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6cc385118e1410e7702330233a4201f8baa8dac6e095d62cf67fa48f5f74b6c5 | 2026-02-02T00:00:00-05:00 | Capacity of Two-User Wireless Systems Aided by Movable Signals | arXiv:2601.22358v1 Announce Type: new Abstract: Movable signals have emerged as a third approach to enable smart radio environments (SREs), complementing reconfigurable intelligent surfaces (RISs) and flexible antennas. This paper investigates their potential to enhance multi-user wireless systems. Focusing on two-user... | https://arxiv.org/abs/2601.22358 | Academic Papers | svg |
dabdc7a3b27658250c7c67310bc11fb77de477ab0d34c57477430d14c15821b9 | 2026-02-02T00:00:00-05:00 | The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples | arXiv:2601.22359v1 Announce Type: new Abstract: Machine unlearning offers a practical alternative to avoid full model re-training by approximately removing the influence of specific user data. While existing methods certify unlearning via statistical indistinguishability from re-trained models, these guarantees do not ... | https://arxiv.org/abs/2601.22359 | Academic Papers | svg |
0ac1baaa260e7617d5a881d1d6119a1222a05c4a62fa524fda73e2efefdaa967 | 2026-02-02T00:00:00-05:00 | MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment | arXiv:2601.22361v1 Announce Type: new Abstract: Assessing the veracity of online content has become increasingly critical. Large language models (LLMs) have recently enabled substantial progress in automated veracity assessment, including automated fact-checking and claim verification systems. Typical veracity assessme... | https://arxiv.org/abs/2601.22361 | Academic Papers | svg |
aafb278c93428b105efc7bc2e36ccd673a253c7fd399893b400a05bbba2f5287 | 2026-02-02T00:00:00-05:00 | Understanding Efficiency: Quantization, Batching, and Serving Strategies in LLM Energy Use | arXiv:2601.22362v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in production, contributing towards shifting the burden in terms of computational resources and energy demands from training to inference. While prior work has examined the energy cost of inference per prompt or per t... | https://arxiv.org/abs/2601.22362 | Academic Papers | svg |
7be097148ade36649dc7fd301a088b4b0b8a2f0ab9ec5f82b40b3e131d8e89d3 | 2026-02-02T00:00:00-05:00 | Context Structure Reshapes the Representational Geometry of Language Models | arXiv:2601.22364v1 Announce Type: new Abstract: Large Language Models (LLMs) have been shown to organize the representations of input sequences into straighter neural trajectories in their deep layers, which has been hypothesized to facilitate next-token prediction via linear extrapolation. Language models can also ada... | https://arxiv.org/abs/2601.22364 | Academic Papers | svg |
e565097f43db4f8ad7304e7b216ba6996777af1db727736a93912c7efd555e48 | 2026-02-02T00:00:00-05:00 | Towards Solving the Gilbert-Pollak Conjecture via Large Language Models | arXiv:2601.22365v1 Announce Type: new Abstract: The Gilbert-Pollak Conjecture \citep{gilbert1968steiner}, also known as the Steiner Ratio Conjecture, states that for any finite point set in the Euclidean plane, the Steiner minimum tree has length at least $\sqrt{3}/2 \approx 0.866$ times that of the Euclidean minimum s... | https://arxiv.org/abs/2601.22365 | Academic Papers | svg |
8ed2efc6e4bb640c7c8aee23935c0c2573102f73dcd515ade7c5fd9864cedf65 | 2026-02-02T00:00:00-05:00 | Learning Provably Correct Distributed Protocols Without Human Knowledge | arXiv:2601.22369v1 Announce Type: new Abstract: Provably correct distributed protocols, which are a critical component of modern distributed systems, are highly challenging to design and have often required decades of human effort. These protocols allow multiple agents to coordinate to come to a common agreement in an ... | https://arxiv.org/abs/2601.22369 | Academic Papers | svg |
3ffc401941c987cd4b15c451c946ba1a61f7e515134c459c128bde5433d0aca4 | 2026-02-02T00:00:00-05:00 | FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models | arXiv:2601.22371v1 Announce Type: new Abstract: Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance, where the commonly-used Gaussian-process (GP) surrogates struggle with cubic scaling costs and overfit to sparse high-fidelity observations, limiting efficiency and generalization in real-... | https://arxiv.org/abs/2601.22371 | Academic Papers | svg |
c15272ba0173583f0003e68952c835133bfa74d10eeb7ce3d2f42493575b8269 | 2026-02-02T00:00:00-05:00 | Stability-Aware Prompt Optimization for Clinical Data Abstraction | arXiv:2601.22373v1 Announce Type: new Abstract: Large language models used for clinical abstraction are sensitive to prompt wording, yet most work treats prompts as fixed and studies uncertainty in isolation. We argue these should be treated jointly. Across two clinical tasks (MedAlign applicability/correctness and MS ... | https://arxiv.org/abs/2601.22373 | Academic Papers | svg |
f57fb429ab187e619b9222f4fa7b703a3ee5f505c25308b07985f093024f1978 | 2026-02-02T00:00:00-05:00 | FlexMap: Generalized HD Map Construction from Flexible Camera Configurations | arXiv:2601.22376v1 Announce Type: new Abstract: High-definition (HD) maps provide essential semantic information of road structures for autonomous driving systems, yet current HD map construction methods require calibrated multi-camera setups and either implicit or explicit 2D-to-BEV transformations, making them fragil... | https://arxiv.org/abs/2601.22376 | Academic Papers | svg |
6355b80b7ebaf34a4de680cae5155a67432234b16d06a01becc6f1d1a5948e84 | 2026-02-02T00:00:00-05:00 | SPLA: Block Sparse Plus Linear Attention for Long Context Modeling | arXiv:2601.22379v1 Announce Type: new Abstract: Block-wise sparse attention offers significant efficiency gains for long-context modeling, yet existing methods often suffer from low selection fidelity and cumulative contextual loss by completely discarding unselected blocks. To address these limitations, we introduce S... | https://arxiv.org/abs/2601.22379 | Academic Papers | svg |
a1a7d798d0f7dcb88788526bda6c0c10485db1310c607274f4f9afd435d0f5a0 | 2026-02-02T00:00:00-05:00 | Lantern: A Minimalist Robotic Object Platform | arXiv:2601.22381v1 Announce Type: new Abstract: Robotic objects are simple actuated systems that subtly blend into human environments. We design and introduce Lantern, a minimalist robotic object platform to enable building simple robotic artifacts. We conducted in-depth design and engineering iterations of Lantern's m... | https://arxiv.org/abs/2601.22381 | Academic Papers | svg |
b462b3c6186cef7ddf3a95c21ac67e17252dcd26d864e50e6213f7434b96fdb5 | 2026-02-02T00:00:00-05:00 | Purely Agentic Black-Box Optimization for Biological Design | arXiv:2601.22382v1 Announce Type: new Abstract: Many key challenges in biological design-such as small-molecule drug discovery, antimicrobial peptide development, and protein engineering-can be framed as black-box optimization over vast, complex structured spaces. Existing methods rely mainly on raw structural data and... | https://arxiv.org/abs/2601.22382 | Academic Papers | svg |
2c24332432eee5485640815edd8e4ee0756e845f603813c053f44e99c233180e | 2026-02-02T00:00:00-05:00 | Graph is a Substrate Across Data Modalities | arXiv:2601.22384v1 Announce Type: new Abstract: Graphs provide a natural representation of relational structure that arises across diverse domains. Despite this ubiquity, graph structure is typically learned in a modality- and task-isolated manner, where graph representations are constructed within individual task cont... | https://arxiv.org/abs/2601.22384 | Academic Papers | svg |
1290ede9d2c91feaea87f023b551ee7bab0dfbf2b943f453f769b5f954b3de4c | 2026-02-02T00:00:00-05:00 | SP^2DPO: An LLM-assisted Semantic Per-Pair DPO Generalization | arXiv:2601.22385v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) controls the trade-off between fitting preference labels and staying close to a reference model using a single global temperature beta, implicitly treating all preference pairs as equally informative. Real-world preference corpora are ... | https://arxiv.org/abs/2601.22385 | Academic Papers | svg |
8065c0c4dd411ec2e189778d6073acb8f673b5136e827246aed3cfa105fb34da | 2026-02-02T00:00:00-05:00 | Specialists or Generalists? Multi-Agent and Single-Agent LLMs for Essay Grading | arXiv:2601.22386v1 Announce Type: new Abstract: Automated essay scoring (AES) systems increasingly rely on large language models, yet little is known about how architectural choices shape their performance across different essay quality levels. This paper evaluates single-agent and multi-agent LLM architectures for ess... | https://arxiv.org/abs/2601.22386 | Academic Papers | svg |
67d47a47331147656750a5134139afc94d10fc66ff41d18554842e73ddad1948 | 2026-02-02T00:00:00-05:00 | Plant-Inspired Robot Design Metaphors for Ambient HRI | arXiv:2601.22387v1 Announce Type: new Abstract: Plants offer a paradoxical model for interaction: they are ambient, low-demand presences that nonetheless shape atmosphere, routines, and relationships through temporal rhythms and subtle expressions. In contrast, most human-robot interaction (HRI) has been grounded in an... | https://arxiv.org/abs/2601.22387 | Academic Papers | svg |
f3ade658a60747faf661114f2eb9fade5360d657bfa2ecc3c21a9940cb04a7ed | 2026-02-02T00:00:00-05:00 | An Effective Energy Mask-based Adversarial Evasion Attacks against Misclassification in Speaker Recognition Systems | arXiv:2601.22390v1 Announce Type: new Abstract: Evasion attacks pose significant threats to AI systems, exploiting vulnerabilities in machine learning models to bypass detection mechanisms. The widespread use of voice data, including deepfakes, in promising future industries is currently hindered by insufficient legal ... | https://arxiv.org/abs/2601.22390 | Academic Papers | svg |
df9db1b0e7212bef5a79a0984bce537c1b865affba6f9db30ef30cd80c34d1e0 | 2026-02-02T00:00:00-05:00 | Proof Complexity of Linear Logics | arXiv:2601.22393v1 Announce Type: new Abstract: Proving proof-size lower bounds for $\mathbf{LK}$, the sequent calculus for classical propositional logic, remains a major open problem in proof complexity. We shed new light on this challenge by isolating the power of structural rules, showing that their combination is e... | https://arxiv.org/abs/2601.22393 | Academic Papers | svg |
9d9da62f4cdeba33fbabb528c9b8b1d1ef9e313f9541ddee09d899aa9c96cb7f | 2026-02-02T00:00:00-05:00 | Conversational Inoculation to Enhance Resistance to Misinformation | arXiv:2601.22394v1 Announce Type: new Abstract: Proliferation of misinformation is a globally acknowledged problem. Cognitive Inoculation helps build resistance to different forms of persuasion, such as misinformation. We investigate Conversational Inoculation, a method to help people build resistance to misinformation... | https://arxiv.org/abs/2601.22394 | Academic Papers | svg |
cbfd8345289f49ca90ddddc0855ca7859945931c25d319737dfdf73376cd72e1 | 2026-02-02T00:00:00-05:00 | Regional Transportation Modeling for Equitable Electric Vehicle Charging Infrastructure Design | arXiv:2601.22395v1 Announce Type: new Abstract: The widespread adoption of battery electric vehicles (BEVs) holds promise for mitigating emission-related health impacts, particularly for low-income communities disproportionately affected by exposure to traffic-related air pollution. However, designing effective chargin... | https://arxiv.org/abs/2601.22395 | Academic Papers | svg |
8b69390d43f0dcd41894a9373ec851d097f6d3ef225f09e2c4da20028da64741 | 2026-02-02T00:00:00-05:00 | Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworks | arXiv:2601.22396v1 Announce Type: new Abstract: Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain. This paper investigates the ... | https://arxiv.org/abs/2601.22396 | Academic Papers | svg |
6602ce61e143ef9ebdb25094af14b541f2f0dfb727a8660259fe55eec4835c38 | 2026-02-02T00:00:00-05:00 | SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning | arXiv:2601.22397v1 Announce Type: new Abstract: Multi-stage ML inference pipelines are difficult to autoscale due to heterogeneous resources, cross-stage coupling, and dynamic bottleneck migration. We present SAIR, an autoscaling framework that uses an LLM as an in-context reinforcement learning controller, improving i... | https://arxiv.org/abs/2601.22397 | Academic Papers | svg |
022c83522432d63fd0faf79fb173441d1b212744f5d26e44047157ced1f078b2 | 2026-02-02T00:00:00-05:00 | Jailbreaks on Vision Language Model via Multimodal Reasoning | arXiv:2601.22398v1 Announce Type: new Abstract: Vision-language models (VLMs) have become central to tasks such as visual question answering, image captioning, and text-to-image generation. However, their outputs are highly sensitive to prompt variations, which can reveal vulnerabilities in safety alignment. In this wo... | https://arxiv.org/abs/2601.22398 | Academic Papers | svg |
c0296ed2fb70b495913219ec49638e3a9bbcd66476df89710e2f3a78bb0a994a | 2026-02-02T00:00:00-05:00 | Score-based Integrated Gradient for Root Cause Explanations of Outliers | arXiv:2601.22399v1 Announce Type: new Abstract: Identifying the root causes of outliers is a fundamental problem in causal inference and anomaly detection. Traditional approaches based on heuristics or counterfactual reasoning often struggle under uncertainty and high-dimensional dependencies. We introduce SIREN, a nov... | https://arxiv.org/abs/2601.22399 | Academic Papers | svg |
3739ca10867f6c3c614b533118678c5d721b3c16f002a62196dd9d98d8f6abba | 2026-02-02T00:00:00-05:00 | Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erd\H{o}s Problems | arXiv:2601.22401v1 Announce Type: new Abstract: We present a case study in semi-autonomous mathematics discovery, using Gemini to systematically evaluate 700 conjectures labeled 'Open' in Bloom's Erd\H{o}s Problems database. We employ a hybrid methodology: AI-driven natural language verification to narrow the search sp... | https://arxiv.org/abs/2601.22401 | Academic Papers | svg |
d29081b2a9534b82a2e738d136e9ed1667017f26e5c2a8d09a28e829849cf18c | 2026-02-02T00:00:00-05:00 | Bifocal Attention: Harmonizing Geometric and Spectral Positional Embeddings for Algorithmic Generalization | arXiv:2601.22402v1 Announce Type: new Abstract: Rotary Positional Embeddings (RoPE) have become the standard for Large Language Models (LLMs) due to their ability to encode relative positions through geometric rotation. However, we identify a significant limitation we term ''Spectral Rigidity'': standard RoPE utilizes ... | https://arxiv.org/abs/2601.22402 | Academic Papers | svg |
5e5c1124d931050e8c0cb63747697afcfa71eeaccda6fd1eb2b7f4209d8d9995 | 2026-02-02T00:00:00-05:00 | Modeling of Non-linear Dynamics of Lithium-ion Batteries via Delay-Embedded Dynamic Mode Decomposition | arXiv:2601.22403v1 Announce Type: new Abstract: The complex electrochemical behavior of lithium-ion batteries results in non-linear dynamics and appropriate modeling of this non-linear dynamical system is of interest for better management and control. In this work, we proposed a family of dynamic mode decomposition (DM... | https://arxiv.org/abs/2601.22403 | Academic Papers | svg |
8e12edb1ab35a16efc824b84333286a124708f805c2403efdaeba471f1a2f0c0 | 2026-02-02T00:00:00-05:00 | Accurate Pedestrian Tracking in Urban Canyons: A Multi-Modal Fusion Approach | arXiv:2601.22406v1 Announce Type: new Abstract: The contribution describes a pedestrian navigation approach designed to improve localization accuracy in urban environments where GNSS performance is degraded, a problem that is especially critical for blind or low-vision users who depend on precise guidance such as ident... | https://arxiv.org/abs/2601.22406 | Academic Papers | svg |
8e777f2a7df23f1ca849acf314e4d1c97e7ec03ac55df31e8048ceda881a5e08 | 2026-02-02T00:00:00-05:00 | Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks | arXiv:2601.22409v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) have recently emerged as a structured alternative to standard MLPs, yet a principled theory for their training dynamics, generalization, and privacy properties remains limited. In this paper, we analyze gradient descent (GD) for training... | https://arxiv.org/abs/2601.22409 | Academic Papers | svg |
7ab7b6f520af985ab2e63577d6d0532daf03812b21a95cdc3dcd87cdc0185ccf | 2026-02-02T00:00:00-05:00 | Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking | arXiv:2601.22410v1 Announce Type: new Abstract: We propose an interpretable, graph-based framework for analyzing semantic shift in diachronic corpora. For each target word and time slice, we induce a word-centered semantic network that integrates distributional similarity from diachronic Skip-gram embeddings with lexic... | https://arxiv.org/abs/2601.22410 | Academic Papers | svg |
111c19ee665b30d3e1ed47dfe69fc715b64739a20d6e920ca1d354664dcb3a9d | 2026-02-02T00:00:00-05:00 | EMBC Special Issue: Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture | arXiv:2601.22412v1 Announce Type: new Abstract: Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation and trust of multi-view markerless motion capture (MMMC) require that, in addition to being accurate, these systems produce ... | https://arxiv.org/abs/2601.22412 | Academic Papers | svg |
7a02408aad314fa2f2ce29ddf7da6e14a7b16e64505f2bdafa8e32072180d9d3 | 2026-02-02T00:00:00-05:00 | PriviSense: A Frida-Based Framework for Multi-Sensor Spoofing on Android | arXiv:2601.22414v1 Announce Type: new Abstract: Mobile apps increasingly rely on real-time sensor and system data to adapt their behavior to user context. While emulators and instrumented builds offer partial solutions, they often fail to support reproducible testing of context-sensitive app behavior on physical device... | https://arxiv.org/abs/2601.22414 | Academic Papers | svg |
2c42e65271268e92cc6fea7b8883546cefc7cc0869bcba1b6684d130bb67c82b | 2026-02-02T00:00:00-05:00 | MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning | arXiv:2601.22416v1 Announce Type: new Abstract: Multimodal-attributed graphs (MMAGs) provide a unified framework for modeling complex relational data by integrating heterogeneous modalities with graph structures. While centralized learning has shown promising performance, MMAGs in real-world applications are often dist... | https://arxiv.org/abs/2601.22416 | Academic Papers | svg |
3560a373ec6a047f0235af0dde510fc17e633a433c4a5e01fe07e1bcd09d551c | 2026-02-02T00:00:00-05:00 | AI-Enabled Waste Classification as a Data-Driven Decision Support Tool for Circular Economy and Urban Sustainability | arXiv:2601.22418v1 Announce Type: new Abstract: Efficient waste sorting is crucial for enabling circular-economy practices and resource recovery in smart cities. This paper evaluates both traditional machine-learning (Random Forest, SVM, AdaBoost) and deep-learning techniques including custom CNNs, VGG16, ResNet50, and... | https://arxiv.org/abs/2601.22418 | Academic Papers | svg |
3f22a3ba4cac6910c073bd857f8e11c36dfb7bf81750894c9ab8c2a596e737ff | 2026-02-02T00:00:00-05:00 | Dynamic Welfare-Maximizing Pooled Testing | arXiv:2601.22419v1 Announce Type: new Abstract: Pooled testing is a common strategy for public health disease screening under limited testing resources, allowing multiple biological samples to be tested together with the resources of a single test, at the cost of reduced individual resolution. While dynamic and adaptiv... | https://arxiv.org/abs/2601.22419 | Academic Papers | svg |
64913b81c90d17a2afacb4a95aac8500e86497260e625042e9f03bd2b94c477a | 2026-02-02T00:00:00-05:00 | MetaLead: A Comprehensive Human-Curated Leaderboard Dataset for Transparent Reporting of Machine Learning Experiments | arXiv:2601.22420v1 Announce Type: new Abstract: Leaderboards are crucial in the machine learning (ML) domain for benchmarking and tracking progress. However, creating leaderboards traditionally demands significant manual effort. In recent years, efforts have been made to automate leaderboard generation, but existing da... | https://arxiv.org/abs/2601.22420 | Academic Papers | svg |
f0db071893200fd59ef5208eef6b69c3754ac7153673a0dc407ef72a9972b7e8 | 2026-02-02T00:00:00-05:00 | Toward Third-Party Assurance of AI Systems: Design Requirements, Prototype, and Early Testing | arXiv:2601.22424v1 Announce Type: new Abstract: As Artificial Intelligence (AI) systems proliferate, the need for systematic, transparent, and actionable processes for evaluating them is growing. While many resources exist to support AI evaluation, they have several limitations. Few address both the process of designin... | https://arxiv.org/abs/2601.22424 | Academic Papers | svg |
d560346ce45662e6e7ad35d04cda3de15c9de448a95e39a9120560c7beb5cc71 | 2026-02-02T00:00:00-05:00 | ScamPilot: Simulating Conversations with LLMs to Protect Against Online Scams | arXiv:2601.22426v1 Announce Type: new Abstract: Fraud continues to proliferate online, from phishing and ransomware to impersonation scams. Yet automated prevention approaches adapt slowly and may not reliably protect users from falling prey to new scams. To better combat online scams, we developed ScamPilot, a convers... | https://arxiv.org/abs/2601.22426 | Academic Papers | svg |
40fbf6c4e581c6edd76184c9d88aa04332c66c9a209f874770a7dd579437ddc8 | 2026-02-02T00:00:00-05:00 | CoDCL: Counterfactual Data Augmentation Contrastive Learning for Continuous-Time Dynamic Network Link Prediction | arXiv:2601.22427v1 Announce Type: new Abstract: The rapid growth and continuous structural evolution of dynamic networks make effective predictions increasingly challenging. To enable prediction models to adapt to complex temporal environments, they need to be robust to emerging structural changes. We propose a dynamic... | https://arxiv.org/abs/2601.22427 | Academic Papers | svg |
ecc95697f9f9b09eebee97c963333c233c35e6e8741301008bebb98a42a69cdc | 2026-02-02T00:00:00-05:00 | Why Johnny Can't Think: GenAI's Impacts on Cognitive Engagement | arXiv:2601.22430v1 Announce Type: new Abstract: Context: Many students now use generative AI in their coursework, yet its effects on intellectual development remain poorly understood. While prior work has investigated students' cognitive offloading during episodic interactions, it remains unclear whether using genAI ro... | https://arxiv.org/abs/2601.22430 | Academic Papers | svg |
77e57185aaa84614cfb40ea72b205f7bd128fc3654a17cd50e9fafd6b2f8ff2d | 2026-02-02T00:00:00-05:00 | ReNCE: Learning to Reason by Noise Contrastive Estimation | arXiv:2601.22432v1 Announce Type: new Abstract: GRPO is a standard approach to endowing pretrained LLMs with reasoning capabilities. It estimates the advantage of an outcome from a group of $K$ outcomes, and promotes those with positive advantages inside a trust region. Since GRPO discriminates between good and bad out... | https://arxiv.org/abs/2601.22432 | Academic Papers | svg |
b1fdb1c34a55126586112df95ab891c0904778e3a8cd2179d794d53872ab3fcf | 2026-02-02T00:00:00-05:00 | When LLM meets Fuzzy-TOPSIS for Personnel Selection through Automated Profile Analysis | arXiv:2601.22433v1 Announce Type: new Abstract: In this highly competitive employment environment, the selection of suitable personnel is essential for organizational success. This study presents an automated personnel selection system that utilizes sophisticated natural language processing (NLP) methods to assess and ... | https://arxiv.org/abs/2601.22433 | Academic Papers | svg |
1c94d04ad8e38f9c9353b098b27117f37845aad114c70fc7e9e0f682ee61b425 | 2026-02-02T00:00:00-05:00 | Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective | arXiv:2601.22434v1 Announce Type: new Abstract: Training generative machine learning models to produce synthetic tabular data has become a popular approach for enhancing privacy in data sharing. As this typically involves processing sensitive personal information, releasing either the trained model or generated synthet... | https://arxiv.org/abs/2601.22434 | Academic Papers | svg |
57de24234cad11485e43032c14e611c63a49a06421121fcac83b7534dae58a73 | 2026-02-02T00:00:00-05:00 | Large Language Model Agents Are Not Always Faithful Self-Evolvers | arXiv:2601.22436v1 Announce Type: new Abstract: Self-evolving large language model (LLM) agents continually improve by accumulating and reusing past experience, yet it remains unclear whether they faithfully rely on that experience to guide their behavior. We present the first systematic investigation of experience fai... | https://arxiv.org/abs/2601.22436 | Academic Papers | svg |
2a8d492c6583874a72f92c66aa663e981652a986408a2cbd711c13b11465e030 | 2026-02-02T00:00:00-05:00 | Towards Resiliency in Large Language Model Serving with KevlarFlow | arXiv:2601.22438v1 Announce Type: new Abstract: Large Language Model (LLM) serving systems remain fundamentally fragile, where frequent hardware faults in hyperscale clusters trigger disproportionate service outages in the software stack. Current recovery mechanisms are prohibitively slow, often requiring up to 10 minu... | https://arxiv.org/abs/2601.22438 | Academic Papers | svg |
87552b579468541fe3e9e5005eb9ad4811ef3bae107e9d18ad009b94a5bf1d04 | 2026-02-02T00:00:00-05:00 | Stop Jostling: Adaptive Negative Sampling Reduces the Marginalization of Low-Resource Language Tokens by Cross-Entropy Loss | arXiv:2601.22439v1 Announce Type: new Abstract: Neural language models often struggle with low-resource languages due to the limited availability of training data, making tokens from these languages rare in the training set. This paper addresses a specific challenge during training: rare tokens are disproportionately a... | https://arxiv.org/abs/2601.22439 | Academic Papers | svg |
e85f29f2f797a75ceab5522c7063a30ae66949742a0805281f37515ea0e5221c | 2026-02-02T00:00:00-05:00 | AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations | arXiv:2601.22440v1 Announce Type: new Abstract: Does AI understand human values? While this remains an open philosophical question, we take a pragmatic stance by introducing VAPT, the Value-Alignment Perception Toolkit, for studying how LLMs reflect people's values and how people judge those reflections. 20 participant... | https://arxiv.org/abs/2601.22440 | Academic Papers | svg |
003e81f4e85c8a4b5f5b8af8c42d341213b62e9c17e3a7e5e21838b6cba4ffc2 | 2026-02-02T00:00:00-05:00 | AsyncMesh: Fully Asynchronous Optimization for Data and Pipeline Parallelism | arXiv:2601.22442v1 Announce Type: new Abstract: Data and pipeline parallelism are key strategies for scaling neural network training across distributed devices, but their high communication cost necessitates co-located computing clusters with fast interconnects, limiting their scalability. We address this communication... | https://arxiv.org/abs/2601.22442 | Academic Papers | svg |
52940c9385086dbe9c3f31f06a8bcabe5992b4e57112ce2f2325b2debb58d711 | 2026-02-02T00:00:00-05:00 | Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance | arXiv:2601.22443v1 Announce Type: new Abstract: Can a diffusion model trained on bedrooms recover human faces? Diffusion models are widely used as priors for inverse problems, but standard approaches usually assume a high-fidelity model trained on data that closely match the unknown signal. In practice, one often must ... | https://arxiv.org/abs/2601.22443 | Academic Papers | svg |
83b8a606c64b9245d6105b0e4b7bdb5a9d8ca152c2e48d854c4da2a5fd7c327a | 2026-02-02T00:00:00-05:00 | Automating Forecasting Question Generation and Resolution for AI Evaluation | arXiv:2601.22444v1 Announce Type: new Abstract: Forecasting future events is highly valuable in decision-making and is a robust measure of general intelligence. As forecasting is probabilistic, developing and evaluating AI forecasters requires generating large numbers of diverse and difficult questions, and accurately ... | https://arxiv.org/abs/2601.22444 | Academic Papers | svg |
8a7c70ade5ad329e9057085a3fa9aaf3138eb78ff018d30ed88092fc4ff6e39c | 2026-02-02T00:00:00-05:00 | High-Definition 5MP Stereo Vision Sensing for Robotics | arXiv:2601.22445v1 Announce Type: new Abstract: High-resolution (5MP+) stereo vision systems are essential for advancing robotic capabilities, enabling operation over longer ranges and generating significantly denser and accurate 3D point clouds. However, realizing the full potential of high-angular-resolution sensors ... | https://arxiv.org/abs/2601.22445 | Academic Papers | svg |
d22c5c42951f405813870bcf4e0faab480b8e436296ccd77bad2c4f833cb6d6f | 2026-02-02T00:00:00-05:00 | Anytime Safe PAC Efficient Reasoning | arXiv:2601.22446v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks but suffer from high computational costs and latency. While selective thinking strategies improve efficiency by routing easy queries to non-thinking models, existing approaches often i... | https://arxiv.org/abs/2601.22446 | Academic Papers | svg |
a5525c40223569df3c1164db3d9424dc90f5c195ff21ccc35049d8768ec9e17b | 2026-02-02T00:00:00-05:00 | Beyond Activation Patterns: A Weight-Based Out-of-Context Explanation of Sparse Autoencoder Features | arXiv:2601.22447v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have emerged as a powerful technique for decomposing language model representations into interpretable features. Current interpretation methods infer feature semantics from activation patterns, but overlook that features are trained to reconstru... | https://arxiv.org/abs/2601.22447 | Academic Papers | svg |
23f1fadf5298eb91ff03be15d69e754097573e2c1e01ced018f9110185484549 | 2026-02-02T00:00:00-05:00 | HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning | arXiv:2601.22448v1 Announce Type: new Abstract: RLVR is now a standard way to train LLMs on reasoning tasks with verifiable outcomes, but when rollout generation dominates the cost, efficiency depends heavily on which prompts you sample and when. In practice, prompt pools are often static or only loosely tied to the mo... | https://arxiv.org/abs/2601.22448 | Academic Papers | svg |
f6f12a6349d562b438d7b80eaddbcde71e0c25dc310f9c1076ff3819f354ed38 | 2026-02-02T00:00:00-05:00 | Controllable Information Production | arXiv:2601.22449v1 Announce Type: new Abstract: Intrinsic Motivation (IM) is a paradigm for generating intelligent behavior without external utilities. The existing information-theoretic methods for IM are predominantly based on information transmission, which explicitly depends on the designer's choice of which random... | https://arxiv.org/abs/2601.22449 | Academic Papers | svg |
fd33bb63ef3dd44ab90e23a52aa2f1b069200c040586b83ac2d667f930527f7d | 2026-02-02T00:00:00-05:00 | Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity | arXiv:2601.22450v1 Announce Type: new Abstract: Masked Diffusion Language Models have recently emerged as a powerful generative paradigm, yet their generalization properties remain understudied compared to their auto-regressive counterparts. In this work, we investigate these properties within the setting of the $k$-pa... | https://arxiv.org/abs/2601.22450 | Academic Papers | svg |
bbb8ced6cb6904670e10b726561a5371fb1587099e61e731a014d7b1a36d73de | 2026-02-02T00:00:00-05:00 | Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework | arXiv:2601.22451v1 Announce Type: new Abstract: Despite progress in Large Vision Language Models (LVLMs), object hallucination remains a critical issue in image captioning task, where models generate descriptions of non-existent objects, compromising their reliability. Previous work attributes this to LVLMs' over-relia... | https://arxiv.org/abs/2601.22451 | Academic Papers | svg |
0a5137a0e485d3f20702a436e87970e2fa804a287251a7d843935d954c6b0ad0 | 2026-02-02T00:00:00-05:00 | Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interaction | arXiv:2601.22452v1 Announce Type: new Abstract: AI chatbots are shifting from tools to companions. This raises critical questions about agency: who drives conversations and sets boundaries in human-AI chatrooms? We report a month-long longitudinal study with 22 adults who chatted with Day, an LLM companion we built, fo... | https://arxiv.org/abs/2601.22452 | Academic Papers | svg |
3c4b4ed160e910aa6c1bd19a20f1349d17db7ce313da75d568b2b9a3e6820409 | 2026-02-02T00:00:00-05:00 | Temporal Graph Pattern Machine | arXiv:2601.22454v1 Announce Type: new Abstract: Temporal graph learning is pivotal for deciphering dynamic systems, where the core challenge lies in explicitly modeling the underlying evolving patterns that govern network transformation. However, prevailing methods are predominantly task-centric and rely on restrictive... | https://arxiv.org/abs/2601.22454 | Academic Papers | svg |
b4e0baad5b5786620a8f1ce60e59606e58960dd54d6dbb7a60e2e4616aadabb5 | 2026-02-02T00:00:00-05:00 | ScribbleSense: Generative Scribble-Based Texture Editing with Intent Prediction | arXiv:2601.22455v1 Announce Type: new Abstract: Interactive 3D model texture editing presents enhanced opportunities for creating 3D assets, with freehand drawing style offering the most intuitive experience. However, existing methods primarily support sketch-based interactions for outlining, while the utilization of c... | https://arxiv.org/abs/2601.22455 | Academic Papers | svg |
54552551ab066202a233e2e7d9def27917e60744c7e8bd2c310fb584a71504ac | 2026-02-02T00:00:00-05:00 | Machine Unlearning in Low-Dimensional Feature Subspace | arXiv:2601.22456v1 Announce Type: new Abstract: Machine Unlearning (MU) aims at removing the influence of specific data from a pretrained model while preserving performance on the remaining data. In this work, a novel perspective for MU is presented upon low-dimensional feature subspaces, which gives rise to the potent... | https://arxiv.org/abs/2601.22456 | Academic Papers | svg |
e5c489e24831a2fcab994c54fd2761e9065cee93bd8ff106410a92f96e8b3b0b | 2026-02-02T00:00:00-05:00 | Toward Non-Expert Customized Congestion Control | arXiv:2601.22461v1 Announce Type: new Abstract: General-purpose congestion control algorithms (CCAs) are designed to achieve general congestion control goals, but they may not meet the specific requirements of certain users. Customized CCAs can meet certain users' specific requirements; however, non-expert users often ... | https://arxiv.org/abs/2601.22461 | Academic Papers | svg |
7af4a322d7ebadb2d8687b56b0b2ac565dbb092fdf88db49813fa0bc09a9f46c | 2026-02-02T00:00:00-05:00 | EvoEGF-Mol: Evolving Exponential Geodesic Flow for Structure-based Drug Design | arXiv:2601.22466v1 Announce Type: new Abstract: Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for continuous atomic coordinates and discrete chemical categories, leading to a mismatch with the u... | https://arxiv.org/abs/2601.22466 | Academic Papers | svg |
a443cf930e84e36eb074b66e2bcb05fa96ac02d35146de83a1f07d264b1fcc5f | 2026-02-02T00:00:00-05:00 | CARE: Multi-Task Pretraining for Latent Continuous Action Representation in Robot Control | arXiv:2601.22467v1 Announce Type: new Abstract: Recent advances in Vision-Language-Action (VLA) models have shown promise for robot control, but their dependence on action supervision limits scalability and generalization. To address this challenge, we introduce CARE, a novel framework designed to train VLA models for ... | https://arxiv.org/abs/2601.22467 | Academic Papers | svg |
cf256d7f87e81467bfc6373c5437113d449c35e3d17d5666ce194d791fe8f833 | 2026-02-02T00:00:00-05:00 | Training-Free Representation Guidance for Diffusion Models with a Representation Alignment Projector | arXiv:2601.22468v1 Announce Type: new Abstract: Recent progress in generative modeling has enabled high-quality visual synthesis with diffusion-based frameworks, supporting controllable sampling and large-scale training. Inference-time guidance methods such as classifier-free and representative guidance enhance semanti... | https://arxiv.org/abs/2601.22468 | Academic Papers | svg |
b5cbda9c4caed7898c9a2159c4dae986bb2be07bdea0748c79461d201bdedb15 | 2026-02-02T00:00:00-05:00 | 5G LDPC Codes as Root LDPC Codes via Diversity Alignment | arXiv:2601.22470v1 Announce Type: new Abstract: This paper studies the diversity of protographbased quasi-cyclic low-density parity-check (QC-LDPC) codes over nonergodic block-fading channels under iterative beliefpropagation decoding. We introduce diversity evolution (DivE), a Boolean-function-based analysis method th... | https://arxiv.org/abs/2601.22470 | Academic Papers | svg |
6ebfb089b8ea23f5d00ed678dd0ca5128e0f12f6e72fa42af45406a11b2f9f90 | 2026-02-02T00:00:00-05:00 | The Third-Party Access Effect: An Overlooked Challenge in Secondary Use of Educational Real-World Data | arXiv:2601.22472v1 Announce Type: new Abstract: Secondary use of growing real-world data (RWD) in education offers significant opportunities for research, yet privacy practices intended to enable third-party access to such RWD are rarely evaluated for their implications for downstream analyses. As a result, potential p... | https://arxiv.org/abs/2601.22472 | Academic Papers | svg |
62395d8974a7ec1c46c528cdd324ff042538ee28bb08ba49a3a028bed556d872 | 2026-02-02T00:00:00-05:00 | Unrewarded Exploration in Large Language Models Reveals Latent Learning from Psychology | arXiv:2601.22474v1 Announce Type: new Abstract: Latent learning, classically theorized by Tolman, shows that biological agents (e.g., rats) can acquire internal representations of their environment without rewards, enabling rapid adaptation once rewards are introduced. In contrast, from a cognitive science perspective,... | https://arxiv.org/abs/2601.22474 | Academic Papers | svg |
fffdfb40e9925e3448e04b9ea6ddc2e4fb6dd6b0f0860cc5b4f1677c2f42f331 | 2026-02-02T00:00:00-05:00 | Continual Policy Distillation from Distributed Reinforcement Learning Teachers | arXiv:2601.22475v1 Announce Type: new Abstract: Continual Reinforcement Learning (CRL) aims to develop lifelong learning agents to continuously acquire knowledge across diverse tasks while mitigating catastrophic forgetting. This requires efficiently managing the stability-plasticity dilemma and leveraging prior experi... | https://arxiv.org/abs/2601.22475 | Academic Papers | svg |
8db910f5df00f17a68adf14e95f4f1a859b1cf9ae3bd3d37915a7340017a34e2 | 2026-02-02T00:00:00-05:00 | RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning | arXiv:2601.22476v1 Announce Type: new Abstract: Floorplanning determines the coordinate and shape of each module in Integrated Circuits. With the scaling of technology nodes, in floorplanning stage especially 3D scenarios with multiple stacked layers, it has become increasingly challenging to adhere to complex hardware... | https://arxiv.org/abs/2601.22476 | Academic Papers | svg |
2be1f60cee0cc08e4fa4a8029e6f5110a44bcc77287a5d132cf972c8e6fe1f83 | 2026-02-02T00:00:00-05:00 | Transform-Augmented GRPO Improves Pass@k | arXiv:2601.22478v1 Announce Type: new Abstract: Large language models trained via next-token prediction are fundamentally pattern-matchers: sensitive to superficial phrasing variations even when the underlying problem is identical. Group Relative Policy Optimization (GRPO) was designed to improve reasoning, but in fact... | https://arxiv.org/abs/2601.22478 | Academic Papers | svg |
7552c9d9a6e590464eeceec9e90914cb949c7b16d18a261215c337bc6e9eeadb | 2026-02-02T00:00:00-05:00 | Rethinking Speech Representation Aggregation in Speech Enhancement: A Phonetic Mutual Information Perspective | arXiv:2601.22480v1 Announce Type: new Abstract: Recent speech enhancement (SE) models increasingly leverage self-supervised learning (SSL) representations for their rich semantic information. Typically, intermediate features are aggregated into a single representation via a lightweight adaptation module. However, most ... | https://arxiv.org/abs/2601.22480 | Academic Papers | svg |
df23aa3ef7096fcb19874f688fb3ab39d918a778fa2819f4c7591b6a8634fc9b | 2026-02-02T00:00:00-05:00 | Successive Cancellation List Decoding of Extended Reed-Solomon Codes | arXiv:2601.22482v1 Announce Type: new Abstract: Reed-Solomon (RS) codes are an important class of non-binary error-correction codes. They are particularly competent in correcting burst errors, being widely applied in modern communications and data storage systems. This also thanks to their distance property of reaching... | https://arxiv.org/abs/2601.22482 | Academic Papers | svg |
69441c612fbc53b6a1b30184595c93ebbf36ae09c7513b4249fa52062e4cd6f8 | 2026-02-02T00:00:00-05:00 | Head-Aware Visual Cropping: Enhancing Fine-Grained VQA with Attention-Guided Subimage | arXiv:2601.22483v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) show strong performance in Visual Question Answering (VQA) but remain limited in fine-grained reasoning due to low-resolution inputs and noisy attention aggregation. We propose \textbf{Head Aware Visual Cropping (HAVC)}, a training... | https://arxiv.org/abs/2601.22483 | Academic Papers | svg |
bb375a367600e653ce4826e8bc1476940a497a5df4a5bc00891fa2d7c7ce4e6a | 2026-02-02T00:00:00-05:00 | Mitigating Cognitive Inertia in Large Reasoning Models via Latent Spike Steering | arXiv:2601.22484v1 Announce Type: new Abstract: While Large Reasoning Models (LRMs) have achieved remarkable performance by scaling test-time compute, they frequently suffer from Cognitive Inertia, a failure pattern manifesting as either overthinking (inertia of motion) or reasoning rigidity (inertia of direction). Exi... | https://arxiv.org/abs/2601.22484 | Academic Papers | svg |
27af68a031db7faac669de6b1ee71c3ee6ac9675474e778408e91b405a191769 | 2026-02-02T00:00:00-05:00 | FraudShield: Knowledge Graph Empowered Defense for LLMs against Fraud Attacks | arXiv:2601.22485v1 Announce Type: new Abstract: Large language models (LLMs) have been widely integrated into critical automated workflows, including contract review and job application processes. However, LLMs are susceptible to manipulation by fraudulent information, which can lead to harmful outcomes. Although advan... | https://arxiv.org/abs/2601.22485 | Academic Papers | svg |
16daed65e78bd392400af2b91e1cadf5461a26f617f1a1a276415cf002eab222 | 2026-02-02T00:00:00-05:00 | AI Literacy, Safety Awareness, and STEM Career Aspirations of Australian Secondary Students: Evaluating the Impact of Workshop Interventions | arXiv:2601.22486v1 Announce Type: new Abstract: Deepfakes and other forms of synthetic media pose growing safety risks for adolescents, yet evidence on students' exposure and related behaviours remains limited. This study evaluates the impact of Day of AI Australia's workshop-based intervention designed to improve AI l... | https://arxiv.org/abs/2601.22486 | Academic Papers | svg |
1f664cdd670c8b384238df2c00ddfc9c07ffc0f7875797104cdd0f4bef9acb89 | 2026-02-02T00:00:00-05:00 | Coordinating Power Grid Frequency Regulation Service with Data Center Load Flexibility | arXiv:2601.22487v1 Announce Type: new Abstract: AI/ML data center growth have led to higher energy consumption and carbon emissions. The shift to renewable energy and growing data center energy demands can destabilize the power grid. Power grids rely on frequency regulation reserves, typically fossil-fueled power plant... | https://arxiv.org/abs/2601.22487 | Academic Papers | svg |
5331ff7de484b80f62a9c1861d8ddddfee37d6e7ad393237f82b83d4bdcf929d | 2026-02-02T00:00:00-05:00 | Elastic Spectral State Space Models for Budgeted Inference | arXiv:2601.22488v1 Announce Type: new Abstract: Foundation models are typically trained at a fixed computational capacity, while real-world applications require deployment across platforms with different resource constraints. Current approaches usually rely on training families of model variants or model distillation, ... | https://arxiv.org/abs/2601.22488 | Academic Papers | svg |
4d661f9f6a20c845a9b0c0fb86c22d60e6c379d3f63010bead3e2b2139cda055 | 2026-02-02T00:00:00-05:00 | SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization | arXiv:2601.22491v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has emerged as a powerful paradigm for training intelligent agents. However, existing methods typically employ binary rewards that fail to capture quality differences among trajectories achieving identical outcomes, thereby o... | https://arxiv.org/abs/2601.22491 | Academic Papers | svg |
954a37cd0b4dfe5bae0e67d6c8acdf7accd9a4f25e36d729d72459722ccf706a | 2026-02-02T00:00:00-05:00 | PromptMAD: Cross-Modal Prompting for Multi-Class Visual Anomaly Localization | arXiv:2601.22492v1 Announce Type: new Abstract: Visual anomaly detection in multi-class settings poses significant challenges due to the diversity of object categories, the scarcity of anomalous examples, and the presence of camouflaged defects. In this paper, we propose PromptMAD, a cross-modal prompting framework for... | https://arxiv.org/abs/2601.22492 | Academic Papers | svg |
1f4631bd7ee5f35b14637a838cfce8cd77d1622cbb9d72e87672486b237ef84d | 2026-02-02T00:00:00-05:00 | Do AI Overviews Benefit Search Engines? An Ecosystem Perspective | arXiv:2601.22493v1 Announce Type: new Abstract: The integration of AI Overviews into search engines enhances user experience but diverts traffic from content creators, potentially discouraging high-quality content creation and causing user attrition that undermines long-term search engine profit. To address this issue,... | https://arxiv.org/abs/2601.22493 | Academic Papers | svg |
f49579c3cefa870a5f5db388d87648584e39015d5fd6af863a5edd8174559cb8 | 2026-02-02T00:00:00-05:00 | Nethira: A Heterogeneity-aware Hierarchical Pre-trained Model for Network Traffic Classification | arXiv:2601.22494v1 Announce Type: new Abstract: Network traffic classification is vital for network security and management. The pre-training technology has shown promise by learning general traffic representations from raw byte sequences, thereby reducing reliance on labeled data. However, existing pre-trained models ... | https://arxiv.org/abs/2601.22494 | Academic Papers | svg |
88881f0100320667d116c4685d3ffa93a32e9d33f99f65d7633c31e9bb172f7e | 2026-02-02T00:00:00-05:00 | Gradual Fine-Tuning for Flow Matching Models | arXiv:2601.22495v1 Announce Type: new Abstract: Fine-tuning flow matching models is a central challenge in settings with limited data, evolving distributions, or strict efficiency demands, where unconstrained fine-tuning can erode the accuracy and efficiency gains learned during pretraining. Prior work has produced the... | https://arxiv.org/abs/2601.22495 | Academic Papers | svg |
2a5b8b2631caadea19fc3fdb02d7a67630f820b59342eb42a6faad29ae129928 | 2026-02-02T00:00:00-05:00 | Action-Sufficient Goal Representations | arXiv:2601.22496v1 Announce Type: new Abstract: Hierarchical policies in offline goal-conditioned reinforcement learning (GCRL) addresses long-horizon tasks by decomposing control into high-level subgoal planning and low-level action execution. A critical design choice in such architectures is the goal representation-t... | https://arxiv.org/abs/2601.22496 | Academic Papers | svg |
b61bbf0b710f7dc8f7e7c8cf89c8953d3f47f832c7c286d6975bd7704e4f777a | 2026-02-02T00:00:00-05:00 | Fairness-Aware Performance Evaluation for Multi-Party Multi-Objective Optimization | arXiv:2601.22497v1 Announce Type: new Abstract: In multiparty multiobjective optimization problems, solution sets are usually evaluated using classical performance metrics, aggregated across DMs. However, such mean-based evaluations may be unfair by favoring certain parties, as they assume identical geometric approxima... | https://arxiv.org/abs/2601.22497 | Academic Papers | svg |
05733e7ae6a7eac0fc4729c692a5c963f1b2e1698e694d9acdd51d14a1148a5c | 2026-02-02T00:00:00-05:00 | FITMM: Adaptive Frequency-Aware Multimodal Recommendation via Information-Theoretic Representation Learning | arXiv:2601.22498v1 Announce Type: new Abstract: Multimodal recommendation aims to enhance user preference modeling by leveraging rich item content such as images and text. Yet dominant systems fuse modalities in the spatial domain, obscuring the frequency structure of signals and amplifying misalignment and redundancy.... | https://arxiv.org/abs/2601.22498 | Academic Papers | svg |
2b884ad8aa043a7e814599f2af81e92ea8e141045138b949b1c5f3407eb4eccc | 2026-02-02T00:00:00-05:00 | Chance-Constrained Secrecy Optimization in Hybrid RIS-Empowered and UAV-Assisted Networks | arXiv:2601.22499v1 Announce Type: new Abstract: This paper considers a hybrid reconfigurable environment comprising a UAV-mounted reflecting RIS, an outdoor STAR-RIS enabling simultaneous transmission and reflection, and an indoor holographic RIS (H-RIS), jointly enhancing secure downlink communication for indoor and o... | https://arxiv.org/abs/2601.22499 | Academic Papers | svg |
4f764676f3181420443acbb5154bda2bca96cc8cdadebae19a063bb3a66ee449 | 2026-02-02T00:00:00-05:00 | MIRRORTALK: Forging Personalized Avatars Via Disentangled Style and Hierarchical Motion Control | arXiv:2601.22501v1 Announce Type: new Abstract: Synthesizing personalized talking faces that uphold and highlight a speaker's unique style while maintaining lip-sync accuracy remains a significant challenge. A primary limitation of existing approaches is the intrinsic confounding of speaker-specific talking style and s... | https://arxiv.org/abs/2601.22501 | Academic Papers | svg |
dd45e7bbb5637bdec1ad551692914bb0033d0555ce2144ba6b7f87e519bf6bb4 | 2026-02-02T00:00:00-05:00 | Constructing BERT Models: How Team Dynamics and Focus Shape AI Model Impact | arXiv:2601.22505v1 Announce Type: new Abstract: The rapid evolution of AI technologies, exemplified by BERT-family models, has transformed scientific research, yet little is known about their production and recognition dynamics in the scientific system. This study investigates the development and impact of BERT-family ... | https://arxiv.org/abs/2601.22505 | Academic Papers | svg |
332355f6c496ea530c4d518de97f992da54e7b121f61bd9abedf57819b62efc9 | 2026-02-02T00:00:00-05:00 | DreamVAR: Taming Reinforced Visual Autoregressive Model for High-Fidelity Subject-Driven Image Generation | arXiv:2601.22507v1 Announce Type: new Abstract: Recent advances in subject-driven image generation using diffusion models have attracted considerable attention for their remarkable capabilities in producing high-quality images. Nevertheless, the potential of Visual Autoregressive (VAR) models, despite their unified arc... | https://arxiv.org/abs/2601.22507 | Academic Papers | svg |
c4b07026836c8d78135a67c10868b7b72fa4e1946af049874bb0b45dedc0fd15 | 2026-02-02T00:00:00-05:00 | CoVA: Text-Guided Composed Video Retrieval for Audio-Visual Content | arXiv:2601.22508v1 Announce Type: new Abstract: Composed Video Retrieval (CoVR) aims to retrieve a target video from a large gallery using a reference video and a textual query specifying visual modifications. However, existing benchmarks consider only visual changes, ignoring videos that differ in audio despite visual... | https://arxiv.org/abs/2601.22508 | Academic Papers | svg |
d30037ac03e457fb54aee952ea6db22af13603d050267a5a40acce734a84e51e | 2026-02-02T00:00:00-05:00 | Keep Rehearsing and Refining: Lifelong Learning Vehicle Routing under Continually Drifting Tasks | arXiv:2601.22509v1 Announce Type: new Abstract: Existing neural solvers for vehicle routing problems (VRPs) are typically trained either in a one-off manner on a fixed set of pre-defined tasks or in a lifelong manner on several tasks arriving sequentially, assuming sufficient training on each task. Both settings overlo... | https://arxiv.org/abs/2601.22509 | Academic Papers | svg |
af28e232665fc0b645177b39a2c1be4059eb9ca0201bfcfa6435061418eb9cfe | 2026-02-02T00:00:00-05:00 | Shattered Compositionality: Counterintuitive Learning Dynamics of Transformers for Arithmetic | arXiv:2601.22510v1 Announce Type: new Abstract: Large language models (LLMs) often exhibit unexpected errors or unintended behavior, even at scale. While recent work reveals the discrepancy between LLMs and humans in skill compositions, the learning dynamics of skill compositions and the underlying cause of non-human b... | https://arxiv.org/abs/2601.22510 | Academic Papers | svg |
d6e3d5b4220c7ddfe2770ae9725940057d51ea2a627f2050f1b3b7119bce5d01 | 2026-02-02T00:00:00-05:00 | Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards | arXiv:2601.22511v1 Announce Type: new Abstract: Small LLMs often struggle to match the agentic capabilities of large, costly models. While reinforcement learning can help, progress has been limited by two structural bottlenecks: existing open-source agentic training data are narrow in task variety and easily solved; re... | https://arxiv.org/abs/2601.22511 | Academic Papers | svg |
3bdb7370435b55c9633add759e7ffc9e519843dfd5fafa288f62e69ffa450bc9 | 2026-02-02T00:00:00-05:00 | DRL-Enabled Trajectory Planing for UAV-Assisted VLC: Optimal Altitude and Reward Design | arXiv:2601.22512v1 Announce Type: new Abstract: Recently, the integration of unmanned aerial vehicle (UAV) and visible light communication (VLC) technologies has emerged as a promising solution to offer flexible communication and efficient lighting. This letter investigates the three-dimensional trajectory planning in ... | https://arxiv.org/abs/2601.22512 | Academic Papers | svg |
e9e91808d558ea9d550623bfc9db2e59f25e24b2eea4265932bd8dbc864d1a14 | 2026-02-02T00:00:00-05:00 | Why Self-Rewarding Works: Theoretical Guarantees for Iterative Alignment of Language Models | arXiv:2601.22513v1 Announce Type: new Abstract: Self-Rewarding Language Models (SRLMs) achieve notable success in iteratively improving alignment without external feedback. Yet, despite their striking empirical progress, the core mechanisms driving their capabilities remain unelucidated, leaving a critical gap in theor... | https://arxiv.org/abs/2601.22513 | Academic Papers | svg |
051c3d5914447233a502cab03ecbf24e306213d6e2424b46a5ee5029017a6d54 | 2026-02-02T00:00:00-05:00 | DNA: Uncovering Universal Latent Forgery Knowledge | arXiv:2601.22515v1 Announce Type: new Abstract: As generative AI achieves hyper-realism, superficial artifact detection has become obsolete. While prevailing methods rely on resource-intensive fine-tuning of black-box backbones, we propose that forgery detection capability is already encoded within pre-trained models r... | https://arxiv.org/abs/2601.22515 | Academic Papers | svg |
b77a2b9dd1c007d85335ad3b56572868dbdcb7f49900962222314eacca00a007 | 2026-02-02T00:00:00-05:00 | SCOPE-PD: Explainable AI on Subjective and Clinical Objective Measurements of Parkinson's Disease for Precision Decision-Making | arXiv:2601.22516v1 Announce Type: new Abstract: Parkinson's disease (PD) is a chronic and complex neurodegenerative disorder influenced by genetic, clinical, and lifestyle factors. Predicting this disease early is challenging because it depends on traditional diagnostic methods that face issues of subjectivity, which c... | https://arxiv.org/abs/2601.22516 | Academic Papers | svg |
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