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5a2a60da57d4c109fc243f41b42fd2d209db7f5733e7d9b6e371285f27bb3d73 | 2026-02-02T00:00:00-05:00 | MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration | arXiv:2601.23049v1 Announce Type: new Abstract: Medical calculators are fundamental to quantitative, evidence-based clinical practice. However, their real-world use is an adaptive, multi-stage process, requiring proactive EHR data acquisition, scenario-dependent calculator selection, and multi-step computation, whereas... | https://arxiv.org/abs/2601.23049 | Academic Papers | svg |
7b0730cf18914adf82512d49d031f189d5e73c65a45de798afe39dbad65588f7 | 2026-02-02T00:00:00-05:00 | Digital Twin Synchronization: towards a data-centric architecture | arXiv:2601.23051v1 Announce Type: new Abstract: Digital Twin (DT) technology revolutionizes industrial processes by enabling the representation of physical entities and their dynamics to enhance productivity and operational efficiency. It has emerged as a vital enabling technology in the Industry 4.0 context. The prese... | https://arxiv.org/abs/2601.23051 | Academic Papers | svg |
f914eb066e01789dff1c0fd45cd60cab9621fa1e29ea98ae308b1437630e932a | 2026-02-02T00:00:00-05:00 | Adaptive Edge Learning for Density-Aware Graph Generation | arXiv:2601.23052v1 Announce Type: new Abstract: Generating realistic graph-structured data is challenging due to discrete structures, variable sizes, and class-specific connectivity patterns that resist conventional generative modelling. While recent graph generation methods employ generative adversarial network (GAN) ... | https://arxiv.org/abs/2601.23052 | Academic Papers | svg |
abbe845e81c30b609979ce649c348183df38ab92ae51181dbbeaa254e394688c | 2026-02-02T00:00:00-05:00 | From Absolute to Relative: Rethinking Reward Shaping in Group-Based Reinforcement Learning | arXiv:2601.23058v1 Announce Type: new Abstract: Reinforcement learning has become a cornerstone for enhancing the reasoning capabilities of Large Language Models, where group-based approaches such as GRPO have emerged as efficient paradigms that optimize policies by leveraging intra-group performance differences. Howev... | https://arxiv.org/abs/2601.23058 | Academic Papers | svg |
d3efc5e00bf76351edec9ac1259cddb987acfb017694ea20dee488316b44d571 | 2026-02-02T00:00:00-05:00 | On the Impact of Code Comments for Automated Bug-Fixing: An Empirical Study | arXiv:2601.23059v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly relevant in Software Engineering research and practice, with Automated Bug Fixing (ABF) being one of their key applications. ABF involves transforming a buggy method into its fixed equivalent. A common preprocessing step in AB... | https://arxiv.org/abs/2601.23059 | Academic Papers | svg |
7ddeb1d234a442f08f5933bf94fec77a6cfc119a11e3fbcd22d22b09e397fc1c | 2026-02-02T00:00:00-05:00 | Evaluating the Effectiveness of OpenAI's Parental Control System | arXiv:2601.23062v1 Announce Type: new Abstract: We evaluate how effectively platform-level parental controls moderate a mainstream conversational assistant used by minors. Our two-phase protocol first builds a category-balanced conversation corpus via PAIR-style iterative prompt refinement over API, then has trained hu... | https://arxiv.org/abs/2601.23062 | Academic Papers | svg |
076464471cebe33755369de2bc1748b710c918a58e147050e1f88ffc3deeff60 | 2026-02-02T00:00:00-05:00 | Gender Disparities in StackOverflow's Community-Based Question Answering: A Matter of Quantity versus Quality | arXiv:2601.23063v1 Announce Type: new Abstract: Community Question-Answering platforms, such as Stack Overflow (SO), are valuable knowledge exchange and problem-solving resources. These platforms incorporate mechanisms to assess the quality of answers and participants' expertise, ideally free from discriminatory biases... | https://arxiv.org/abs/2601.23063 | Academic Papers | svg |
81bd916388583a08bbe2d980f9958691d3db287499f23d98991c052ab152a962 | 2026-02-02T00:00:00-05:00 | HierLoc: Hyperbolic Entity Embeddings for Hierarchical Visual Geolocation | arXiv:2601.23064v1 Announce Type: new Abstract: Visual geolocalization, the task of predicting where an image was taken, remains challenging due to global scale, visual ambiguity, and the inherently hierarchical structure of geography. Existing paradigms rely on either large-scale retrieval, which requires storing a la... | https://arxiv.org/abs/2601.23064 | Academic Papers | svg |
e75a11a39018768c6986e1f783541e1cddec3297f685c1b0a195175070329e6c | 2026-02-02T00:00:00-05:00 | EAG-PT: Emission-Aware Gaussians and Path Tracing for Indoor Scene Reconstruction and Editing | arXiv:2601.23065v1 Announce Type: new Abstract: Recent reconstruction methods based on radiance field such as NeRF and 3DGS reproduce indoor scenes with high visual fidelity, but break down under scene editing due to baked illumination and the lack of explicit light transport. In contrast, physically based inverse rend... | https://arxiv.org/abs/2601.23065 | Academic Papers | svg |
fcc1148924dd8588868f0572da3504a3b1217bbeaf5a50f5227b362ef88d514a | 2026-02-02T00:00:00-05:00 | Towards Explicit Acoustic Evidence Perception in Audio LLMs for Speech Deepfake Detection | arXiv:2601.23066v1 Announce Type: new Abstract: Speech deepfake detection (SDD) focuses on identifying whether a given speech signal is genuine or has been synthetically generated. Existing audio large language model (LLM)-based methods excel in content understanding; however, their predictions are often biased toward ... | https://arxiv.org/abs/2601.23066 | Academic Papers | svg |
19351c93c15dbf72617464a02ee664fe12364ba26ad52f94ea72b7d9a3f6c3f5 | 2026-02-02T00:00:00-05:00 | ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations | arXiv:2601.23068v1 Announce Type: new Abstract: Computing the importance of features in supervised classification tasks is critical for model interpretability. Shapley values are a widely used approach for explaining model predictions, but require direct access to the underlying model, an assumption frequently violated... | https://arxiv.org/abs/2601.23068 | Academic Papers | svg |
948abee66aba19f6d9f1222ef198c17ecf9fafac2d6137615747353b57a8f4e8 | 2026-02-02T00:00:00-05:00 | SplineFlow: Flow Matching for Dynamical Systems with B-Spline Interpolants | arXiv:2601.23072v1 Announce Type: new Abstract: Flow matching is a scalable generative framework for characterizing continuous normalizing flows with wide-range applications. However, current state-of-the-art methods are not well-suited for modeling dynamical systems, as they construct conditional paths using linear in... | https://arxiv.org/abs/2601.23072 | Academic Papers | svg |
1cb8d20338b6e3f00622cb323bca4d00867c7c4274646b1896dba1ed8b50f717 | 2026-02-02T00:00:00-05:00 | Computing braids from approximate data | arXiv:2601.23073v1 Announce Type: new Abstract: We study the theoretical and practical aspects of computing braids described by approximate descriptions of paths in the plane. Exact algorithms rely on the lexicographic ordering of the points in the plane, which is unstable under numerical uncertainty. Instead, we forma... | https://arxiv.org/abs/2601.23073 | Academic Papers | svg |
35f4c41534d4be8baaac9f493cdf089986804269d58256b8254fc926434dbea5 | 2026-02-02T00:00:00-05:00 | RN-D: Discretized Categorical Actors with Regularized Networks for On-Policy Reinforcement Learning | arXiv:2601.23075v1 Announce Type: new Abstract: On-policy deep reinforcement learning remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies, often leading to brittle optimization when gradients are noisy and policy updates must be co... | https://arxiv.org/abs/2601.23075 | Academic Papers | svg |
109cc753d80ac08a20a9cfbe402d198c7b74cc9cced9663e5ff18a8f6b8a5f1f | 2026-02-02T00:00:00-05:00 | Robust and Generalized Humanoid Motion Tracking | arXiv:2601.23080v1 Announce Type: new Abstract: Learning a general humanoid whole-body controller is challenging because practical reference motions can exhibit noise and inconsistencies after being transferred to the robot domain, and local defects may be amplified by closed-loop execution, causing drift or failure in... | https://arxiv.org/abs/2601.23080 | Academic Papers | svg |
c70c1f1f0b16306afaf246eb256ffeb1c30e7081e67f03afe6d71e786c5ea17d | 2026-02-02T00:00:00-05:00 | Character as a Latent Variable in Large Language Models: A Mechanistic Account of Emergent Misalignment and Conditional Safety Failures | arXiv:2601.23081v1 Announce Type: new Abstract: Emergent Misalignment refers to a failure mode in which fine-tuning large language models (LLMs) on narrowly scoped data induces broadly misaligned behavior. Prior explanations mainly attribute this phenomenon to the generalization of erroneous or unsafe content. In this ... | https://arxiv.org/abs/2601.23081 | Academic Papers | svg |
8a8447391609557a1a2ec983bdea92bbe357dfe266a99df5e115febbe41076c6 | 2026-02-02T00:00:00-05:00 | A Complete Finitary Refinement Type System for Scott-Open Properties | arXiv:2601.23082v1 Announce Type: new Abstract: We are interested in proving input-output properties of functions that handle infinite data such as streams or non-wellfounded trees. We provide a finitary refinement type system which is sound and complete for Scott-open properties defined in a fixpoint-like logic. Worki... | https://arxiv.org/abs/2601.23082 | Academic Papers | svg |
c217a7a5b35333c1466501d735f7591a3b8b613b79ddf27a4216ff6444bbde0a | 2026-02-02T00:00:00-05:00 | Solving 4-Block Integer Linear Programs Faster Using Affine Decompositions of the Right-Hand Sides | arXiv:2601.23083v1 Announce Type: new Abstract: We present a new and faster algorithm for the 4-block integer linear programming problem, overcoming the long-standing runtime barrier faced by previous algorithms that rely on Graver complexity or proximity bounds. The 4-block integer linear programming problem asks to c... | https://arxiv.org/abs/2601.23083 | Academic Papers | svg |
35b1b5450c405e119106d160b3d27416c74bc10892f7c470c0145e7e0eb08050 | 2026-02-02T00:00:00-05:00 | OrLog: Resolving Complex Queries with LLMs and Probabilistic Reasoning | arXiv:2601.23085v1 Announce Type: new Abstract: Resolving complex information needs that come with multiple constraints should consider enforcing the logical operators encoded in the query (i.e., conjunction, disjunction, negation) on the candidate answer set. Current retrieval systems either ignore these constraints i... | https://arxiv.org/abs/2601.23085 | Academic Papers | svg |
70548251cc281c834d4d86691e6a7b8bb4f8f475a8764b4d1b95bae3805c8abd | 2026-02-02T00:00:00-05:00 | Chain-of-thought obfuscation learned from output supervision can generalise to unseen tasks | arXiv:2601.23086v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning provides a significant performance uplift to LLMs by enabling planning, exploration, and deliberation of their actions. CoT is also a powerful tool for monitoring the behaviours of these agents: when faithful, they offer interpretations of... | https://arxiv.org/abs/2601.23086 | Academic Papers | svg |
91412e4ceed3f880d60df965dfdd4b42f182024bda3344a538c54d5e0d19c2ac | 2026-02-02T00:00:00-05:00 | Temporally Coherent Imitation Learning via Latent Action Flow Matching for Robotic Manipulation | arXiv:2601.23087v1 Announce Type: new Abstract: Learning long-horizon robotic manipulation requires jointly achieving expressive behavior modeling, real-time inference, and stable execution, which remains challenging for existing generative policies. Diffusion-based approaches provide strong modeling capacity but typic... | https://arxiv.org/abs/2601.23087 | Academic Papers | svg |
1fb818896b237eb811504b6a44cae24418b689227c63966eb34bc5746723dae0 | 2026-02-02T00:00:00-05:00 | From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching | arXiv:2601.23088v1 Announce Type: new Abstract: Semantic caching has emerged as a pivotal technique for scaling LLM applications, widely adopted by major providers including AWS and Microsoft. By utilizing semantic embedding vectors as cache keys, this mechanism effectively minimizes latency and redundant computation f... | https://arxiv.org/abs/2601.23088 | Academic Papers | svg |
af957d428dcdf61619e9bb09393bad6a5393baeb23cde221e305c460b34b065f | 2026-02-02T00:00:00-05:00 | Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model | arXiv:2601.23090v1 Announce Type: new Abstract: Self-supervised fMRI foundation models have shown promising transfer performance, yet most rely on predefined region-level parcellations that discard fine-grained voxel information and introduce atlas-dependent biases. We propose Omni-fMRI, an atlas-free foundation model ... | https://arxiv.org/abs/2601.23090 | Academic Papers | svg |
5ff41a48c4a3b330cb9d17f6ea82ab37d11ba272a7c48e235fb583ccaef548a9 | 2026-02-02T00:00:00-05:00 | WiFiPenTester: Advancing Wireless Ethical Hacking with Governed GenAI | arXiv:2601.23092v1 Announce Type: new Abstract: Wireless ethical hacking relies heavily on skilled practitioners manually interpreting reconnaissance results and executing complex, time-sensitive sequences of commands to identify vulnerable targets, capture authentication handshakes, and assess password resilience; a p... | https://arxiv.org/abs/2601.23092 | Academic Papers | svg |
df33ecf447dcbdd224034ed9b92552ebf17863bbebc1066b4f27709d59aadb89 | 2026-02-02T00:00:00-05:00 | Safer Policy Compliance with Dynamic Epistemic Fallback | arXiv:2601.23094v1 Announce Type: new Abstract: Humans develop a series of cognitive defenses, known as epistemic vigilance, to combat risks of deception and misinformation from everyday interactions. Developing safeguards for LLMs inspired by this mechanism might be particularly helpful for their application in high-s... | https://arxiv.org/abs/2601.23094 | Academic Papers | svg |
14d89ffce21e4604d8049d97ae573857348ac2c3083f69f2dc8da50b168830c7 | 2026-02-02T00:00:00-05:00 | Exploring Sidewalk Sheds in New York City through Chatbot Surveys and Human Computer Interaction | arXiv:2601.23095v1 Announce Type: new Abstract: Sidewalk sheds are a common feature of the streetscape in New York City, reflecting ongoing construction and maintenance activities. However, policymakers and local business owners have raised concerns about reduced storefront visibility and altered pedestrian navigation.... | https://arxiv.org/abs/2601.23095 | Academic Papers | svg |
079fe2795509b9e3a36bba00c57d69dafbdbf478ecff23f8e1aa0c2368c429a4 | 2026-02-02T00:00:00-05:00 | CATTO: Balancing Preferences and Confidence in Language Models | arXiv:2601.23096v1 Announce Type: new Abstract: Large language models (LLMs) often make accurate next token predictions but their confidence in these predictions can be poorly calibrated: high-confidence predictions are frequently wrong, and low-confidence predictions may be correct. This miscalibration is exacerbated ... | https://arxiv.org/abs/2601.23096 | Academic Papers | svg |
b7eb15099a313f98d3cf4d34c25e2e5679a577a181e17fba8d5108c2510166dd | 2026-02-02T00:00:00-05:00 | Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective | arXiv:2601.23102v1 Announce Type: new Abstract: Transferable adversarial attacks on point clouds remain challenging, as existing methods often rely on model-specific gradients or heuristics that limit generalization to unseen architectures. In this paper, we rethink adversarial transferability from a compact subspace p... | https://arxiv.org/abs/2601.23102 | Academic Papers | svg |
93748d5809addc5b24ea979744823a0abed9878847ba4cedba699a86877f65d7 | 2026-02-02T00:00:00-05:00 | Lossy Compression of Cellular Network KPIs | arXiv:2601.23105v1 Announce Type: new Abstract: Network Key Performance Indicators (KPIs) are a fundamental component of mobile cellular network monitoring and optimization. Their massive volume, resulting from fine-grained measurements collected across many cells over long time horizons, poses significant challenges f... | https://arxiv.org/abs/2601.23105 | Academic Papers | svg |
9eb7f76f49ccf3b47ef9e1b5897cddb7db2a5c3a8787262bcb55bbc2c0c48a01 | 2026-02-02T00:00:00-05:00 | FlowCalib: LiDAR-to-Vehicle Miscalibration Detection using Scene Flows | arXiv:2601.23107v1 Announce Type: new Abstract: Accurate sensor-to-vehicle calibration is essential for safe autonomous driving. Angular misalignments of LiDAR sensors can lead to safety-critical issues during autonomous operation. However, current methods primarily focus on correcting sensor-to-sensor errors without c... | https://arxiv.org/abs/2601.23107 | Academic Papers | svg |
e25be8926fa0ef671509aa7496c4ec9b40a867a72c2cb8c42e97877c78b96e62 | 2026-02-02T00:00:00-05:00 | Energy Management Strategies for Electric Aircraft Charging Leveraging Active Landside Vehicle-to-Grid | arXiv:2601.23108v1 Announce Type: new Abstract: The deployment of medium-range battery electric aircraft is a promising pathway to improve the environmental footprint of air mobility. Yet such a deployment would be accompanied by significant electric power requirements at airports due to aircraft charging. Given the gr... | https://arxiv.org/abs/2601.23108 | Academic Papers | svg |
8582be17ba52ecf5b4a5b2d9e78e051eb2295facd37fc08fdfee2ad82d095c9f | 2026-02-02T00:00:00-05:00 | How should AI Safety Benchmarks Benchmark Safety? | arXiv:2601.23112v1 Announce Type: new Abstract: AI safety benchmarks are pivotal for safety in advanced AI systems; however, they have significant technical, epistemic, and sociotechnical shortcomings. We present a review of 210 safety benchmarks that maps out common challenges in safety benchmarking, documenting failu... | https://arxiv.org/abs/2601.23112 | Academic Papers | svg |
d6fa49a8b75e04061ffc5a92035170d1abe17773d4b6551b26354a56cc384e02 | 2026-02-02T00:00:00-05:00 | To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series | arXiv:2601.23114v1 Announce Type: new Abstract: The prevailing Direct Forecasting (DF) paradigm dominates Long-term Time Series Forecasting (LTSF) by forcing models to predict the entire future horizon in a single forward pass. While efficient, this rigid coupling of output and evaluation horizons necessitates computat... | https://arxiv.org/abs/2601.23114 | Academic Papers | svg |
c91ca98c0654aef78c9b782276e5c72e00d4bd810b7e5b905c836c7d9968ede4 | 2026-02-02T00:00:00-05:00 | An Automatic Deep Learning Approach for Trailer Generation through Large Language Models | arXiv:2601.23121v1 Announce Type: new Abstract: Trailers are short promotional videos designed to provide audiences with a glimpse of a movie. The process of creating a trailer typically involves selecting key scenes, dialogues and action sequences from the main content and editing them together in a way that effective... | https://arxiv.org/abs/2601.23121 | Academic Papers | svg |
9fe06e697bc7b67f1f04b0e83cccadac792d3c247a50b769127f96d5aa0d41a7 | 2026-02-02T00:00:00-05:00 | Greedy Routing Reachability Games | arXiv:2601.23126v1 Announce Type: new Abstract: Today's networks consist of many autonomous entities that follow their own objectives, i.e., smart devices or parts of large AI systems, that are interconnected. Given the size and complexity of most communication networks, each entity typically only has a local view and ... | https://arxiv.org/abs/2601.23126 | Academic Papers | svg |
83179115837960e579f9a2574c249e25c93365562bc9a1af19f6ab6242260c07 | 2026-02-02T00:00:00-05:00 | "I Choose to Live, for Life Itself": Understanding Agency of Home-Based Care Patients Through Information Practices and Relational Dynamics in Care Networks | arXiv:2601.23127v1 Announce Type: new Abstract: Home-based care (HBC) delivers medical and care services in patients' living environments, offering unique opportunities for patient-centered care. However, patient agency is often inadequately represented in shared HBC planning processes. Through 23 multi-stakeholder int... | https://arxiv.org/abs/2601.23127 | Academic Papers | svg |
4e6acf4171d302f1a1abacbb2b2cd7345b5d8ceedc1021df2c6b8c48c1199c25 | 2026-02-02T00:00:00-05:00 | Distribution-informed Efficient Conformal Prediction for Full Ranking | arXiv:2601.23128v1 Announce Type: new Abstract: Quantifying uncertainty is critical for the safe deployment of ranking models in real-world applications. Recent work offers a rigorous solution using conformal prediction in a full ranking scenario, which aims to construct prediction sets for the absolute ranks of test i... | https://arxiv.org/abs/2601.23128 | Academic Papers | svg |
ce7598bcc3368a8deb4cd947183d13a4849fdf416c76a14b281a6e05d9acbfbf | 2026-02-02T00:00:00-05:00 | Evaluating the Utility of Grounding Documents with Reference-Free LLM-based Metrics | arXiv:2601.23129v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG)'s success depends on the utility the LLM derives from the content used for grounding. Quantifying content utility does not have a definitive specification and existing metrics ignore model-specific capabilities and/or rely on costly an... | https://arxiv.org/abs/2601.23129 | Academic Papers | svg |
2f242f6ff4c353c39ae56550094123884a108acf124bea91e83affe1ac733cf6 | 2026-02-02T00:00:00-05:00 | Synthesizing Petri Nets from Labelled Petri Nets using Token Trail Regions | arXiv:2601.23130v1 Announce Type: new Abstract: Synthesis automatically generates a process model from a behavioural specification. When the target model is a Petri net, we address synthesis through region theory. Researchers have studied region-based synthesis extensively for state-based specifications, such as transi... | https://arxiv.org/abs/2601.23130 | Academic Papers | svg |
7fcfcedb0ad78fc814f502fe1e3954272499f5465c428f81408a85896f14ee5b | 2026-02-02T00:00:00-05:00 | Regularisation in neural networks: a survey and empirical analysis of approaches | arXiv:2601.23131v1 Announce Type: new Abstract: Despite huge successes on a wide range of tasks, neural networks are known to sometimes struggle to generalise to unseen data. Many approaches have been proposed over the years to promote the generalisation ability of neural networks, collectively known as regularisation ... | https://arxiv.org/abs/2601.23131 | Academic Papers | svg |
d7d0d020833cd8b7ca3b4597a150b3d5b83b008aa7338183407133d7b45a66d6 | 2026-02-02T00:00:00-05:00 | Secure Tool Manifest and Digital Signing Solution for Verifiable MCP and LLM Pipelines | arXiv:2601.23132v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly adopted in sensitive domains such as healthcare and financial institutions' data analytics; however, their execution pipelines remain vulnerable to manipulation and unverifiable behavior. Existing control mechanisms, such as t... | https://arxiv.org/abs/2601.23132 | Academic Papers | svg |
c115886ca19756d1c0ded275e01a4a9e1411892da42d249d83e246bc98672a16 | 2026-02-02T00:00:00-05:00 | RAudit: A Blind Auditing Protocol for Large Language Model Reasoning | arXiv:2601.23133v1 Announce Type: new Abstract: Inference-time scaling can amplify reasoning pathologies: sycophancy, rung collapse, and premature certainty. We present RAudit, a diagnostic protocol for auditing LLM reasoning without ground truth access. The key constraint is blindness: the auditor evaluates only wheth... | https://arxiv.org/abs/2601.23133 | Academic Papers | svg |
5c62ed8add874b61fb7bf9dcf0b444c8eac3c6f2e74c44986ab73f3ce82900a6 | 2026-02-02T00:00:00-05:00 | Machine Learning for Energy-Performance-aware Scheduling | arXiv:2601.23134v1 Announce Type: new Abstract: In the post-Dennard era, optimizing embedded systems requires navigating complex trade-offs between energy efficiency and latency. Traditional heuristic tuning is often inefficient in such high-dimensional, non-smooth landscapes. In this work, we propose a Bayesian Optimi... | https://arxiv.org/abs/2601.23134 | Academic Papers | svg |
bf138b4a836331b525cbb0f66945c82e418667d27ea2a4a93ea83f595a1456f6 | 2026-02-02T00:00:00-05:00 | Why GRPO Needs Normalization: A Local-Curvature Perspective on Adaptive Gradients | arXiv:2601.23135v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key driver of language model reasoning. Among RL algorithms, Group Relative Policy Optimization (GRPO) is the de facto standard, avoiding the need for a critic by using per-prompt baselines and variance normalization. Yet why and w... | https://arxiv.org/abs/2601.23135 | Academic Papers | svg |
18edc6fd4e236317365341f4e4c05ff4679597011ca78b4e9d8763c1706ecf34 | 2026-02-02T00:00:00-05:00 | Automated Testing of Prevalent 3D User Interactions in Virtual Reality Applications | arXiv:2601.23139v1 Announce Type: new Abstract: Virtual Reality (VR) technologies offer immersive user experiences across various domains, but present unique testing challenges compared to traditional software. Existing VR testing approaches enable scene navigation and interaction activation, but lack the ability to au... | https://arxiv.org/abs/2601.23139 | Academic Papers | svg |
f39cde0cc5024bccf2734b220d8aa80e2e6c48279a06a740885fb013d22f17f6 | 2026-02-02T00:00:00-05:00 | From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks | arXiv:2601.23141v1 Announce Type: new Abstract: Software modernisation through the migration from monolithic architectures to microservices has become increasingly critical, yet identifying effective service boundaries remains a complex and unresolved challenge. Although numerous automated microservice decomposition fr... | https://arxiv.org/abs/2601.23141 | Academic Papers | svg |
0e2d0379c9aa041ad662a5fd511af65c6994a0ee86610d0292342641c7e0d379 | 2026-02-02T00:00:00-05:00 | Do Good, Stay Longer? Temporal Patterns and Predictors of Newcomer-to-Core Transitions in Conventional OSS and OSS4SG | arXiv:2601.23142v1 Announce Type: new Abstract: Open Source Software (OSS) sustainability relies on newcomers transitioning to core contributors, but this pipeline is broken, with most newcomers becoming inactive after initial contributions. Open Source Software for Social Good (OSS4SG) projects, which prioritize socie... | https://arxiv.org/abs/2601.23142 | Academic Papers | svg |
7e2012a92068b4f648ed7404bc2426aa0f03c25e44e5d885ac700ad5316ce70e | 2026-02-02T00:00:00-05:00 | THINKSAFE: Self-Generated Safety Alignment for Reasoning Models | arXiv:2601.23143v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain-of-thought (CoT) reasoning. However, this over-optimization often prioritizes compliance, making models vulnerable to harmful p... | https://arxiv.org/abs/2601.23143 | Academic Papers | svg |
1843e46c9db14709228bc740f32bbd71860e8d3b5460f8a71083187048644dbf | 2026-02-02T00:00:00-05:00 | Securing Time in Energy IoT: A Clock-Dynamics-Aware Spatio-Temporal Graph Attention Network for Clock Drift Attacks and Y2K38 Failures | arXiv:2601.23147v1 Announce Type: new Abstract: The integrity of time in distributed Internet of Things (IoT) devices is crucial for reliable operation in energy cyber-physical systems, such as smart grids and microgrids. However, IoT systems are vulnerable to clock drift, time-synchronization manipulation, and timesta... | https://arxiv.org/abs/2601.23147 | Academic Papers | svg |
0f167da813db857f3f6e6c7216a26c1e70c88cf66d175507c528222bfe3d33a9 | 2026-02-02T00:00:00-05:00 | Hearing is Believing? Evaluating and Analyzing Audio Language Model Sycophancy with SYAUDIO | arXiv:2601.23149v1 Announce Type: new Abstract: Audio Language Models (ALMs) have recently shown strong capabilities in unified reasoning over speech, sound, and natural language; yet they inherit behavioral issues observed in Large Language Models, including sycophancy--the tendency to agree with user assertions even ... | https://arxiv.org/abs/2601.23149 | Academic Papers | svg |
56fa22028be740d04e52082e23f717077c496d0664ab591a05ea478f35970876 | 2026-02-02T00:00:00-05:00 | Manifold-Aware Perturbations for Constrained Generative Modeling | arXiv:2601.23151v1 Announce Type: new Abstract: Generative models have enjoyed widespread success in a variety of applications. However, they encounter inherent mathematical limitations in modeling distributions where samples are constrained by equalities, as is frequently the setting in scientific domains. In this wor... | https://arxiv.org/abs/2601.23151 | Academic Papers | svg |
a333228f05843547c6b42130855d960c48a712406af07ed6fb5b864727302415 | 2026-02-02T00:00:00-05:00 | Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data | arXiv:2601.23153v1 Announce Type: new Abstract: As artificial neural networks, and specifically large language models, have improved rapidly in capabilities and quality, they have increasingly been deployed in real-world applications, from customer service to Google search, despite the fact that they frequently make fa... | https://arxiv.org/abs/2601.23153 | Academic Papers | svg |
42f1b7e1a5c5722b1eea6044914f8115ba7abd868a5f57dd5e7e76e59ade166e | 2026-02-02T00:00:00-05:00 | On Safer Reinforcement Learning Policies for Sedation and Analgesia in Intensive Care | arXiv:2601.23154v1 Announce Type: new Abstract: Pain management in intensive care usually involves complex trade-offs between therapeutic goals and patient safety, since both inadequate and excessive treatment may induce serious sequelae. Reinforcement learning can help address this challenge by learning medication dos... | https://arxiv.org/abs/2601.23154 | Academic Papers | svg |
5b56a6e592858b9054f1f62a6c1cc90e4c7378ec8e052c66727270b57419e6ee | 2026-02-02T00:00:00-05:00 | SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training | arXiv:2601.23155v1 Announce Type: new Abstract: Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monotone submodular objective, enabling greedy algorithms to achieve a $(1-1/e)$ approximation under a cardinality budget. In practice,... | https://arxiv.org/abs/2601.23155 | Academic Papers | svg |
d1d888d91d1490ba8180df1c1f711228de4c67f6ba0052be17ae855605cf45be | 2026-02-02T00:00:00-05:00 | Unsupervised Hierarchical Skill Discovery | arXiv:2601.23156v1 Announce Type: new Abstract: We consider the problem of unsupervised skill segmentation and hierarchical structure discovery in reinforcement learning. While recent approaches have sought to segment trajectories into reusable skills or options, most rely on action labels, rewards, or handcrafted anno... | https://arxiv.org/abs/2601.23156 | Academic Papers | svg |
529b7a2866b75cc1e9d5c287ad6a43dd62ab128ed0a6dbd158a3c9d09a1d7954 | 2026-02-02T00:00:00-05:00 | No More, No Less: Least-Privilege Language Models | arXiv:2601.23157v1 Announce Type: new Abstract: Least privilege is a core security principle: grant each request only the minimum access needed to achieve its goal. Deployed language models almost never follow it, instead being exposed through a single API endpoint that serves all users and requests. This gap exists no... | https://arxiv.org/abs/2601.23157 | Academic Papers | svg |
0d08fa509aece83c1ec0c172dd7d6a75d9e12a8ba68ba8f8e6a3aebe3dec5876 | 2026-02-02T00:00:00-05:00 | Segment Any Events with Language | arXiv:2601.23159v1 Announce Type: new Abstract: Scene understanding with free-form language has been widely explored within diverse modalities such as images, point clouds, and LiDAR. However, related studies on event sensors are scarce or narrowly centered on semantic-level understanding. We introduce SEAL, the first ... | https://arxiv.org/abs/2601.23159 | Academic Papers | svg |
97c34b016d2dbaf065f0ca3b8d1a23c50fd4af37d32e4e92de618d4fa5cb048f | 2026-02-02T00:00:00-05:00 | Robust Control of Constrained Linear Systems using Online Convex Optimization and a Reference Governor | arXiv:2601.23160v1 Announce Type: new Abstract: This article develops a control method for linear time-invariant systems subject to time-varying and a priori unknown cost functions, that satisfies state and input constraints, and is robust to exogenous disturbances. To this end, we combine the online convex optimizatio... | https://arxiv.org/abs/2601.23160 | Academic Papers | svg |
27efecb3f4e99a7df1f3464ec780d0a54165f0b1d801ad4b036ad3018ce87baf | 2026-02-02T00:00:00-05:00 | DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding | arXiv:2601.23161v1 Announce Type: new Abstract: Autoregressive (AR) large audio language models (LALMs) such as Qwen-2.5-Omni have achieved strong performance on audio understanding and interaction, but scaling them remains costly in data and computation, and strictly sequential decoding limits inference efficiency. Di... | https://arxiv.org/abs/2601.23161 | Academic Papers | svg |
b09298cfb96d3dd75149ecb53aa433702295990e165d7c1b8cb4997f22b9ab0e | 2026-02-02T00:00:00-05:00 | Probing the Trajectories of Reasoning Traces in Large Language Models | arXiv:2601.23163v1 Announce Type: new Abstract: Large language models (LLMs) increasingly solve difficult problems by producing "reasoning traces" before emitting a final response. However, it remains unclear how accuracy and decision commitment evolve along a reasoning trajectory, and whether intermediate trace segmen... | https://arxiv.org/abs/2601.23163 | Academic Papers | svg |
c596bbb92627be3515d4f93348d1e975f57f8baa2d3d68240eb4ba285f571a3b | 2026-02-02T00:00:00-05:00 | Stochastic Linear Bandits with Parameter Noise | arXiv:2601.23164v1 Announce Type: new Abstract: We study the stochastic linear bandits with parameter noise model, in which the reward of action $a$ is $a^\top \theta$ where $\theta$ is sampled i.i.d. We show a regret upper bound of $\widetilde{O} (\sqrt{d T \log (K/\delta) \sigma^2_{\max})}$ for a horizon $T$, general... | https://arxiv.org/abs/2601.23164 | Academic Papers | svg |
3f16d78556f30123694a41613a11066dada4da9a3811b1ca3e7f039440467ca5 | 2026-02-02T00:00:00-05:00 | Monotonic Reference-Free Refinement for Autoformalization | arXiv:2601.23166v1 Announce Type: new Abstract: While statement autoformalization has advanced rapidly, full-theorem autoformalization remains largely unexplored. Existing iterative refinement methods in statement autoformalization typicall improve isolated aspects of formalization, such as syntactic correctness, but s... | https://arxiv.org/abs/2601.23166 | Academic Papers | svg |
bbb673e3de67ae2892797c1c478087a63dbea3ada45195947118126b6bb04e54 | 2026-02-02T00:00:00-05:00 | Hi-Light: A Path to high-fidelity, high-resolution video relighting with a Novel Evaluation Paradigm | arXiv:2601.23167v1 Announce Type: new Abstract: Video relighting offers immense creative potential and commercial value but is hindered by challenges, including the absence of an adequate evaluation metric, severe light flickering, and the degradation of fine-grained details during editing. To overcome these challenges... | https://arxiv.org/abs/2601.23167 | Academic Papers | svg |
a79c97479a3f48edf0db68c8924e3d23b6d54c26c3ed7b1e43c80d61a7f77dbc | 2026-02-02T00:00:00-05:00 | Names Don't Matter: Symbol-Invariant Transformer for Open-Vocabulary Learning | arXiv:2601.23169v1 Announce Type: new Abstract: Current neural architectures lack a principled way to handle interchangeable tokens, i.e., symbols that are semantically equivalent yet distinguishable, such as bound variables. As a result, models trained on fixed vocabularies often struggle to generalize to unseen symbo... | https://arxiv.org/abs/2601.23169 | Academic Papers | svg |
6cbb6bcbd6fb5bc6a7fc45a308fe1d7675f2c505794c7e6560e5563bf86d815f | 2026-02-02T00:00:00-05:00 | Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization | arXiv:2601.23174v1 Announce Type: new Abstract: Neural audio codecs are at the core of modern conversational speech technologies, converting continuous speech into sequences of discrete tokens that can be processed by LLMs. However, existing codecs typically operate at fixed frame rates, allocating tokens uniformly in ... | https://arxiv.org/abs/2601.23174 | Academic Papers | svg |
00831f1b0674c9c77c60561fa790522bea1ce078e55c585b3dcb90ab91e6232a | 2026-02-02T00:00:00-05:00 | MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics | arXiv:2601.23177v1 Announce Type: new Abstract: We present MeshGraphNet-Transformer (MGN-T), a novel architecture that combines the global modeling capabilities of Transformers with the geometric inductive bias of MeshGraphNets, while preserving a mesh-based graph representation. MGN-T overcomes a key limitation of sta... | https://arxiv.org/abs/2601.23177 | Academic Papers | svg |
dd06d363a6890696584059f5f454338e75593df8d9c982a63d66a4abe84a2279 | 2026-02-02T00:00:00-05:00 | Make Anything Match Your Target: Universal Adversarial Perturbations against Closed-Source MLLMs via Multi-Crop Routed Meta Optimization | arXiv:2601.23179v1 Announce Type: new Abstract: Targeted adversarial attacks on closed-source multimodal large language models (MLLMs) have been increasingly explored under black-box transfer, yet prior methods are predominantly sample-specific and offer limited reusability across inputs. We instead study a more string... | https://arxiv.org/abs/2601.23179 | Academic Papers | svg |
96cf2c20b3be3b9901df059cb4ce2b4d9c1f2d25f3ddcf7edc9dedfccc80a954 | 2026-02-02T00:00:00-05:00 | TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification | arXiv:2601.23180v1 Announce Type: new Abstract: Inference efficiency in Large Language Models (LLMs) is fundamentally limited by their serial, autoregressive generation, especially as reasoning becomes a key capability and response sequences grow longer. Speculative decoding (SD) offers a powerful solution, providing s... | https://arxiv.org/abs/2601.23180 | Academic Papers | svg |
62f14c712b1e0a464b03451653c6faec480275f742e1750966f9a0a984e7a1ad | 2026-02-02T00:00:00-05:00 | Ensuring Semantics in Weights of Implicit Neural Representations through the Implicit Function Theorem | arXiv:2601.23181v1 Announce Type: new Abstract: Weight Space Learning (WSL), which frames neural network weights as a data modality, is an emerging field with potential for tasks like meta-learning or transfer learning. Particularly, Implicit Neural Representations (INRs) provide a convenient testbed, where each set of... | https://arxiv.org/abs/2601.23181 | Academic Papers | svg |
7cff210352548a80de868ba180d368d224474b91a232e82c8191848d2a8a58ee | 2026-02-02T00:00:00-05:00 | FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation | arXiv:2601.23182v1 Announce Type: new Abstract: Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to fully unlock the potential of arbitrary generation. In this work, we delve into the inherent spectral characteristics of dLL... | https://arxiv.org/abs/2601.23182 | Academic Papers | svg |
145d3b5c7e22106fb5ddb7f1a8e6e19f8026e23dafbf1da8bcdb7c43bdff468f | 2026-02-02T00:00:00-05:00 | JobResQA: A Benchmark for LLM Machine Reading Comprehension on Multilingual R\'esum\'es and JDs | arXiv:2601.23183v1 Announce Type: new Abstract: We introduce JobResQA, a multilingual Question Answering benchmark for evaluating Machine Reading Comprehension (MRC) capabilities of LLMs on HR-specific tasks involving r\'esum\'es and job descriptions. The dataset comprises 581 QA pairs across 105 synthetic r\'esum\'e-j... | https://arxiv.org/abs/2601.23183 | Academic Papers | svg |
8e7580daf8259a00b3b5d2630476475ee1633e52125056b168b2957ee25925df | 2026-02-02T00:00:00-05:00 | ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought | arXiv:2601.23184v1 Announce Type: new Abstract: While Chain-of-Thought (CoT) significantly enhances the performance of Large Language Models (LLMs), explicit reasoning chains introduce substantial computational redundancy. Recent latent reasoning methods attempt to mitigate this by compressing reasoning processes into ... | https://arxiv.org/abs/2601.23184 | Academic Papers | svg |
386482977d495b7002e88927ec7f1523c3d6fdefa1551fc9f1d6736061509871 | 2026-02-02T00:00:00-05:00 | Preconditioning and Numerical Stability in Neural Network Training for Parametric PDEs | arXiv:2601.23185v1 Announce Type: new Abstract: In the context of training neural network-based approximations of solutions of parameter-dependent PDEs, we investigate the effect of preconditioning via well-conditioned frame representations of operators and demonstrate a significant improvement on the performance of st... | https://arxiv.org/abs/2601.23185 | Academic Papers | svg |
dd7312fe0c0ac38673ab56f6ebeab4e8b59ac59bf114224183bc94ae7a438ee8 | 2026-02-02T00:00:00-05:00 | Deep Search with Hierarchical Meta-Cognitive Monitoring Inspired by Cognitive Neuroscience | arXiv:2601.23188v1 Announce Type: new Abstract: Deep search agents powered by large language models have demonstrated strong capabilities in multi-step retrieval, reasoning, and long-horizon task execution. However, their practical failures often stem from the lack of mechanisms to monitor and regulate reasoning and re... | https://arxiv.org/abs/2601.23188 | Academic Papers | svg |
44a5a8be70f2ae21c8ba2c6fd68d18f03dd617ff2580e815d176fbaf289531cf | 2026-02-02T00:00:00-05:00 | Network analysis and link prediction in competitive women's basketball | arXiv:2601.23193v1 Announce Type: new Abstract: Network structure and its role in prediction are examined in competitive basketball at the team and player levels. Adversarial game outcome networks from NCAA Division I women's basketball from 2021 to 2024 are used to compute the common out-neighbor score and PageRank, w... | https://arxiv.org/abs/2601.23193 | Academic Papers | svg |
694ae6143b26f9b01c5c2a45935f1cecdb9844fc2e7d35082bef177726f908a1 | 2026-02-02T00:00:00-05:00 | Planar Graph Homomorphisms: A Dichotomy and a Barrier from Quantum Groups | arXiv:2601.23198v1 Announce Type: new Abstract: We study the complexity of counting (weighted) planar graph homomorphism problem $\tt{Pl\text{-}GH}(M)$ parametrized by an arbitrary symmetric non-negative real valued matrix $M$. For matrices with pairwise distinct diagonal values, we prove a complete dichotomy theorem: ... | https://arxiv.org/abs/2601.23198 | Academic Papers | svg |
46b283f546e22c3e44225488711b18f056cd8aa544eabc9444add4eca2177ca9 | 2026-02-02T00:00:00-05:00 | Large Language Models for Patent Classification: Strengths, Trade-offs, and the Long Tail Effect | arXiv:2601.23200v1 Announce Type: new Abstract: Patent classification into CPC codes underpins large scale analyses of technological change but remains challenging due to its hierarchical, multi label, and highly imbalanced structure. While pre Generative AI supervised encoder based models became the de facto standard ... | https://arxiv.org/abs/2601.23200 | Academic Papers | svg |
9d57683b3707c2ebe52a44673ba2eeaa9901988c397850a9f9dbbf7a00162cd4 | 2026-02-02T00:00:00-05:00 | TSAQA: Time Series Analysis Question And Answering Benchmark | arXiv:2601.23204v1 Announce Type: new Abstract: Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science. While recent work has begun to explore multi-task time series question answering (QA), current benchmarks remain limited to foreca... | https://arxiv.org/abs/2601.23204 | Academic Papers | svg |
661691b69cf7dc1d7b952f73f31a82190989f6a8c34743c2c410c44fe1883fbb | 2026-02-02T00:00:00-05:00 | High-quality generation of dynamic game content via small language models: A proof of concept | arXiv:2601.23206v1 Announce Type: new Abstract: Large language models (LLMs) offer promise for dynamic game content generation, but they face critical barriers, including narrative incoherence and high operational costs. Due to their large size, they are often accessed in the cloud, limiting their application in offlin... | https://arxiv.org/abs/2601.23206 | Academic Papers | svg |
6a003e43b339a06295b730a6babbc026dbd973a145a7ed167de52a63c16a0d13 | 2026-02-02T00:00:00-05:00 | Learning to Execute Graph Algorithms Exactly with Graph Neural Networks | arXiv:2601.23207v1 Announce Type: new Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical challenge. In this work, we prove exact learnability results for graph algorithms under bounded-degree and finite-precision constrain... | https://arxiv.org/abs/2601.23207 | Academic Papers | svg |
7af5f80b3fcb8ba9dde63e43cf9ac3be6652edd1a5b5a6bb3664a8d8ab0bc92b | 2026-02-02T00:00:00-05:00 | Evaluating the Viability of Additive Models to Predict Task Completion Time for 3D Interactions in Augmented Reality | arXiv:2601.23209v1 Announce Type: new Abstract: Additive models of interaction performance, such as the Keystroke-Level Model (KLM), are tools that allow designers to compare and optimize the performance of user interfaces by summing the predicted times for the atomic components of a specific interaction to predict the... | https://arxiv.org/abs/2601.23209 | Academic Papers | svg |
7a02907d8438e84ffd493875df9842f0ffb6078a318f9a5439677af9b1f7650f | 2026-02-02T00:00:00-05:00 | Multi-Agent Systems Should be Treated as Principal-Agent Problems | arXiv:2601.23211v1 Announce Type: new Abstract: Consider a multi-agent systems setup in which a principal (a supervisor agent) assigns subtasks to specialized agents and aggregates their responses into a single system-level output. A core property of such systems is information asymmetry: agents observe task-specific i... | https://arxiv.org/abs/2601.23211 | Academic Papers | svg |
86598d0aaf035fa1a14444a061501d9dec91e6d05fff1ecebf0aa517042b2e88 | 2026-02-02T00:00:00-05:00 | A complete characterisation of conditional entropies | arXiv:2601.23213v1 Announce Type: new Abstract: Entropies are fundamental measures of uncertainty with central importance in information theory and statistics and applications across all the quantitative sciences. Under a natural set of operational axioms, the most general form of entropy is captured by the family of R... | https://arxiv.org/abs/2601.23213 | Academic Papers | svg |
24c50395a8d0c2498546516513abf0f2d35ab03ab692cf81c0755ffd2fefeb1b | 2026-02-02T00:00:00-05:00 | Tackling air quality with SAPIENS | arXiv:2601.23215v1 Announce Type: new Abstract: Air pollution is a chronic problem in large cities worldwide and awareness is rising as the long-term health implications become clearer. Vehicular traffic has been identified as a major contributor to poor air quality. In a lot of cities the publicly available air qualit... | https://arxiv.org/abs/2601.23215 | Academic Papers | svg |
a5f385f3abd0a2a273cabb925f88174c86db42d25db15b3ff71cb1e6746597b9 | 2026-02-02T00:00:00-05:00 | Secure Integrated Sensing and Communication against Communication and Sensing Eavesdropping | arXiv:2601.23216v1 Announce Type: new Abstract: Sensing privacy and communication confidentiality play fundamentally different but interconnected roles in adversarial wireless environments. Capturing this interplay within a single physical-layer framework is particularly challenging in integrated sensing and communicat... | https://arxiv.org/abs/2601.23216 | Academic Papers | svg |
8a091140180ff7db11f6e014b8f37e1eebbdc2abd91bd5cf07f70d1e91b8bb41 | 2026-02-02T00:00:00-05:00 | MonoScale: Scaling Multi-Agent System with Monotonic Improvement | arXiv:2601.23219v1 Announce Type: new Abstract: In recent years, LLM-based multi-agent systems (MAS) have advanced rapidly, using a router to decompose tasks and delegate subtasks to specialized agents. A natural way to expand capability is to scale up the agent pool by continually integrating new functional agents or ... | https://arxiv.org/abs/2601.23219 | Academic Papers | svg |
e79e7a88fa5fd5011365ef61a4814e211e03e7fb7fbf77324e80e279cc4b1337 | 2026-02-02T00:00:00-05:00 | Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training | arXiv:2601.23220v1 Announce Type: new Abstract: Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit: geometric blindness. This failure to ground outputs in objective geometric constraints leads to... | https://arxiv.org/abs/2601.23220 | Academic Papers | svg |
dd083c486dc8e080009d10b306b508cedbf6ca944353a6d083e4f90a7e3151d8 | 2026-02-02T00:00:00-05:00 | Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints | arXiv:2601.23221v1 Announce Type: new Abstract: As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort. Aggregating subjective labels, though, may amplify individual biases, particularly regarding sensitive features, rai... | https://arxiv.org/abs/2601.23221 | Academic Papers | svg |
33ad64e3f08949e22b6624d598d6f394623f0bac2f93ed1145faa603ffb6c117 | 2026-02-02T00:00:00-05:00 | Region-Normalized DPO for Medical Image Segmentation under Noisy Judges | arXiv:2601.23222v1 Announce Type: new Abstract: While dense pixel-wise annotations remain the gold standard for medical image segmentation, they are costly to obtain and limit scalability. In contrast, many deployed systems already produce inexpensive automatic quality-control (QC) signals like model agreement, uncerta... | https://arxiv.org/abs/2601.23222 | Academic Papers | svg |
909b85c9c45374711eed3b437d0223cb7549d73f0a9240f24b774fd9b58e6c53 | 2026-02-02T00:00:00-05:00 | Are you going to finish that? A Practical Study of the Tokenization Boundary Problem | arXiv:2601.23223v1 Announce Type: new Abstract: Language models (LMs) are trained over sequences of tokens, whereas users interact with LMs via text. This mismatch gives rise to the partial token problem, which occurs when a user ends their prompt in the middle of the expected next-token, leading to distorted next-toke... | https://arxiv.org/abs/2601.23223 | Academic Papers | svg |
a97d627bc6d1f54ef56cf21e69fa3107c82a41e56e4b567e85855d8bfb9a7edf | 2026-02-02T00:00:00-05:00 | Video-o3: Native Interleaved Clue Seeking for Long Video Multi-Hop Reasoning | arXiv:2601.23224v1 Announce Type: new Abstract: Existing multimodal large language models for long-video understanding predominantly rely on uniform sampling and single-turn inference, limiting their ability to identify sparse yet critical evidence amid extensive redundancy. We introduce Video-o3, a novel framework tha... | https://arxiv.org/abs/2601.23224 | Academic Papers | svg |
01ef36971b250b3fa1f6338e734ce9d9d612bf47f09b823c6ca417afd91e56e4 | 2026-02-02T00:00:00-05:00 | Agile Reinforcement Learning through Separable Neural Architecture | arXiv:2601.23225v1 Announce Type: new Abstract: Deep reinforcement learning (RL) is increasingly deployed in resource-constrained environments, yet the go-to function approximators - multilayer perceptrons (MLPs) - are often parameter-inefficient due to an imperfect inductive bias for the smooth structure of many value... | https://arxiv.org/abs/2601.23225 | Academic Papers | svg |
c6b8c6186f8720a71b557c5ff530289e3be36f0597a9568d7bc7b581a30d5b7b | 2026-02-02T00:00:00-05:00 | Toward Digital Twins in 3D IC Packaging: A Critical Review of Physics, Data, and Hybrid Architectures | arXiv:2601.23226v1 Announce Type: new Abstract: Three-dimensional integrated circuit (3D IC) pack-aging and heterogeneous integration have emerged as central pillars of contemporary semiconductor scaling. Yet, the multi-physics coupling inherent to stacked architectures manifesting as thermal hot spots, warpage-induced... | https://arxiv.org/abs/2601.23226 | Academic Papers | svg |
1e8650fff3809bdf39741e96dd44aa091cac6016d221febf441aab550344f5db | 2026-02-02T00:00:00-05:00 | Scaling Multiagent Systems with Process Rewards | arXiv:2601.23228v1 Announce Type: new Abstract: While multiagent systems have shown promise for tackling complex tasks via specialization, finetuning multiple agents simultaneously faces two key challenges: (1) credit assignment across agents, and (2) sample efficiency of expensive multiagent rollouts. In this work, we... | https://arxiv.org/abs/2601.23228 | Academic Papers | svg |
c07f61daa25db99c62e279435b072b37111ccedc6c2fd422547776a29beabd67 | 2026-02-02T00:00:00-05:00 | Strongly Polynomial Time Complexity of Policy Iteration for $L_\infty$ Robust MDPs | arXiv:2601.23229v1 Announce Type: new Abstract: Markov decision processes (MDPs) are a fundamental model in sequential decision making. Robust MDPs (RMDPs) extend this framework by allowing uncertainty in transition probabilities and optimizing against the worst-case realization of that uncertainty. In particular, $(s,... | https://arxiv.org/abs/2601.23229 | Academic Papers | svg |
7fffdd06374f571c29c91409e9d27354d16b7cbdbcfcb92519ee6ee8840c4006 | 2026-02-02T00:00:00-05:00 | ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search | arXiv:2601.23232v1 Announce Type: new Abstract: In recent years, large language models (LLMs) have made rapid progress in information retrieval, yet existing research has mainly focused on text or static multimodal settings. Open-domain video shot retrieval, which involves richer temporal structure and more complex sem... | https://arxiv.org/abs/2601.23232 | Academic Papers | svg |
f73086b8f0ff74f22cfa5e4ed064ececf0b7e8ace4fb67de19aa052fb45743d4 | 2026-02-02T00:00:00-05:00 | Sequence Diffusion Model for Temporal Link Prediction in Continuous-Time Dynamic Graph | arXiv:2601.23233v1 Announce Type: new Abstract: Temporal link prediction in dynamic graphs is a fundamental problem in many real-world systems. Existing temporal graph neural networks mainly focus on learning representations of historical interactions. Despite their strong performance, these models are still purely dis... | https://arxiv.org/abs/2601.23233 | Academic Papers | svg |
9af33e6bfa221e53b91dd463e988610675504d1788bae5b1d8b9fbd28853fc57 | 2026-02-02T00:00:00-05:00 | YuriiFormer: A Suite of Nesterov-Accelerated Transformers | arXiv:2601.23236v1 Announce Type: new Abstract: We propose a variational framework that interprets transformer layers as iterations of an optimization algorithm acting on token embeddings. In this view, self-attention implements a gradient step of an interaction energy, while MLP layers correspond to gradient updates o... | https://arxiv.org/abs/2601.23236 | Academic Papers | svg |
f8dce92a85b48b156e655cd7749fdf8ec9f5e6ca41a9783cea227c579b3889e8 | 2026-02-02T00:00:00-05:00 | Applications of QR-based Vector-Valued Rational Approximation | arXiv:2601.23237v1 Announce Type: new Abstract: Several applications of the QR-AAA algorithm, a greedy scheme for vector-valued rational approximation, are presented. The focus is on demonstrating the flexibility and practical effectiveness of QR-AAA in a variety of computational settings, including Stokes flow computa... | https://arxiv.org/abs/2601.23237 | Academic Papers | svg |
05c11e281a14c949b2931be9962df17cda8b322f884725ec9d67011b8f93ac7e | 2026-02-02T00:00:00-05:00 | How well do generative models solve inverse problems? A benchmark study | arXiv:2601.23238v1 Announce Type: new Abstract: Generative learning generates high dimensional data based on low dimensional conditions, also called prompts. Therefore, generative learning algorithms are eligible for solving (Bayesian) inverse problems. In this article we compare a traditional Bayesian inverse approach... | https://arxiv.org/abs/2601.23238 | Academic Papers | svg |
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