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47f2574b5d1fc25eb33b80356a3f9471382441b0bec5de411e4f2f60f15aeb8b | 2026-01-07T00:00:00-05:00 | Exploring Blockchain Interoperability: Frameworks, Use Cases, and Future Challenges | arXiv:2601.02949v1 Announce Type: new Abstract: Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption, enabling users to work in a trus... | https://arxiv.org/abs/2601.02949 | Academic Papers | svg |
2c114c823bb649277dbe203e775b00c01027f26a6af8c479853133aaffcbe3c0 | 2026-01-07T00:00:00-05:00 | Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning | arXiv:2601.02950v1 Announce Type: new Abstract: Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constraints. We introduce Batch-of-Thought (BoT), a training-free method that processes related queri... | https://arxiv.org/abs/2601.02950 | Academic Papers | svg |
a10cbda24cf58e1c586845765da2614374481dd56d1044f3ab70f627813aca46 | 2026-01-07T00:00:00-05:00 | The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models | arXiv:2601.02954v1 Announce Type: new Abstract: Existing large audio-language models perceive the world as "mono" -- a single stream of audio that ignores the critical spatial dimension ("where") required for universal acoustic scene analysis. To bridge this gap, we first introduce a hierarchical framework for Auditory... | https://arxiv.org/abs/2601.02954 | Academic Papers | svg |
23d4a7e518f71d92dde30c8f088099b1c40e06483a136683a629af88a3474b46 | 2026-01-07T00:00:00-05:00 | HarmonRank: Ranking-aligned Multi-objective Ensemble for Live-streaming E-commerce Recommendation | arXiv:2601.02955v1 Announce Type: new Abstract: Recommendation for live-streaming e-commerce is gaining increasing attention due to the explosive growth of the live streaming economy. Different from traditional e-commerce, live-streaming e-commerce shifts the focus from products to streamers, which requires ranking mec... | https://arxiv.org/abs/2601.02955 | Academic Papers | svg |
32fa1fdb9b8be5e4c6cd956614cf14526e9191f503abb15a4bee9adb9ca61360 | 2026-01-07T00:00:00-05:00 | Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion | arXiv:2601.02956v1 Announce Type: new Abstract: Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, resulting in the widespread adoption of English pivoting. While prior studies attribute this advantage to the superior Englis... | https://arxiv.org/abs/2601.02956 | Academic Papers | svg |
8a6a2075b4a5447b77dbbd1af584c8fdaa18111396669863edaacb1f8b8ee129 | 2026-01-07T00:00:00-05:00 | LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation | arXiv:2601.02957v1 Announce Type: new Abstract: This paper introduces a novel changepoint detection framework that combines ensemble statistical methods with Large Language Models (LLMs) to enhance both detection accuracy and the interpretability of regime changes in time series data. Two critical limitations in the fi... | https://arxiv.org/abs/2601.02957 | Academic Papers | svg |
d0ba1e08a4feb08b3bb74d7308d64d2f8672cc436b141cff2fe276823aeccc76 | 2026-01-07T00:00:00-05:00 | Post-Earthquake Restoration of Electricity-Gas Distribution Systems with Damage Information Collection and Repair Vehicle Routing | arXiv:2601.02958v1 Announce Type: new Abstract: Extreme events such as earthquakes pose significant threats to integrated electricity-gas distribution systems (IEGDS) by causing widespread damage. Existing restoration approaches typically assume full awareness of damage, which may not be true if monitoring and communic... | https://arxiv.org/abs/2601.02958 | Academic Papers | svg |
b3e37758de39933a200c4c56d1cb709a8845a2e02ad039eda98b1c6046f0f13e | 2026-01-07T00:00:00-05:00 | Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation | arXiv:2601.02962v1 Announce Type: new Abstract: Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten sug... | https://arxiv.org/abs/2601.02962 | Academic Papers | svg |
6cd9e7ae2ec983e5d5dd240e0518392f0014b9b943f9681224e60c561ed85d1a | 2026-01-07T00:00:00-05:00 | Low-Resource Heuristics for Bahnaric Optical Character Recognition Improvement | arXiv:2601.02965v1 Announce Type: new Abstract: Bahnar, a minority language spoken across Vietnam, Cambodia, and Laos, faces significant preservation challenges due to limited research and data availability. This study addresses the critical need for accurate digitization of Bahnar language documents through optical ch... | https://arxiv.org/abs/2601.02965 | Academic Papers | svg |
2dc2482c7f0b81a5c6bf0f01badfaed927188b1d597df3cc06b16eeacafc0687 | 2026-01-07T00:00:00-05:00 | MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free | arXiv:2601.02967v1 Announce Type: new Abstract: Extending the input modality of Large Language Models~(LLMs) to the audio domain is essential for achieving comprehensive multimodal perception. However, it is well-known that acoustic information is intrinsically \textit{heterogeneous}, entangling attributes such as spee... | https://arxiv.org/abs/2601.02967 | Academic Papers | svg |
3c30b28c7ea8ec4261122c4d92a1ac6aba95f4092142ba138983ead1b399bc2c | 2026-01-07T00:00:00-05:00 | Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models | arXiv:2601.02968v1 Announce Type: new Abstract: The underperformance of existing multimodal large language models for time series reasoning lies in the absence of rationale priors that connect temporal observations to their downstream outcomes, which leads models to rely on superficial pattern matching rather than prin... | https://arxiv.org/abs/2601.02968 | Academic Papers | svg |
db99c9ff82b51baad555936cb72149275c53bdee90d774f2ebccb67f1a18bd6f | 2026-01-07T00:00:00-05:00 | Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning | arXiv:2601.02970v1 Announce Type: new Abstract: Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost. Adaptive self-consistency methods mitigate this issue by adjusting the sampling budget; however, they rely on count-based stopping rules that treat all... | https://arxiv.org/abs/2601.02970 | Academic Papers | svg |
f07b6b73339db700d20aace8026502e187e584d24a9034d9fea7a204c431f0af | 2026-01-07T00:00:00-05:00 | Few-shot learning for security bug report identification | arXiv:2601.02971v1 Announce Type: new Abstract: Security bug reports require prompt identification to minimize the window of vulnerability in software systems. Traditional machine learning (ML) techniques for classifying bug reports to identify security bug reports rely heavily on large amounts of labeled data. However... | https://arxiv.org/abs/2601.02971 | Academic Papers | svg |
56f6e7dd732e768672708191058f5899d96d06861ef5032f4256cfa35832baed | 2026-01-07T00:00:00-05:00 | Correct, Concise and Complete: Multi-stage Training For Adaptive Reasoning | arXiv:2601.02972v1 Announce Type: new Abstract: The reasoning capabilities of large language models (LLMs) have improved substantially through increased test-time computation, typically in the form of intermediate tokens known as chain-of-thought (CoT). However, CoT often becomes unnecessarily long, increasing computat... | https://arxiv.org/abs/2601.02972 | Academic Papers | svg |
e24972c760bee3e3b2a65094bfaa6bf995457c700060303c00cee3547837d5d2 | 2026-01-07T00:00:00-05:00 | A Fourth-Order Cut-cell Multigrid Method for Generic Elliptic Equations on Arbitrary Domains | arXiv:2601.02975v1 Announce Type: new Abstract: To numerically solve a generic elliptic equation on two-dimensional domains with rectangular Cartesian grids, we propose a cut-cell geometric multigrid method that features (1) general algorithmic steps that apply to all forms of elliptic equations and all types of bounda... | https://arxiv.org/abs/2601.02975 | Academic Papers | svg |
45b40ba1b9f006dece531cd6322689b0ad1a5285447a25e16388bd82fe8f97be | 2026-01-07T00:00:00-05:00 | Mechanistic Knobs in LLMs: Retrieving and Steering High-Order Semantic Features via Sparse Autoencoders | arXiv:2601.02978v1 Announce Type: new Abstract: Recent work in Mechanistic Interpretability (MI) has enabled the identification and intervention of internal features in Large Language Models (LLMs). However, a persistent challenge lies in linking such internal features to the reliable control of complex, behavior-level... | https://arxiv.org/abs/2601.02978 | Academic Papers | svg |
bde238847e8580714b25f9c02693249488ed49c1d6a0dd9718760a51a52db06e | 2026-01-07T00:00:00-05:00 | Developing and Evaluating Lightweight Cryptographic Algorithms for Secure Embedded Systems in IoT Devices | arXiv:2601.02981v1 Announce Type: new Abstract: The high rate of development of Internet of Things (IoT) devices has brought to attention new challenges in the area of data security, especially within the resource-limited realm of RFID tags, sensors, and embedded systems. Traditional cryptographic implementations can b... | https://arxiv.org/abs/2601.02981 | Academic Papers | svg |
d0e9d6e10a60bbb2e679c54e892f1b0117f6207f863cf121aceeac5370c01aa3 | 2026-01-07T00:00:00-05:00 | Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning | arXiv:2601.02983v1 Announce Type: new Abstract: Recent advances in audio large language models (ALLMs) have made high-quality synthetic audio widely accessible, increasing the risk of malicious audio deepfakes across speech, environmental sounds, singing voice, and music. Real-world audio deepfake detection (ADD) there... | https://arxiv.org/abs/2601.02983 | Academic Papers | svg |
5825224d8ee7f6e0c9bd26d5fe4d798dd3cee5b19d4cdec70db4d890aab0693b | 2026-01-07T00:00:00-05:00 | Selfish Mining in Multi-Attacker Scenarios: An Empirical Evaluation of Nakamoto, Fruitchain, and Strongchain | arXiv:2601.02984v1 Announce Type: new Abstract: The aim of this work is to enhance blockchain security by deepening the understanding of selfish mining attacks in various consensus protocols, especially the ones that have the potential to mitigate selfish mining. Previous research was mainly focused on a particular pro... | https://arxiv.org/abs/2601.02984 | Academic Papers | svg |
00d2e3144f3688283ee09c72db8d8339a66f27bde76c785a3cc4c4856e790bbb | 2026-01-07T00:00:00-05:00 | P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist | arXiv:2601.02986v1 Announce Type: new Abstract: Recent approaches in personalized reward modeling have primarily focused on leveraging user interaction history to align model judgments with individual preferences. However, existing approaches largely treat user context as a static or implicit conditioning signal, faili... | https://arxiv.org/abs/2601.02986 | Academic Papers | svg |
e4e4a3908aa842d7c027ad4c9df601e0c68a89a27beb3a72600fc463327eb405 | 2026-01-07T00:00:00-05:00 | LAMS-Edit: Latent and Attention Mixing with Schedulers for Improved Content Preservation in Diffusion-Based Image and Style Editing | arXiv:2601.02987v1 Announce Type: new Abstract: Text-to-Image editing using diffusion models faces challenges in balancing content preservation with edit application and handling real-image editing. To address these, we propose LAMS-Edit, leveraging intermediate states from the inversion process--an essential step in r... | https://arxiv.org/abs/2601.02987 | Academic Papers | svg |
8c7a47282c1e5e43da50fb797dd86afd4635008dda9be8262427eb745b8fb916 | 2026-01-07T00:00:00-05:00 | ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation | arXiv:2601.02988v1 Announce Type: new Abstract: In this study, we present ULS+, an enhanced version of the Universal Lesion Segmentation (ULS) model. The original ULS model segments lesions across the whole body in CT scans given volumes of interest (VOIs) centered around a click-point. Since its release, several new p... | https://arxiv.org/abs/2601.02988 | Academic Papers | svg |
b883e9fc2d7fe1a719df2a7c4a51d83bc00186582d3effd3c64b64cad4ddcaf4 | 2026-01-07T00:00:00-05:00 | Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy | arXiv:2601.02989v1 Announce Type: new Abstract: Large language models (LLMs), despite strong performance on complex mathematical problems, exhibit systematic limitations in counting tasks. This issue arises from architectural limits of transformers, where counting is performed across layers, leading to degraded precisi... | https://arxiv.org/abs/2601.02989 | Academic Papers | svg |
1a359faf17abffcb7bae7ce19b25eb3bb951f28e5ece72c5e827cb8bf15426e8 | 2026-01-07T00:00:00-05:00 | Towards Faithful Reasoning in Comics for Small MLLMs | arXiv:2601.02991v1 Announce Type: new Abstract: Comic-based visual question answering (CVQA) poses distinct challenges to multimodal large language models (MLLMs) due to its reliance on symbolic abstraction, narrative logic, and humor, which differ from conventional VQA tasks. Although Chain-of-Thought (CoT) prompting ... | https://arxiv.org/abs/2601.02991 | Academic Papers | svg |
a02f7e2460c6c024f86c95272aa404842b70e0dd779a3fb7c35e6a75d61990b5 | 2026-01-07T00:00:00-05:00 | Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation | arXiv:2601.02993v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in large language models (LLMs), yet little is known about how the order of retrieved documents affects model behavior. We empirically show that under Top-5 retrieval with t... | https://arxiv.org/abs/2601.02993 | Academic Papers | svg |
ff3866a03346e58c1ce4fda65eb7881f5b6c972321398834657477626738558c | 2026-01-07T00:00:00-05:00 | Learning to Act Robustly with View-Invariant Latent Actions | arXiv:2601.02994v1 Announce Type: new Abstract: Vision-based robotic policies often struggle with even minor viewpoint changes, underscoring the need for view-invariant visual representations. This challenge becomes more pronounced in real-world settings, where viewpoint variability is unavoidable and can significantly... | https://arxiv.org/abs/2601.02994 | Academic Papers | svg |
31049411ff3f8035715314b921a2b80b2d3df47e70ab0a05e17aff6317e1edef | 2026-01-07T00:00:00-05:00 | Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners | arXiv:2601.02996v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit chain-of-thought (CoT) explanations. However, recent work shows that LRMs often arrive at the correct answer before completi... | https://arxiv.org/abs/2601.02996 | Academic Papers | svg |
1c571805ba8fad110d6d15d5491858aab95b6c2b61dbf1303d09a35dbd0f3754 | 2026-01-07T00:00:00-05:00 | From Memorization to Creativity: LLM as a Designer of Novel Neural-Architectures | arXiv:2601.02997v1 Announce Type: new Abstract: Large language models (LLMs) excel in program synthesis, yet their ability to autonomously navigate neural architecture design--balancing syntactic reliability, performance, and structural novelty--remains underexplored. We address this by placing a code-oriented LLM with... | https://arxiv.org/abs/2601.02997 | Academic Papers | svg |
e6f93de9c89d29c4289f1fc5597362a22082519d271a4ccb546b1e8b99b021e6 | 2026-01-07T00:00:00-05:00 | Multi-Distribution Robust Conformal Prediction | arXiv:2601.02998v1 Announce Type: new Abstract: In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them. We study the problem of constructing a conformal prediction set that is unifo... | https://arxiv.org/abs/2601.02998 | Academic Papers | svg |
3f93cf62e384440ad0ec3aa3dc9aa5e940c9acbc6f06347ffbd3140b3f8e1ec4 | 2026-01-07T00:00:00-05:00 | Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selection | arXiv:2601.03001v1 Announce Type: new Abstract: Vehicle-Infrastructure Collaborative Perception (VICP) is pivotal for resolving occlusion in autonomous driving, yet the trade-off between communication bandwidth and feature redundancy remains a critical bottleneck. While intermediate fusion mitigates data volume compare... | https://arxiv.org/abs/2601.03001 | Academic Papers | svg |
b9ccb63ad1e52917c1307cfa856217e968324fd9ae315efbd901a12b0b491f59 | 2026-01-07T00:00:00-05:00 | Closed-Loop Transmission Power Control for Reliable and Low-Power BLE Communication in Dynamic IoT Settings | arXiv:2601.03003v1 Announce Type: new Abstract: Reliable and energy-efficient Bluetooth Low Energy (BLE) communication is crucial for Internet of Things (IoT) applications in dynamic environments. However, the Received Signal Strength Indicator (RSSI) and data throughput in BLE are highly susceptible to environmental v... | https://arxiv.org/abs/2601.03003 | Academic Papers | svg |
fab536e458445dc5c3e8c69ab1961607ef147d209014120827745baf58eba0b0 | 2026-01-07T00:00:00-05:00 | JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification | arXiv:2601.03005v1 Announce Type: new Abstract: Despite extensive safety alignment, Large Language Models (LLMs) often fail against jailbreak attacks. While machine unlearning has emerged as a promising defense by erasing specific harmful parameters, current methods remain vulnerable to diverse jailbreaks. We first con... | https://arxiv.org/abs/2601.03005 | Academic Papers | svg |
e882c729253d9069e006c2bbf92a2e59ebda8f0a17e23cde0b886c7801eee87b | 2026-01-07T00:00:00-05:00 | From inconsistency to decision: explainable operation and maintenance of battery energy storage systems | arXiv:2601.03007v1 Announce Type: new Abstract: Battery Energy Storage Systems (BESSs) are increasingly critical to power-system stability, yet their operation and maintenance remain dominated by reactive, expert-dependent diagnostics. While cell-level inconsistencies provide early warning signals of degradation and sa... | https://arxiv.org/abs/2601.03007 | Academic Papers | svg |
59c1c5bf02bff09bb36d9e680c8f18a54198b07328cfd24e53e0f25887164160 | 2026-01-07T00:00:00-05:00 | A Dataset of Low-Rated Applications from the Amazon Appstore for User Feedback Analysis | arXiv:2601.03009v1 Announce Type: new Abstract: In todays digital landscape, end-user feedback plays a crucial role in the evolution of software applications, particularly in addressing issues that hinder user experience. While much research has focused on high-rated applications, low-rated applications often remain un... | https://arxiv.org/abs/2601.03009 | Academic Papers | svg |
25369758e1220a92363be542877b1c9cc937a6e57bfbca4cbbab7d9a4aee5e08 | 2026-01-07T00:00:00-05:00 | Mathematical aspects of registration methods in bounded domains | arXiv:2601.03010v1 Announce Type: new Abstract: Registration methods in bounded domains have received significant attention in the model reduction literature, as a valuable tool for nonlinear approximation. The aim of this work is to provide a concise yet complete overview of relevant results for registration methods i... | https://arxiv.org/abs/2601.03010 | Academic Papers | svg |
0fa7c922cd6e4d2014411510172e0a5d0075c2186cc99682816d92af5856c803 | 2026-01-07T00:00:00-05:00 | ReCCur: A Recursive Corner-Case Curation Framework for Robust Vision-Language Understanding in Open and Edge Scenarios | arXiv:2601.03011v1 Announce Type: new Abstract: Corner cases are rare or extreme scenarios that drive real-world failures, but they are difficult to curate at scale: web data are noisy, labels are brittle, and edge deployments preclude large retraining. We present ReCCur (Recursive Corner-Case Curation), a low-compute ... | https://arxiv.org/abs/2601.03011 | Academic Papers | svg |
d5d4b382f1729dd66d8fcf06f37d089a1d9e102becf69e4b90d49636363e509e | 2026-01-07T00:00:00-05:00 | LLMs, You Can Evaluate It! Design of Multi-perspective Report Evaluation for Security Operation Centers | arXiv:2601.03013v1 Announce Type: new Abstract: Security operation centers (SOCs) often produce analysis reports on security incidents, and large language models (LLMs) will likely be used for this task in the near future. We postulate that a better understanding of how veteran analysts evaluate reports, including thei... | https://arxiv.org/abs/2601.03013 | Academic Papers | svg |
b24ba1895bd83bd9e16d0d3cc612448a3acdeb464f0bcdec291135dc85f40725 | 2026-01-07T00:00:00-05:00 | SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering | arXiv:2601.03014v1 Announce Type: new Abstract: Traditional Retrieval-Augmented Generation (RAG) effectively supports single-hop question answering with large language models but faces significant limitations in multi-hop question answering tasks, which require combining evidence from multiple documents. Existing chunk... | https://arxiv.org/abs/2601.03014 | Academic Papers | svg |
d6e741619fb086647469b022d9d0a9b6b543d83f55aa408590b67e97a6c84023 | 2026-01-07T00:00:00-05:00 | In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior | arXiv:2601.03015v1 Announce Type: new Abstract: In-context reinforcement learning (ICRL) promises fast adaptation to unseen environments without parameter updates, but current methods either cannot improve beyond the training distribution or require near-optimal data, limiting practical adoption. We introduce SPICE, a ... | https://arxiv.org/abs/2601.03015 | Academic Papers | svg |
6ecc2d023ed5e3dbe63e2ab1b51226f6cf7dff93fb74c266c1c42ff2ee0523d1 | 2026-01-07T00:00:00-05:00 | MMFormalizer: Multimodal Autoformalization in the Wild | arXiv:2601.03017v1 Announce Type: new Abstract: Autoformalization, which translates natural language mathematics into formal statements to enable machine reasoning, faces fundamental challenges in the wild due to the multimodal nature of the physical world, where physics requires inferring hidden constraints (e.g., mas... | https://arxiv.org/abs/2601.03017 | Academic Papers | svg |
34f62b0d651c40f1ba5617da7eded71ce5dcbab998f3155edd7fa659f63b42c8 | 2026-01-07T00:00:00-05:00 | Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis | arXiv:2601.03018v1 Announce Type: new Abstract: While Large Language Models (LLMs) have shown strong performance on clinical text understanding, they struggle with longitudinal prediction tasks such as dementia prognosis, which require reasoning over complex, non-monotonic symptom trajectories across multiple visits. S... | https://arxiv.org/abs/2601.03018 | Academic Papers | svg |
15469c17f99aed1d905d1da63d8d00552a400193630a7a0e4644d219f73fb7fa | 2026-01-07T00:00:00-05:00 | Hardness of Regular Expression Matching with Extensions | arXiv:2601.03020v1 Announce Type: new Abstract: The regular expression matching problem asks whether a given regular expression of length $m$ matches a given string of length $n$. As is well known, the problem can be solved in $O(nm)$ time using Thompson's algorithm. Moreover, recent studies have shown that the matchin... | https://arxiv.org/abs/2601.03020 | Academic Papers | svg |
a5e0f580a489454081be7a812688e4ab7052d49b8e7536a3b756254c32d91380 | 2026-01-07T00:00:00-05:00 | MedDialogRubrics: A Comprehensive Benchmark and Evaluation Framework for Multi-turn Medical Consultations in Large Language Models | arXiv:2601.03023v1 Announce Type: new Abstract: Medical conversational AI (AI) plays a pivotal role in the development of safer and more effective medical dialogue systems. However, existing benchmarks and evaluation frameworks for assessing the information-gathering and diagnostic reasoning abilities of medical large ... | https://arxiv.org/abs/2601.03023 | Academic Papers | svg |
8c502e497885c8a682d5a29743bc7c11faf4ea4fba50b25daf8eae41f7b03a92 | 2026-01-07T00:00:00-05:00 | SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selection | arXiv:2601.03024v1 Announce Type: new Abstract: We propose Self-Augmented Residual 3D Gaussian Splatting (SA-ResGS), a novel framework to stabilize uncertainty quantification and enhancing uncertainty-aware supervision in next-best-view (NBV) selection for active scene reconstruction. SA-ResGS improves both the reliabi... | https://arxiv.org/abs/2601.03024 | Academic Papers | svg |
e9c8aaad0002e5cf178b70f1473ef8ab9bc8f8360b708054b3a2043fd7542f61 | 2026-01-07T00:00:00-05:00 | LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answering | arXiv:2601.03025v1 Announce Type: new Abstract: Long-context question answering (QA) over literary texts poses significant challenges for modern large language models, particularly in low-resource languages. We address the scarcity of long-context QA resources for Indic languages by introducing LittiChoQA, the largest ... | https://arxiv.org/abs/2601.03025 | Academic Papers | svg |
24e6e7c606b47ebb13aa506d915ef4a583fb5bca71c781977a2ed8ac7e057980 | 2026-01-07T00:00:00-05:00 | Reducing Hallucinations in LLMs via Factuality-Aware Preference Learning | arXiv:2601.03027v1 Announce Type: new Abstract: Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce hallucinations when preference judgments reward fluency and confidence over factual correctness. We introduce F-DPO (Factuality-aw... | https://arxiv.org/abs/2601.03027 | Academic Papers | svg |
62a169c503f8cb2f51df65363afd2a66663a03a4e864677751230663e3f0db72 | 2026-01-07T00:00:00-05:00 | Flow Matching and Diffusion Models via PointNet for Generating Fluid Fields on Irregular Geometries | arXiv:2601.03030v1 Announce Type: new Abstract: We present two novel generative geometric deep learning frameworks, termed Flow Matching PointNet and Diffusion PointNet, for predicting fluid flow variables on irregular geometries by incorporating PointNet into flow matching and diffusion models, respectively. In these ... | https://arxiv.org/abs/2601.03030 | Academic Papers | svg |
3a7ed66833d0f365aeb3fb745261fb5eccedc4c4e5dc7412500a13bfa53bda39 | 2026-01-07T00:00:00-05:00 | FlexProofs: A Vector Commitment with Flexible Linear Time for Computing All Proofs | arXiv:2601.03031v1 Announce Type: new Abstract: In this paper, we introduce FlexProofs, a new vector commitment (VC) scheme that achieves two key properties: (1) the prover can generate all individual opening proofs for a vector of size $N$ in optimal time ${\cal O}(N)$, and there is a flexible batch size parameter $b$... | https://arxiv.org/abs/2601.03031 | Academic Papers | svg |
ee185c823d378d163128af6e2c06a738ffb83d9989d94dc9705663e150a58cdf | 2026-01-07T00:00:00-05:00 | Causal Manifold Fairness: Enforcing Geometric Invariance in Representation Learning | arXiv:2601.03032v1 Announce Type: new Abstract: Fairness in machine learning is increasingly critical, yet standard approaches often treat data as static points in a high-dimensional space, ignoring the underlying generative structure. We posit that sensitive attributes (e.g., race, gender) do not merely shift data dis... | https://arxiv.org/abs/2601.03032 | Academic Papers | svg |
27a7f5cef891587d773a600686f3983d9b2e8abee743cc4b5a98f29d9f53fdbf | 2026-01-07T00:00:00-05:00 | NorwAI's Large Language Models: Technical Report | arXiv:2601.03034v1 Announce Type: new Abstract: Norwegian, spoken by approximately five million people, remains underrepresented in many of the most significant breakthroughs in Natural Language Processing (NLP). To address this gap, the NorLLM team at NorwAI has developed a family of models specifically tailored to No... | https://arxiv.org/abs/2601.03034 | Academic Papers | svg |
019bc4a09cec763e824c27064cf3504778cd10d78d778800acb54e1473e116e1 | 2026-01-07T00:00:00-05:00 | A Bi-directional Adaptive Framework for Agile UAV Landing | arXiv:2601.03037v1 Announce Type: new Abstract: Autonomous landing on mobile platforms is crucial for extending quadcopter operational flexibility, yet conventional methods are often too inefficient for highly dynamic scenarios. The core limitation lies in the prevalent ``track-then-descend'' paradigm, which treats the... | https://arxiv.org/abs/2601.03037 | Academic Papers | svg |
36f14383005ef1ea95a82158001872661716df6b441a2f3aa13c88347647b7d5 | 2026-01-07T00:00:00-05:00 | Validating Generalist Robots with Situation Calculus and STL Falsification | arXiv:2601.03038v1 Announce Type: new Abstract: Generalist robots are becoming a reality, capable of interpreting natural language instructions and executing diverse operations. However, their validation remains challenging because each task induces its own operational context and correctness specification, exceeding t... | https://arxiv.org/abs/2601.03038 | Academic Papers | svg |
951f069065a493391bbc27242aa7451af6856e95a2d8a290a50a00be3b07dd1a | 2026-01-07T00:00:00-05:00 | PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms | arXiv:2601.03040v1 Announce Type: new Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information. In these challenging environments, the platform must rely exclusively on inertial sensors, leading to pur... | https://arxiv.org/abs/2601.03040 | Academic Papers | svg |
eb6ce62b6224ac7e3665e1944d8c1951711530fd267423a53579a8583f646ea4 | 2026-01-07T00:00:00-05:00 | BaseCal: Unsupervised Confidence Calibration via Base Model Signals | arXiv:2601.03042v1 Announce Type: new Abstract: Reliable confidence is essential for trusting the outputs of LLMs, yet widely deployed post-trained LLMs (PoLLMs) typically compromise this trust with severe overconfidence. In contrast, we observe that their corresponding base LLMs often remain well-calibrated. This natu... | https://arxiv.org/abs/2601.03042 | Academic Papers | svg |
6d5a9322932fa986b256adb9aa509abcf9bc9f3e1dc588eb9e2a98acf6912e00 | 2026-01-07T00:00:00-05:00 | Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage | arXiv:2601.03043v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency. Prior work typically decomposes inference into prefill and decode stages, with th... | https://arxiv.org/abs/2601.03043 | Academic Papers | svg |
d071942f520e5025741aa035357e83f7cc22d176d1f6b41edf44d71b9c7cde58 | 2026-01-07T00:00:00-05:00 | SOP: A Scalable Online Post-Training System for Vision-Language-Action Models | arXiv:2601.03044v1 Announce Type: new Abstract: Vision-language-action (VLA) models achieve strong generalization through large-scale pre-training, but real-world deployment requires expert-level task proficiency in addition to broad generality. Existing post-training approaches for VLA models are typically offline, si... | https://arxiv.org/abs/2601.03044 | Academic Papers | svg |
ea758b0dba491225c31e671c3be0819f700510428ad43b4c8bf2534f08d32ab8 | 2026-01-07T00:00:00-05:00 | Motion Blur Robust Wheat Pest Damage Detection with Dynamic Fuzzy Feature Fusion | arXiv:2601.03046v1 Announce Type: new Abstract: Motion blur caused by camera shake produces ghosting artifacts that substantially degrade edge side object detection. Existing approaches either suppress blur as noise and lose discriminative structure, or apply full image restoration that increases latency and limits dep... | https://arxiv.org/abs/2601.03046 | Academic Papers | svg |
a51f4540fba8d54dfba00966671320cdcd1ff123d4ad52ce2ce231cf5899bb70 | 2026-01-07T00:00:00-05:00 | When the Coffee Feature Activates on Coffins: An Analysis of Feature Extraction and Steering for Mechanistic Interpretability | arXiv:2601.03047v1 Announce Type: new Abstract: Recent work by Anthropic on Mechanistic interpretability claims to understand and control Large Language Models by extracting human-interpretable features from their neural activation patterns using sparse autoencoders (SAEs). If successful, this approach offers one of th... | https://arxiv.org/abs/2601.03047 | Academic Papers | svg |
eed6496f0ac84feae436779abee968bd5d49d958e0d2a2375c33fe60c963d7a7 | 2026-01-07T00:00:00-05:00 | On the Intrinsic Limits of Transformer Image Embeddings in Non-Solvable Spatial Reasoning | arXiv:2601.03048v1 Announce Type: new Abstract: Vision Transformers (ViTs) excel in semantic recognition but exhibit systematic failures in spatial reasoning tasks such as mental rotation. While often attributed to data scale, we propose that this limitation arises from the intrinsic circuit complexity of the architect... | https://arxiv.org/abs/2601.03048 | Academic Papers | svg |
9d90c84a3f4937f14e6647feada1be3faa5ab713098b3ef68a613f8c875c47cf | 2026-01-07T00:00:00-05:00 | An Empirical Study on User Profile Analysis and SEO Performance: A Case of Taiwan Cultural Memory Bank 2.0 | arXiv:2601.03050v1 Announce Type: new Abstract: Taiwan Cultural Memory Bank 2.0 is an online curation platform that invites the public to become curators, fostering diverse perspectives on Taiwan's society, humanities, natural landscapes, and daily life. Built on a material bank concept, the platform encourages users t... | https://arxiv.org/abs/2601.03050 | Academic Papers | svg |
5a39ce2073043d615b1684675738d5cebbfe778d01f4c2edb76698feb82c7c9e | 2026-01-07T00:00:00-05:00 | Temporal Graph Network: Hallucination Detection in Multi-Turn Conversation | arXiv:2601.03051v1 Announce Type: new Abstract: Hallucinations can be produced by conversational AI systems, particularly in multi-turn conversations where context changes and contradictions may eventually surface. By representing the entire conversation as a temporal graph, we present a novel graph-based method for de... | https://arxiv.org/abs/2601.03051 | Academic Papers | svg |
3a4ca476f23c21b4c95d85dbb69d54d30bde7cc3066494697d698d4b2702be3d | 2026-01-07T00:00:00-05:00 | Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph | arXiv:2601.03052v1 Announce Type: new Abstract: The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress. It can effectively reduce factuality hallucinations, but faithfulness hallucinations still exist. Previous methods for detecting faithfulness hallucinations ... | https://arxiv.org/abs/2601.03052 | Academic Papers | svg |
f7a1240dc661b54ad905f8c2761ca63280fbf39a8c4c4de843b39b35a2d198c3 | 2026-01-07T00:00:00-05:00 | IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation | arXiv:2601.03054v1 Announce Type: new Abstract: Recent research on medical MLLMs has gradually shifted its focus from image-level understanding to fine-grained, pixel-level comprehension. Although segmentation serves as the foundation for pixel-level understanding, existing approaches face two major challenges. First, ... | https://arxiv.org/abs/2601.03054 | Academic Papers | svg |
83ea6c2f16979c532afa4567d780be05fed42a2f9a925caf8ca27178a4058f5e | 2026-01-07T00:00:00-05:00 | A Fast Semidefinite Convex Relaxation for Optimal Control Problems With Spatio-Temporal Constraints | arXiv:2601.03055v1 Announce Type: new Abstract: Solving optimal control problems (OCPs) of autonomous agents operating under spatial and temporal constraints fast and accurately is essential in applications ranging from eco-driving of autonomous vehicles to quadrotor navigation. However, the nonlinear programs approxim... | https://arxiv.org/abs/2601.03055 | Academic Papers | svg |
af2f5348f5487b455e1fa7fed25db4baeb275551a0fae10ae576ed276cb0b3ad | 2026-01-07T00:00:00-05:00 | Fine-Grained Generalization via Structuralizing Concept and Feature Space into Commonality, Specificity and Confounding | arXiv:2601.03056v1 Announce Type: new Abstract: Fine-Grained Domain Generalization (FGDG) presents greater challenges than conventional domain generalization due to the subtle inter-class differences and relatively pronounced intra-class variations inherent in fine-grained recognition tasks. Under domain shifts, the mo... | https://arxiv.org/abs/2601.03056 | Academic Papers | svg |
c051847f7fc17dda66d7191e362701a2534f34101200b3c865fe9d332e00ed8e | 2026-01-07T00:00:00-05:00 | Exploring the Relationship Between Local Election Results and Online Public Opinion in Taiwan: A Case Study of Taitung County | arXiv:2601.03057v1 Announce Type: new Abstract: This study examines the relationship between online buzz and local election outcomes in Taiwan, with a focus on Taitung County. As social media becomes a major channel for public discourse, online buzz is increasingly seen as a factor influencing elections. However, its i... | https://arxiv.org/abs/2601.03057 | Academic Papers | svg |
64b54a0fa23d40966d36faeb7bc8b4771d88a7f04f2b8efc2b37caf1b7b990c7 | 2026-01-07T00:00:00-05:00 | Vertical tacit collusion in AI-mediated markets | arXiv:2601.03061v1 Announce Type: new Abstract: AI shopping agents are being deployed to hundreds of millions of consumers, creating a new intermediary between platforms, sellers, and buyers. We identify a novel market failure: vertical tacit collusion, where platforms controlling rankings and sellers controlling produ... | https://arxiv.org/abs/2601.03061 | Academic Papers | svg |
23c7721f3f387d80d8876e19b2d8d80c98b1a983d11c83d4b5034dccb06cc12f | 2026-01-07T00:00:00-05:00 | Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks | arXiv:2601.03062v1 Announce Type: new Abstract: Timely leak detection in water distribution networks is critical for conserving resources and maintaining operational efficiency. Although Graph Neural Networks (GNNs) excel at capturing spatial-temporal dependencies in sensor data, their black-box nature and the limited ... | https://arxiv.org/abs/2601.03062 | Academic Papers | svg |
31bcf08ca54eb4c5994a3dafabec5035207d75c64235903c22aca4759fe45410 | 2026-01-07T00:00:00-05:00 | Do LLMs Encode Functional Importance of Reasoning Tokens? | arXiv:2601.03066v1 Announce Type: new Abstract: Large language models solve complex tasks by generating long reasoning chains, achieving higher accuracy at the cost of increased computational cost and reduced ability to isolate functionally relevant reasoning. Prior work on compact reasoning shortens such chains throug... | https://arxiv.org/abs/2601.03066 | Academic Papers | svg |
f2c4ca5625874920c3b53455b7e3c1e7613c1e6cee7386fbf13746884e7ea5cf | 2026-01-07T00:00:00-05:00 | Joint Encoding of KV-Cache Blocks for Scalable LLM Serving | arXiv:2601.03067v1 Announce Type: new Abstract: Modern large language models (LLMs) drive interactive AI systems but are bottlenecked by the memory-heavy growth of key-value (KV) caches, which limits real-time throughput under concurrent loads. Existing KV-cache compression methods rely on rigid heuristics, disrupt ten... | https://arxiv.org/abs/2601.03067 | Academic Papers | svg |
f5b89b794db0c7b98d913e750b8f72c1854a8193e5b783d8c68df7cdeff3c441 | 2026-01-07T00:00:00-05:00 | HEXAR: a Hierarchical Explainability Architecture for Robots | arXiv:2601.03070v1 Announce Type: new Abstract: As robotic systems become increasingly complex, the need for explainable decision-making becomes critical. Existing explainability approaches in robotics typically either focus on individual modules, which can be difficult to query from the perspective of high-level behav... | https://arxiv.org/abs/2601.03070 | Academic Papers | svg |
fecd2c8dd1807d0fbc24c104607bf52bf3c0cba2555cf793c22c4f4931bff5cb | 2026-01-07T00:00:00-05:00 | Understanding Multi-Agent Reasoning with Large Language Models for Cartoon VQA | arXiv:2601.03073v1 Announce Type: new Abstract: Visual Question Answering (VQA) for stylised cartoon imagery presents challenges, such as interpreting exaggerated visual abstraction and narrative-driven context, which are not adequately addressed by standard large language models (LLMs) trained on natural images. To in... | https://arxiv.org/abs/2601.03073 | Academic Papers | svg |
30778a4807e0e6492a1b7a44b0ee4b95d2587ee8e860b68ab7acc6da6f02a228 | 2026-01-07T00:00:00-05:00 | Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace | arXiv:2601.03075v1 Announce Type: new Abstract: Trajectory prediction (TP) is crucial for ensuring safety and efficiency in modern air traffic management systems. It is, for example, a core component of conflict detection and resolution tools, arrival sequencing algorithms, capacity planning, as well as several future ... | https://arxiv.org/abs/2601.03075 | Academic Papers | svg |
2bc6b59cf77cea616fb3f75c5d9764089f22d7873d02c3c23dce039dfeed16ac | 2026-01-07T00:00:00-05:00 | Learning to Diagnose and Correct Moral Errors: Towards Enhancing Moral Sensitivity in Large Language Models | arXiv:2601.03079v1 Announce Type: new Abstract: Moral sensitivity is fundamental to human moral competence, as it guides individuals in regulating everyday behavior. Although many approaches seek to align large language models (LLMs) with human moral values, how to enable them morally sensitive has been extremely chall... | https://arxiv.org/abs/2601.03079 | Academic Papers | svg |
06be158c4a21bac6615cf9b578ebe5c3c78db31ceae9ec1d81c65392ec031938 | 2026-01-07T00:00:00-05:00 | Real-Time Adaptive Anomaly Detection in Industrial IoT Environments | arXiv:2601.03085v1 Announce Type: new Abstract: To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous ... | https://arxiv.org/abs/2601.03085 | Academic Papers | svg |
b18b58736762469239378e2bfc6d472e0e3cfc52031ab604456f6f5321df3e00 | 2026-01-07T00:00:00-05:00 | Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators | arXiv:2601.03086v1 Announce Type: new Abstract: We propose a Pretrained Finite Element Method (PFEM),a physics driven framework that bridges the efficiency of neural operator learning with the accuracy and robustness of classical finite element methods (FEM). PFEM consists of a physics informed pretraining stage and an... | https://arxiv.org/abs/2601.03086 | Academic Papers | svg |
a8c3631aa3a41b89726d3cf06f734e0368ca1814ae2712c26f054372a8e4821e | 2026-01-07T00:00:00-05:00 | Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs | arXiv:2601.03087v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit systematic biases across demographic groups. Auditing is proposed as an accountability tool for black-box LLM applications, but suffers from resource-intensive query access. We conceptualise auditing as uncertainty estimation over a ta... | https://arxiv.org/abs/2601.03087 | Academic Papers | svg |
44013b315266fe392789df5157337615a0ad98d2a9a84c4d247542beed43b181 | 2026-01-07T00:00:00-05:00 | Grad-ELLM: Gradient-based Explanations for Decoder-only LLMs | arXiv:2601.03089v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet their black-box nature raises concerns about transparency and faithfulness. Input attribution methods aim to highlight each input token's contributions to the model's output, ... | https://arxiv.org/abs/2601.03089 | Academic Papers | svg |
4d4395d2cdb75dc60f7b619b8f9fd594bc3c61130faeba35628b61b131f07a66 | 2026-01-07T00:00:00-05:00 | LesionTABE: Equitable AI for Skin Lesion Detection | arXiv:2601.03090v1 Announce Type: new Abstract: Bias remains a major barrier to the clinical adoption of AI in dermatology, as diagnostic models underperform on darker skin tones. We present LesionTABE, a fairness-centric framework that couples adversarial debiasing with dermatology-specific foundation model embeddings... | https://arxiv.org/abs/2601.03090 | Academic Papers | svg |
4984eb903d52a1d034a32cfb70138862cdcef20680c5857856ed64123a37565b | 2026-01-07T00:00:00-05:00 | ATLAS: Adaptive Test-Time Latent Steering with External Verifiers for Enhancing LLMs Reasoning | arXiv:2601.03093v1 Announce Type: new Abstract: Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (LLMs) toward improved reasoning and efficiency without additional training. However, most existing approaches rely on fixed ... | https://arxiv.org/abs/2601.03093 | Academic Papers | svg |
0c2e70476ced4e253d374f79c877dd39abc5ac8cbe62e5d2e0c7459163959832 | 2026-01-07T00:00:00-05:00 | Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees | arXiv:2601.03097v1 Announce Type: new Abstract: We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically on $\mathrm{SE}(3)$. The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct c... | https://arxiv.org/abs/2601.03097 | Academic Papers | svg |
62580f5368af6f53fc5a5eefc1fea82c0309fb9270d1ece1d84e64a3bfc9df11 | 2026-01-07T00:00:00-05:00 | From Muscle to Text with MyoText: sEMG to Text via Finger Classification and Transformer-Based Decoding | arXiv:2601.03098v1 Announce Type: new Abstract: Surface electromyography (sEMG) provides a direct neural interface for decoding muscle activity and offers a promising foundation for keyboard-free text input in wearable and mixed-reality systems. Previous sEMG-to-text studies mainly focused on recognizing letters direct... | https://arxiv.org/abs/2601.03098 | Academic Papers | svg |
c034578a03fd6d5856aed9334d91776702723d1023290850d3fae07615730641 | 2026-01-07T00:00:00-05:00 | Time-Aware Synthetic Control | arXiv:2601.03099v1 Announce Type: new Abstract: The synthetic control (SC) framework is widely used for observational causal inference with time-series panel data. SC has been successful in diverse applications, but existing methods typically treat the ordering of pre-intervention time indices interchangeable. This inv... | https://arxiv.org/abs/2601.03099 | Academic Papers | svg |
9aad19760cb19099586e8a340d96fa57072654454f2dbb0221d21727d496bdd7 | 2026-01-07T00:00:00-05:00 | Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs | arXiv:2601.03100v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utilized. MLLMs still suffer from visually ungrounded hallucinations, often relying on language p... | https://arxiv.org/abs/2601.03100 | Academic Papers | svg |
365c196b2e0874f1d72379614d1ab81d1ea0aa0e138785461ca2738d337a7337 | 2026-01-07T00:00:00-05:00 | Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs | arXiv:2601.03103v1 Announce Type: new Abstract: Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In this study, we model heterogeneity in humor preferences in Oogiri, a Japanese creative response game, by clustering users with voting... | https://arxiv.org/abs/2601.03103 | Academic Papers | svg |
9ab78e57bfab59857ac78f3cd3273e9898c6145daa91e7ddde047211198457d4 | 2026-01-07T00:00:00-05:00 | One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling | arXiv:2601.03111v1 Announce Type: new Abstract: The reasoning ability of large language models (LLMs) can be unleashed with reinforcement learning (RL) (OpenAI, 2024; DeepSeek-AI et al., 2025a; Zeng et al., 2025). The success of existing RL attempts in LLMs usually relies on high-quality samples of thousands or beyond.... | https://arxiv.org/abs/2601.03111 | Academic Papers | svg |
bc1b03e669d5cb78edd6d68bd9e699455455cf221b429ac9141a5ce10bc1898f | 2026-01-07T00:00:00-05:00 | A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control | arXiv:2601.03113v1 Announce Type: new Abstract: This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital Twin is intended to support the development and rigorous human-in-the-loop evaluation of AI agents for Air Traffic Control ... | https://arxiv.org/abs/2601.03113 | Academic Papers | svg |
8477b8f6861e8c5311338d81e8ca0ff8afae6e741273424bceeb72f968814b00 | 2026-01-07T00:00:00-05:00 | Stroke Patches: Customizable Artistic Image Styling Using Regression | arXiv:2601.03114v1 Announce Type: new Abstract: We present a novel, regression-based method for artistically styling images. Unlike recent neural style transfer or diffusion-based approaches, our method allows for explicit control over the stroke composition and level of detail in the rendered image through the use of ... | https://arxiv.org/abs/2601.03114 | Academic Papers | svg |
d09f989117984a64a9798b4f9105b906bc7b5e6f2e350b8ffbcaa522d21f071d | 2026-01-07T00:00:00-05:00 | Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models | arXiv:2601.03115v1 Announce Type: new Abstract: Emotion is a central dimension of spoken communication, yet, we still lack a mechanistic account of how modern large audio-language models (LALMs) encode it internally. We present the first neuron-level interpretability study of emotion-sensitive neurons (ESNs) in LALMs a... | https://arxiv.org/abs/2601.03115 | Academic Papers | svg |
f1c1369af2f13274be64760bd521f125dfebe69a488142c8997888dc62be7959 | 2026-01-07T00:00:00-05:00 | A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace | arXiv:2601.03120v1 Announce Type: new Abstract: Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Bluebird, an industry-academic collaboration, has developed a probabilistic Digital Twin of en r... | https://arxiv.org/abs/2601.03120 | Academic Papers | svg |
10b0cc72f3d79719ef442276ac7edd6eefaf26a5913b5723a9ed509a293ba9d9 | 2026-01-07T00:00:00-05:00 | ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generation | arXiv:2601.03121v1 Announce Type: new Abstract: Augmenting toxic language data in a controllable and class-specific manner is crucial for improving robustness in toxicity classification, yet remains challenging due to limited supervision and distributional skew. We propose ToxiGAN, a class-aware text augmentation frame... | https://arxiv.org/abs/2601.03121 | Academic Papers | svg |
5dc4e631fb1d6d4b57f55686efc9aad618854435161b1532d83740fdcb476284 | 2026-01-07T00:00:00-05:00 | LeafLife: An Explainable Deep Learning Framework with Robustness for Grape Leaf Disease Recognition | arXiv:2601.03124v1 Announce Type: new Abstract: Plant disease diagnosis is essential to farmers' management choices because plant diseases frequently lower crop yield and product quality. For harvests to flourish and agricultural productivity to boost, grape leaf disease detection is important. The plant disease datase... | https://arxiv.org/abs/2601.03124 | Academic Papers | svg |
1c67ea7b24fc63a33df27ab0a28dfe0bfe6fbef142790ee49b6569f62622dd01 | 2026-01-07T00:00:00-05:00 | Dualities for finite abelian groups and applications to coding theory | arXiv:2601.03126v1 Announce Type: new Abstract: The choice of an isomorphism, a duality, between a finite abelian group $A$ and its character group allows one to define dual codes of additive codes over $A$. Properties of dualities and dual codes are studied, continuing work of Delsarte from 1973 and more recent work o... | https://arxiv.org/abs/2601.03126 | Academic Papers | svg |
81fd9d2043c9063e5c45deebb9c1c852d1419f6ebee7ca31cfe740f276d08ab8 | 2026-01-07T00:00:00-05:00 | Unified Thinker: A General Reasoning Modular Core for Image Generation | arXiv:2601.03127v1 Announce Type: new Abstract: Despite impressive progress in high-fidelity image synthesis, generative models still struggle with logic-intensive instruction following, exposing a persistent reasoning--execution gap. Meanwhile, closed-source systems (e.g., Nano Banana) have demonstrated strong reasoni... | https://arxiv.org/abs/2601.03127 | Academic Papers | svg |
69ebb7ca3fcaf3a12ecafc16b03b22696e1a4cf75005de756c919010eccd1778 | 2026-01-07T00:00:00-05:00 | Density Matters: A Complexity Dichotomy of Deleting Edges to Bound Subgraph Density | arXiv:2601.03129v1 Announce Type: new Abstract: We study $\tau$-Bounded-Density Edge Deletion ($\tau$-BDED), where given an undirected graph $G$, the task is to remove as few edges as possible to obtain a graph $G'$ where no subgraph of $G'$ has density more than $\tau$. The density of a (sub)graph is the number of edg... | https://arxiv.org/abs/2601.03129 | Academic Papers | svg |
e3f3ec90b2cd22baaa40b8d333eea31e01769d52ff46cff9230e5a9965cfa6a4 | 2026-01-07T00:00:00-05:00 | Automatic Prompt Engineering with No Task Cues and No Tuning | arXiv:2601.03130v1 Announce Type: new Abstract: This paper presents a system for automatic prompt engineering that is much simpler in both design and application and yet as effective as the existing approaches. It requires no tuning and no explicit clues about the task. We evaluated our approach on cryptic column name ... | https://arxiv.org/abs/2601.03130 | Academic Papers | svg |
efa91bae4ee2047717a064e9746227b797d6fe9e6ce2b7948b6664ba5e505067 | 2026-01-07T00:00:00-05:00 | Finite Memory Belief Approximation for Optimal Control in Partially Observable Markov Decision Processes | arXiv:2601.03132v1 Announce Type: new Abstract: We study finite memory belief approximation for partially observable (PO) stochastic optimal control (SOC) problems. While belief states are sufficient for SOC in partially observable Markov decision processes (POMDPs), they are generally infinite-dimensional and impracti... | https://arxiv.org/abs/2601.03132 | Academic Papers | svg |
1d5209b1271182b1c0ce2c3aa65da5972c83de59d31696a89691ae21a15e6e4c | 2026-01-07T00:00:00-05:00 | The Anatomy of Conversational Scams: A Topic-Based Red Teaming Analysis of Multi-Turn Interactions in LLMs | arXiv:2601.03134v1 Announce Type: new Abstract: As LLMs gain persuasive agentic capabilities through extended dialogues, they introduce novel risks in multi-turn conversational scams that single-turn safety evaluations fail to capture. We systematically study these risks using a controlled LLM-to-LLM simulation framewo... | https://arxiv.org/abs/2601.03134 | Academic Papers | svg |
0f1da87a43fc1e0354ee4589ecd4de5e9c8cb9c1c92871a14bee92f5c2363bbd | 2026-01-07T00:00:00-05:00 | Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing | arXiv:2601.03135v1 Announce Type: new Abstract: Low-resource indigenous languages often lack the parallel corpora required for effective neural machine translation (NMT). Synthetic data generation offers a practical strategy for mitigating this limitation in data-scarce settings. In this work, we augment curated parall... | https://arxiv.org/abs/2601.03135 | Academic Papers | svg |
02b61fbef4f49db116c1f18c5aade0664c9a10d675c431d4896cdcd19310105e | 2026-01-07T00:00:00-05:00 | Limited Linguistic Diversity in Embodied AI Datasets | arXiv:2601.03136v1 Announce Type: new Abstract: Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate these systems remain poorly documented. In this work, we present a systematic dataset audit of several widely used VLA corp... | https://arxiv.org/abs/2601.03136 | Academic Papers | svg |
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