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Tessera: Secure, Near-Line-Rate Weight Streaming for UMA Edge Accelerators

arXiv:2604.23205v1 Announce Type: cross Abstract: Deploying proprietary Deep Neural Networks (DNNs) on commodity edge devices demands hardware-backed Digital Rights Management (DRM) capable of withsta

researcharxiv-cs-lg

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Test of Time: Rethinking Temporal Signal of Benchmark Contamination

DGX agent

arXiv:2509.00072v3 Announce Type: replace Abstract: Post-cutoff performance decay has been widely interpreted as a temporal signal for benchmark contamination. We critically examine this belief and de

model-releasesarxiv-cs-ai

28 Apr 2026

Local Ai

Test-Time Adaptation for Unsupervised Combinatorial Optimization

arXiv:2601.21048v2 Announce Type: replace Abstract: Unsupervised neural combinatorial optimization (NCO) enables learning powerful solvers without access to ground-truth solutions. Existing approaches

local-aiarxiv-cs-lg

TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction

model-releasesarxiv-cs-cl

arXiv:2604.22880v1 Announce Type: new Abstract: Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientif

Text-Guided Multimodal Unified Industrial Anomaly Detection

arXiv:2604.22899v1 Announce Type: new Abstract: Industrial anomaly detection based on RGB-3D multimodal data has emerged as a mainstream paradigm for intelligent quality inspection. However, existing

safetyarxiv-cs-cv

Text Tells the Cost: Predicting and Analyzing Repayment Effort of Self-Admitted Technical Debt

arXiv:2309.06020v3 Announce Type: replace-cross Abstract: Technical debt refers to the consequences of sub-optimal decisions made during software development that prioritize short-term benefits over l

TextGround4M: A Prompt-Aligned Dataset for Layout-Aware Text Rendering

model-releasesarxiv-cs-cv

arXiv:2604.24459v1 Announce Type: new Abstract: Despite recent advances in text-to-image generation, models still struggle to accurately render prompt-specified text with correct spatial layout -- esp

The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers

arXiv:2604.24155v1 Announce Type: cross Abstract: The quest to align machine behavior with human values raises fundamental questions about the moral frameworks that should govern AI decision-making. M

The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models

arXiv:2604.24698v1 Announce Type: new Abstract: Applications based on large language models (LLMs), such as multi-agent simulations, require population diversity among agents. We identify a pervasive

agentsarxiv-cs-cl

The Collapse of Heterogeneity in Silicon Philosophers

arXiv:2604.23575v1 Announce Type: cross Abstract: Silicon samples are increasingly used as a low-cost substitute for human panels and have been shown to reproduce aggregate human opinion with high fid

safetyarxiv-cs-cl

The Consensus Trap: Dissecting Subjectivity and the 'Ground Truth' Illusion in Data Annotation

arXiv:2602.11318v3 Announce Type: replace Abstract: In machine learning, 'ground truth' refers to the assumed correct labels used to train and evaluate models. However, the foundational 'ground truth'

The High Cost of Incivility: Quantifying Interaction Inefficiency via Multi-Agent Monte Carlo Simulations

arXiv:2512.08345v2 Announce Type: replace Abstract: Workplace toxicity is widely recognized as detrimental to organizational culture, yet quantifying its direct impact on operational efficiency remain

agentsarxiv-cs-ai

The Imbalanced User-AI Relationships as an Ethical Failure of Front-End Design in Healthcare AI

arXiv:2604.22767v1 Announce Type: cross Abstract: Ethical discourse on AI in healthcare has focused predominantly on back-end concerns such as bias, fairness and explainability, while the front-end in

The Kerimov-Alekberli Model: An Information-Geometric Framework for Real-Time System Stability

arXiv:2604.24083v1 Announce Type: new Abstract: This study introduces the Kerimov-Alekberli model, a novel information-geometric framework that redefines AI safety by formally linking non-equilibrium

The Last Human-Written Paper: Agent-Native Research Artifacts

arXiv:2604.24658v1 Announce Type: new Abstract: Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along

agentsarxiv-cs-lg

The Limits of Artificial Companionship

arXiv:2604.23601v1 Announce Type: cross Abstract: This Article argues that conversations with companion chatbot should be subject to a clear structural distinction between commercial and non-commercia

applicationsarxiv-cs-cl

The Optimal Sample Complexity of Multiclass and List Learning

model-releasesarxiv-cs-lg

arXiv:2604.24749v1 Announce Type: new Abstract: While the optimal sample complexity of binary classification in terms of the VC dimension is well-established, determining the optimal sample complexity

The Override Gap: A Magnitude Account of Knowledge Conflict Failure in Hypernetwork-Based Instant LLM Adaptation

arXiv:2604.23750v1 Announce Type: cross Abstract: Hypernetwork-based methods such as Doc-to-LoRA internalize a document into an LLM's weights in a single forward pass, but they fail systematically on

Tutorials

The Power of Power Law: Asymmetry Enables Compositional Reasoning

arXiv:2604.22951v1 Announce Type: new Abstract: Natural language data follows a power-law distribution, with most knowledge and skills appearing at very low frequency. While a common intuition suggest

tutorialsarxiv-cs-ai

The Pragmatic Persona: Discovering LLM Persona through Bridging Inference

arXiv:2604.24079v1 Announce Type: cross Abstract: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue. However, most existing persona discovery approaches rely on su

The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications

arXiv:2604.24668v1 Announce Type: new Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode

The Randomness Floor: Measuring Intrinsic Non-Randomness in Language Model Token Distributions

arXiv:2604.22771v1 Announce Type: cross Abstract: Language models cannot be random. This paper introduces Entropic Deviation (ED), the normalised KL divergence between a model's token distribution and

The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding

arXiv:2601.15485v2 Announce Type: replace-cross Abstract: Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly

The Security Cost of Intelligence: AI Capability, Cyber Risk, and Deployment Paradox

arXiv:2604.23058v1 Announce Type: cross Abstract: Firms are deploying more capable AI systems, but organizational controls often have not kept pace. These systems can generate greater productivity gai

The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction

arXiv:2503.09101v4 Announce Type: replace-cross Abstract: Uniform manifold approximation and projection (UMAP) is among the most popular neighbor embedding methods. The method samples pairs of point i

The Spectral Lifecycle of Transformer Training: Transient Compression Waves, Persistent Spectral Gradients, and the Q/K--V Asymmetry

arXiv:2604.22778v1 Announce Type: cross Abstract: We present the first systematic study of weight matrix singular value spectra during transformer pretraining, tracking full SVD decompositions of ever

The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage

model-releasesarxiv-cs-cl

arXiv:2508.09603v2 Announce Type: replace Abstract: Membership inference attacks serves as useful tool for fair use of language models, such as detecting potential copyright infringement and auditing

The Swarm Intelligence Freeway-Urban Trajectories (SWIFTraj) Dataset -- Part II: A Graph-Based Approach for Trajectory Connection

arXiv:2602.21954v2 Announce Type: replace-cross Abstract: In Part I of this companion paper series, we introduced SWIFTraj, a new open-source vehicle trajectory dataset collected using a unmanned aeri

safetyarxiv-cs-ro

Think Anywhere in Code Generation

arXiv:2603.29957v3 Announce Type: replace-cross Abstract: Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answe

agentsarxiv-cs-lg

Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

arXiv:2511.08577v2 Announce Type: replace-cross Abstract: Improving reasoning abilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications.

Thinking Like a Clinician: A Cognitive AI Agent for Clinical Diagnosis via Panoramic Profiling and Adversarial Debate

arXiv:2604.23605v1 Announce Type: new Abstract: The application of large language models (LLMs) in clinical decision support faces significant challenges of 'tunnel vision' and diagnostic hallucinatio

agentsarxiv-cs-ai

Time-Series Forecasting in Safety-Critical Environments: An EU-AI-Act-Compliant Open-Source Package / Zeitreihenprognose in sicherheitskritischen Umgebungen: Ein KI-VO-konformes Open-Source-Paket

arXiv:2604.23859v1 Announce Type: new Abstract: With spotforecast2-safe we present an integrated Compliance-by-Design approach to Python-based point forecasting of time series in safety-critical envir

TimingLLM: A Two-Stage Retrieval-Augmented Framework for Pre-Synthesis Timing Prediction from Verilog

arXiv:2604.23602v1 Announce Type: cross Abstract: Early, tool-free prediction of post-synthesis timing remains a key obstacle to rapid RTL iteration. We introduce TimingLLM, a two-stage retrieval-augm

researcharxiv-cs-lg

Token Is All You Price

arXiv:2510.09859v4 Announce Type: replace-cross Abstract: A seller of a dynamic information service under an information-throughput constraint screens buyers who privately differ in urgency. We charac

applicationsarxiv-cs-ai

Tutorials

TokenTrace: Multi-Concept Attribution through Watermarked Token Recovery

arXiv:2602.19019v2 Announce Type: replace Abstract: Generative AI models pose a significant challenge to intellectual property (IP), as they can replicate unique artistic styles and concepts without a

tutorialsarxiv-cs-cv

TOL: Textual Localization with OpenStreetMap

model-releasesarxiv-cs-cv

arXiv:2604.01644v2 Announce Type: replace Abstract: Natural language provides an intuitive way to express spatial intent in geospatial applications. While existing localization methods often rely on d

TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations

model-releasesarxiv-cs-cv

arXiv:2604.24119v1 Announce Type: new Abstract: Topology reasoning is crucial for autonomous driving. Current methods primarily focus on instance-level learning for centerline detection, followed by a

Touchless Intraoperative Image Access System Based on Vision-Based Hand Tracking

arXiv:2604.24235v1 Announce Type: new Abstract: Touchless interaction with medical images is becoming increasingly important in the surgical field, where sterility and continuity of the operational wo

researcharxiv-cs-cv

Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning

arXiv:2604.23231v1 Announce Type: cross Abstract: Semantic Communication (SC) backdoor attacks aim to utilize triggers to manipulate the system into producing predetermined outputs via backdoored shar

Toward Real-World Adoption of Portrait Relighting via Hybrid Domain Knowledge Fusion

arXiv:2604.23094v1 Announce Type: new Abstract: The real-world adoption of portrait relighting is hindered by dataset domain gaps, camera sensitivity, and computational costs. We address these challen

applicationsarxiv-cs-cv

Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging

arXiv:2505.16024v2 Announce Type: replace-cross Abstract: Diffusion trajectory distillation accelerates sampling by training a student model to approximate the multi-step denoising trajectories of a p

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement

arXiv:2601.02018v2 Announce Type: replace Abstract: Segment Anything Models (SAMs), known for their exceptional zero-shot segmentation performance, have garnered significant attention in the research

safetyarxiv-cs-cv

Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach

arXiv:2604.23090v1 Announce Type: new Abstract: Automatically generating formal ontologies from unstructured natural language remains a central challenge in knowledge engineering. While large language

agentsarxiv-cs-ai

Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction

arXiv:2604.22979v1 Announce Type: new Abstract: We address Human Activity Recognition (HAR) utilizing Wi-Fi Channel State Information (CSI) under the joint requirements of causal interpretability, sym

Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis

arXiv:2509.26158v2 Announce Type: replace-cross Abstract: The performance of deep neural networks is strongly influenced by the quality of their training data. However, mitigating dataset bias by manu

Towards Fair and Robust Volumetric CT Classification via KL-Regularised Group Distributionally Robust Optimisation

arXiv:2603.15941v2 Announce Type: replace Abstract: Automated diagnosis from chest computed tomography (CT) scans faces two persistent challenges in clinical deployment: distribution shift across acqu

safetyarxiv-cs-cv

Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding

arXiv:2603.11831v2 Announce Type: replace Abstract: The field of Computer-Aided Design (CAD) generation has made significant progress in recent years. Existing methods typically fall into two separate

applicationsarxiv-cs-cv

Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey

arXiv:2505.15957v4 Announce Type: replace-cross Abstract: With advancements in large audio-language models (LALMs), which enhance large language models (LLMs) with auditory capabilities, these models