://arxiv.org/abs/2409.04970 | Academic Papers | svg |
52f544b9e5d230b441b4eb2c467a57d3e4c76005bfa35e67a44ff2838a64f56a | 2026-01-16T00:00:00-05:00 | Error-Minimizing Measurements in Postselected One-Shot Symmetric Quantum State Discrimination and Acceptance as a Performance Metric | arXiv:2409.13379v2 Announce Type: replace-cross Abstract: In hypothesis testing with quantum states, given a black box containing one of the two possible states, measurement is performed to detect in favor of one of the hypotheses. In postselected hypothesis testing, a third outcome is added, corresponding to not selec... | https://arxiv.org/abs/2409.13379 | Academic Papers | svg |
13b49065230a5b34c5bc450efaba4b2c5fb73e615211ee2b5696a03bd5ded367 | 2026-01-16T00:00:00-05:00 | Convex optimization with $p$-norm oracles | arXiv:2410.24158v2 Announce Type: replace-cross Abstract: In recent years, there have been significant advances in efficiently solving $\ell_s$-regression using linear system solvers and $\ell_2$-regression [Adil-Kyng-Peng-Sachdeva, J. ACM'24]. Would efficient smoothed $\ell_p$-norm solvers lead to even faster rates fo... | https://arxiv.org/abs/2410.24158 | Academic Papers | svg |
ecb8fa4ac65b2ea20d043815746972b53194b590fac405eac95df2835c1fbf62 | 2026-01-16T00:00:00-05:00 | Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance | arXiv:2412.05554v3 Announce Type: replace-cross Abstract: The significant progress of quantum sensing technologies offer numerous radical solutions for measuring a multitude of physical quantities at an unprecedented precision. Among them, Rydberg atomic quantum receivers (RAQRs) emerge as an eminent solution for detec... | https://arxiv.org/abs/2412.05554 | Academic Papers | svg |
089994d1a77454172d930a77203153e4f0df935f254c5230b2cbf0450d2d73cf | 2026-01-16T00:00:00-05:00 | Assessing fault-tolerant quantum advantage for $k$-SAT with structure | arXiv:2412.13274v3 Announce Type: replace-cross Abstract: For many problems, quantum algorithms promise speedups over their classical counterparts. However, these results predominantly rely on asymptotic worst-case analysis, which overlooks significant overheads due to error correction and the fact that real-world inst... | https://arxiv.org/abs/2412.13274 | Academic Papers | svg |
680158355ab008abf655f96e1c7e7660d68351f93f456e005eca3f5b31725211 | 2026-01-16T00:00:00-05:00 | Non-Expansive Mappings in Two-Time-Scale Stochastic Approximation: Finite-Time Analysis | arXiv:2501.10806v3 Announce Type: replace-cross Abstract: Two-time-scale stochastic approximation algorithms are iterative methods used in applications such as optimization, reinforcement learning, and control. Finite-time analysis of these algorithms has primarily focused on fixed point iterations where both time-scal... | https://arxiv.org/abs/2501.10806 | Academic Papers | svg |
c39ba46df3705f0560df9f56f640a3bd4598a34d88739ec7e015d991c85292cf | 2026-01-16T00:00:00-05:00 | Topological constraints on self-organisation in locally interacting systems | arXiv:2501.13188v2 Announce Type: replace-cross Abstract: All intelligence is collective intelligence, in the sense that it is made of parts which must align with respect to system-level goals. Understanding the dynamics which facilitate or limit navigation of problem spaces by aligned parts thus impacts many fields ra... | https://arxiv.org/abs/2501.13188 | Academic Papers | svg |
7eb1127e7415f85f16564f3f40dece54f7d00a467800802483f2f0217017ed28 | 2026-01-16T00:00:00-05:00 | Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentations | arXiv:2503.03283v2 Announce Type: replace-cross Abstract: Drawing parallels with the way biological networks are studied, we adapt the treatment--control paradigm to explainable artificial intelligence research and enrich it through multi-parametric input alterations. In this study, we propose a framework for investiga... | https://arxiv.org/abs/2503.03283 | Academic Papers | svg |
f83ff717ad9b8e1935a2ce4b2a590b690e19317863bee1d4e497e0f13cca60ac | 2026-01-16T00:00:00-05:00 | Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning | arXiv:2503.03565v2 Announce Type: replace-cross Abstract: We investigate efficient exploration strategies of environments with unknown stochastic dynamics and sparse rewards. Specifically, we analyze first the impact of parallel simulations on the probability of reaching rare states within a finite time budget. Using s... | https://arxiv.org/abs/2503.03565 | Academic Papers | svg |
19291cb921b1635a5003e1f0d7b36e200fe6458dd2db3954fbea9fda4eef889c | 2026-01-16T00:00:00-05:00 | End-to-End PET Image Reconstruction via a Posterior-Mean Diffusion Model | arXiv:2503.08546v2 Announce Type: replace-cross Abstract: Positron Emission Tomography (PET) is a functional imaging modality that enables the visualization of biochemical and physiological processes across various tissues. Recently, deep learning (DL)-based methods have demonstrated significant progress in directly ma... | https://arxiv.org/abs/2503.08546 | Academic Papers | svg |
4a2cd789389153c332a845636257978f839a0e43d3b2e2195dcce201ee511744 | 2026-01-16T00:00:00-05:00 | Sparse Nonparametric Contextual Bandits | arXiv:2503.16382v2 Announce Type: replace-cross Abstract: We study the benefits of sparsity in nonparametric contextual bandit problems, in which the set of candidate features is countably or uncountably infinite. Our contribution is two-fold. First, using a novel reduction to sequences of multi-armed bandit problems, ... | https://arxiv.org/abs/2503.16382 | Academic Papers | svg |
28fd9ee71065988c5a070d603aba8be726a85882d5e0cb59c5a73e662f71f123 | 2026-01-16T00:00:00-05:00 | Information-theoretic coordinate subset and partition selection of multivariate Markov chains via submodular optimization | arXiv:2503.23340v2 Announce Type: replace-cross Abstract: We study the problem of optimally projecting the transition matrix of a finite ergodic multivariate Markov chain onto a lower-dimensional state space, as well as the problem of finding an optimal partition of coordinates such that the factorized Markov chain giv... | https://arxiv.org/abs/2503.23340 | Academic Papers | svg |
0593f91e7757cef421837cc7c39c7f2cfe02ab21898d631e5b61ad01221a69ea | 2026-01-16T00:00:00-05:00 | From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks | arXiv:2506.12308v3 Announce Type: replace-cross Abstract: In this article, we introduce a novel low-altitude wireless network (LAWN), which is a reconfigurable, three-dimensional (3D) layered architecture. In particular, the LAWN integrates connectivity, sensing, control, and computing across aerial and terrestrial nod... | https://arxiv.org/abs/2506.12308 | Academic Papers | svg |
d4926a00a1e5407a09ed92137af19b253937b24c39c964ff74c858a22bc3f7e1 | 2026-01-16T00:00:00-05:00 | A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning | arXiv:2506.14432v2 Announce Type: replace-cross Abstract: We present FOMO300K, a large-scale, heterogeneous dataset of 318,877 brain Magnetic Resonance Imaging (MRI) scans from 82,678 MRI sessions and 59,969 subjects, aggregated from 920 publicly available sources. The dataset includes both clinical- and research-grade... | https://arxiv.org/abs/2506.14432 | Academic Papers | svg |
5f1affa048c92702018b213b8106dfadc491a0bfbe250e593f96dd348ab0f8dd | 2026-01-16T00:00:00-05:00 | Data-Driven Dynamic Factor Modeling via Manifold Learning | arXiv:2506.19945v2 Announce Type: replace-cross Abstract: We introduce a data-driven dynamic factor framework for modeling the joint evolution of high-dimensional covariates and responses without parametric assumptions. Standard factor models applied to covariates alone often lose explanatory power for responses. Our a... | https://arxiv.org/abs/2506.19945 | Academic Papers | svg |
165b9bca324a167cbde0a36fbed4178398919bee5993b7526f18689d5a42487a | 2026-01-16T00:00:00-05:00 | Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation | arXiv:2507.06363v3 Announce Type: replace-cross Abstract: In recent years, artificial intelligence has significantly advanced medical image segmentation. Nonetheless, challenges remain, including efficient 3D medical image processing across diverse modalities and handling data variability. In this work, we introduce Hi... | https://arxiv.org/abs/2507.06363 | Academic Papers | svg |
6eca5de14172a7debd744a9f4264393b0d6892ef5078ae2cdaf6dec62a7337ec | 2026-01-16T00:00:00-05:00 | prNet: Data-Driven Phase Retrieval via Stochastic Refinement | arXiv:2507.09608v2 Announce Type: replace-cross Abstract: Phase retrieval is an ill-posed inverse problem in which classical and deep learning-based methods struggle to jointly achieve measurement fidelity and perceptual realism. We propose a novel framework for phase retrieval that leverages Langevin dynamics to enabl... | https://arxiv.org/abs/2507.09608 | Academic Papers | svg |
eb1823bb5c04591b0aed78f724381f5aa5f88db5f87d7350679d6ddf54ea0ed0 | 2026-01-16T00:00:00-05:00 | Life Finds A Way: Emergence of Cooperative Structures in Adaptive Threshold Networks | arXiv:2507.13253v3 Announce Type: replace-cross Abstract: There has been a long debate on how new levels of organization have evolved. It might seem unlikely, as cooperation must prevail over competition. One well-studied example is the emergence of autocatalytic sets, which seem to be a prerequisite for the evolution ... | https://arxiv.org/abs/2507.13253 | Academic Papers | svg |
56615377c4aa501b7b37b838d4a004d5d1e7cf4786b7ddacd9aa53f284aa6019 | 2026-01-16T00:00:00-05:00 | Quantum circuit complexity and unsupervised machine learning of topological order | arXiv:2508.04486v2 Announce Type: replace-cross Abstract: Inspired by the close relationship between Kolmogorov complexity and unsupervised machine learning, we explore quantum circuit complexity, an important concept in quantum computation and quantum information science, as a pivot to understand and to build interpre... | https://arxiv.org/abs/2508.04486 | Academic Papers | svg |
692c5f3900d3d642524d25277c2588f6157991c9025487f9b14ad67ec88d8851 | 2026-01-16T00:00:00-05:00 | Random Walk Learning and the Pac-Man Attack | arXiv:2508.05663v3 Announce Type: replace-cross Abstract: Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing applications in decentralized learning. However, their reliance on local interactions makes them inherently vulnerable to ma... | https://arxiv.org/abs/2508.05663 | Academic Papers | svg |
3f097f166848ce19d16c2c53759a02520d8f9dc9c6d4242456638f971a30620f | 2026-01-16T00:00:00-05:00 | A reduced-order derivative-informed neural operator for subsurface fluid-flow | arXiv:2509.13620v2 Announce Type: replace-cross Abstract: Neural operators have emerged as cost-effective surrogates for expensive fluid-flow simulators, particularly in computationally intensive tasks such as permeability inversion from time-lapse seismic data, and uncertainty quantification. In these applications, th... | https://arxiv.org/abs/2509.13620 | Academic Papers | svg |
224f6e206c33b640d89b5b73a1e7143a3a51662ec846a35dc93ea17aa7524fca | 2026-01-16T00:00:00-05:00 | Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models | arXiv:2509.18349v3 Announce Type: replace-cross Abstract: Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available ("few-shot" learning). Increased task diversity is often believed to enhance meta... | https://arxiv.org/abs/2509.18349 | Academic Papers | svg |
a861d9d7712098514d13f8490183a9d7f9099049a86d4c9d792e244c432dafc5 | 2026-01-16T00:00:00-05:00 | Relative Information Gain and Gaussian Process Regression | arXiv:2510.04277v2 Announce Type: replace-cross Abstract: The sample complexity of estimating or maximising an unknown function in a reproducing kernel Hilbert space is known to be linked to both the effective dimension and the information gain associated with the kernel. While the information gain has an attractive in... | https://arxiv.org/abs/2510.04277 | Academic Papers | svg |
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