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An Optimal Agnostic PAC Algorithm

arXiv:2608.06363v1 Announce Type: cross Abstract: Let Hsubseteq{-1,+1}^X be a class of finite VC dimension dge1. Writing L for the binary risk and L^*=min_{hin H}L(h), we construct a learner achieving

Answer First, Reason Later: Commitment Order in Diffusion LLMs

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arXiv:2608.05687v1 Announce Type: cross Abstract: Masked diffusion language models (dLLMs) can commit tokens in any order -- a freedom marketed as their core advantage over autoregressive decoding. We

researcharxiv-cs-ai

7 Aug 2026

Arbitrage: Efficient Reasoning via Advantage-Aware Speculation

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Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inference, and this motivates techniques to imp

Automatic Detection of Deaths from Social Networking Sites

arXiv:2608.05183v1 Announce Type: cross Abstract: This dissertation analysed and discussed the differences in linguistic characteristics between pre-mortem and post-mortem social media content, and re

BaKron: Efficient Quantization with Kronecker-Factored Hessians

arXiv:2608.06291v1 Announce Type: cross Abstract: We accelerate a family of algorithms for neural network quantization whose geometry is informed by any Kronecker-factored approximation of the Hessian

BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

arXiv:2608.05926v1 Announce Type: cross Abstract: Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference main

Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation

arXiv:2608.05815v1 Announce Type: new Abstract: Few-shot learning has emerged as a promising approach for anatomical segmentation when labelled data are scarce. However, different few-shot learning al

Bayesian Expected Uncertainty Reduction (B-EUR) Model: A Computational Account of What Makes Design Options Worth Trying

arXiv:2608.05642v1 Announce Type: new Abstract: This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model, which formalizes the value of trying a candidate design action as its exp

Behavioral Residualization for Unsupervised Intrusion Detection in Automotive CAN Networks

arXiv:2608.05548v1 Announce Type: cross Abstract: Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message aut

BendTwin: Robust Dense-to-Sparse Physical Reconstruction with Bending-Aware Differentiable Spring-Mass Models

arXiv:2608.06164v1 Announce Type: new Abstract: Reconstructing objects with mechanical properties from video observations enables physically consistent dynamic prediction, benefiting robotics planning

Berkeley and Heiserman as an Unexhausted Architecture for Embodied Machine Intelligence

arXiv:2607.16465v2 Announce Type: replace Abstract: Edmund C. Berkeley is usually remembered as a mediator between symbolic logic and early computing, yet that standard description understates the sco

Beyond Flat Policies: Hierarchical Post-Training for Embodied Agents in Robotic Manipulation

arXiv:2608.05999v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated remarkable capabilities in robotic manipulation by leveraging pretrained vision-language models. H

researcharxiv-cs-ro

Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs

arXiv:2608.05592v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved strong progress in video understanding, yet it remains challenging because the token limitation m

Beyond Information Retrieval: Generative AI as an Epistemic Arbiter to Enhance Collaborative Problem-Solving

arXiv:2608.05171v1 Announce Type: cross Abstract: Generative AI (GAI) creates new opportunities for collaborative problem-solving (CPS), yet its role in shaping student interaction remains unclear. To

Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models

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Large Language Models (LLMs) have achieved state-of-the-art performance on a broad range of Natural Language Processing (NLP) tasks, including document processing and code generation. Autoregressive L

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages

arXiv:2606.16891v2 Announce Type: replace-cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to captur

🚨 BREAKING — Anthropic investors worry Dario Amodei’s AI doom marketing could hurt its upcoming IPO. “He’s more of a religious leader than …

🚨 BREAKING — Anthropic investors worry Dario Amodei’s AI doom marketing could hurt its upcoming IPO. “He’s more of a religious leader than he is a CEO.” > used to write sensitive OpenAI memos on an of

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CDSeg: A Renderable Gaussian Carrier for Image-to-3D Label Transfer

arXiv:2608.05482v1 Announce Type: new Abstract: Modern image models provide strong cues about what should be segmented in each view, but their masks do not by themselves determine where those labels s

CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection

arXiv:2608.06205v1 Announce Type: new Abstract: RGB--T object detection exploits the complementary strengths of visible and infrared imagery, supporting robust perception in low-light, adverse-weather

CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

arXiv:2603.08652v2 Announce Type: replace Abstract: Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integra

Coherence-Oriented Dream Scene Visualisation

arXiv:2608.05233v1 Announce Type: new Abstract: Dreams can be emotionally intense but difficult to communicate. We describe the Dream Scene Visualiser (DSV) system which turns written dream descriptio

CohortHijack: Robustness of Single Cell Annotation to Companion Cell Removal

arXiv:2608.05900v1 Announce Type: new Abstract: Many single-cell annotation tools refine an initial cell label using nearby cells or cluster-level voting. We study whether this refinement can be manip

Confidence matters: Leveraging Multi-view Geometric Priors for GS-based Reconstruction

arXiv:2608.06117v1 Announce Type: new Abstract: 3D Gaussian splatting (3DGS) has emerged as a widely-used tool for novel view synthesis, offering real-time rendering in a sparse representation. Howeve

Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading

arXiv:2608.05675v1 Announce Type: new Abstract: Label-free reliability for vision-language models rests on invariance: perturb the input and a faithful reader's answer should not change. This has a kn

Constraint-First Reasoning: A Training-Free Protocol for Exploiting Answer-Space Constraints in Mathematical Problem Solving

arXiv:2608.05254v1 Announce Type: new Abstract: Large language models can derive a plausible mathematical object yet still violate explicit requirements--for example, by omitting a modular reduction,

Context Matters: Support Set Selection and Failure Detection for In-Context Medical Image Segmentation

arXiv:2608.05333v1 Announce Type: new Abstract: In-context learning (ICL) adapts medical image segmentation models to unseen structures and modalities without retraining by conditioning on a task-spec

Controllable Clothing: Precise Labels and Generation for Virtual Try-On with Latent Diffusion Models

arXiv:2608.05834v1 Announce Type: new Abstract: In this technical report, I present a new method for guiding image generation in the context of Virtual- Try-On (VITON). The proposed method leverages n

Coordinated Multi-Robot Disassembly for Makespan Optimization of Large-Scale Assemblies

arXiv:2608.05830v1 Announce Type: new Abstract: Multi-robot task and motion planning for disassembly tasks requires robots to operate in confined workspaces while coordinating their motions with other

researcharxiv-cs-ro

Counterfactual Analysis via Large Language Models

arXiv:2608.05367v1 Announce Type: new Abstract: Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making. This paper inve

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis

arXiv:2507.14022v2 Announce Type: replace Abstract: This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The

d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation

arXiv:2601.07568v3 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random-orde

DBLAST: Dependent Block Drafting for Stochastic Speculative Decoding

arXiv:2608.05448v1 Announce Type: new Abstract: Speculative decoding accelerates large language models' inference by using a lightweight drafter to propose multiple future tokens and a target model to

Decomposed Entailment for Factuality Checking and Hallucination Detection

arXiv:2608.05823v1 Announce Type: new Abstract: The reliability of Large Language Models (LLMs) is often compromised by factual inconsistencies, including hallucinations---cases where generated conten

Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting

arXiv:2608.06082v1 Announce Type: new Abstract: Proper short-term forecasting of precipitation is crucial in disaster management and preparedness. Nonetheless, the variability and nonlinearity of prec

Depth-Guided Video Object Counting in Crowded Scenes

arXiv:2608.06236v1 Announce Type: cross Abstract: Our primary objective is to advance video object counting in crowded scenes, aiming to robustly count all instances of a target category based on give

Diff-VF: Training-free High-quality Long Video Generation via Diffusion Model

arXiv:2608.05976v1 Announce Type: new Abstract: Recently, diffusion models have made great progress in video generation. However, most existing video diffusion models are trained with short videos, an

Discrete energy as an exact label-free training objective for finite-element surrogates

arXiv:2608.05437v1 Announce Type: cross Abstract: Supervised training of finite-element (FE) surrogate models requires reference solutions, and each reference solution is obtained by solving the syste

Does FLAIR super-resolution erase or hallucinate small white-matter lesions?

arXiv:2608.06311v1 Announce Type: cross Abstract: White matter hyperintensities (WMH), bright regions on Fluid-attenuated Inversion Recovery (FLAIR) scans are associated with cerebrovascular pathology

Dual-Output Multi-Exposure HDR Reconstruction via SDR Fusion and Gain Map Inverse Tone Mapping

arXiv:2608.05626v1 Announce Type: new Abstract: We propose DOME-HDR, a dual-output multi-exposure HDR reconstruction framework that jointly produces a perceptually balanced SDR image and a consistent

Engram-E2VID: Reference-Based Event-to-Video Reconstruction via Generative Activation of Appearance Engrams

arXiv:2608.05728v1 Announce Type: new Abstract: Reference-based event-to-video reconstruction aims to recover target RGB frames from a reference frame and the event stream captured over the reference-

EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver

arXiv:2602.11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from

Equipment-centric workpiece localization in near real-time using deep learning-based vision and event-driven finite state machines

arXiv:2608.05744v1 Announce Type: new Abstract: Continuous workpiece localization is essential for traceability and process coordination in hot forging, but direct tracking is unreliable because of ex

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction

arXiv:2608.05265v1 Announce Type: cross Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in r

Example-Guided Prompting for Document-Level Text Simplification

arXiv:2608.05447v1 Announce Type: new Abstract: Document-level text simplification requires large language models (LLMs) to rewrite complex documents while preserving meaning, readability, and discour

Flow-Map Distillation on Relation Manifolds for Image Restoration

arXiv:2608.05769v1 Announce Type: new Abstract: Knowledge distillation for image restoration typically aligns intermediate features or relation matrices between teacher and student networks as static

From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

arXiv:2608.05203v1 Announce Type: new Abstract: Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by

Gender-Based Heterogeneity in Youth Privacy-Protective Behavior for Smart Voice Assistants: Evidence from Multigroup PLS-SEM

arXiv:2603.27117v2 Announce Type: replace-cross Abstract: This paper investigates how gender shapes privacy decision-making in youth smart voice assistant (SVA) ecosystems. Using survey data from 469

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI

arXiv:2608.05258v1 Announce Type: new Abstract: Gradient-weighted Class Activation Mapping (Grad-CAM) is widely used to visualize model decisions, but it was originally formulated for convolutional ne