Search: “papers”

Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models

arXiv:2608.21377v1 Announce Type: cross Abstract: Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied pr

agentsarxiv-cs-ai

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An offline approach to fNIRS-guided reinforcement learning for robot behavior

DGX agent

arXiv:2607.14393v2 Announce Type: replace-cross Abstract: Human-in-the-loop Reinforcement Learning has become a popular approach for training, finetuning, and aligning robot behavior with user prefere

model-releasesarxiv-cs-ai

25 Aug 2026

Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate

arXiv:2505.21430v3 Announce Type: replace Abstract: Attribute-efficient PAC learning of sparse halfspaces has been a fundamental problem in machine learning theory. In recent years, machine learning a

tutorialsarxiv-cs-lg

Automating Multi-Hop RAG Evaluation via TRIAD: From Context Extraction to Validated Dataset Generation

arXiv:2608.21558v1 Announce Type: cross Abstract: Recent advances in LLMs and the adoption of RAG systems in industry have created a need for domain-specific question-answer datasets that can assess R

BanglaVeilGuard: Cross-Script Safety Benchmarking and Lightweight Guardrails for Bangla Large Language Models

model-releasesarxiv-cs-cl

arXiv:2608.21880v1 Announce Type: new Abstract: Bangla large language model (LLM) safety is difficult to evaluate with English-centric or standard-script benchmarks because Bangla users routinely writ

Betting for Sim-to-Real Performance Certificates

arXiv:2608.21572v1 Announce Type: new Abstract: Consider a typical test of a robot system: one observes a sequence of outcomes concerning some aspect of interest (crash or no crash, tracking error, ti

applicationsarxiv-cs-ro

Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap

arXiv:2508.18646v3 Announce Type: replace Abstract: Despite their rapid advancement, large language models (LLMs) suffer from a critical disconnect between benchmark scores and real-world utility. Cur

Beyond Success and Failure: Length-Aware Contrastive Learning for GUI Agents

arXiv:2608.21830v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) have shown strong potential for automating tasks across divers

safetyarxiv-cs-ai

Beyond What Meets the Eye: Unveiling Situational Illusions for Multimodal Large Language Models

arXiv:2608.22232v1 Announce Type: new Abstract: Real-world situation appearances can deviate from their underlying physical states, challenging the reliability of multimodal large language models (MLL

Boosting Knowledge-based Visual Question Answering with Structured Context Reasoning

arXiv:2608.21431v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information.

Local Ai

Breaking the Assumptions: Auditing Input-Side Jailbreak Defenses Against Semantic Attacks

arXiv:2608.21895v1 Announce Type: cross Abstract: Locally deployed Large Language Models (LLMs) via inference engines such as Ollama run without the moderation and abuse detection present in API-serve

local-aiarxiv-cs-ai

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

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

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

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

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

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

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

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

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

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

Constructing Predictive Surgical Path for AI-based Capsulorhexis Skill Transfer

arXiv:2608.21441v1 Announce Type: cross Abstract: Automated training of surgeons is one of the most crucial factors that significantly minimize surgical training risks and expenses. With recent advanc

agentsarxiv-cs-ai

Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classification

arXiv:2608.22804v1 Announce Type: new Abstract: Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased

applicationsarxiv-cs-lg

Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation

model-releasesarxiv-cs-cv

arXiv:2603.00604v2 Announce Type: replace Abstract: High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to ob

Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures

model-releasesarxiv-cs-lg

arXiv:2403.14830v2 Announce Type: replace-cross Abstract: Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dim

Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis

arXiv:2606.18395v2 Announce Type: replace-cross Abstract: The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single n

Dialects of Translationese Shape Language Model Learning

arXiv:2602.16469v2 Announce Type: replace Abstract: Machine-translated data is widely used in multilingual NLP, particularly where native text is scarce. However, translated text differs systematicall

tutorialsarxiv-cs-cl

DIGing--SGLD: Decentralized and Scalable Langevin Sampling over Time--Varying Networks

arXiv:2511.12836v2 Announce Type: replace-cross Abstract: Sampling from a target distribution induced by training data is central to Bayesian learning, with Stochastic Gradient Langevin Dynamics (SGLD

safetyarxiv-cs-lg

Direct, Parallel, or Sequential? A Comparative Study of Training-Free Multi-Subject Image-to-Video Generation

arXiv:2608.22819v1 Announce Type: new Abstract: Text-conditioned image-to-video (I2V) generation has advanced rapidly, yet generating videos with multiple subjects remains challenging. A model must si

Diversity-Based Active Learning: An Evaluation of Metric Spaces for Active Learning Selection

arXiv:2608.23461v1 Announce Type: new Abstract: With rapid advancement over the last few years, many different methods are now widely used for classification. However, training these models requires s

applicationsarxiv-cs-lg

Do Not Copy/Paste: Soft Barriers for Copying in AI-Assisted Programming

arXiv:2608.22638v1 Announce Type: cross Abstract: Copying a function from a chat window into an editor takes less than a second. For many uses of AI coding tools, that speed is the point; in settings

safetyarxiv-cs-ai

Don' t Box Me In: Dynamic Cultural Adaptation and Cognitive Tracking for Social Understanding

arXiv:2608.22411v1 Announce Type: new Abstract: Social interaction increasingly takes place in multicultural settings, where individuals may draw on multiple cultural influences and adapt their commun

safetyarxiv-cs-cl

Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair

arXiv:2601.19066v3 Announce Type: replace-cross Abstract: Bug Reproduction Tests (BRTs) have been used in many Automated Program Repair (APR) systems, primarily for validating fixes and aiding fix gen

agentsarxiv-cs-ai

DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning

arXiv:2603.16459v2 Announce Type: replace Abstract: Diffusion large language models (D-LLMs) have emerged as a promising alternative to auto-regressive models due to their iterative refinement capabil

researcharxiv-cs-cl

EGAMA-RC: Risk-Calibrated Evidence-Gated Adaptive Malware Analysis for Robust and Interpretable Memory-Forensic Triage

arXiv:2608.22721v1 Announce Type: cross Abstract: Machine-learning malware detectors often achieve high clean-data accuracy, but operational triage also requires evidence about uncertainty, novelty, r

researcharxiv-cs-lg

Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads

arXiv:2508.02609v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNN) have been extensively applied to industry recommendation systems, as seen in models like GraphSageite{GraphSage},

tutorialsarxiv-cs-ai

Environmental Injection Attacks against GUI Agents in Realistic Dynamic Environments

arXiv:2509.11250v3 Announce Type: replace-cross Abstract: Graphical User Interface (GUI) agents are increasingly deployed to interact with online web services, yet their exposure to open-world content

agentsarxiv-cs-cv

Evaluating Human and LLM-Generated Thematic Analysis in HRI for Vulnerable Populations: A Comparative and Ethical Analysis

arXiv:2608.21420v1 Announce Type: new Abstract: Thematic analysis (TA) has long been regarded as an inherently human, reflexive, and interpretive process. However, the extent to which LLM-generated TA

researcharxiv-cs-ro

Evidence-State Reliability Under Controlled Degradation: Parser-Validity Divergence in a Multi-Stage LLM Pipeline

arXiv:2608.21559v1 Announce Type: new Abstract: Multi-stage LLM pipelines can remain structurally valid even when evidence available to downstream stages becomes incomplete, compressed, or conflicting

researcharxiv-cs-cl

Exact Finite-Length Theory of Uniform Car Parking: Spatial Laws, Absorption, and Aggregation

arXiv:2608.22671v1 Announce Type: new Abstract: The uniform car-parking process is the one-dimensional random sequential adsorption of unit cars on a segment of finite length s: cars arrive at uniform

researcharxiv-cs-ro

ExplainGuard: A Zero Trust Framework for Post-Hoc Explanation Integrity Guarantees in Blackbox XAI Models

arXiv:2608.21803v1 Announce Type: cross Abstract: As machine learning (ML) models are increasingly deployed in high-stakes environments, explainable AI (XAI) methods like SHAP and LIME have become ess

FaCT-GS: Fast and Scalable CT Reconstruction with Gaussian Splatting

arXiv:2604.01844v2 Announce Type: replace Abstract: Gaussian Splatting (GS) has emerged as a dominating technique for image rendering and has quickly been adapted for the X-ray Computed Tomography (CT

Local Ai

Falsification-Based Verification of LLM-Generated Optimization Models: Sound Test Batteries and Their Detection Limits

arXiv:2607.16646v2 Announce Type: replace-cross Abstract: Large language models now translate natural-language descriptions of decision problems into solver-ready optimization models, and they fail si

local-aiarxiv-cs-ai

Few-Shot Cross-Dataset Adaptation for Tuberculosis Detection Using DenseNet

arXiv:2608.21427v1 Announce Type: new Abstract: Tuberculosis (TB) is one of the most common and dangerous bacterial ailments. Every year, it causes a large number of deaths worldwide. Although many de

applicationsarxiv-cs-cv

Force/Torque-Based Kinematic Adaptation for Robotic Manipulation Tasks

arXiv:2608.21592v1 Announce Type: new Abstract: Contact-rich robotic manipulation requires an accurate model of the kinematic relationship between a robot's joints and the task features it senses. Thi

safetyarxiv-cs-ro

From Detrimental to Beneficial: Dynamic Influence-based Valuation and Editing

arXiv:2608.22522v1 Announce Type: new Abstract: Data valuation is a cornerstone of data-centric learning, where prior efforts primarily focus on designing algorithms to classify training samples as ei

researcharxiv-cs-lg

From Diagnosis to Redesign: Using Quantitative Ethnography to Improve Multi-Agent LLM Reasoning

arXiv:2608.22566v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems are designed to improve reasoning by decomposing tasks across multiple agents with specialized functions,

agentsarxiv-cs-cl

From Generation to Simulation: How Far Are World Models from Being True Simulators?

arXiv:2608.23070v1 Announce Type: new Abstract: With the rapid progress of diffusion models and large-scale video generation, generative world models are increasingly expected to replace traditional s