All entries

Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement

arXiv:2608.23136v1 Announce Type: new Abstract: Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspecti

researcharxiv-cs-cv

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models

DGX agent

arXiv:2408.11827v2 Announce Type: replace Abstract: Understanding how language models compose meaning from linguistic input remains a central problem in interpretability research. Mechanistic studies

safetyarxiv-cs-cl

25 Aug 2026

Budget-Constrained Embodied Perception: Four Resource Walls and a Pre-Registered Evaluation of Access-Structured Perception on Open Models at less than 31B

arXiv:2608.22975v1 Announce Type: new Abstract: Embodied multimodal agents must answer from growing observation streams under a fixed per-decision token budget. We formalize this constraint through fo

Bulbul: A Dataset for Dialectal Arabic Speech Recognition

arXiv:2608.21950v1 Announce Type: cross Abstract: Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited speech resources

Applications

BulletTime: Decoupled Control of Time and Camera Pose for Video Generation

arXiv:2512.05076v2 Announce Type: replace Abstract: Emerging video diffusion models achieve high visual fidelity but fundamentally couple scene dynamics with camera motion, limiting their ability to p

applicationsarxiv-cs-cv

Buried in Textual Debt: Context Pruning with Visual Evidence Preservation for MLLM Agents

arXiv:2608.22963v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed as multi-step agents, where explicit reasoning supports task decomposition and tool c

safetyarxiv-cs-ai

Tutorials

ByteAction: Byte-space Action Recognition Foundation Model

arXiv:2608.22760v1 Announce Type: new Abstract: Byte-space Action Recognition (BAR) aims to recognize human actions directly from compressed image bitstreams without any pixel decoding. By operating e

tutorialsarxiv-cs-cv

C^2Path: Class-Conditional Pathway Decoupling for Vision-Language Incremental Object Detection

model-releasesarxiv-cs-cv

arXiv:2608.21937v1 Announce Type: new Abstract: Incremental Object Detection (IOD) aims to enable detectors to continuously learn novel categories while preserving previously acquired knowledge. Howev

CacheRouter: A Dual-Path Tool Routing Architecture with Cache-Preserving Main-Model Isolation for Long-Tail Tool Discovery

arXiv:2608.22708v1 Announce Type: new Abstract: Tool use in LLM systems faces a structural trade-off. Progressive disclosure keeps the prompt small by showing only the tools relevant to the current ta

Tutorials

CAI-DLLM: Convergence Aware Inference for Diffusion Language Models

arXiv:2608.22646v1 Announce Type: new Abstract: Diffusion language models can generate many tokens in parallel, but they still require repeated denoising steps during inference. This makes generation

tutorialsarxiv-cs-ai

CAIA in Practice: Field Evaluation of an AI-Assisted Support System for Text-Based Online Counselling

arXiv:2608.22251v1 Announce Type: cross Abstract: Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a cruci

safetyarxiv-cs-cl

Calibrate What You SHIP: Post-Selection Risk Control for Verifier-Guided Text-to-Image Generation

model-releasesarxiv-cs-cv

arXiv:2608.21748v1 Announce Type: new Abstract: Verifier-guided text-to-image systems increasingly use test-time search to select, refine, or stop among multiple candidates, yet release thresholds are

Calibrating Perception Uncertainty for Autonomous Driving

arXiv:2211.04340v2 Announce Type: replace Abstract: Autonomous driving systems must be capable of making quick decisions based on the perceived environment and specific driving conditions. Perception

safetyarxiv-cs-cv

CALIBURN: Self-Calibrated LLM Unlearning Alignment

arXiv:2602.02824v2 Announce Type: replace Abstract: LLM unlearning aims to remove the influence of undesirable knowledge from pretrained language models, which offers a practical mechanism for address

safetyarxiv-cs-cl

CallScreenBench: Benchmarking Small Language Models as Phone Secretaries

arXiv:2608.01033v2 Announce Type: replace-cross Abstract: Language models small enough to run on a handset, quantized to a few bits, are increasingly capable of acting on their user's behalf -- which

Can Coding Agents Build Robust Baselines? A Skill-Based Approach for Automating the Medical Imaging Model-Development Pipeline

arXiv:2608.23336v1 Announce Type: new Abstract: Developing competitive deep learning baselines for medical imaging remains a highly iterative process requiring literature review, implementation, exper

agentsarxiv-cs-cv

Can Large Language Models 'Hyper-Thread'?

arXiv:2608.22376v1 Announce Type: new Abstract: Large language models generate tokens sequentially, but can they execute multiple tasks concurrently while forming each token? Broader attention allocat

Can LLMs Truly Forget? Revealing Unlearning Gaps Through Adversarial Evaluation

model-releasesarxiv-cs-cl

arXiv:2608.21606v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of targeted training data from a model while preserving its remaining capabilities, but evaluating wheth

Can We Perform Online RL for Image Editing without Editing Rewards?

arXiv:2608.22780v1 Announce Type: new Abstract: Reinforcement learning (RL) enables direct preference optimization for image editing through editing-specific rewards, which remain less developed due t

safetyarxiv-cs-cv

Applications

CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data

arXiv:2607.15721v2 Announce Type: replace Abstract: Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular diseas

applicationsarxiv-cs-lg

CaRGo-T: Causal Reasoning Graph-of-Thought improves Multimodal Humor Comprehension

arXiv:2608.23172v1 Announce Type: new Abstract: Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humor

Cascaded Learned Bloom Filter for Optimizing Model-Filter Size Balance and Fast Rejection

arXiv:2502.03696v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that learned Bloom filters (LBFs), which combine machine learning with the classical Bloom filter, can achiev

CatchBench: When Can an Agent Failure Be Caught?

arXiv:2608.22808v1 Announce Type: new Abstract: When can an agent failure be caught? An audit is usually limited by the record rather than by the method. CatchBench therefore puts one auditor's questi

CausalCache: Conditional High-Fidelity Restoration for Long-Horizon GUI Agents

arXiv:2608.22577v1 Announce Type: new Abstract: Long-horizon GUI agents can retain a complete interaction trace cheaply as textual action records, but expose only a few past events to the policy in hi

CD-LoRA: Consistency-Driven Low-Rank Adaptation for Multi-Task Fine-Tuning

arXiv:2608.21909v1 Announce Type: new Abstract: While Multi-Task Learning (MTL) is essential for adapting Large Language Models (LLMs) to diverse domains, prevailing LoRA-based methods rely on complex

safetyarxiv-cs-lg

CDEG: Learning Decision-Critical Evidence for Long-Horizon Diagnostic Agents

arXiv:2608.22899v1 Announce Type: new Abstract: Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquired

ChainPrune: Evaluating and Reducing Redundancy in Long Chain-of-Thought Reasoning

arXiv:2608.21860v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) reasoning has significantly enhanced the multi-step problem-solving capabilities of large language models (LLMs) by introducing

researcharxiv-cs-ai

Change Detection in Probability Flow ODE: Online Testing in Diffusion Latent Spaces

arXiv:2608.22807v1 Announce Type: new Abstract: A rapidly growing range of sequential data tasks, such as identifying trend reversals in financial markets, auto-segmenting video and audio recordings,

researcharxiv-cs-lg

Channel-Token Attention for Reliable Dynamic Spectrum Access under Bursty Primary-User Traffic

arXiv:2608.22992v1 Announce Type: cross Abstract: Dynamic spectrum access must coordinate secondary users under bursty primary-user activity while preserving packet reliability and delay. We present T

safetyarxiv-cs-lg

Characterizing Necessary Losers to Explain Tournaments Losers

arXiv:2608.23446v1 Announce Type: new Abstract: We study the problem of formally explaining why a candidate was not selected by a given tournament rule, by identifying sub-tournaments in which the can

researcharxiv-cs-ai

ChebBooster: A Training-Free Approach for Efficient Diffusion Transformer Inference via Chebyshev-Inspired Extrapolation

arXiv:2608.23429v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have shown strong performance in high-fidelity image generation, but their sampling process remains computationally inte

researcharxiv-cs-ai

ChemDIRT: A Diversified Instruction, Representation, and Task Benchmark for Robust Chemistry-LLM Evaluation

arXiv:2608.21504v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has led to increasing interest in their application to scientific domains such as chemistry. Howev

ChequeMark: An Ensemble Machine Learning Framework for After-Hours Business Deposit Fraud Detection

arXiv:2608.21629v1 Announce Type: new Abstract: Cheque fraud is a material risk in after-hours business deposit operations because funds may be released within one business day, while cheque clearing

CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets

model-releasesarxiv-cs-cv

arXiv:2608.21497v1 Announce Type: cross Abstract: Biochemical recurrence (BCR), defined as any detectable prostate-specific antigen level after prostatectomy with confirmatory elevation, is widely use

Local Ai

Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT

arXiv:2512.15206v3 Announce Type: replace Abstract: A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes

local-aiarxiv-cs-lg

CIDER: Continual Interactive Distillation for Embodied Reinforcement Learning

arXiv:2608.21899v1 Announce Type: new Abstract: Human-in-the-loop real-world reinforcement learning enables rapid acquisition of effective robotic manipulation policies for individual tasks, often wit

safetyarxiv-cs-ro

Local Ai

CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation

arXiv:2608.22281v1 Announce Type: cross Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computatio

local-aiarxiv-cs-cv

Claim-Level Confidence Calibration for Reliable Decision Making with Large Language Models

model-releasesarxiv-cs-cl

arXiv:2608.22483v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is misa

Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency

arXiv:2608.22266v1 Announce Type: new Abstract: In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A cr

Class-Conditioned Gaussian Mixture Modeling for Imbalanced Time Series Quantification

arXiv:2608.21473v1 Announce Type: new Abstract: Quantification, estimating class prevalences in bags of unlabeled instances is vital in domains where aggregate statistics are more important than indiv

researcharxiv-cs-lg

ClawProBench: Trace-Aware Evaluation of AI Agents with Runtime Coverage and Frozen Workplace-Style Holdouts

arXiv:2608.22510v1 Announce Type: new Abstract: Agent benchmarks often evaluate only final answers even when agents run on stateful runtimes. We argue this under-specifies what is being evaluated: the

Clinical Graph-JEPA: Predictive Patient-State Knowledge Graphs for Cognitive Decision Support

arXiv:2608.22583v1 Announce Type: cross Abstract: Clinical records contain rich evidence about patient state, but converting that evidence into reliable, structured knowledge graphs remains difficult

ClinicalGPT-R1: Pushing reasoning capability of generalist disease diagnosis with large language model

arXiv:2504.09421v3 Announce Type: replace-cross Abstract: Recent advances in reasoning with large language models (LLMs)has shown remarkable reasoning capabilities in domains such as mathematics and c

Clinically Grounded Privacy Evaluation of Medical LMs

arXiv:2606.09590v2 Announce Type: replace Abstract: Medical language models (LMs) can memorize and reproduce protected health information, but privacy evaluations often focus on recovery of training t

Close Shortcut Wins Long: Seeking Diverse and Stable Generators for Data-Free Knowledge Distillation

arXiv:2608.22003v1 Announce Type: new Abstract: Data-Free Knowledge Distillation (DFKD) preserves privacy by transferring knowledge without real data access. However, existing generator-based DFKD met

researcharxiv-cs-cv

Closed-loop AI achieves certifiable engineering design

arXiv:2608.21976v1 Announce Type: new Abstract: Agentic AI has automated parts of scientific discovery, including paper generation, expert-level coding, therapeutic proposal, and autonomous experiment

Closed-Loop Bayesian Molecular Inverse Design with Semantic LLM Surrogates

arXiv:2608.22967v1 Announce Type: new Abstract: Practical molecular inverse design is rarely a one-shot generation problem; it often takes the form of closed-loop candidate-pool enrichment, where unde

Closing the Curvature Gap: Full Transformer Hessians

arXiv:2510.16927v2 Announce Type: replace Abstract: The optimization landscape of Transformer models remains poorly understood despite their widespread adoption. While recent studies have derived curv

researcharxiv-cs-lg