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bf6bc7fad104dd645cc3d193fd6393872334e0642d5eec51c46963030eec349d | 2026-01-13T00:00:00-05:00 | PsyAgent: Constructing Human-like Agents Based on Psychological Modeling and Contextual Interaction | arXiv:2601.06158v1 Announce Type: new Abstract: Human-like agents require modeling how dispositions interact with social structure. We present PsyAgent, which couples a Big Five trait prior with Bourdieu's cognitive-social co-structure. PsyAgent comprises: (i) Individual Structure (IS), a machine-usable profile encodin... | https://arxiv.org/abs/2601.06158 | Academic Papers | svg |
fc8458d5d54f7024445da5fb1b41cb62478944ca0d38074f894ef62f17839c2a | 2026-01-13T00:00:00-05:00 | Can we Improve Prediction of Psychotherapy Outcomes Through Pretraining With Simulated Data? | arXiv:2601.06159v1 Announce Type: new Abstract: In the context of personalized medicine, machine learning algorithms are growing in popularity. These algorithms require substantial information, which can be acquired effectively through the usage of previously gathered data. Open data and the utilization of synthetizati... | https://arxiv.org/abs/2601.06159 | Academic Papers | svg |
dc7ba22c193c18f996bd62dcf0284cff42cffb3e6f560e8b3ed3405661589a5e | 2026-01-13T00:00:00-05:00 | Student Guides Teacher: Weak-to-Strong Inference via Spectral Orthogonal Exploration | arXiv:2601.06160v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate near-human capabilities, they often suffer from "Reasoning Collapse" in complex mathematical proving and long-horizon planning. Models tend to degenerate into low-rank Bias Manifold, where stochastic sampling merely produces ... | https://arxiv.org/abs/2601.06160 | Academic Papers | svg |
938a7f7593794b9ec156d6d60d17f842e154d08da691768a88ab4d5d581418d2 | 2026-01-13T00:00:00-05:00 | Beyond Accuracy: A Decision-Theoretic Framework for Allocation-Aware Healthcare AI | arXiv:2601.06161v1 Announce Type: new Abstract: Artificial intelligence (AI) systems increasingly achieve expert-level predictive accuracy in healthcare, yet improvements in model performance often fail to produce corresponding gains in patient outcomes. We term this disconnect the allocation gap and provide a decision... | https://arxiv.org/abs/2601.06161 | Academic Papers | svg |
c3e0e18ebc9f51947d0765bb3e0f00ec9d7efa043ef7fe1e01e98199849451f9 | 2026-01-13T00:00:00-05:00 | Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models | arXiv:2601.06162v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable progress, yet their use raises copyright and misuse concerns, prompting research into machine unlearning. However, extending multi-concept unlearning to large-scale scenarios remains difficult due to three challenges... | https://arxiv.org/abs/2601.06162 | Academic Papers | svg |
83baff6a9b4d66b6071f83220811f2b25fa38553b0ed9ac7953b762b4dcf23f6 | 2026-01-13T00:00:00-05:00 | Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking | arXiv:2601.06163v1 Announce Type: new Abstract: The widespread adoption of text-to-image (T2I) diffusion models has raised concerns about their potential to generate copyrighted, inappropriate, or sensitive imagery learned from massive training corpora. As a practical solution, machine unlearning aims to selectively er... | https://arxiv.org/abs/2601.06163 | Academic Papers | svg |
a1af822f570d682df9a2a11aa01d1003040ff17887e2238926f0e4080ed8ff68 | 2026-01-13T00:00:00-05:00 | Contract2Plan: Verified Contract-Grounded Retrieval-Augmented Optimization for BOM-Aware Procurement and Multi-Echelon Inventory Planning | arXiv:2601.06164v1 Announce Type: new Abstract: Procurement and inventory planning is governed not only by demand forecasts and bills of materials (BOMs), but also by operational terms in contracts and supplier documents (e.g., MOQs, lead times, price tiers, allocation caps, substitution approvals). LLM-based extractio... | https://arxiv.org/abs/2601.06164 | Academic Papers | svg |
ef3eb5fa4e769b2985cfd53a4233e351054468bf9befe72f2ed44ecaf654d464 | 2026-01-13T00:00:00-05:00 | What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models | arXiv:2601.06165v1 Announce Type: new Abstract: Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries are often informal and underspecified. Users naturally leave much unsaid, relying on images to convey context. We introduce HAERAE-V... | https://arxiv.org/abs/2601.06165 | Academic Papers | svg |
4f8abb4541111bf22fa2954422fc087827e89a08ed1e5520d969d0b81c0f6d8b | 2026-01-13T00:00:00-05:00 | B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI | arXiv:2601.06166v1 Announce Type: new Abstract: Accelerated dynamic volumetric magnetic resonance imaging (4DMRI) is essential for applications relying on motion resolution. Existing 4DMRI produces acceptable artifacts of averaged breathing phases, which can blur and misrepresent instantaneous dynamic information. Reco... | https://arxiv.org/abs/2601.06166 | Academic Papers | svg |
ab74bc4f09bfaadce744e4bcdfac6fc4c44e0efff6cbf0e581b676c42850529e | 2026-01-13T00:00:00-05:00 | Parent-Guided Adaptive Reliability (PGAR): A Behavioural Meta-Learning Framework for Stable and Trustworthy AI | arXiv:2601.06167v1 Announce Type: new Abstract: Parent-Guided Adaptive Reliability (PGAR) is a lightweight behavioural meta-learning framework that adds a supervisory "parent" layer on top of a standard learner to improve stability, calibration, and recovery under disturbances. PGAR computes three reflex-level signals ... | https://arxiv.org/abs/2601.06167 | Academic Papers | svg |
3c3e00493714ef4c3e1cb8db5f7f40061ab548ce132728b446d5257134decb67 | 2026-01-13T00:00:00-05:00 | Analyzing the Structure of Handwritten Digits: A Comparative Study of PCA, Factor Analysis, and UMAP | arXiv:2601.06168v1 Announce Type: new Abstract: Handwritten digit images lie in a high-dimensional pixel space but exhibit strong geometric and statistical structure. This paper investigates the latent organization of handwritten digits in the MNIST dataset using three complementary dimensionality reduction techniques:... | https://arxiv.org/abs/2601.06168 | Academic Papers | svg |
cf21006106f695ad4a18bf8f9d14cc10994fd0531542ea0a9a20e1a93048162c | 2026-01-13T00:00:00-05:00 | Think Bright, Diffuse Nice: Enhancing T2I-ICL via Inductive-Bias Hint Instruction and Query Contrastive Decoding | arXiv:2601.06169v1 Announce Type: new Abstract: Text-to-Image In-Context Learning (T2I-ICL) enables customized image synthesis via interleaved text-image examples but faces two mutually reinforcing bottlenecks, compliance failure and prior-dominated hallucination, that form a vicious cycle degrading generation quality.... | https://arxiv.org/abs/2601.06169 | Academic Papers | svg |
984e1ec4fa9db72b176c53f05ac5dff92f7ae2889ccdddce570b0dbf77790c2c | 2026-01-13T00:00:00-05:00 | From Individual Prompts to Collective Intelligence: Mainstreaming Generative AI in the Classroom | arXiv:2601.06171v1 Announce Type: new Abstract: Engineering classrooms are increasingly experimenting with generative AI (GenAI), but most uses remain confined to individual prompting and isolated assistance. This narrow framing risks reinforcing equity gaps and only rewarding the already privileged or motivated studen... | https://arxiv.org/abs/2601.06171 | Academic Papers | svg |
c22499021a62843c4d697eea21347f6831c78e474d66837be83f470a16f6d7a9 | 2026-01-13T00:00:00-05:00 | The Psychology of Learning from Machines: Anthropomorphic AI and the Paradox of Automation in Education | arXiv:2601.06172v1 Announce Type: new Abstract: As AI tutors enter classrooms at unprecedented speed, their deployment increasingly outpaces our grasp of the psychological and social consequences of such technology. Yet decades of research in automation psychology, human factors, and human-computer interaction provide ... | https://arxiv.org/abs/2601.06172 | Academic Papers | svg |
6f5afe587dc4fad0376a0db8c18f16c137bb458bceba76d91d8ac58cbdd9ad94 | 2026-01-13T00:00:00-05:00 | The environmental impact of ICT in the era of data and artificial intelligence | arXiv:2601.06174v1 Announce Type: new Abstract: The technology industry promotes artificial intelligence (AI) as a key enabler to solve a vast number of problems, including the environmental crisis. However, when looking at the emissions of datacenters from worldwide service providers, we observe a rapid increase align... | https://arxiv.org/abs/2601.06174 | Academic Papers | svg |
f248dd0fe075c6f6e3c3b772210bd2905ee5c511b9a9b8fef12f45f47ddb25f5 | 2026-01-13T00:00:00-05:00 | A Mixed Methods Systematic Analysis of Issues and Factors Influencing Organizational Cloud Computing Adoption and Usage in the Public Sector: Initial Findings | arXiv:2601.06175v1 Announce Type: new Abstract: Cloud computing has been shown to be an essential enabling technology for public sector organizations PSOs and offers numerous potential benefits, including reduced information technology infrastructure costs, increased innovation potential, and improved resource resilien... | https://arxiv.org/abs/2601.06175 | Academic Papers | svg |
c5915e101bbf889d00bad2f93863e2f3b7835cefe7ad614298963b04da3f398d | 2026-01-13T00:00:00-05:00 | TIR-Flow: Active Video Search and Reasoning with Frozen VLMs | arXiv:2601.06176v1 Announce Type: new Abstract: While Large Video-Language Models (Video-LLMs) have achieved remarkable progress in perception, their reasoning capabilities remain a bottleneck. Existing solutions typically resort to a heavy "data engineering" paradigm-synthesizing large-scale Chain-of-Thought (CoT) dat... | https://arxiv.org/abs/2601.06176 | Academic Papers | svg |
e68ba551bb74fbf75a8536dc3e05c5bf70cf2ab019531cc0d6bf37b58b81c453 | 2026-01-13T00:00:00-05:00 | AutoVulnPHP: LLM-Powered Two-Stage PHP Vulnerability Detection and Automated Localization | arXiv:2601.06177v1 Announce Type: new Abstract: PHP's dominance in web development is undermined by security challenges: static analysis lacks semantic depth, causing high false positives; dynamic analysis is computationally expensive; and automated vulnerability localization suffers from coarse granularity and impreci... | https://arxiv.org/abs/2601.06177 | Academic Papers | svg |
b1a3c899480c005ab2565e15f25ae01fdc2420bb866a0ed14396f8bfb4daba6e | 2026-01-13T00:00:00-05:00 | Performance of models for monitoring sustainable development goals from remote sensing: A three-level meta-regression | arXiv:2601.06178v1 Announce Type: new Abstract: Machine learning (ML) is a tool to exploit remote sensing data for the monitoring and implementation of the United Nations' Sustainable Development Goals (SDGs). In this paper, we report on a meta-analysis to evaluate the performance of ML applied to remote sensing data t... | https://arxiv.org/abs/2601.06178 | Academic Papers | svg |
af60b94edeec7ab9465614e3d89a82e8d1e3535d4282cc2e25096f63b06d2c45 | 2026-01-13T00:00:00-05:00 | MixDPO: Modeling Preference Strength for Pluralistic Alignment | arXiv:2601.06180v1 Announce Type: new Abstract: Preference based alignment objectives implicitly assume that all human preferences are expressed with equal strength. In practice, however, preference strength varies across individuals and contexts -- a phenomenon established in behavioral economics and discrete choice t... | https://arxiv.org/abs/2601.06180 | Academic Papers | svg |
f33416ba2e7efa10d04082a47ec7d59d4e27d63c73a9b33d1bd5b781dd199e80 | 2026-01-13T00:00:00-05:00 | Neuro-Symbolic Compliance: Integrating LLMs and SMT Solvers for Automated Financial Legal Analysis | arXiv:2601.06181v1 Announce Type: new Abstract: Financial regulations are increasingly complex, hindering automated compliance-especially the maintenance of logical consistency with minimal human oversight. We introduce a Neuro-Symbolic Compliance Framework that integrates Large Language Models (LLMs) with Satisfiabili... | https://arxiv.org/abs/2601.06181 | Academic Papers | svg |
0dfbb63169b124d84ebcecb186b3551d0f05350ffa438981367eeac5c119d62f | 2026-01-13T00:00:00-05:00 | Geo-Standardizing 3D Modeling of Surface Objects and Related Logical Spaces on Celestial Bodies: Case Studies for Moon and Mars | arXiv:2601.06182v1 Announce Type: new Abstract: Establishing frameworks for promoting the realization of various activities on celestial bodies sustainably is of great significance for different contexts, such as preserving the scientific evidence and space heritage. Therefore, this research first proposes a conceptual... | https://arxiv.org/abs/2601.06182 | Academic Papers | svg |
d57e73922a51b3af37c5da786d318c3717b09ba63bcf5614a729b4bf9fc7357f | 2026-01-13T00:00:00-05:00 | Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge | arXiv:2601.06183v1 Announce Type: new Abstract: We introduce a community challenge designed to facilitate direct comparisons between data-driven methods for compression, forecasting, and sensing of complex aerospace flows. The challenge is organized into three tracks that target these complementary capabilities: compre... | https://arxiv.org/abs/2601.06183 | Academic Papers | svg |
8bcea24923344cbd9ff53a8e382984980df5727b4d00c683452d1f3fbd898d8f | 2026-01-13T00:00:00-05:00 | Attention Mechanism and Heuristic Approach: Context-Aware File Ranking Using Multi-Head Self-Attention | arXiv:2601.06185v1 Announce Type: new Abstract: The identification and ranking of impacted files within software reposi-tories is a key challenge in change impact analysis. Existing deterministic approaches that combine heuristic signals, semantic similarity measures, and graph-based centrality metrics have demonstrate... | https://arxiv.org/abs/2601.06185 | Academic Papers | svg |
00c1341aab5a876dff8912c028885ae080c020e7038c5e0f55a2a2b17f3f2b17 | 2026-01-13T00:00:00-05:00 | Time-Series Anomaly Classification for Launch Vehicle Propulsion Systems: Fast Statistical Detectors Enhancing LSTM Accuracy and Data Quality | arXiv:2601.06186v1 Announce Type: new Abstract: Supporting Go/No-Go decisions prior to launch requires assessing real-time telemetry data against redline limits established during the design qualification phase. Family data from ground testing or previous flights is commonly used to detect initiating failure modes and ... | https://arxiv.org/abs/2601.06186 | Academic Papers | svg |
b53dfd7bf25b0616d2848b9dc37f2c295ada6972b2ee8786068cbb337ee8ada3 | 2026-01-13T00:00:00-05:00 | A Unified Attention U-Net Framework for Cross-Modality Tumor Segmentation in MRI and CT | arXiv:2601.06187v1 Announce Type: new Abstract: This study presents a unified Attention U-Net architecture trained jointly on MRI (BraTS 2021) and CT (LIDC-IDRI) datasets to investigate the generalizability of a single model across diverse imaging modalities and anatomical sites. Our proposed pipeline incorporates moda... | https://arxiv.org/abs/2601.06187 | Academic Papers | svg |
1f90bbfa06cb77e05d1e3d90c03554e28a692472b7e63ca3050624c6a3671215 | 2026-01-13T00:00:00-05:00 | Large-Scale Continual Scheduling and Execution for Dynamic Distributed Satellite Constellation Observation Allocation | arXiv:2601.06188v1 Announce Type: new Abstract: The size and capabilities of Earth-observing satellite constellations are rapidly increasing. Leveraging distributed onboard control, we can enable novel time-sensitive measurements and responses. However, deploying autonomy to satellites requires efficient computation an... | https://arxiv.org/abs/2601.06188 | Academic Papers | svg |
20860931f6b577d3a0f5f5b5d6c562d1452886e0e8fe8a13e0871d72a11b51cb | 2026-01-13T00:00:00-05:00 | Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering | arXiv:2601.06189v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is the prevailing paradigm for grounding Large Language Models (LLMs), yet the mechanisms governing how models integrate groups of conflicting retrieved evidence remain opaque. Does an LLM answer a certain way because the evidence is f... | https://arxiv.org/abs/2601.06189 | Academic Papers | svg |
a48eb3013401b3d46fc96c5228f2bd5cfa240b906c225eb7f4f8b6729d07f385 | 2026-01-13T00:00:00-05:00 | TimeGNN-Augmented Hybrid-Action MARL for Fine-Grained Task Partitioning and Energy-Aware Offloading in MEC | arXiv:2601.06191v1 Announce Type: new Abstract: With the rapid growth of IoT devices and latency-sensitive applications, the demand for both real-time and energy-efficient computing has surged, placing significant pressure on traditional cloud computing architectures. Mobile edge computing (MEC), an emerging paradigm, ... | https://arxiv.org/abs/2601.06191 | Academic Papers | svg |
4c6f94f246f202328c56b87ad104fdba7d745b78d7dce0c615c72e20797057af | 2026-01-13T00:00:00-05:00 | MLB: A Scenario-Driven Benchmark for Evaluating Large Language Models in Clinical Applications | arXiv:2601.06193v1 Announce Type: new Abstract: The proliferation of Large Language Models (LLMs) presents transformative potential for healthcare, yet practical deployment is hindered by the absence of frameworks that assess real-world clinical utility. Existing benchmarks test static knowledge, failing to capture the... | https://arxiv.org/abs/2601.06193 | Academic Papers | svg |
40495125cd0dc3a9cdb54a53b95281a177e0b09a0be09204d95806b1378dc44d | 2026-01-13T00:00:00-05:00 | Political Alignment in Large Language Models: A Multidimensional Audit of Psychometric Identity and Behavioral Bias | arXiv:2601.06194v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into social decision-making, understanding their political positioning and alignment behavior is critical for safety and fairness. This study presents a sociotechnical audit of 26 prominent LLMs, triangulating th... | https://arxiv.org/abs/2601.06194 | Academic Papers | svg |
08b2680150933df9440d10f76d0e085bec240d512a879dae398c8026cef4d6bb | 2026-01-13T00:00:00-05:00 | EntroLnn: Entropy-Guided Liquid Neural Networks for Operando Refinement of Battery Capacity Fade Trajectories | arXiv:2601.06195v1 Announce Type: new Abstract: Battery capacity degradation prediction has long been a central topic in battery health analytics, and most studies focus on state of health (SoH) estimation and end of life (EoL) prediction. This study extends the scope to online refinement of the entire capacity fade tr... | https://arxiv.org/abs/2601.06195 | Academic Papers | svg |
4fea0a29dd105802fbfc0170baae49136b39f259100c088d1cf343b47786dd70 | 2026-01-13T00:00:00-05:00 | Manifold-based Sampling for In-Context Hallucination Detection in Large Language Models | arXiv:2601.06196v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate factually incorrect or unsupported content, commonly referred to as hallucinations. Prior work has explored decoding strategies, retrieval augmentation, and supervised fine-tuning for hallucination detection, while recent s... | https://arxiv.org/abs/2601.06196 | Academic Papers | svg |
0e8758b4ebf21fe245c9a51ec49a3c3ae74aeb2a97f0c56f4be2d99996d7d185 | 2026-01-13T00:00:00-05:00 | AI Safeguards, Generative AI and the Pandora Box: AI Safety Measures to Protect Businesses and Personal Reputation | arXiv:2601.06197v1 Announce Type: new Abstract: Generative AI has unleashed the power of content generation and it has also unwittingly opened the pandora box of realistic deepfake causing a number of social hazards and harm to businesses and personal reputation. The investigation & ramification of Generative AI techno... | https://arxiv.org/abs/2601.06197 | Academic Papers | svg |
37237ed6bb6f26276411c37672be46196f8ef0f098a79772961f4139f7bab44d | 2026-01-13T00:00:00-05:00 | How Does India Cook Biryani? | arXiv:2601.06198v1 Announce Type: new Abstract: Biryani, one of India's most celebrated dishes, exhibits remarkable regional diversity in its preparation, ingredients, and presentation. With the growing availability of online cooking videos, there is unprecedented potential to study such culinary variations using compu... | https://arxiv.org/abs/2601.06198 | Academic Papers | svg |
68b2b6e01f0a9e25f23d36c27d058a79bff050870d7cd87cff86703358723438 | 2026-01-13T00:00:00-05:00 | Leveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Images | arXiv:2601.06200v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training while keeping training data localized, allowing us to preserve privacy in various domains including remote sensing. However, recent studies show that FL models may still leak sensitive information through their ... | https://arxiv.org/abs/2601.06200 | Academic Papers | svg |
1a20a8513ac325a855677718b1e2f339c6bde92e047b7d0966238ecf2274e57f | 2026-01-13T00:00:00-05:00 | RiskBridge: Turning CVEs into Business-Aligned Patch Priorities | arXiv:2601.06201v1 Announce Type: new Abstract: Enterprises are confronted with an unprece- dented escalation in cybersecurity vulnerabil- ities, with thousands of new CVEs disclosed each month. Conventional prioritization frame- works such as CVSS offer static severity met- rics that fail to account for exploit probab... | https://arxiv.org/abs/2601.06201 | Academic Papers | svg |
7c0f0a501d3ca0015b6640b27d796a82c3a2ae741a596baabb5dc396ae6a45e5 | 2026-01-13T00:00:00-05:00 | QwenStyle: Content-Preserving Style Transfer with Qwen-Image-Edit | arXiv:2601.06202v1 Announce Type: new Abstract: Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to its internal entangled content and style features. In this technical report, we propose the first content-preserving style transfer model tr... | https://arxiv.org/abs/2601.06202 | Academic Papers | svg |
455143d093647c2cd5ce0da0766ae7d46ea3b8f470313d2b47fc591bac1dbef9 | 2026-01-13T00:00:00-05:00 | Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification | arXiv:2601.06204v1 Announce Type: new Abstract: Intelligent anomaly detection in dynamic visual environments requires reconciling real-time performance with semantic interpretability. Conventional approaches address only fragments of this challenge. Reconstruction-based models capture low-level deviations without conte... | https://arxiv.org/abs/2601.06204 | Academic Papers | svg |
3f0d750e8b4b077379f77b35566c3430a6363b066354ae642d7bf5a3135aa42f | 2026-01-13T00:00:00-05:00 | Towards Public Administration Research Based on Interpretable Machine Learning | arXiv:2601.06205v1 Announce Type: new Abstract: Causal relationships play a pivotal role in research within the field of public administration. Ensuring reliable causal inference requires validating the predictability of these relationships, which is a crucial precondition. However, prediction has not garnered adequate... | https://arxiv.org/abs/2601.06205 | Academic Papers | svg |
c4b43221be9ddde20f336f5651612df583dafcc7dd4543aa984798f09121e1ac | 2026-01-13T00:00:00-05:00 | When Imbalance Comes Twice: Active Learning under Simulated Class Imbalance and Label Shift in Binary Semantic Segmentation | arXiv:2601.06209v1 Announce Type: new Abstract: The aim of Active Learning is to select the most informative samples from an unlabelled set of data. This is useful in cases where the amount of data is large and labelling is expensive, such as in machine vision or medical imaging. Two particularities of machine vision a... | https://arxiv.org/abs/2601.06209 | Academic Papers | svg |
fce314f595e74aff9aaa4d09a63f7169f51b2e53d82e8c0a3355284721c341ff | 2026-01-13T00:00:00-05:00 | Large Multimodal Model-Aided Scheduling for 6G Autonomous Communications | arXiv:2601.06211v1 Announce Type: new Abstract: Recently, large language models (LLMs) have gained significant attention for their ability to generate fast and accurate answer to the given query. These models have evolved into large multimodal models (LMMs), which can interpret and analyze multimodal inputs such as ima... | https://arxiv.org/abs/2601.06211 | Academic Papers | svg |
7506d70ebf7b13d64cad85434ea002a6d46a970a8337ef8ca028cdafabeedd89 | 2026-01-13T00:00:00-05:00 | Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur | arXiv:2601.06212v1 Announce Type: new Abstract: We present Akasha 2, a state-of-the-art multimodal architecture that integrates Hamiltonian State Space Duality (H-SSD) with Visual-Language Joint Embedding Predictive Architecture (VL-JEPA). The system leverages the Mamba-3 Selective State Space Model (SSM) augmented by ... | https://arxiv.org/abs/2601.06212 | Academic Papers | svg |
533aac873ccc0b3b565b558fa793a3295d72eed83f52ce8ed9ad0d963d403648 | 2026-01-13T00:00:00-05:00 | Cyber Threat Detection and Vulnerability Assessment System using Generative AI and Large Language Model | arXiv:2601.06213v1 Announce Type: new Abstract: Background: Cyber-attacks have evolved rapidly in recent years, many individuals and business owners have been affected by cyber-attacks in various ways. Cyber-attacks include various threats such as ransomware, malware, phishing, and Denial of Service (DoS)-related attac... | https://arxiv.org/abs/2601.06213 | Academic Papers | svg |
10f9c6c44cbfdad5407bfa37eac7e3762115b88c7d28a2e53195d4a1a98ac275 | 2026-01-13T00:00:00-05:00 | Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling | arXiv:2601.06214v1 Announce Type: new Abstract: Protein-protein interaction (PPI) represents a central challenge within the biology field, and accurately predicting the consequences of mutations in this context is crucial for drug design and protein engineering. Deep learning (DL) has shown promise in forecasting the e... | https://arxiv.org/abs/2601.06214 | Academic Papers | svg |
04441a8596a039d90b6405da00d1697f5bafe4005574301f79b1f498d3980368 | 2026-01-13T00:00:00-05:00 | LLM Agents in Law: Taxonomy, Applications, and Challenges | arXiv:2601.06216v1 Announce Type: new Abstract: Large language models (LLMs) have precipitated a dramatic improvement in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability. Recently, LLM agents have attracted significa... | https://arxiv.org/abs/2601.06216 | Academic Papers | svg |
e1af818baec89f4d822a482559410e76a90375f317109ba8972a33a0be33bc18 | 2026-01-13T00:00:00-05:00 | CEEMDAN-Based Multiscale CNN for Wind Turbine Gearbox Fault Detection | arXiv:2601.06217v1 Announce Type: new Abstract: Wind turbines play a critical role in the shift toward sustainable energy generation. Their operation relies on multiple interconnected components, and a failure in any of these can compromise the entire system's functionality. Detecting faults accurately is challenging d... | https://arxiv.org/abs/2601.06217 | Academic Papers | svg |
8c02895e98e05c0603153abf855a3f51dd2a5ee27aa25003ee76b8b0bfb2c452 | 2026-01-13T00:00:00-05:00 | Two-step Authentication: Multi-biometric System Using Voice and Facial Recognition | arXiv:2601.06218v1 Announce Type: new Abstract: We present a cost-effective two-step authentication system that integrates face identification and speaker verification using only a camera and microphone available on common devices. The pipeline first performs face recognition to identify a candidate user from a small e... | https://arxiv.org/abs/2601.06218 | Academic Papers | svg |
b075ea51f8eb5064abbe8a0abc0948bfc6f4a3886b1138480ce4fc5d94ae40cc | 2026-01-13T00:00:00-05:00 | AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections | arXiv:2601.06219v1 Announce Type: new Abstract: The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need ... | https://arxiv.org/abs/2601.06219 | Academic Papers | svg |
4c84b82f123e40ae2b04ba2c46601f3a2970f7e0aed958056b4ca136db9f69c6 | 2026-01-13T00:00:00-05:00 | Breaking Model Lock-in: Cost-Efficient Zero-Shot LLM Routing via a Universal Latent Space | arXiv:2601.06220v1 Announce Type: new Abstract: The rapid proliferation of Large Language Models (LLMs) has led to a fragmented and inefficient ecosystem, a state of ``model lock-in'' where seamlessly integrating novel models remains a significant bottleneck. Current routing frameworks require exhaustive, costly retrai... | https://arxiv.org/abs/2601.06220 | Academic Papers | svg |
e93f1f4bcc31226128b59b64de69e040b08b13a4dd9219ba5dce6988494e06bd | 2026-01-13T00:00:00-05:00 | LDTC: Lifelong deep temporal clustering for multivariate time series | arXiv:2601.06221v1 Announce Type: new Abstract: Clustering temporal and dynamically changing multivariate time series from real-world fields, called temporal clustering for short, has been a major challenge due to inherent complexities. Although several deep temporal clustering algorithms have demonstrated a strong adv... | https://arxiv.org/abs/2601.06221 | Academic Papers | svg |
c7a75910388616ff513e2cdab111fad4a68870b526c05c03c84501abb123b995 | 2026-01-13T00:00:00-05:00 | SAPL: Semantic-Agnostic Prompt Learning in CLIP for Weakly Supervised Image Manipulation Localization | arXiv:2601.06222v1 Announce Type: new Abstract: Malicious image manipulation threatens public safety and requires efficient localization methods. Existing approaches depend on costly pixel-level annotations which make training expensive. Existing weakly supervised methods rely only on image-level binary labels and focu... | https://arxiv.org/abs/2601.06222 | Academic Papers | svg |
f7208c0b026a97b3e1e8529d39d48186de6567c42bd36255d9f109b09a1aaceb | 2026-01-13T00:00:00-05:00 | Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness | arXiv:2601.06223v1 Announce Type: new Abstract: This paper presents a conceptual and operational framework for developing and operating safe and trustworthy AI agents based on a Three-Pillar Model grounded in transparency, accountability, and trustworthiness. Building on prior work in Human-in-the-Loop systems, reinfor... | https://arxiv.org/abs/2601.06223 | Academic Papers | svg |
836d2fe269db3d82026eddd87b69f8ca2fdb2a25be7f9d3e81d52f26a08f2d46 | 2026-01-13T00:00:00-05:00 | Ground What You See: Hallucination-Resistant MLLMs via Caption Feedback, Diversity-Aware Sampling, and Conflict Regularization | arXiv:2601.06224v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable success across diverse tasks, their practical deployment is severely hindered by hallucination issues, which become particularly acute during Reinforcement Learning (RL) optimization. This paper syste... | https://arxiv.org/abs/2601.06224 | Academic Papers | svg |
f0b80d822891f0e2ed4acc907d74b3ae52d1325f1039ce40751720fc450abd43 | 2026-01-13T00:00:00-05:00 | Classroom AI: Large Language Models as Grade-Specific Teachers | arXiv:2601.06225v1 Announce Type: new Abstract: Large Language Models (LLMs) offer a promising solution to complement traditional teaching and address global teacher shortages that affect hundreds of millions of children, but they fail to provide grade-appropriate responses for students at different educational levels.... | https://arxiv.org/abs/2601.06225 | Academic Papers | svg |
ff6964089bd170d81d72069ca3c395943c0de49dde844582890e940053b11a6b | 2026-01-13T00:00:00-05:00 | Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification | arXiv:2601.06226v1 Announce Type: new Abstract: Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content, restricting their safe deployment. While traditional methods (e.g., alignment) adjust output preferences, they fail to eliminate underlying toxic regions in p... | https://arxiv.org/abs/2601.06226 | Academic Papers | svg |
45335948c0d6204b4d375e90c8ba729f2330b44d5dd08b93c7594d921491a534 | 2026-01-13T00:00:00-05:00 | When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics | arXiv:2601.06227v1 Announce Type: new Abstract: Battery management systems increasingly require accurate battery health prognostics under strict on-device constraints. This paper presents DLNet, a practical framework with dual-stage distillation of liquid neural networks that turns a high-capacity model into compact an... | https://arxiv.org/abs/2601.06227 | Academic Papers | svg |
d50f373bb3046d54d563a83f2ba640dd9f17df3d42879b11ec082c4faf36cf1a | 2026-01-13T00:00:00-05:00 | Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model | arXiv:2601.06228v1 Announce Type: new Abstract: The scarcity and low diversity of well-annotated automotive radar datasets often limit the performance of deep-learning-based environmental perception. To overcome these challenges, we propose a conditional generative framework for synthesizing realistic Frequency-Modulat... | https://arxiv.org/abs/2601.06228 | Academic Papers | svg |
6ac9b61dc0f6533f82d69fb09921e1dd6c51a9acb273467b94ba668352df289f | 2026-01-13T00:00:00-05:00 | Triadic Concept Analysis for Logic Interpretation of Simple Artificial Networks | arXiv:2601.06229v1 Announce Type: new Abstract: An artificial neural network (ANN) is a numerical method used to solve complex classification problems. Due to its high classification power, the ANN method often outperforms other classification methods in terms of accuracy. However, an ANN model lacks interpretability c... | https://arxiv.org/abs/2601.06229 | Academic Papers | svg |
cae2fa770a669b110e3a2425875d828c08c5867bfafb9db64107baa6174097a4 | 2026-01-13T00:00:00-05:00 | Employ SmartNICs' Data Path Accelerators for Ordered Key-Value Stores | arXiv:2601.06231v1 Announce Type: new Abstract: Remote in-memory key-value (KV) stores serve as a cornerstone for diverse modern workloads, and high-speed range scans are frequently a requirement. However, current architectures rarely achieve a simultaneous balance of peak efficiency, architectural simplicity, and nati... | https://arxiv.org/abs/2601.06231 | Academic Papers | svg |
d5bb225ed670f286b0811f040546b1799724f4711119faee648e64db8419e6f5 | 2026-01-13T00:00:00-05:00 | Multi-Agent Framework for Controllable and Protected Generative Content Creation: Addressing Copyright and Provenance in AI-Generated Media | arXiv:2601.06232v1 Announce Type: new Abstract: The proliferation of generative AI systems creates unprecedented opportunities for content creation while raising critical concerns about controllability, copyright infringement, and content provenance. Current generative models operate as "black boxes" with limited user ... | https://arxiv.org/abs/2601.06232 | Academic Papers | svg |
1848152e7c87231cd78e919665e6fa2391da95fee6409bae6521e6af8d74bc0f | 2026-01-13T00:00:00-05:00 | PCoKG: Personality-aware Commonsense Reasoning with Debate | arXiv:2601.06234v1 Announce Type: new Abstract: Most commonsense reasoning models overlook the influence of personality traits, limiting their effectiveness in personalized systems such as dialogue generation. To address this limitation, we introduce the Personality-aware Commonsense Knowledge Graph (PCoKG), a structur... | https://arxiv.org/abs/2601.06234 | Academic Papers | svg |
f69c8e62785d4673b6790695adb4e77278b2307186944443f72675cfd8847a61 | 2026-01-13T00:00:00-05:00 | An Intelligent AI glasses System with Multi-Agent Architecture for Real-Time Voice Processing and Task Execution | arXiv:2601.06235v1 Announce Type: new Abstract: This paper presents an AI glasses system that integrates real-time voice processing, artificial intelligence(AI) agents, and cross-network streaming capabilities. The system employs dual-agent architecture where Agent 01 handles Automatic Speech Recognition (ASR) and Agen... | https://arxiv.org/abs/2601.06235 | Academic Papers | svg |
e2352a67dc5f40d3dafecde505a2d06a91701f77edb8d04dd73faef5bd6e7862 | 2026-01-13T00:00:00-05:00 | Data-Dependent Goal Modeling for ML-Enabled Law Enforcement Systems | arXiv:2601.06237v1 Announce Type: new Abstract: Investigating serious crimes is inherently complex and resource-constrained. Law enforcement agencies (LEAs) grapple with overwhelming volumes of offender and incident data, making effective suspect identification difficult. Although machine learning (ML)-enabled systems ... | https://arxiv.org/abs/2601.06237 | Academic Papers | svg |
62f3c20a6bc989cfb6eb3a1df0922fa94d5c6e3cc4c2f7af4bc2c577234a0b60 | 2026-01-13T00:00:00-05:00 | SPINAL -- Scaling-law and Preference Integration in Neural Alignment Layers | arXiv:2601.06238v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is a principled, scalable alternative to RLHF for aligning large language models from pairwise preferences, but its internal geometric footprint remains undercharacterized, limiting audits, checkpoint comparisons, and failure predictio... | https://arxiv.org/abs/2601.06238 | Academic Papers | svg |
bc4db04cdd83aa76af97f93604dbcc718418583db90ca457755e7daaef925edd | 2026-01-13T00:00:00-05:00 | A survey of facial recognition techniques | arXiv:2601.06239v1 Announce Type: new Abstract: As multimedia content is quickly growing, the field of facial recognition has become one of the major research fields, particularly in the recent years. The most problematic area to researchers in image processing and computer vision is the human face which is a complex o... | https://arxiv.org/abs/2601.06239 | Academic Papers | svg |
37e3bfdda1710573a472ee86957b9770089a0fff0db697d53cc6ffceb2bf36b8 | 2026-01-13T00:00:00-05:00 | Agentic AI Microservice Framework for Deepfake and Document Fraud Detection in KYC Pipelines | arXiv:2601.06241v1 Announce Type: new Abstract: The rapid proliferation of synthetic media, presentation attacks, and document forgeries has created significant vulnerabilities in Know Your Customer (KYC) workflows across financial services, telecommunications, and digital-identity ecosystems. Traditional monolithic KY... | https://arxiv.org/abs/2601.06241 | Academic Papers | svg |
7f5577814cadf16d1462ef9ca510bb3c1f636f28d3c5503d4e14f3807e2301f4 | 2026-01-13T00:00:00-05:00 | Matrix Factorization Framework for Community Detection under the Degree-Corrected Block Model | arXiv:2601.06262v1 Announce Type: new Abstract: Community detection is a fundamental task in data analysis. Block models form a standard approach to partition nodes according to a graph model, facilitating the analysis and interpretation of the network structure. By grouping nodes with similar connection patterns, they... | https://arxiv.org/abs/2601.06262 | Academic Papers | svg |
3a362356d32725128ad0a18be114f07309479bb4cd268bd3ef18c92c3aa25c31 | 2026-01-13T00:00:00-05:00 | Self-Admitted Technical Debt in LLM Software: An Empirical Comparison with ML and Non-ML Software | arXiv:2601.06266v1 Announce Type: new Abstract: Self-admitted technical debt (SATD), referring to comments flagged by developers that explicitly acknowledge suboptimal code or incomplete functionality, has received extensive attention in machine learning (ML) and traditional (Non-ML) software. However, little is known ... | https://arxiv.org/abs/2601.06266 | Academic Papers | svg |
1413198506408db5afe5d9011b4a69ab428d2aca00be4308801cecfbbe00e432 | 2026-01-13T00:00:00-05:00 | Automated QoR improvement in OpenROAD with coding agents | arXiv:2601.06268v1 Announce Type: new Abstract: EDA development and innovation has been constrained by scarcity of expert engineering resources. While leading LLMs have demonstrated excellent performance in coding and scientific reasoning tasks, their capacity to advance EDA technology itself has been largely untested.... | https://arxiv.org/abs/2601.06268 | Academic Papers | svg |
aa1cc5913aa1c8895a12b9d0833d22316ada7007f02356a448afda1899eb7c58 | 2026-01-13T00:00:00-05:00 | FairSCOSCA: Fairness At Arterial Signals -- Just Around The Corner | arXiv:2601.06275v1 Announce Type: new Abstract: Traffic signal control at intersections, especially in arterial networks, is a key lever for mitigating the growing issue of traffic congestion in cities. Despite the widespread deployment of SCOOTS and SCATS, which prioritize efficiency, fairness has remained largely abs... | https://arxiv.org/abs/2601.06275 | Academic Papers | svg |
56366885878cd1e6d3d9e8c020fe607c28a1828f0bd52180a8f2d47dfb349089 | 2026-01-13T00:00:00-05:00 | Automated Generation of Accurate Privacy Captions From Android Source Code Using Large Language Models | arXiv:2601.06276v1 Announce Type: new Abstract: Privacy captions are short sentences that succinctly describe what personal information is used, how it is used, and why, within an app. These captions can be utilized in various notice formats, such as privacy policies, app rationales, and app store descriptions. However... | https://arxiv.org/abs/2601.06276 | Academic Papers | svg |
cbf45f3e01c7910e4c3764d36ed8da9e5d787470907ab91f985e8cb884b5ec43 | 2026-01-13T00:00:00-05:00 | EyeTheia: A Lightweight and Accessible Eye-Tracking Toolbox | arXiv:2601.06279v1 Announce Type: new Abstract: We introduce EyeTheia, a lightweight and open deep learning pipeline for webcam-based gaze estimation, designed for browser-based experimental platforms and real-world cognitive and clinical research. EyeTheia enables real-time gaze tracking using only a standard laptop w... | https://arxiv.org/abs/2601.06279 | Academic Papers | svg |
6a4cfc0b4b927b51086c021555daea84bb02dabb7b763bb6664ed5ec532a90f5 | 2026-01-13T00:00:00-05:00 | The Potential of Erroneous Outbound Traffic Analysis to Unveil Silent Internal Anomalies | arXiv:2601.06280v1 Announce Type: new Abstract: Passive measurement has traditionally focused on inbound traffic to detect malicious activity, based on the assumption that threats originate externally. In this paper, we offer a complementary perspective by examining outbound traffic, and argue that a narrow subset -- w... | https://arxiv.org/abs/2601.06280 | Academic Papers | svg |
1e181cd9a8bff26cc06c511285080fa246f1d13c22fc22e288ad601b6066a0fd | 2026-01-13T00:00:00-05:00 | Mining Quantum Software Patterns in Open-Source Projects | arXiv:2601.06281v1 Announce Type: new Abstract: Quantum computing has become an active research field in recent years, as its applications in fields such as cryptography, optimization, and materials science are promising. Along with these developments, challenges and opportunities exist in the field of Quantum Software... | https://arxiv.org/abs/2601.06281 | Academic Papers | svg |
73914bb3c9c432060859a32b869673687edc76b530b1e7e81a698229585476dc | 2026-01-13T00:00:00-05:00 | Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning | arXiv:2601.06282v1 Announce Type: new Abstract: Long-term conversational agents face a fundamental scalability challenge as interactions extend over time: repeatedly processing entire conversation histories becomes computationally prohibitive. Current approaches attempt to solve this through memory frameworks that pred... | https://arxiv.org/abs/2601.06282 | Academic Papers | svg |
49588cb3964e13d28a0500493f4ad664ba393b7f92c811934a4f1dfbac6b7ba4 | 2026-01-13T00:00:00-05:00 | NAS-GS: Noise-Aware Sonar Gaussian Splatting | arXiv:2601.06285v1 Announce Type: new Abstract: Underwater sonar imaging plays a crucial role in various applications, including autonomous navigation in murky water, marine archaeology, and environmental monitoring. However, the unique characteristics of sonar images, such as complex noise patterns and the lack of ele... | https://arxiv.org/abs/2601.06285 | Academic Papers | svg |
56e0dfb470133d4394abfb188038e106dc072ad9ec09197048daecf5885b9efb | 2026-01-13T00:00:00-05:00 | Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds | arXiv:2601.06286v1 Announce Type: new Abstract: Bipedal humanoid robots must precisely coordinate balance, timing, and contact decisions when locomoting on constrained footholds such as stepping stones, beams, and planks -- even minor errors can lead to catastrophic failure. Classical optimization and control pipelines... | https://arxiv.org/abs/2601.06286 | Academic Papers | svg |
5f90953b8fca2a3e6c41f5b81724f995c70145e082f23494a794b33715515732 | 2026-01-13T00:00:00-05:00 | Perception Test 2025: Challenge Summary and a Unified VQA Extension | arXiv:2601.06287v1 Announce Type: new Abstract: The Third Perception Test challenge was organised as a full-day workshop alongside the IEEE/CVF International Conference on Computer Vision (ICCV) 2025. Its primary goal is to benchmark state-of-the-art video models and measure the progress in multimodal perception. This ... | https://arxiv.org/abs/2601.06287 | Academic Papers | svg |
cb4fe7b08c85093366daeee732b79e25d7274fe49bc4acf22f68367f45934f0b | 2026-01-13T00:00:00-05:00 | AIConfigurator: Lightning-Fast Configuration Optimization for Multi-Framework LLM Serving | arXiv:2601.06288v1 Announce Type: new Abstract: Optimizing Large Language Model (LLM) inference in production systems is increasingly difficult due to dynamic workloads, stringent latency/throughput targets, and a rapidly expanding configuration space. This complexity spans not only distributed parallelism strategies (... | https://arxiv.org/abs/2601.06288 | Academic Papers | svg |
84e6f2ef5dfb649e1f85862e3606a2ecdef3a9d75fbd1b8a51899b11ae3d68dc | 2026-01-13T00:00:00-05:00 | How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning? | arXiv:2601.06289v1 Announce Type: new Abstract: Mass spectrometry (MS) is a powerful analytical technique for identifying small molecules, yet determining complete molecular structures directly from tandem mass spectra (MS/MS) remains a long-standing challenge due to complex fragmentation patterns and the vast diversit... | https://arxiv.org/abs/2601.06289 | Academic Papers | svg |
d16f936801c5b36b613c95855605c15f9caeab5592af5f8b0a28c8afbd4f71d3 | 2026-01-13T00:00:00-05:00 | A Structure-Preserving Numerical Scheme for Optimal Control and Design of Mixing in Incompressible Flows | arXiv:2601.06294v1 Announce Type: new Abstract: We develop a structure-preserving computational framework for optimal mixing control in incompressible flows. Our approach exactly conserves the continuous system's key invariants (mass and $L^2$-energy), while also maintaining discrete state-adjoint duality at every time... | https://arxiv.org/abs/2601.06294 | Academic Papers | svg |
e2f73b6c1efac3e149ba9e45ae78cee7861fe96c5326e7bd318154f6fa04a46c | 2026-01-13T00:00:00-05:00 | Separation Results for Constant-Depth and Multilinear Ideal Proof Systems | arXiv:2601.06299v1 Announce Type: new Abstract: In this work, we establish separation theorems for several subsystems of the Ideal Proof System (IPS), an algebraic proof system introduced by Grochow and Pitassi (J. ACM, 2018). Separation theorems are well-studied in the context of classical complexity theory, Boolean c... | https://arxiv.org/abs/2601.06299 | Academic Papers | svg |
f0bdc22a5c1777793be290b69254e56602e9149d4fc17f84d648609489b70b0c | 2026-01-13T00:00:00-05:00 | $\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials | arXiv:2601.06300v1 Announce Type: new Abstract: Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component. We introduce \textit{eligibility criteria amendment prediction}, a novel NLP task that aims to forecast ... | https://arxiv.org/abs/2601.06300 | Academic Papers | svg |
7c4a85fd27af648087fc76ee6f43db9b1ec524c144c53a468aae3c36e9483cfd | 2026-01-13T00:00:00-05:00 | Beyond BeautifulSoup: Benchmarking LLM-Powered Web Scraping for Everyday Users | arXiv:2601.06301v1 Announce Type: new Abstract: Web scraping has historically required technical expertise in HTML parsing, session management, and authentication circumvention, which limited large-scale data extraction to skilled developers. We argue that large language models (LLMs) have democratized web scraping, en... | https://arxiv.org/abs/2601.06301 | Academic Papers | svg |
e63871169947e6bc06c00f8d4d1b5bb984ee14cdd87356308faaf1292d99d6bb | 2026-01-13T00:00:00-05:00 | Why LoRA Fails to Forget: Regularized Low-Rank Adaptation Against Backdoors in Language Models | arXiv:2601.06305v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning of large language models, but it is notably ineffective at removing backdoor behaviors from poisoned pretrained models when fine-tuning on clean dataset. Contrary to the common belief that this ... | https://arxiv.org/abs/2601.06305 | Academic Papers | svg |
c866ef408c1fd5169fb27301da9b3296dd4bce2814ce8f2443001b395d366012 | 2026-01-13T00:00:00-05:00 | SyntaxMind at BLP-2025 Task 1: Leveraging Attention Fusion of CNN and GRU for Hate Speech Detection | arXiv:2601.06306v1 Announce Type: new Abstract: This paper describes our system used in the BLP-2025 Task 1: Hate Speech Detection. We participated in Subtask 1A and Subtask 1B, addressing hate speech classification in Bangla text. Our approach employs a unified architecture that integrates BanglaBERT embeddings with m... | https://arxiv.org/abs/2601.06306 | Academic Papers | svg |
e835302ef7e33b37e4de065d5d45c6974e8272da9d7eff18d0f9313cb0de015b | 2026-01-13T00:00:00-05:00 | A Rising Tide Lifts All Boats: MTQE Rewards for Idioms Improve General Translation Quality | arXiv:2601.06307v1 Announce Type: new Abstract: Non-compositional expressions (e.g., idioms, proverbs, and metaphors) pose significant challenges for neural machine translation systems because their meanings cannot be derived from individual words alone. These expressions encode rich, cultural meaning, and have both fi... | https://arxiv.org/abs/2601.06307 | Academic Papers | svg |
449ff86c69f2ccce5a04bc21a2846ab18524e03d2c0d42b29940bada8a990204 | 2026-01-13T00:00:00-05:00 | VideoWeave: A Data-Centric Approach for Efficient Video Understanding | arXiv:2601.06309v1 Announce Type: new Abstract: Training video-language models is often prohibitively expensive due to the high cost of processing long frame sequences and the limited availability of annotated long videos. We present VideoWeave, a simple yet effective approach to improve data efficiency by constructing... | https://arxiv.org/abs/2601.06309 | Academic Papers | svg |
1cbe416f52b69b337f7569025d840eca8da0a01737b5565fab6fd07fc2df9788 | 2026-01-13T00:00:00-05:00 | C-EQ-ALINEA: Distributed, Coordinated, and Equitable Ramp Metering Strategy for Sustainable Freeway Operations | arXiv:2601.06311v1 Announce Type: new Abstract: Ramp metering is a widely deployed traffic management strategy for improving freeway efficiency, yet conventional approaches often lead to highly uneven delay distributions across on-ramps, undermining user acceptance and long-term sustainability. While existing fairness-... | https://arxiv.org/abs/2601.06311 | Academic Papers | svg |
061358a1dd34196e70a8a6f4271edd0243b3d69021efcf96a8220d9ab80736fa | 2026-01-13T00:00:00-05:00 | Koopman Model Dimension Reduction via Variational Bayesian Inference and Graph Search | arXiv:2601.06315v1 Announce Type: new Abstract: Koopman operator recently gained increasing attention in the control systems community for its abilities to bridge linear and nonlinear systems. Data driven Koopman operator approximations have established themselves as key enablers for system identification and model pre... | https://arxiv.org/abs/2601.06315 | Academic Papers | svg |
978c620040a2bb1eb28379017026b5ea893195361107a6434fff06f63e42065b | 2026-01-13T00:00:00-05:00 | Annotating Dimensions of Social Perception in Text: The First Sentence-Level Dataset of Warmth and Competence | arXiv:2601.06316v1 Announce Type: new Abstract: Warmth (W) (often further broken down into Trust (T) and Sociability (S)) and Competence (C) are central dimensions along which people evaluate individuals and social groups (Fiske, 2018). While these constructs are well established in social psychology, they are only sta... | https://arxiv.org/abs/2601.06316 | Academic Papers | svg |
c12e1829e206474d28575ac092b1d773e21ede7ca0a785722aef16497b9f068d | 2026-01-13T00:00:00-05:00 | Random is Faster than Systematic in Multi-Objective Local Search | arXiv:2601.06318v1 Announce Type: new Abstract: Local search is a fundamental method in operations research and combinatorial optimisation. It has been widely applied to a variety of challenging problems, including multi-objective optimisation where multiple, often conflicting, objectives need to be simultaneously cons... | https://arxiv.org/abs/2601.06318 | Academic Papers | svg |
fd7c7d1e06b0f97ef7aab2f4ab2f753c4587db8817050d1c35b6635a2a929acc | 2026-01-13T00:00:00-05:00 | SourceNet: Interpretable Sim-to-Real Inference on Variable-Geometry Sensor Arrays for Earthquake Source Inversion | arXiv:2601.06320v1 Announce Type: new Abstract: Inferring high-dimensional physical states from sparse, ad-hoc sensor arrays is a fundamental challenge across AI for Science, as they are complicated by irregular geometries and the profound Sim-to-Real gap in physical modeling. Taking earthquake source characterization ... | https://arxiv.org/abs/2601.06320 | Academic Papers | svg |
8ccd56915b8022e7ef8b732901caf2c1d5ab9f9abf10bbaa53efbd381c527ac3 | 2026-01-13T00:00:00-05:00 | A Data-Driven Surrogate Modeling and Sensor/Actuator Placement Framework for Flexible Spacecraft | arXiv:2601.06325v1 Announce Type: new Abstract: Flexible spacecraft structures present significant challenges for physical and control system design due to nonlinear dynamics, mission constraints, environmental variables, and changing operational conditions. This paper presents a data-driven framework for constructing ... | https://arxiv.org/abs/2601.06325 | Academic Papers | svg |
f365f3ef06ed232100a6a6aa578312e33b1c30f9f1e343cfa8dd3536943ef4f5 | 2026-01-13T00:00:00-05:00 | From Lagging to Leading: Validating Hard Braking Events as High-Density Indicators of Segment Crash Risk | arXiv:2601.06327v1 Announce Type: new Abstract: Identifying high crash risk road segments and accurately predicting crash incidence is fundamental to implementing effective safety countermeasures. While collision data inherently reflects risk, the infrequency and inconsistent reporting of crashes present a major challe... | https://arxiv.org/abs/2601.06327 | Academic Papers | svg |
7f00bbc72c84fc4ecf4f8eb5f9ed0918c2d80035888b7ff1c862c92d24deb756 | 2026-01-13T00:00:00-05:00 | ToolGym: an Open-world Tool-using Environment for Scalable Agent Testing and Data Curation | arXiv:2601.06328v1 Announce Type: new Abstract: Tool-using LLM agents still struggle in open-world settings with large tool pools, long-horizon objectives, wild constraints, and unreliable tool states. For scalable and realistic training and testing, we introduce an open-world tool-using environment, built on 5,571 for... | https://arxiv.org/abs/2601.06328 | Academic Papers | svg |
298cccee64875ff5632926a08cca1c74b1b92960f410111f5b9f6483efd0ec8b | 2026-01-13T00:00:00-05:00 | On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation | arXiv:2601.06329v1 Announce Type: new Abstract: Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content while preserving attributes like speaker and emotion, serving as foundation models for spoken dialogue. In prior literature, these models are often ... | https://arxiv.org/abs/2601.06329 | Academic Papers | svg |
6e534eddd060b0f66b1e2aadf3396d613afdb86ba5475aaa4a99f40b8e00801a | 2026-01-13T00:00:00-05:00 | Rethinking Inter-Process Communication with Memory Operation Offloading | arXiv:2601.06331v1 Announce Type: new Abstract: As multimodal and AI-driven services exchange hundreds of megabytes per request, existing IPC runtimes spend a growing share of CPU cycles on memory copies. Although both hardware and software mechanisms are exploring memory offloading, current IPC stacks lack a unified r... | https://arxiv.org/abs/2601.06331 | Academic Papers | svg |
bb66294b71cfc725af67bee23319d4cabcab84fbb39babf35d5bbb732554eb2b | 2026-01-13T00:00:00-05:00 | Kolmogorov-Arnold Networks-Based Tolerance-Aware Manufacturability Assessment Integrating Design-for-Manufacturing Principles | arXiv:2601.06334v1 Announce Type: new Abstract: Manufacturability assessment is a critical step in bridging the persistent gap between design and production. While artificial intelligence (AI) has been widely applied to this task, most existing frameworks rely on geometry-driven methods that require extensive preproces... | https://arxiv.org/abs/2601.06334 | Academic Papers | svg |
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