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d9e7c2494e5f00830f3a0705321a3ad62eb8e50aed75e574a224025ec8352b31 | 2025-12-31T09:23:43-05:00 | The Dreame X40 Ultra robovac is about $700 off, nearly matching its best price | The Dreame X40 Ultra used to be one of our robot vacuums on the market before its successor took its place. With the year coming to a close, now’s a good time to set yourself up for a cleaner, more organized start in 2026. The Dreame X40 Ultra — one of our favorite robot vacuum deals from Black Friday — can handle most... | https://www.theverge.com/gadgets/851325/dreame-x40-ultra-baseus-163w-retractable-car-charger-deal-sale | Technology | svg |
56a2a10ed8302ef67b837f3567c0b3d5093a40dbd3556fa87bf34a881623e191 | 2025-12-31T08:30:00-05:00 | Killing in the name of… nothing | Read the full story at The Verge. | https://www.theverge.com/policy/849609/charlie-kirk-shooting-ideology-literacy-politics | Technology | svg |
6144ae484c60806adba1ed681d0ba359414a9ebee59be18f502a57e7f658f037 | 2025-12-31T08:00:00-05:00 | The 11 best Nintendo Switch 2 games we played in 2025 | Read the full story at The Verge. | https://www.theverge.com/games/845401/nintendo-switch-2-best-games | Technology | svg |
a2cb967e1503cf6f8357b916445ddac37d48656db02c27b765b09eabe5f06071 | 2026-01-01T00:00:00-05:00 | Enriching Historical Records: An OCR and AI-Driven Approach for Database Integration | arXiv:2512.23710v1 Announce Type: new Abstract: This research digitizes and analyzes the Leidse hoogleraren en lectoren 1575-1815 books written between 1983 and 1985, which contain biographic data about professors and curators of Leiden University. It addresses the central question: how can we design an automated pipel... | https://arxiv.org/abs/2512.23710 | Academic Papers | svg |
eec7bf8139c13c0e80658b0f9fce4391c5e93478139c5780c47b45028c11926c | 2026-01-01T00:00:00-05:00 | CAT: A Metric-Driven Framework for Analyzing the Consistency-Accuracy Relation of LLMs under Controlled Input Variations | arXiv:2512.23711v1 Announce Type: new Abstract: We introduce \textsc{CAT}, a framework designed to evaluate and visualize the \emph{interplay} of \emph{accuracy} and \emph{response consistency} of Large Language Models (LLMs) under controllable input variations, using multiple-choice (MC) benchmarks as a case study. Cu... | https://arxiv.org/abs/2512.23711 | Academic Papers | svg |
1f3fc524f9cf8cadbd3a6bba2bf3e78f23abb0b9caf1e48492bd0bfd2b8ffe9c | 2026-01-01T00:00:00-05:00 | STED and Consistency Scoring: A Framework for Evaluating LLM Structured Output Reliability | arXiv:2512.23712v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed for structured data generation, yet output consistency remains critical for production applications. We introduce a comprehensive framework for evaluating and improving consistency in LLM-generated structured outputs.... | https://arxiv.org/abs/2512.23712 | Academic Papers | svg |
fa819cdf03ff5878182cd307c447bf52040665755c6578093f5f2262358644a7 | 2026-01-01T00:00:00-05:00 | PyBangla at BLP-2025 Task 2: Enhancing Bangla-to-Python Code Generation with Iterative Self-Correction and Multilingual Agents | arXiv:2512.23713v1 Announce Type: new Abstract: LLMs excel at code generation from English prompts, but this progress has not extended to low-resource languages. We address Bangla-to-Python code generation by introducing BanglaCodeAct, an agent-based framework that leverages multi-agent prompting and iterative self-cor... | https://arxiv.org/abs/2512.23713 | Academic Papers | svg |
f3f32fd3e27ee4f978613d169bd43b7f111c0b26f5d990c2466a0b7ab381324c | 2026-01-01T00:00:00-05:00 | PharmaShip: An Entity-Centric, Reading-Order-Supervised Benchmark for Chinese Pharmaceutical Shipping Documents | arXiv:2512.23714v1 Announce Type: new Abstract: We present PharmaShip, a real-world Chinese dataset of scanned pharmaceutical shipping documents designed to stress-test pre-trained text-layout models under noisy OCR and heterogeneous templates. PharmaShip covers three complementary tasks-sequence entity recognition (SE... | https://arxiv.org/abs/2512.23714 | Academic Papers | svg |
efee8544eb0506287a81d630bc1589b418f3368974d5d3e5d1a69ed4867d899e | 2026-01-01T00:00:00-05:00 | Wind Speed Weibull Model Identification in Oman, and Computed Normalized Annual Energy Production (NAEP) From Wind Turbines Based on Data From Weather Stations | arXiv:2512.23715v1 Announce Type: new Abstract: Using observation records of wind speeds from weather stations in the Sultanate of Oman between 2000 and 2023, we compute estimators of the two Weibull distribution parameters (namely, the Weibull distribution's shape parameter and the Weibull distribution's scale paramet... | https://arxiv.org/abs/2512.23715 | Academic Papers | svg |
0f5d15d4a9eaf213403010538bc160c9b3e10b5be7702a76cf15285e9d436225 | 2026-01-01T00:00:00-05:00 | Noise-Driven Persona Formation in Reflexive Neural Language Generation | arXiv:2512.23716v1 Announce Type: new Abstract: This paper introduces the Luca-Noise Reflex Protocol (LN-RP), a computational framework for analyzing noise-driven persona emergence in large language models. By injecting stochastic noise seeds into the initial generation state, we observe nonlinear transitions in lingui... | https://arxiv.org/abs/2512.23716 | Academic Papers | svg |
a569c16324cd39959830ca14be82748c0629bbf9ffd32876aa5dce993a0c8c41 | 2026-01-01T00:00:00-05:00 | HarmTransform: Transforming Explicit Harmful Queries into Stealthy via Multi-Agent Debate | arXiv:2512.23717v1 Announce Type: new Abstract: Large language models (LLMs) are equipped with safety mechanisms to detect and block harmful queries, yet current alignment approaches primarily focus on overtly dangerous content and overlook more subtle threats. However, users can often disguise harmful intent through c... | https://arxiv.org/abs/2512.23717 | Academic Papers | svg |
efb9947e1d18d40b826129edb546d39ff897c0aec3280a8b3b3bc5093a708f6e | 2026-01-01T00:00:00-05:00 | Network Traffic Analysis with Process Mining: The UPSIDE Case Study | arXiv:2512.23718v1 Announce Type: new Abstract: Online gaming is a popular activity involving the adoption of complex systems and network infrastructures. The relevance of gaming, which generates large amounts of market revenue, drove research in modeling network devices' behavior to evaluate bandwidth consumption, pre... | https://arxiv.org/abs/2512.23718 | Academic Papers | svg |
54cbc4aa4109ec031835bf17b4180ddf2d6bc2811f5f1f2a65674ca488d8e8c3 | 2026-01-01T00:00:00-05:00 | A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation | arXiv:2512.23719v1 Announce Type: new Abstract: Artificial intelligence is beginning to ease long-standing bottlenecks in the CAD-to-mesh pipeline. This survey reviews recent advances where machine learning aids part classification, mesh quality prediction, and defeaturing. We explore methods that improve unstructured ... | https://arxiv.org/abs/2512.23719 | Academic Papers | svg |
8e1a0d93d49924b2493f969179ddc4aefd204a0f09ee26e68bf7cd7a8c552909 | 2026-01-01T00:00:00-05:00 | An Electronic Ising Machine | arXiv:2512.23720v1 Announce Type: new Abstract: We develop a custom printed circuit board (PCB) as a low-power and high-speed accelerator for NP-Hard graph problems. Based on the annealing principle, it uses an analog computing architecture of coupled nonlinear electronic oscillators. Using an energy-based representati... | https://arxiv.org/abs/2512.23720 | Academic Papers | svg |
5be7f90af35df20181c87ea5bd00845806eb9c58ad167af4438c4c96727e4191 | 2026-01-01T00:00:00-05:00 | Emergent World Beliefs: Exploring Transformers in Stochastic Games | arXiv:2512.23722v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) have demonstrated strong reasoning abilities across diverse fields, from solving programming challenges to competing in strategy-intensive games such as chess. Prior work has shown that LLMs can develop emergent world models ... | https://arxiv.org/abs/2512.23722 | Academic Papers | svg |
2e76570217a974c32603f2c6e0a9e0ee6dda4b53601c448a57126e9562b3af14 | 2026-01-01T00:00:00-05:00 | New Exam Security Questions in the AI Era: Comparing AI-Generated Item Similarity Between Naive and Detail-Guided Prompting Approaches | arXiv:2512.23729v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as powerful tools for generating domain-specific multiple-choice questions (MCQs), offering efficiency gains for certification boards but raising new concerns about examination security. This study investigated whether LLM-generat... | https://arxiv.org/abs/2512.23729 | Academic Papers | svg |
ffeeaedec15303891acd925d93cd29e3d1c74f93a407639d9ca4ed457927dbc9 | 2026-01-01T00:00:00-05:00 | When in Doubt, Deliberate: Confidence-Based Routing to Expert Debate for Sexism Detection | arXiv:2512.23732v1 Announce Type: new Abstract: Sexist content online increasingly appears in subtle, context-dependent forms that evade traditional detection methods. Its interpretation often depends on overlapping linguistic, psychological, legal, and cultural dimensions, which produce mixed and sometimes contradicto... | https://arxiv.org/abs/2512.23732 | Academic Papers | svg |
2765f6c3727928c9ab993f50cca84084b5776cfac46303648a69a4105c5431b8 | 2026-01-01T00:00:00-05:00 | Biochemical Computing Mode for Sequential Logic | arXiv:2512.23734v1 Announce Type: new Abstract: Recent years have witnessed the growing scholarly interest in the next-generation general-purpose computers. Various innovative computing modes have been proposed, such as optical, quantum phenomena, and DNA-based modes. Sequential logic circuits are a critical factor tha... | https://arxiv.org/abs/2512.23734 | Academic Papers | svg |
bb155e501e3c2abb70899522b9f1fc5c6561efb9479d2c9b78133dec523ee0fc | 2026-01-01T00:00:00-05:00 | Ovonic switches enable energy-efficient dendrite-like computing | arXiv:2512.23736v1 Announce Type: new Abstract: Over the last decade, dendrites within individual biological neurons, which were previously thought to generally perform information pooling and networking, have now been shown to express complex temporal dynamics, Boolean-like logic, arithmetic, signal discrimination, an... | https://arxiv.org/abs/2512.23736 | Academic Papers | svg |
e010b789ddb99c4c42f78c5443250f6724554354c4d0bf461df7d20c012bac22 | 2026-01-01T00:00:00-05:00 | Governing Cloud Data Pipelines with Agentic AI | arXiv:2512.23737v1 Announce Type: new Abstract: Cloud data pipelines increasingly operate under dynamic workloads, evolving schemas, cost constraints, and strict governance requirements. Despite advances in cloud-native orchestration frameworks, most production pipelines rely on static configurations and reactive opera... | https://arxiv.org/abs/2512.23737 | Academic Papers | svg |
4cf2be4410fa3785f640578ef1178686e604ded0116ce4f747cecf42918bab14 | 2026-01-01T00:00:00-05:00 | Enforcing Temporal Constraints for LLM Agents | arXiv:2512.23738v1 Announce Type: new Abstract: LLM-based agents are deployed in safety-critical applications, yet current guardrail systems fail to prevent violations of temporal safety policies, requirements that govern the ordering and sequencing of agent actions. For instance, agents may access sensitive data befor... | https://arxiv.org/abs/2512.23738 | Academic Papers | svg |
ae8d9ab0129cbd395c160ec47166a5971857aac93b9fc5760aa61e39f80f1e21 | 2026-01-01T00:00:00-05:00 | Break Out the Silverware -- Semantic Understanding of Stored Household Items | arXiv:2512.23739v1 Announce Type: new Abstract: ``Bring me a plate.'' For domestic service robots, this simple command reveals a complex challenge: inferring where everyday items are stored, often out of sight in drawers, cabinets, or closets. Despite advances in vision and manipulation, robots still lack the commonsen... | https://arxiv.org/abs/2512.23739 | Academic Papers | svg |
0434af8a0747a48537b44735f8e60016dab5fd0f47e8efe29530d3e55886f3f2 | 2026-01-01T00:00:00-05:00 | Towards representation agnostic probabilistic programming | arXiv:2512.23740v1 Announce Type: new Abstract: Current probabilistic programming languages and tools tightly couple model representations with specific inference algorithms, preventing experimentation with novel representations or mixed discrete-continuous models. We introduce a factor abstraction with five fundamenta... | https://arxiv.org/abs/2512.23740 | Academic Papers | svg |
d8c0e2611b3ee09b80e7e763a85c86feacac8db06fe662f9e099a22e8c0360dc | 2026-01-01T00:00:00-05:00 | AgenticTCAD: A LLM-based Multi-Agent Framework for Automated TCAD Code Generation and Device Optimization | arXiv:2512.23742v1 Announce Type: new Abstract: With the continued scaling of advanced technology nodes, the design-technology co-optimization (DTCO) paradigm has become increasingly critical, rendering efficient device design and optimization essential. In the domain of TCAD simulation, however, the scarcity of open-s... | https://arxiv.org/abs/2512.23742 | Academic Papers | svg |
7af6400e30cbd9dae923ab5a2d4a30d5a4e682b8e53d423b851b0c513816d48a | 2026-01-01T00:00:00-05:00 | Hybrid-Code: A Privacy-Preserving, Redundant Multi-Agent Framework for Reliable Local Clinical Coding | arXiv:2512.23743v1 Announce Type: new Abstract: Clinical coding automation using cloud-based Large Language Models (LLMs) poses privacy risks and latency bottlenecks, rendering them unsuitable for on-premise healthcare deployment. We introduce Hybrid-Code, a hybrid neuro-symbolic multi-agent framework for local clinica... | https://arxiv.org/abs/2512.23743 | Academic Papers | svg |
20f8409a8eeb4119824c19a76d19956c857827067693bb06b0647a4673279e28 | 2026-01-01T00:00:00-05:00 | A Comprehensive Study of Deep Learning Model Fixing Approaches | arXiv:2512.23745v1 Announce Type: new Abstract: Deep Learning (DL) has been widely adopted in diverse industrial domains, including autonomous driving, intelligent healthcare, and aided programming. Like traditional software, DL systems are also prone to faults, whose malfunctioning may expose users to significant risk... | https://arxiv.org/abs/2512.23745 | Academic Papers | svg |
fe2c405eec1021bad03b514db15d2a387fee8a8e071581e3ecc3589e9411c071 | 2026-01-01T00:00:00-05:00 | DEFT: Differentiable Automatic Test Pattern Generation | arXiv:2512.23746v1 Announce Type: new Abstract: Modern IC complexity drives test pattern growth, with the majority of patterns targeting a small set of hard-to-detect (HTD) faults. This motivates new ATPG algorithms to improve test effectiveness specifically for HTD faults. This paper presents DEFT (Differentiable Auto... | https://arxiv.org/abs/2512.23746 | Academic Papers | svg |
66ea766a5d1fe799372184b84551372a057adbea7bf65760cbc89c3152bfc189 | 2026-01-01T00:00:00-05:00 | State-of-the-art Small Language Coder Model: Mify-Coder | arXiv:2512.23747v1 Announce Type: new Abstract: We present Mify-Coder, a 2.5B-parameter code model trained on 4.2T tokens using a compute-optimal strategy built on the Mify-2.5B foundation model. Mify-Coder achieves comparable accuracy and safety while significantly outperforming much larger baseline models on standard... | https://arxiv.org/abs/2512.23747 | Academic Papers | svg |
8250fd5ca3fcdce447c88d1cbdcfad2979ee83a19fd061c7953c930abf29beed | 2026-01-01T00:00:00-05:00 | A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios | arXiv:2512.23748v1 Announce Type: new Abstract: For complex simulation problems, inferring parameters of scientific interest often precludes the use of classical likelihood-based techniques due to intractable likelihood functions. Simulation-based inference (SBI) methods forego the need for explicit likelihoods by dire... | https://arxiv.org/abs/2512.23748 | Academic Papers | svg |
3a46cfa4d510bf6f9b351b4c7629afc8dc8d4447de2d97cd21b1d03fb9c7c8fb | 2026-01-01T00:00:00-05:00 | Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents | arXiv:2512.23749v1 Announce Type: new Abstract: Human-level concept learning argues that humans typically learn new concepts from a single example, whereas machine learning algorithms typically require hundreds of samples to learn a single concept. Our brain subconsciously identifies important features and learns more ... | https://arxiv.org/abs/2512.23749 | Academic Papers | svg |
0dba88e5b0a3e8c4e95c24ed9c5cf0342be0925f309a092ff411598bef4175ab | 2026-01-01T00:00:00-05:00 | Geometric Scaling of Bayesian Inference in LLMs | arXiv:2512.23752v1 Announce Type: new Abstract: Recent work has shown that small transformers trained in controlled "wind-tunnel'' settings can implement exact Bayesian inference, and that their training dynamics produce a geometric substrate -- low-dimensional value manifolds and progressively orthogonal keys -- that ... | https://arxiv.org/abs/2512.23752 | Academic Papers | svg |
baf0c94f227a0fb68c5aa4b9774b76af1bc3eb7222151c661f2a27a12596f396 | 2026-01-01T00:00:00-05:00 | Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation | arXiv:2512.23753v1 Announce Type: new Abstract: Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence... | https://arxiv.org/abs/2512.23753 | Academic Papers | svg |
44153405f969adfe1f721b004b321fbe8bc7fb35f55639f3b666e08ea7a633b6 | 2026-01-01T00:00:00-05:00 | HINTS: Extraction of Human Insights from Time-Series Without External Sources | arXiv:2512.23755v1 Announce Type: new Abstract: Human decision-making, emotions, and collective psychology are complex factors that shape the temporal dynamics observed in financial and economic systems. Many recent time series forecasting models leverage external sources (e.g., news and social media) to capture human ... | https://arxiv.org/abs/2512.23755 | Academic Papers | svg |
ffd1cac01f896bd36bcffd52236e084fea663a052a96ef1eeefec0ae69e93a28 | 2026-01-01T00:00:00-05:00 | Sparse Random Matrices for Dimensionality Reduction | arXiv:2512.23756v1 Announce Type: new Abstract: The Johnson-Lindenstrauss (JL) theorem states that a set of points in high-dimensional space can be embedded into a lower-dimensional space while approximately preserving pairwise distances with high probability Johnson and Lindenstrauss (1984). The standard JL theorem us... | https://arxiv.org/abs/2512.23756 | Academic Papers | svg |
78c9637f01e969592b86841e5b6b2f6887f1cb44281e960d00ad4b1f5278041d | 2026-01-01T00:00:00-05:00 | Audited Skill-Graph Self-Improvement for Agentic LLMs via Verifiable Rewards, Experience Synthesis, and Continual Memory | arXiv:2512.23760v1 Announce Type: new Abstract: Reinforcement learning is increasingly used to transform large language models into agentic systems that act over long horizons, invoke tools, and manage memory under partial observability. While recent work has demonstrated performance gains through tool learning, verifi... | https://arxiv.org/abs/2512.23760 | Academic Papers | svg |
0abfc82c61b4af430caae73ffa60cd273496b2d9b90bd49ea90ce5d7c0eac9f7 | 2026-01-01T00:00:00-05:00 | Learning Coupled System Dynamics under Incomplete Physical Constraints and Missing Data | arXiv:2512.23761v1 Announce Type: new Abstract: Advances in data acquisition and computational methods have accelerated the use of differential equation based modelling for complex systems. Such systems are often described by coupled (or more) variables, yet governing equation is typically available for one variable, w... | https://arxiv.org/abs/2512.23761 | Academic Papers | svg |
79ee5e65d99f7ef5ba5f8ea6be06a6d729e7b3954e7e5aea2de81623bb504017 | 2026-01-01T00:00:00-05:00 | Drift-Based Dataset Stability Benchmark | arXiv:2512.23762v1 Announce Type: new Abstract: Machine learning (ML) represents an efficient and popular approach for network traffic classification. However, network traffic classification is a challenging domain, and trained models may degrade soon after deployment due to the obsolete datasets and quick evolution of... | https://arxiv.org/abs/2512.23762 | Academic Papers | svg |
3b46999c9236561993e7c451f7bf927e3e51a672658a28025fef32532507f5e2 | 2026-01-01T00:00:00-05:00 | Neural Optimal Design of Experiment for Inverse Problems | arXiv:2512.23763v1 Announce Type: new Abstract: We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and indirect sparsity regularization. NODE jointly trains a neural reconstruction model and a fixed... | https://arxiv.org/abs/2512.23763 | Academic Papers | svg |
276c73c1c9fcd1626b3b19ff965d48503a3f160cd3ef340ef08c17a76552a50b | 2026-01-01T00:00:00-05:00 | Exploring Cumulative Effects in Survival Data Using Deep Learning Networks | arXiv:2512.23764v1 Announce Type: new Abstract: In epidemiological research, modeling the cumulative effects of time-dependent exposures on survival outcomes presents a challenge due to their intricate temporal dynamics. Conventional spline-based statistical methods, though effective, require repeated data transformati... | https://arxiv.org/abs/2512.23764 | Academic Papers | svg |
905959d75fd15d668b556ab9d3a94670343d4bcb3f9c3c4153cf9175e17f724f | 2026-01-01T00:00:00-05:00 | Entropy-Aware Speculative Decoding Toward Improved LLM Reasoning | arXiv:2512.23765v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates large language model (LLM) reasoning by using a small draft model to generate candidate tokens, which the target LLM either accepts directly or regenerates upon rejection. However, excessive alignment between the draft and target mode... | https://arxiv.org/abs/2512.23765 | Academic Papers | svg |
d9c64ca6a9b6f2707e43c4d2f3c5c9c438d284097a5446d248a0eefa420ba76b | 2026-01-01T00:00:00-05:00 | A Granular Grassmannian Clustering Framework via the Schubert Variety of Best Fit | arXiv:2512.23766v1 Announce Type: new Abstract: In many classification and clustering tasks, it is useful to compute a geometric representative for a dataset or a cluster, such as a mean or median. When datasets are represented by subspaces, these representatives become points on the Grassmann or flag manifold, with di... | https://arxiv.org/abs/2512.23766 | Academic Papers | svg |
7fe5b66c78a4267e97feb7a803d2bfb9795eeb9a3d05ad60903ad73c4472520e | 2026-01-01T00:00:00-05:00 | Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics | arXiv:2512.23767v1 Announce Type: new Abstract: Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recovery (MR), which identifies governing equations from sensor data, is a key primitive for safe... | https://arxiv.org/abs/2512.23767 | Academic Papers | svg |
0a69ddbab3cd2fa827b6f129470f27c28b83d838f828c468d9ef6fe359a63f13 | 2026-01-01T00:00:00-05:00 | VGC: A High-Performance Zone-Based Garbage Collector Architecture for Python with Partitioning and Parallel Execution | arXiv:2512.23768v1 Announce Type: new Abstract: The Virtual Garbage Collector (VGC) introduces a novel memory management framework designed to optimize performance across diverse systems, ranging from resource constrained embedded devices to high performance parallel architectures. Unlike conventional garbage collector... | https://arxiv.org/abs/2512.23768 | Academic Papers | svg |
06b9302578a892bbea03656f388e6f8e218e57dd7858a84d306e535fd26c6002 | 2026-01-01T00:00:00-05:00 | Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations | arXiv:2512.23769v1 Announce Type: new Abstract: Fairness in algorithmic decision-making is often framed in terms of individual fairness, which requires that similar individuals receive similar outcomes. A system violates individual fairness if there exists a pair of inputs differing only in protected attributes (such a... | https://arxiv.org/abs/2512.23769 | Academic Papers | svg |
ede5c2df40f4159fedb8829619bfe62bfa19b3e4f18fe6a6c06c83547021f616 | 2026-01-01T00:00:00-05:00 | Safety-Biased Policy Optimisation: Towards Hard-Constrained Reinforcement Learning via Trust Regions | arXiv:2512.23770v1 Announce Type: new Abstract: Reinforcement learning (RL) in safety-critical domains requires agents to maximise rewards while strictly adhering to safety constraints. Existing approaches, such as Lagrangian and projection-based methods, often either fail to ensure near-zero safety violations or sacri... | https://arxiv.org/abs/2512.23770 | Academic Papers | svg |
3a190472e8db4cba2c779093319aaaf953b2a743c023091105d1a053c9c8e877 | 2026-01-01T00:00:00-05:00 | FineFT: Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading | arXiv:2512.23773v1 Announce Type: new Abstract: Futures are contracts obligating the exchange of an asset at a predetermined date and price, notable for their high leverage and liquidity and, therefore, thrive in the Crypto market. RL has been widely applied in various quantitative tasks. However, most methods focus on... | https://arxiv.org/abs/2512.23773 | Academic Papers | svg |
9c8fa00ca51369e6ed9720288f91c8d6226e5ee4f0c49ce4403fd715bb7d2a5d | 2026-01-01T00:00:00-05:00 | Secure and Governed API Gateway Architectures for Multi-Cluster Cloud Environments | arXiv:2512.23774v1 Announce Type: new Abstract: API gateways serve as critical enforcement points for security, governance, and traffic management in cloud-native systems. As organizations increasingly adopt multi-cluster and hybrid cloud deployments, maintaining consistent policy enforcement, predictable performance, ... | https://arxiv.org/abs/2512.23774 | Academic Papers | svg |
372aa4863ad2e15cf2462f7de84619c0a19c52dcd0293c95adce2f1d27c4197b | 2026-01-01T00:00:00-05:00 | A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms | arXiv:2512.23777v1 Announce Type: new Abstract: This study investigates fraud detection in ride hailing platforms through Graph Neural Networks (GNNs),focusing on the effectiveness of various models. By analyzing prevalent fraudulent activities, the research highlights and compares the existing work related to fraud de... | https://arxiv.org/abs/2512.23777 | Academic Papers | svg |
4a98074a7cefbb4d1974783f970234c235082fd7f8e05d231ee737acb97629c5 | 2026-01-01T00:00:00-05:00 | SyncGait: Robust Long-Distance Authentication for Drone Delivery via Implicit Gait Behaviors | arXiv:2512.23778v1 Announce Type: new Abstract: In recent years, drone delivery, which utilizes unmanned aerial vehicles (UAVs) for package delivery and pickup, has gradually emerged as a crucial method in logistics. Since delivery drones are expensive and may carry valuable packages, they must maintain a safe distance... | https://arxiv.org/abs/2512.23778 | Academic Papers | svg |
0452c1e48ee74c0ca53ee5c85c6c9878c39267c4c6a8de8709cb691043a01b95 | 2026-01-01T00:00:00-05:00 | Prompt-Induced Over-Generation as Denial-of-Service: A Black-Box Attack-Side Benchmark | arXiv:2512.23779v1 Announce Type: new Abstract: Large language models (LLMs) can be driven into over-generation, emitting thousands of tokens before producing an end-of-sequence (EOS) token. This degrades answer quality, inflates latency and cost, and can be weaponized as a denial-of-service (DoS) attack. Recent work h... | https://arxiv.org/abs/2512.23779 | Academic Papers | svg |
0a9ed151b200d5844c79a3c76deadb4fb67f5a7222b6311bcc29c700e017deb3 | 2026-01-01T00:00:00-05:00 | Test Case Specification Techniques and System Testing Tools in the Automotive Industry: A Review | arXiv:2512.23780v1 Announce Type: new Abstract: The automotive domain is shifting to software-centric development to meet regulation, market pressure, and feature velocity. This shift increases embedded systems' complexity and strains testing capacity. Despite relevant standards, a coherent system-testing methodology t... | https://arxiv.org/abs/2512.23780 | Academic Papers | svg |
73a88b48be8c53bbfa0ce373f2dc8c25a4b6951c9eea5f69c6b83aded1f0835e | 2026-01-01T00:00:00-05:00 | Personalized Promotions in Practice: Dynamic Allocation and Reference Effects | arXiv:2512.23781v1 Announce Type: new Abstract: Partnering with a large online retailer, we consider the problem of sending daily personalized promotions to a userbase of over 20 million customers. We propose an efficient policy for determining, every day, the promotion that each customer should receive (10%, 12%, 15%,... | https://arxiv.org/abs/2512.23781 | Academic Papers | svg |
b612090a0f0de07eb2c4d7bfde4b1471d632277490660325597171211fc01c6a | 2026-01-01T00:00:00-05:00 | A Systematic Mapping on Software Fairness: Focus, Trends and Industrial Context | arXiv:2512.23782v1 Announce Type: new Abstract: Context: Fairness in systems has emerged as a critical concern in software engineering, garnering increasing attention as the field has advanced in recent years. While several guidelines have been proposed to address fairness, achieving a comprehensive understanding of re... | https://arxiv.org/abs/2512.23782 | Academic Papers | svg |
28dab6e4a77377ab4f3a3ac8202844cfa2db2eb4ced801bae19bfa298a5af5f4 | 2026-01-01T00:00:00-05:00 | Application-Specific Power Side-Channel Attacks and Countermeasures: A Survey | arXiv:2512.23785v1 Announce Type: new Abstract: Side-channel attacks try to extract secret information from a system by analyzing different side-channel signatures, such as power consumption, electromagnetic emanation, thermal dissipation, acoustics, time, etc. Power-based side-channel attack is one of the most promine... | https://arxiv.org/abs/2512.23785 | Academic Papers | svg |
89681de9899d294d448f359c2d0b8dbad0f094df676ba139dc4e80a7abe43067 | 2026-01-01T00:00:00-05:00 | Leveraging Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments | arXiv:2512.23786v1 Announce Type: new Abstract: Accurate Monocular Depth Estimation (MDE) is critical for robotic surgery but remains fragile in specular, fluid-filled endoscopic environments. Existing self-supervised methods, typically relying on foundation models trained with noisy real-world pseudo-labels, often suf... | https://arxiv.org/abs/2512.23786 | Academic Papers | svg |
c331efd40e91d41187d2bfdc978aee47e1abf6eaa6c5485d8933535ac391bb66 | 2026-01-01T00:00:00-05:00 | TabMixNN: A Unified Deep Learning Framework for Structural Mixed Effects Modeling on Tabular Data | arXiv:2512.23787v1 Announce Type: new Abstract: We present TabMixNN, a flexible PyTorch-based deep learning framework that synthesizes classical mixed-effects modeling with modern neural network architectures for tabular data analysis. TabMixNN addresses the growing need for methods that can handle hierarchical data st... | https://arxiv.org/abs/2512.23787 | Academic Papers | svg |
32aaa2564cf277c0f66b6b66ee005549653de7b7c8c2bb2fbab1f9bb1e041a02 | 2026-01-01T00:00:00-05:00 | VBSF: A Visual-Based Spam Filtering Technique for Obfuscated Emails | arXiv:2512.23788v1 Announce Type: new Abstract: Recent spam email techniques exploit visual effects in text messages, such as poisoning text, obfuscating words, and hidden text salting techniques. These effects were able to evade spam detection techniques based on the text. In this paper, we overcome this limitation by... | https://arxiv.org/abs/2512.23788 | Academic Papers | svg |
abbc2e532f1a6247719b70d2fb84cf4aab2183ea92c6ffba5a2fea2d0ee5ae25 | 2026-01-01T00:00:00-05:00 | A note on the space-time variational formulation for the wave equation with source term in $L^2(Q)$ | arXiv:2512.23807v1 Announce Type: new Abstract: We derive a variational formulation for the scalar wave equation in the second-order formulation on bounded Lipschitz domains and homogeneous initial conditions. We investigate a variational framework in a bounded space-time cylinder $Q$ with a new solution space and the ... | https://arxiv.org/abs/2512.23807 | Academic Papers | svg |
baf237a45288267adfa139612d2158e88e15c4893f6ea6e72d3e14d21c633fd7 | 2026-01-01T00:00:00-05:00 | MiMo-Audio: Audio Language Models are Few-Shot Learners | arXiv:2512.23808v1 Announce Type: new Abstract: Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token predicti... | https://arxiv.org/abs/2512.23808 | Academic Papers | svg |
3607c653a44a9020c989d0079804b37defdd99ea86dd8397950b5f55cf010cff | 2026-01-01T00:00:00-05:00 | Zero-Trust Agentic Federated Learning for Secure IIoT Defense Systems | arXiv:2512.23809v1 Announce Type: new Abstract: Recent attacks on critical infrastructure, including the 2021 Oldsmar water treatment breach and 2023 Danish energy sector compromises, highlight urgent security gaps in Industrial IoT (IIoT) deployments. While Federated Learning (FL) enables privacy-preserving collaborat... | https://arxiv.org/abs/2512.23809 | Academic Papers | svg |
c49ce83e3eed0f052c4d9566f6bca37147e35f946836aacd23841e38b3099f15 | 2026-01-01T00:00:00-05:00 | StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection | arXiv:2512.23813v1 Announce Type: new Abstract: The prevalence of chronic stress represents a significant public health concern, with social media platforms like Twitter serving as important venues for individuals to share their experiences. This paper introduces StressRoBERTa, a cross-condition transfer learning appro... | https://arxiv.org/abs/2512.23813 | Academic Papers | svg |
64259469f2608dd9096c43e0a74a2c0934fd5a4a7159208408a30987fb541d79 | 2026-01-01T00:00:00-05:00 | Greedy Rational Approximation for Frequency-Domain Model Reduction of Parametric LTI Systems | arXiv:2512.23814v1 Announce Type: new Abstract: We investigate model reduction of parametric linear time-invariant (LTI) dynamical systems. When posed in the frequency domain, this problem can be formulated as seeking a low-order rational function approximation of a high-order rational function. We propose to use a sta... | https://arxiv.org/abs/2512.23814 | Academic Papers | svg |
a4ef16cb6bf5a658293539ad1235efce7e93509d68b1ef17da1dfdbf51bd3743 | 2026-01-01T00:00:00-05:00 | Improved Bounds for Private and Robust Alignment | arXiv:2512.23816v1 Announce Type: new Abstract: In this paper, we study the private and robust alignment of language models from a theoretical perspective by establishing upper bounds on the suboptimality gap in both offline and online settings. We consider preference labels subject to privacy constraints and/or advers... | https://arxiv.org/abs/2512.23816 | Academic Papers | svg |
4143aab700b864b347468ce89a4116a15069e2766961f37a4c7d27e6684ac091 | 2026-01-01T00:00:00-05:00 | Video-Based Performance Evaluation for ECR Drills in Synthetic Training Environments | arXiv:2512.23819v1 Announce Type: new Abstract: Effective urban warfare training requires situational awareness and muscle memory, developed through repeated practice in realistic yet controlled environments. A key drill, Enter and Clear the Room (ECR), demands threat assessment, coordination, and securing confined spa... | https://arxiv.org/abs/2512.23819 | Academic Papers | svg |
0a467cc0c513f8d25f5204ec86c28792a86fc9f55da5a594e30c50b475993310 | 2026-01-01T00:00:00-05:00 | MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling | arXiv:2512.23824v1 Announce Type: new Abstract: State-space models (SSMs) have recently attention as an efficient alternative to computationally expensive attention-based models for sequence modeling. They rely on linear recurrences to integrate information over time, enabling fast inference, parallelizable training, a... | https://arxiv.org/abs/2512.23824 | Academic Papers | svg |
b3f08ae25d39f6ed9e253eb5e521db660b7a3b3c0fba230a432e4b30629398f9 | 2026-01-01T00:00:00-05:00 | Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations | arXiv:2512.23829v1 Announce Type: new Abstract: Inverse problems are important mathematical problems that seek to recover model parameters from noisy data. Since inverse problems are often ill-posed, they require regularization or incorporation of prior information about the underlying model or unknown variables. Proxi... | https://arxiv.org/abs/2512.23829 | Academic Papers | svg |
a221699166c97960e87251db211bbfe4fcbd719b05fa898feebc6200e3f5bc8c | 2026-01-01T00:00:00-05:00 | Exploiting the Prior of Generative Time Series Imputation | arXiv:2512.23832v1 Announce Type: new Abstract: Time series imputation, i.e., filling the missing values of a time recording, finds various applications in electricity, finance, and weather modelling. Previous methods have introduced generative models such as diffusion probabilistic models and Schrodinger bridge models... | https://arxiv.org/abs/2512.23832 | Academic Papers | svg |
4ab9d928681d5348ec6a2c3eeb823ce28e766a0dd37f1c87facd06cb7e7b429f | 2026-01-01T00:00:00-05:00 | Artificial Intelligence for All? Brazilian Teachers on Ethics, Equity, and the Everyday Challenges of AI in Education | arXiv:2512.23834v1 Announce Type: new Abstract: This study examines the perceptions of Brazilian K-12 education teachers regarding the use of AI in education, specifically General Purpose AI. This investigation employs a quantitative analysis approach, extracting information from a questionnaire completed by 346 educat... | https://arxiv.org/abs/2512.23834 | Academic Papers | svg |
1bb8d367c25f515ac57982d4e700cd7dde8f99f70540a7f32246b6e8ce61b67a | 2026-01-01T00:00:00-05:00 | Explaining News Bias Detection: A Comparative SHAP Analysis of Transformer Model Decision Mechanisms | arXiv:2512.23835v1 Announce Type: new Abstract: Automated bias detection in news text is heavily used to support journalistic analysis and media accountability, yet little is known about how bias detection models arrive at their decisions or why they fail. In this work, we present a comparative interpretability study o... | https://arxiv.org/abs/2512.23835 | Academic Papers | svg |
80e2ed64efd074ff9cb1af3b79ace3ae529a8a87f8cbad9157cbcf6d60d98b8c | 2026-01-01T00:00:00-05:00 | Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance? | arXiv:2512.23836v1 Announce Type: new Abstract: The success of expanded context windows in Large Language Models (LLMs) has driven increased use of broader context in retrieval-augmented generation. We investigate the use of LLMs for retrieval augmented question answering. While longer contexts make it easier to incorp... | https://arxiv.org/abs/2512.23836 | Academic Papers | svg |
184a56b89a4006e9ff8f3023d09255f1f745b77e27dc7b608e72c97c1ef48909 | 2026-01-01T00:00:00-05:00 | Adversarial Lens: Exploiting Attention Layers to Generate Adversarial Examples for Evaluation | arXiv:2512.23837v1 Announce Type: new Abstract: Recent advances in mechanistic interpretability suggest that intermediate attention layers encode token-level hypotheses that are iteratively refined toward the final output. In this work, we exploit this property to generate adversarial examples directly from attention-l... | https://arxiv.org/abs/2512.23837 | Academic Papers | svg |
0bb98ee836abb9c7ecaccd38994487a5fdc647eb9cce419b996044c93ce7df44 | 2026-01-01T00:00:00-05:00 | From Correctness to Collaboration: Toward a Human-Centered Framework for Evaluating AI Agent Behavior in Software Engineering | arXiv:2512.23844v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve from code generators into collaborative partners for software engineers, our methods for evaluation are lagging. Current benchmarks, focused on code correctness, fail to capture the nuanced, interactive behaviors essential for succes... | https://arxiv.org/abs/2512.23844 | Academic Papers | svg |
fffeba1afb49eae406a7276c8f4c3258ae11dbb900eea5fb777342c6b6b002c1 | 2026-01-01T00:00:00-05:00 | Integrating Domain Knowledge for Financial QA: A Multi-Retriever RAG Approach with LLMs | arXiv:2512.23848v1 Announce Type: new Abstract: This research project addresses the errors of financial numerical reasoning Question Answering (QA) tasks due to the lack of domain knowledge in finance. Despite recent advances in Large Language Models (LLMs), financial numerical questions remain challenging because they... | https://arxiv.org/abs/2512.23848 | Academic Papers | svg |
142c4e30c648872cc791c4024c6dd7aa5ef9c6c6f16a15fdbdc1319f4d399385 | 2026-01-01T00:00:00-05:00 | Security Without Detection: Economic Denial as a Primitive for Edge and IoT Defense | arXiv:2512.23849v1 Announce Type: new Abstract: Detection-based security fails against sophisticated attackers using encryption, stealth, and low-rate techniques, particularly in IoT/edge environments where resource constraints preclude ML-based intrusion detection. We present Economic Denial Security (EDS), a detectio... | https://arxiv.org/abs/2512.23849 | Academic Papers | svg |
d097a11d6110aac4dc6989076b44db14015997957a6ea5ba25048a4ee83fe618 | 2026-01-01T00:00:00-05:00 | The Drill-Down and Fabricate Test (DDFT): A Protocol for Measuring Epistemic Robustness in Language Models | arXiv:2512.23850v1 Announce Type: new Abstract: Current language model evaluations measure what models know under ideal conditions but not how robustly they know it under realistic stress. Static benchmarks like MMLU and TruthfulQA cannot distinguish a model that lacks knowledge from one whose verification mechanisms c... | https://arxiv.org/abs/2512.23850 | Academic Papers | svg |
0227b46d9951fce368d9ee5844e2ce81c6a8122bddf1e5dcb10217922576646f | 2026-01-01T00:00:00-05:00 | Pretraining Frame Preservation in Autoregressive Video Memory Compression | arXiv:2512.23851v1 Announce Type: new Abstract: We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temporal positions. The baseline model can compress a 20-second video into a... | https://arxiv.org/abs/2512.23851 | Academic Papers | svg |
2c46d3a77fefed32f633b0b6cd2f9cb367dfda5b22c877780750d6bb348dc74a | 2026-01-01T00:00:00-05:00 | Trellis: Learning to Compress Key-Value Memory in Attention Models | arXiv:2512.23852v1 Announce Type: new Abstract: Transformers, while powerful, suffer from quadratic computational complexity and the ever-growing Key-Value (KV) cache of the attention mechanism. This paper introduces Trellis, a novel Transformer architecture with bounded memory that learns how to compress its key-value... | https://arxiv.org/abs/2512.23852 | Academic Papers | svg |
c748975706eb71e9c4b7fde8d43b80bc9587b435ef55a06db317c46782d710d1 | 2026-01-01T00:00:00-05:00 | Flow Matching Neural Processes | arXiv:2512.23853v1 Announce Type: new Abstract: Neural processes (NPs) are a class of models that learn stochastic processes directly from data and can be used for inference, sampling and conditional sampling. We introduce a new NP model based on flow matching, a generative modeling paradigm that has demonstrated stron... | https://arxiv.org/abs/2512.23853 | Academic Papers | svg |
e6ee28cb5caf0a182b95e9ee57f40d2e6d2d0c32c8896dc748c25ef032c6d147 | 2026-01-01T00:00:00-05:00 | Simultaneous Extrinsic Contact and In-Hand Pose Estimation via Distributed Tactile Sensing | arXiv:2512.23856v1 Announce Type: new Abstract: Prehensile autonomous manipulation, such as peg insertion, tool use, or assembly, require precise in-hand understanding of the object pose and the extrinsic contacts made during interactions. Providing accurate estimation of pose and contacts is challenging. Tactile senso... | https://arxiv.org/abs/2512.23856 | Academic Papers | svg |
1b9d293b42a39e5185c7f646c8097fcdeb54084fd5755b4c5a9790a3671684e6 | 2026-01-01T00:00:00-05:00 | Yggdrasil: Bridging Dynamic Speculation and Static Runtime for Latency-Optimal Tree-Based LLM Decoding | arXiv:2512.23858v1 Announce Type: new Abstract: Speculative decoding improves LLM inference by generating and verifying multiple tokens in parallel, but existing systems suffer from suboptimal performance due to a mismatch between dynamic speculation and static runtime assumptions. We present Yggdrasil, a co-designed s... | https://arxiv.org/abs/2512.23858 | Academic Papers | svg |
a2304f5add2e177b5c20246ea92167656e42b81df0527162dd628f9b027f961e | 2026-01-01T00:00:00-05:00 | Seeking Late Night Life Lines: Experiences of Conversational AI Use in Mental Health Crisis | arXiv:2512.23859v1 Announce Type: new Abstract: Online, people often recount their experiences turning to conversational AI agents (e.g., ChatGPT, Claude, Copilot) for mental health support -- going so far as to replace their therapists. These anecdotes suggest that AI agents have great potential to offer accessible me... | https://arxiv.org/abs/2512.23859 | Academic Papers | svg |
509462654add02bee93167745ffe02d8cdd238c5463d252ab0ff270ae97c499b | 2026-01-01T00:00:00-05:00 | Lifelong Domain Adaptive 3D Human Pose Estimation | arXiv:2512.23860v1 Announce Type: new Abstract: 3D Human Pose Estimation (3D HPE) is vital in various applications, from person re-identification and action recognition to virtual reality. However, the reliance on annotated 3D data collected in controlled environments poses challenges for generalization to diverse in-t... | https://arxiv.org/abs/2512.23860 | Academic Papers | svg |
888b77ca2e503ed0aa8d0e0460a2504a832cd61e73d29f7986a8ef0f945323b4 | 2026-01-01T00:00:00-05:00 | Probing the Limits of Compressive Memory: A Study of Infini-Attention in Small-Scale Pretraining | arXiv:2512.23862v1 Announce Type: new Abstract: This study investigates small-scale pretraining for Small Language Models (SLMs) to enable efficient use of limited data and compute, improve accessibility in low-resource settings and reduce costs. To enhance long-context extrapolation in compact models, we focus on Infi... | https://arxiv.org/abs/2512.23862 | Academic Papers | svg |
ad98f4f591d4ab63efc83dc76efcb273bfbc94899b8a79992c29fcaae28ccf06 | 2026-01-01T00:00:00-05:00 | Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation | arXiv:2512.23864v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable generalization by mapping web-scale knowledge to robotic control, yet they remain blind to physical contact. Consequently, they struggle with contact-rich manipulation tasks that require reasoning about force, text... | https://arxiv.org/abs/2512.23864 | Academic Papers | svg |
b46e24d2d4b83d2eac6398a4dc93f044ac0c7200d5dee92ad6e417d81352b2c2 | 2026-01-01T00:00:00-05:00 | Improving Reliability of Human Trafficking Alerts in Airports | arXiv:2512.23865v1 Announce Type: new Abstract: This paper investigates the latter scenario of individual emergency alerts in airports by applying two existing benchmark delay tolerant network protocols and evaluating their performance of delivery ratio and latency. First, the paper provides a background on Mobile Ad H... | https://arxiv.org/abs/2512.23865 | Academic Papers | svg |
27ab863fc1540ab08729e8c86a3836a8a665e93ef656e5e5c4ba68c076bf1e95 | 2026-01-01T00:00:00-05:00 | Max-Entropy Reinforcement Learning with Flow Matching and A Case Study on LQR | arXiv:2512.23870v1 Announce Type: new Abstract: Soft actor-critic (SAC) is a popular algorithm for max-entropy reinforcement learning. In practice, the energy-based policies in SAC are often approximated using simple policy classes for efficiency, sacrificing the expressiveness and robustness. In this paper, we propose... | https://arxiv.org/abs/2512.23870 | Academic Papers | svg |
c68e3b3cdae71027aee7fcb58732f1b263c1843a807835b8935305cc759effed | 2026-01-01T00:00:00-05:00 | Hierarchical Quasi-cyclic Codes from Reed-Solomon and Polynomial Evaluation Codes | arXiv:2512.23872v1 Announce Type: new Abstract: We introduce the first example of algebraically constructed hierarchical quasi-cyclic codes. These codes are built from Reed-Solomon codes using a 1964 construction of superimposed codes by Kautz and Singleton. We show both the number of levels in the hierarchy and the in... | https://arxiv.org/abs/2512.23872 | Academic Papers | svg |
d60ff6d1e0094c9868e5ff14351773f2b44e6288eca10f4ac8b6e9c775e306a7 | 2026-01-01T00:00:00-05:00 | From Illusion to Insight: Change-Aware File-Level Software Defect Prediction Using Agentic AI | arXiv:2512.23875v1 Announce Type: new Abstract: Much of the reported progress in file-level software defect prediction (SDP) is, in reality, nothing but an illusion of accuracy. Over the last decades, machine learning and deep learning models have reported increasing performance across software versions. However, since... | https://arxiv.org/abs/2512.23875 | Academic Papers | svg |
b733be13efd94260ad71d45772eed47a3bd19c6a5c1058a174d869a11067013f | 2026-01-01T00:00:00-05:00 | CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution | arXiv:2512.23880v1 Announce Type: new Abstract: Large language model (LLM) agents currently depend on predefined tools or brittle tool generation, constraining their capability and adaptability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of th... | https://arxiv.org/abs/2512.23880 | Academic Papers | svg |
5cf3ba3d1eb56bb1f2466e351cc10c8d5f0f4db7e03e665681a8ea58433c4100 | 2026-01-01T00:00:00-05:00 | Breaking Audio Large Language Models by Attacking Only the Encoder: A Universal Targeted Latent-Space Audio Attack | arXiv:2512.23881v1 Announce Type: new Abstract: Audio-language models combine audio encoders with large language models to enable multimodal reasoning, but they also introduce new security vulnerabilities. We propose a universal targeted latent space attack, an encoder-level adversarial attack that manipulates audio la... | https://arxiv.org/abs/2512.23881 | Academic Papers | svg |
adde824f987f228ab83aa1bce68a74b6862f59b0fb96756d80b70124e65fd65a | 2026-01-01T00:00:00-05:00 | Institutional cooperations in Austrian research: An analysis of shared researchers | arXiv:2512.23882v1 Announce Type: new Abstract: Multiple organisational affiliations are an increasingly common feature of research systems, yet their implications for organisational performance had received limited systematic attention. We developed a scalable, network-based analytical framework that represents simult... | https://arxiv.org/abs/2512.23882 | Academic Papers | svg |
25faba5a3cb603063a7eb7c216760a1255413978e6357f066d8fb414cd5a627b | 2026-01-01T00:00:00-05:00 | How Large Language Models Systematically Misrepresent American Climate Opinions | arXiv:2512.23889v1 Announce Type: new Abstract: Federal agencies and researchers increasingly use large language models to analyze and simulate public opinion. When AI mediates between the public and policymakers, accuracy across intersecting identities becomes consequential; inaccurate group-level estimates can mislea... | https://arxiv.org/abs/2512.23889 | Academic Papers | svg |
d8a24c25809c077442e61b9c6d0b5d623fe000b4a9e2fb958f2074fe116e1293 | 2026-01-01T00:00:00-05:00 | MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework | arXiv:2512.23894v1 Announce Type: new Abstract: Quantifying normative pediatric cranial development and suture ossification is crucial for diagnosing and treating growth-related cephalic disorders. Computed tomography (CT) is widely used to evaluate cranial and sutural deformities; however, its ionizing radiation is co... | https://arxiv.org/abs/2512.23894 | Academic Papers | svg |
2476d3081f003ed597bccd29b2137d2125a01ef75b9d5ccdb1f82eeb5b6cf604 | 2026-01-01T00:00:00-05:00 | Wireless Multimodal Foundation Model (WMFM): Integrating Vision and Communication Modalities for 6G ISAC Systems | arXiv:2512.23897v1 Announce Type: new Abstract: The emergence of multimodal foundation models has revolutionized learning paradigms by enabling joint understanding across diverse data types. In the context of next-generation wireless networks, integrating sensing and communication modalities presents a unique opportuni... | https://arxiv.org/abs/2512.23897 | Academic Papers | svg |
a17e2938535ae78e16ba67b9fdd378f8fa85e8a533d8972d7bec986afbf3fffa | 2026-01-01T00:00:00-05:00 | Efficient Deep Learning for Short-Term Solar Irradiance Time Series Forecasting: A Benchmark Study in Ho Chi Minh City | arXiv:2512.23898v1 Announce Type: new Abstract: Reliable forecasting of Global Horizontal Irradiance (GHI) is essential for mitigating the variability of solar energy in power grids. This study presents a comprehensive benchmark of ten deep learning architectures for short-term (1-hour ahead) GHI time series forecastin... | https://arxiv.org/abs/2512.23898 | Academic Papers | svg |
8a9290612f1d63bb37b0e4851c429a2c065208cd07be6a5c4233c3ffe8fa7931 | 2026-01-01T00:00:00-05:00 | Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations | arXiv:2512.23900v1 Announce Type: new Abstract: Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a ... | https://arxiv.org/abs/2512.23900 | Academic Papers | svg |
0033dfdb88770f0db13c6fa80b7cfe944c68b589280731cd9a571d40a9540088 | 2026-01-01T00:00:00-05:00 | Road Rules for Radio: Why Your Wi-Fi Got Better | arXiv:2512.23901v1 Announce Type: new Abstract: WiFi allows for the connection of devices and people around the globe. It has proven to be a monumental and revolutionary tool that keeps the world connected. However, recent WiFi advancements are numerous and at times confusing. WiFi has grown significantly over the year... | https://arxiv.org/abs/2512.23901 | Academic Papers | svg |
6b8850ef0f7a865ee14d3237c8de3ee3d1a5dbc8da10e0793e9927d687f6691e | 2026-01-01T00:00:00-05:00 | Scaling Remote Sensing Foundation Models: Data Domain Tradeoffs at the Peta-Scale | arXiv:2512.23903v1 Announce Type: new Abstract: We explore the scaling behaviors of artificial intelligence to establish practical techniques for training foundation models on high-resolution electro-optical (EO) datasets that exceed the current state-of-the-art scale by orders of magnitude. Modern multimodal machine l... | https://arxiv.org/abs/2512.23903 | Academic Papers | svg |
d839b1cf1cc44aa87d9c9f1ea7508fede5bc322f7a0cad17aba7e4aeba222233 | 2026-01-01T00:00:00-05:00 | Rethinking Dense Linear Transformations: Stagewise Pairwise Mixing (SPM) for Near-Linear Training in Neural Networks | arXiv:2512.23905v1 Announce Type: new Abstract: Dense linear layers are a dominant source of computational and parametric cost in modern machine learning models, despite their quadratic complexity and often being misaligned with the compositional structure of learned representations. We introduce Stagewise Pairwise Mix... | https://arxiv.org/abs/2512.23905 | Academic Papers | svg |
7f2c551e3be35b146d6b9ebd1042b0bfc4b84098297fc1dcf7329b11875cd239 | 2026-01-01T00:00:00-05:00 | Deletion Considered Harmful | arXiv:2512.23907v1 Announce Type: new Abstract: In a world of information overload, understanding how we can most effectively manage information is crucial to success. We set out to understand how people view deletion, the removal of material no longer needed: does it help by reducing clutter and improving the signal t... | https://arxiv.org/abs/2512.23907 | Academic Papers | svg |
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