Search: “persistence”

Don't Forget Your Embeddings: Robust Knowledge Erasure via Precise Editing of Embeddings

model-releasesarxiv-cs-cl

arXiv:2606.03695v1 Announce Type: new Abstract: As language models are increasingly deployed in real-world applications, the ability to erase specific knowledge from them becomes critical for safety a

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FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

DGX agent

arXiv:2606.03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps. Real-world benchmarks off

model-releasesarxiv-cs-lg

3 Jun 2026

Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation

arXiv:2606.03761v1 Announce Type: new Abstract: Frame analysis of migration news is a socially consequential task: media scholars and researchers who study how migration is narrated need tools that ar

local-aiarxiv-cs-cl

From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models

arXiv:2606.03660v1 Announce Type: new Abstract: Large language models are increasingly used as chemistry assistants, yet most chemistry benchmarks still score only final answers. This masks a critical

Gender-Dependent Diagnostic Substitution in LLM Medical Triage: Same Symptoms, Unequal Urgency

arXiv:2606.03641v1 Announce Type: new Abstract: We investigate whether large language models produce different medical triage recommendations for identical neurological symptoms when only the patient'

Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization

arXiv:2606.03022v1 Announce Type: cross Abstract: Hallucination in Large Language Models (LLMs), characterized by the generation of content inconsistent with contextual facts or logical constraints --

KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators

arXiv:2606.02963v1 Announce Type: new Abstract: Production inference increasingly targets a heterogeneous mix of accelerators. Agentic pipelines interleave reasoning, tool calls, and multi-agent coord

Agents

Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models

arXiv:2606.02914v1 Announce Type: new Abstract: Background: Oral diseases affect nearly 3.5 billion people worldwide, yet the comparative clinical potential of large-scale AI models in dentistry remai

agentsarxiv-cs-ai

Let There Be Light: Reflection, Refraction and Scattering for Neural Operators

arXiv:2606.03262v1 Announce Type: new Abstract: Neural operators learn mappings between infinite-dimensional function spaces and provide a data-driven surrogate modeling paradigm for parametric partia

safetyarxiv-cs-lg

Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions

arXiv:2606.03382v1 Announce Type: cross Abstract: While Proximal Policy Optimization (PPO) demonstrates strong performance in stationary settings, we show that its standard optimization paradigm strug

local-aiarxiv-cs-ai

Multi-Robot Bearing-only Pose Estimation via Angle Rigidity

arXiv:2606.03931v1 Announce Type: new Abstract: This letter proposes a novel distributed bearing-based pose estimator for time-varying multi-robot systems. The method uses angles computed from body-fr

researcharxiv-cs-ro

Agents

PhotoCraft: Agentic Reasoning with Hierarchical Self-Evolving Memory for Deep Image Search

arXiv:2606.03099v1 Announce Type: cross Abstract: Deep Image Search requires multi-step reasoning over rich contextual cues, such as time, location, and event relations. However, most existing LLM-bas

agentsarxiv-cs-ai

Hardware

Pruning Deep Neural Networks via the Marchenko--Pastur Distribution

arXiv:2606.02608v1 Announce Type: new Abstract: We study a Marchenko--Pastur (MP) random-matrix approach to pruning deep neural networks with very small post-pruning fine-tuning budgets. The main prac

hardwarearxiv-cs-lg

Quantifying Faithful Confidence Expression in Large Reasoning Models

arXiv:2606.03969v1 Announce Type: cross Abstract: Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic

Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps

model-releasesarxiv-cs-ro

arXiv:2606.03421v1 Announce Type: new Abstract: Specular glare on reflective floors, glass boundaries, and glossy indoor surfaces frequently corrupts active-stereo RGB-D depth measurements, producing

Static and Dynamic Representations for Tactile Contact-Angle Estimation with Event-Based Sensors

arXiv:2606.03545v1 Announce Type: new Abstract: Event-based tactile sensing offers low-latency signal acquisition for contact-rich robotic interaction. This paper investigates contact-angle estimation

researcharxiv-cs-ro

T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation

model-releasesarxiv-cs-cv

arXiv:2512.21094v2 Announce Type: replace Abstract: Text-to-Audio-Video (T2AV) generation aims to synthesize temporally coherent video and semantically synchronized audio from natural language, yet it

Testing LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks

arXiv:2606.03606v1 Announce Type: cross Abstract: Large language models achieve strong performance on arithmetic reasoning benchmarks, and one common response to arithmetic brittleness is to delegate

Towards a Science of AI Agent Reliability

arXiv:2602.16666v3 Announce Type: replace Abstract: AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many age

VistaHop: Benchmarking Multi-hop Visual Reasoning for Visual DeepSearch

arXiv:2606.03273v1 Announce Type: cross Abstract: Visual DeepSearch requires multimodal large reasoning model (MLRM) agents to answer complex visual queries by repeatedly inspecting image regions, gro

Tutorials

Visual Graph Scaffolds for Structural Reasoning in Large Language Models

arXiv:2606.02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at t

tutorialsarxiv-cs-ai

WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts

arXiv:2606.03220v1 Announce Type: cross Abstract: Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions

local-aiarxiv-cs-ai

A Grammar of Machine Learning Workflows: Rejecting Data Leakage at Call Time

arXiv:2603.10742v4 Announce Type: replace Abstract: Data leakage has been identified in 648 published papers across 30 scientific fields. The knowledge to prevent it has existed for over a decade; the

researcharxiv-cs-lg

Active Exploring like a Pigeon: Reinforcing Spatial Reasoning via Agentic Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2606.02459v1 Announce Type: new Abstract: Enabling Vision-Language Models (VLMs) to perform spatial reasoning remains challenging. Existing approaches treat VLMs as passive observers, which is d

Agents

AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Robotics

arXiv:2606.01015v1 Announce Type: cross Abstract: The convergence of Artificial Intelligence, the Internet of Things, and Robotics is no longer a futuristic vision; it is rapidly becoming the foundati

agentsarxiv-cs-ai

An Effective Solution for the CVPR 2026 8th UG2+ Challenge Track 3: Dynamic Object Segmentation in Turbulence

arXiv:2606.00522v1 Announce Type: new Abstract: In this work, we present our solution for the 8th UG2+ Challenge (CVPR 2026) Track 3: Dynamic Object Segmentation in Turbulence (DOST). Our method is bu

researcharxiv-cs-cv

Beyond Task-Agnostic: Task-Aware Grouping for Communication-Efficient Multi-Task MoE Inference

arXiv:2606.01007v1 Announce Type: cross Abstract: Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert

local-aiarxiv-cs-ai

CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Cloud-Edge Systems

arXiv:2606.00756v1 Announce Type: new Abstract: Deploying lightweight Large Language Model (LLM) agents on edge servers can reduce latency and move agentic services closer to users, but resource-const

Cross-modal linkage risk in clinical vision-language models

arXiv:2606.02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-leve

CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences

arXiv:2606.00931v1 Announce Type: cross Abstract: Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edi

DECK: A Consistency x Confidence Taxonomy of LLM Hallucinations

model-releasesarxiv-cs-cl

arXiv:2606.02289v1 Announce Type: new Abstract: Existing hallucination taxonomies classify LLM errors by what is wrong with the output -- memorised misconceptions, reasoning failures, fluent fabricati

Demystifying the Optimal Fair Classifier in Multi-Class Classification

arXiv:2606.00656v1 Announce Type: cross Abstract: Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to th

Design-MLLM: A Reinforcement Alignment Framework for Verifiable and Aesthetic Interior Design

arXiv:2603.13312v2 Announce Type: replace-cross Abstract: Interior design is a requirements-to-visual-plan generation process that must simultaneously satisfy verifiable spatial feasibility and compar

Diagnosing LLM Arbitration Behavior over Pre-evidence Epistemic States in RAG-based Fact-Checking

arXiv:2606.01120v1 Announce Type: new Abstract: In RAG-based fact-checking, LLMs are increasingly used as verifiers to check given claims against retrieved evidence. Their parametric knowledge can ind

Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores

arXiv:2601.00175v2 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years prior to diagnosis using

Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking

arXiv:2602.03912v4 Announce Type: replace Abstract: This paper investigates the performance of Echo State Networks (ESNs) for univariate forecasting of monthly and quarterly time series from the M4 Fo

Empathy Applicability Modeling for General Health Queries

model-releasesarxiv-cs-cl

arXiv:2601.09696v2 Announce Type: replace Abstract: LLMs are increasingly being integrated into clinical workflows, yet they often lack clinical empathy, an essential aspect of effective doctor-patien

EvoCut: Multi-Layer Evolution-Aware Visual Token Compression for Efficient Large Vision-Language Models

arXiv:2606.01756v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong performance on image and video understanding tasks, but their inference efficiency is constrained by

researcharxiv-cs-cv

Failure of contextual invariance in large language models

arXiv:2603.23485v2 Announce Type: replace-cross Abstract: Standard evaluation practices assume that large language model (LLM) outputs are stable when prompts are embedded in contextually equivalent d

Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift

arXiv:2512.15647v3 Announce Type: replace Abstract: Soft labels from teacher models are a de facto practice for knowledge transfer and large-scale dataset distillation (e.g., SRe2L, LPLD). However, wh

safetyarxiv-cs-cv

Honey, I Shrunk the Arc de Triomphe!

arXiv:2606.02379v1 Announce Type: new Abstract: Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from

researcharxiv-cs-cv

hZACH-ViT: Curved Latent Geometry for Compact Vision Transformers in Low-Data Medical Imaging

model-releasesarxiv-cs-cv

arXiv:2606.00906v1 Announce Type: new Abstract: Compact Vision Transformers are attractive for medical imaging in low-data and resource-constrained settings, but most existing variants assume that Euc

IDEAFix: Evaluation Framework for Creative Defixation Prompting in LLMs

arXiv:2606.00875v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for tasks involving creative problem solving and idea generation. However, there is a lack of consens

researcharxiv-cs-cl

Implicit Geographic Inference in LLM Medical Triage: Language-Driven Disparities in Emergency Recommendations

arXiv:2606.01204v1 Announce Type: cross Abstract: We investigate whether large language models produce different medical triage recommendations for identical symptoms based solely on the language of t

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

arXiv:2606.02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets (arepsilon_i) according to insti

Joint Agent Memory and Exploration Learning via Novelty Signals

arXiv:2606.01528v1 Announce Type: new Abstract: In open-ended environments, exploration is fundamental for autonomous agents, yet current language model agents struggle with this. Effective exploratio

KnowledgeBerg: Evaluating Systematic Knowledge Coverage and Compositional Reasoning in Large Language Models

arXiv:2604.17621v2 Announce Type: replace Abstract: Many real-world questions appear deceptively simple yet implicitly demand two capabilities: (i) systematic coverage of a bounded knowledge universe

Make Your VLA More Robust Without More Data By Interleaving Motion Planning

model-releasesarxiv-cs-ro

arXiv:2606.00985v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable progress for mobile manipulation, but their performance on long-horizon tasks remains poor. Th