All entries

Do LLM Recommenders Know When They're Hallucinating? Auditing Confidence Calibration in Catalog Faithfulness

model-releasesarxiv-cs-cl

arXiv:2608.10008v1 Announce Type: cross Abstract: LLM recommenders for top-K item suggestion regularly emit titles outside the target catalog. Prior audits measure this as a binary out-of-domain rate;

Do LLMs Benefit From Their Own Words?

DGX agent

arXiv:2602.24287v2 Announce Type: replace-cross Abstract: In multi-turn conversations, large language models typically condition on the full conversation history: both past user prompts and assistant

researcharxiv-cs-ai

12 Aug 2026

Agents

Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Histories

arXiv:2608.10319v1 Announce Type: cross Abstract: Large language model (LLM)-powered agents have rapidly evolved from code-completion tools into solvers of complex software engineering tasks. As devel

agentsarxiv-cs-ai

Do Time-Series Forecasters Use the Right History: Recoverability, Recovery, and Functional Use of Temporal Delays

arXiv:2608.10433v1 Announce Type: new Abstract: Forecast accuracy does not tell us which past inputs produced a prediction. We separate three questions for time-series models with known delay structur

DocLang: a markup language for LLMs

IBM has introduced **DocLang**, a new markup language specifically designed for large‑language models (LLMs). Developed by lead researcher Peter Staar and IBM’s document‑parsing team, DocLang aims to

researchibm-research

Agents

DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents

arXiv:2608.10037v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical groundin

agentsarxiv-cs-ai

Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding

arXiv:2603.25251v2 Announce Type: replace-cross Abstract: Explainable AI (XAI) methods are commonly evaluated using functional correctness metrics, sometimes termed faithfulness or fidelity, which est

DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy

arXiv:2608.10173v1 Announce Type: new Abstract: Most radiotherapy dose-prediction models use only CT images and anatomical structures, although intensity-modulated proton therapy (IMPT) dose also depe

researcharxiv-cs-cv

Applications

DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches

arXiv:2509.05663v4 Announce Type: replace Abstract: Truly unsupervised approaches for time series anomaly detection are rare in the literature. Those that exist suffer from a poorly set threshold, whi

applicationsarxiv-cs-lg

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving

arXiv:2608.10386v1 Announce Type: new Abstract: Sample-efficient reinforcement learning for autonomous driving is often limited by the trade-off between data efficiency and model bias. While world mod

DreamOmni3: Scribble-based Editing and Generation

model-releasesarxiv-cs-cv

arXiv:2512.22525v2 Announce Type: replace Abstract: Recently unified generation and editing models have achieved remarkable success with their impressive performance. These models rely mainly on text

DriveVLA-M0: Failure-Aware Memory Augmentation for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2608.10413v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for end-to-end autonomous driving by enabling unified reasoning across

DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?

arXiv:2608.10366v1 Announce Type: new Abstract: Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coor

Tutorials

DSAR: Dual-Stream Autoregressive Modeling of Temporal Cloth Dynamics for Photorealistic Animatable Avatars

arXiv:2608.10500v1 Announce Type: new Abstract: Creating photorealistic and temporally coherent animatable human avatars from RGB videos remains challenging. Current methods struggle to capture realis

tutorialsarxiv-cs-cv

Dual-Loop Self-Evolution via Verifiable Emotion Feedback for Multi-Turn Empathetic Dialogue

arXiv:2608.10626v1 Announce Type: new Abstract: Large language models have demonstrated conversational capabilities, yet empathetic competence remains challenging. Empathetic support is inherently mul

safetyarxiv-cs-cl

Dual Space Preconditioning for Gradient Descent in the Overparameterized Regime

arXiv:2603.10485v3 Announce Type: replace-cross Abstract: In this work, we study the convergence properties of the Dual Space Preconditioned Gradient Descent, encompassing optimizers such as Normalize

Dual Stress: Runtime Safety Monitoring for Safety-Constrained MPC Navigation

arXiv:2608.10791v1 Announce Type: new Abstract: Runtime hazard monitors for autonomous naviga- tion are conventionally built from geometric quantities: predicted clearance, time to collision, and requ

safetyarxiv-cs-ro

Applications

DualSpectralCF: Training-Free Sign-Aware Spectral Collaborative Filtering

arXiv:2608.10247v1 Announce Type: cross Abstract: Real-world recommendation platforms routinely collect explicit negative feedback such as 1-star reviews, hate-button clicks, distrust between users, a

applicationsarxiv-cs-lg

Agents

DuplexWorld: Can voice agents help you get through the day?

arXiv:2608.10716v1 Announce Type: cross Abstract: Speech-to-speech (S2S) voice agents are increasingly being incorporated into enterprise for customer care and as daily companions for consumers owing

agentsarxiv-cs-ai

Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models

arXiv:2608.10525v1 Announce Type: cross Abstract: Historical context integration presents a fundamental challenge for Vision-Language Models (VLMs) in sequential decision-making tasks. Current VLMs pr

Tutorials

DynaPPI: A Large-scale Dynamic Protein Dataset for AI-driven Advances in Protein Interactomics

arXiv:2608.10435v1 Announce Type: new Abstract: Diffusion models have been widely explored in protein backbone generation due to their powerful generation capabilities.However, in today's AI-driven bi

tutorialsarxiv-cs-cv

E^3mo-Bench: A Scalable Benchmark for Multimodal Evoked and Expressed Emotion Understanding via Bayesian Pairwise Alignment

model-releasesarxiv-cs-cv

arXiv:2608.10796v1 Announce Type: new Abstract: Understanding both expressed and evoked emotions is critical for multimodal large language models (MLLMs) to achieve comprehensive affect-aware interact

Easy3D-Labels: Supervising Semantic Occupancy Estimation with 3D Pseudo-Labels for Automotive Perception

arXiv:2509.26087v5 Announce Type: replace Abstract: In perception for automated vehicles, safety is critical not only for the driver but also for other agents in the scene, particularly vulnerable roa

Edge Phoneme Recognition for Children's Speech through Age-Aware Training

arXiv:2608.10206v1 Announce Type: new Abstract: Detecting phonemes from children's speech has historically been difficult due to the scarcity of training data, and unique characteristics of children's

Efficient Hypergradient Descent for Inverse Reinforcement Learning

arXiv:2608.11052v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) aims to recover a reward function under which the resulting policy reproduces the behavior observed in expert demon

Efficient Reinforcement Learning for Long-Horizon Tool-Use Agentic Tasks

arXiv:2608.10357v1 Announce Type: cross Abstract: Long-horizon tool-using agents must reason over user goals, domain policies, tool calls, simulator state, and delayed verifiable rewards. Reinforcemen

Efficient Uncoupled Learning Dynamics with ilde{O}!left(T^{-1/4}right) Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback

arXiv:2602.21436v2 Announce Type: replace-cross Abstract: In this paper, we study last-iterate convergence of learning algorithms in bilinear saddle-point problems, a preferable notion of convergence

researcharxiv-cs-lg

Efficient Weak-Entropy PINN for Solving Hyperbolic Conservation Laws

arXiv:2608.10389v1 Announce Type: cross Abstract: In recent years, neural networks have significantly advanced numerical solutions of partial differential equations (PDEs). However, solving PDEs with

researcharxiv-cs-lg

Elbow Angle Guidance System Based on Surface Haptic Sensations Elicited by Lightweight Wearable Fabric Actuator

arXiv:2608.10404v1 Announce Type: cross Abstract: The demand for wearable haptic devices has rapidly increased for various applications. However, many haptic devices interfere with the wearer's activi

researcharxiv-cs-ro

Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems

arXiv:2608.09964v1 Announce Type: cross Abstract: The Brazilian Conference on Intelligent Systems (BRACIS) is the main national venue for Artificial Intelligence research in Brazil, hosted by the Braz

ELMER: Evolutionary Language Model that Explores and Refines

arXiv:2608.10196v1 Announce Type: cross Abstract: Program evolution can measure whether a mutation helped, but it rarely controls how far the mutation moves in behavior space. Syntactic edit size is a

safetyarxiv-cs-ai

ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation

arXiv:2608.10398v1 Announce Type: cross Abstract: Variational autoencoders generate samples from probabilistic latent representations but do not distinguish uncertainty about the latent location from

Embedding Rotation Invariance for Provable Multi-Oriented Scene Text Recognition

arXiv:2608.10684v1 Announce Type: new Abstract: Multi-oriented text is ubiquitous in real-world scenes and remains a major challenge for scene text recognition (STR). Existing rotation-aware methods e

Embodied Multimodal Grounding for Open-Vocabulary Mobile Manipulation via Semantic 3D Gaussian Splatting

arXiv:2608.10756v1 Announce Type: cross Abstract: Embodied mobile manipulation requires language, visual observations, three-dimensional scene structure, and action feasibility to be aligned before ex

Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations

arXiv:2109.13445v3 Announce Type: replace-cross Abstract: The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well u

Enabling Scalable Kinesthetic Teaching via Observer-based Hand-guiding with Active Support

arXiv:2608.10847v1 Announce Type: new Abstract: Kinesthetic teaching through robot hand-guiding provides a natural interface for collecting demonstrations in imitation learning and programming-by-demo

researcharxiv-cs-ro

ENCORE: Efficient Noise Context-Aware Representation for Low-Dose CT Denoising

arXiv:2608.10343v1 Announce Type: new Abstract: While deep learning-based denoising has become widely adopted in low-dose CT, conventional models use generic architectures designed for natural images,

Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection

arXiv:2608.09996v1 Announce Type: cross Abstract: Recent advances in machine learning have greatly improved breast cancer detection, enabling more accurate and timely diagnosis. Deep learning (DL) mod

Enhanced Filtering Algorithms for the Euclidean Traveling Salesperson Problem and its variants in Constraint Logic Programming

arXiv:2608.10881v1 Announce Type: new Abstract: The Traveling Salesperson Problem (TSP) is one of the best-known problems in computer science and arises in many engineering applications, such as smart

Enhancing Automated Essay Scoring With Three Techniques: Two-Stage Fine-Tuning, Score Alignment, and Self-Training

arXiv:2602.01747v2 Announce Type: replace Abstract: Automated Essay Scoring (AES) plays a crucial role in education by providing scalable and efficient assessment tools. However, in real-world setting

safetyarxiv-cs-cl

ENTLORE: A Graph-Grounded Benchmark for Latent Organizational Reasoning in Enterprise Question Answering

arXiv:2608.10679v1 Announce Type: cross Abstract: Enterprise question answering is framed as retrieving internal documents and generating grounded answers. Routine enterprise records, however, are wor

Entropy-Centric Explainable AI for Remote Sensing Image Segmentation

arXiv:2608.11064v1 Announce Type: cross Abstract: Artificial intelligence (AI) has become a powerful approach to solving complex problems in critical domains. Many concerns arise regarding the decisio

eta-VAEs as Effective Theories: Tolerance-Dependent Dimension

arXiv:2608.10599v1 Announce Type: new Abstract: In a eta-VAE, increasing the regularization strength acts as a spectral cutoff by collapsing low-utility latent coordinates. In the linear Gaussian VAE,

researcharxiv-cs-lg

Evaluating Rational Contracting in Natural Language

arXiv:2608.10475v1 Announce Type: new Abstract: The emergence of language-based AI agents promises to transform the scope of machine economic activity. Instead of just proposing bids or following hard

Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery

arXiv:2608.10801v1 Announce Type: new Abstract: Spatio-temporal PV data are essential for understanding adoption processes in off-grid regions, yet such data remain largely unavailable. Automated segm

researcharxiv-cs-cv

Evaluation-Conditioned Training: Teaching Models to Generalize to Stronger Oversight Regimes

arXiv:2608.10209v1 Announce Type: new Abstract: Feedback signals used to train Large Language Models (LLMs) are the primary driver of their behavior and our main lever for instilling alignment with hu

safetyarxiv-cs-ai

Every Packet Counts: Dispersing Information for Loss-Resilient Learned Image Compression

arXiv:2608.11096v1 Announce Type: new Abstract: Learned image compression (LIC) has achieved impressive rate-distortion performance. However, existing methods remain highly vulnerable to packet loss,

researcharxiv-cs-cv

Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biases

arXiv:2608.10503v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed as autonomous agents, accurately evaluating their latent values and biases is critical. The NL

safetyarxiv-cs-cl