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Do LLMs Know What Is Private Internally? Probing and Steering Contextual Privacy Norms in Large Language Model Representations

arXiv:2604.00209v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in high-stakes settings, yet they frequently violate contextual privacy by disclosing private

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Research

Document Optimization for Black-Box Retrieval via Reinforcement Learning

DGX agent

arXiv:2604.05087v3 Announce Type: replace Abstract: Document expansion is a classical technique for improving retrieval quality, and is attractive since it shifts computation offline, avoiding additio

researcharxiv-cs-cl

6 Aug 2026

Does Out-of-Sight Equal Out-of-Mind in CoT Monitorability?

arXiv:2608.04928v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning offers a window into the decision-making of large language models (LLMs), which can be monitored for target behaviors b

DreamWAM: Beyond RGB Future Prediction for World Action Models

model-releasesarxiv-cs-ro

arXiv:2608.04996v1 Announce Type: new Abstract: World Action Models (WAMs) learn action-relevant representations by predicting how the observed world will evolve. Most existing WAMs define this future

DSeq-JEPA: Discriminative Sequential Joint-Embedding Predictive Architecture

arXiv:2511.17354v4 Announce Type: replace Abstract: Recent advances in self-supervised visual representation learning have demonstrated the effectiveness of predictive latent-space objectives for lear

researcharxiv-cs-cv

Dynamic Jailbreaking Attack

arXiv:2510.02422v4 Announce Type: replace-cross Abstract: Existing gradient-based jailbreak attacks typically optimize a fixed-length adversarial suffix toward a predefined target response with a stat

Dynamical Lie Algebras Cannot Describe Shallow QAOA: Cragged Terrains, Barren Plateaus, and Empirical Hardness Models

arXiv:2608.04252v1 Announce Type: cross Abstract: The dynamical Lie algebraic (DLA) theory of variational quantum algorithms (VQAs) predicts commonplace exponentially vanishing loss and gradient varia

E^2M: Double Bounded alpha-Divergence Optimization for Tensor-based Discrete Density Estimation

arXiv:2405.18220v4 Announce Type: replace-cross Abstract: Tensor-based discrete density estimation requires flexible modeling and proper divergence criteria to enable effective learning; however, trad

EA-Graph: Artifact-Anchored Verification Memory for Coding Agents under Upstream Drift

arXiv:2608.04278v1 Announce Type: cross Abstract: Coding agents increasingly work across sessions, but prose notes can preserve a conclusion without the program state that supported it. After an upstr

Easy to Complete, Hard to Choose: Investigating LLM Performance on the ProverbIT Benchmark

arXiv:2608.04670v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed computational linguistics and achieved remarkable performance across numerous natural language processin

EASy: Towards Efficient LLM-Based Agentic System

arXiv:2608.04588v1 Announce Type: cross Abstract: Agentic systems have emerged as a promising paradigm for solving complex tasks by coordinating specialized LLM-based agents. However, most existing sy

agentsarxiv-cs-ai

Echo Flow Networks

model-releasesarxiv-cs-lg

arXiv:2509.24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal d

EDATracer: An Agentic Framework for Large-Scale EDA Artifact Analysis

arXiv:2608.04032v1 Announce Type: cross Abstract: Modern chip design relies on electronic design automation (EDA) tools that generate large, heterogeneous artifacts, including source files, scripts, l

EdgeLM: Edge Demonstrations for Language Models' Table Understanding

arXiv:2608.04390v1 Announce Type: new Abstract: Large language models (LLMs) perform table-centric prediction through in-context learning, making demonstration selection critical to performance. Exist

Efficient Online Lexicographic Generalized Low-Rank Matrix Bandits

arXiv:2608.04324v1 Announce Type: cross Abstract: This paper studies generalized low-rank matrix bandits with multiple prioritized objectives. At each round, the learner selects a matrix-valued arm an

EgoAfford: Task-Oriented Affordance Grounding via Egocentric Referring Segmentation

model-releasesarxiv-cs-cv

arXiv:2608.04533v1 Announce Type: new Abstract: Part-level affordance grounding has advanced the localization of functional object regions associated with elemental actions. Extending this capability

Eigenius: A Typed Knowledge-Graph DBMS with Epistemic Stratification and Institution-Mediated Reasoning

arXiv:2608.04457v1 Announce Type: cross Abstract: As 'AI Scientists' emerge to drive research via the Model Context Protocol (MCP), systems relying on ephemeral scripts will fail. The sheer scale of s

agentsarxiv-cs-ai

Elbow-Based MoE Routing: A Training-Free Inference Time Plugin for Expert Selection

arXiv:2608.04401v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable model scaling while maintaining low inference-time compute by activating only a subset of experts per token. Howe

Eliciting Intrinsic Hallucinations in LLMs via Semantically Equivalent Adversarial Attacks

model-releasesarxiv-cs-cl

arXiv:2608.04286v1 Announce Type: new Abstract: Large language models (LLMs) are often used in conjunction with external knowledge sources to improve their factual accuracy and decrease hallucinations

Embedding Large Language Models into Flow Controls: An Agentic Framework for Adaptive and Trustworthy Automated Cooking

arXiv:2608.04768v1 Announce Type: new Abstract: Automated cooking robots have traditionally relied on predefined procedures and rule-based control, ensuring stable execution but offering limited perso

agentsarxiv-cs-cv

Emergence of Hierarchical Emotion Organization in Large Language Models

arXiv:2507.10599v3 Announce Type: replace-cross Abstract: As large language models (LLMs) increasingly power conversational agents, understanding how they model users' emotional states is critical for

EmpaAva: An Open-source Agentic 3D-Avatar Empathetic Live Chatbot

arXiv:2608.04709v1 Announce Type: new Abstract: This paper presents EmpaAva, to our knowledge the first open-source, agentic 3D-avatar empathetic chatbot, which carries empathetic response generation

agentsarxiv-cs-cl

Enabling Urgency-aware Robot Swarm Intralogistics using Smart IoT Tags

arXiv:2608.04721v1 Announce Type: new Abstract: Warehouse items differ in how urgently they must be moved: perishable goods, pharmaceutical shipments, and just-in-time production materials must be del

safetyarxiv-cs-ro

EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment

arXiv:2608.04472v1 Announce Type: cross Abstract: The development of foundation models (FMs) is crucial for advancing endoscopic image analysis. However, existing endoscopy FMs mainly rely on self-sup

safetyarxiv-cs-ai

Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs

model-releasesarxiv-cs-cl

arXiv:2608.04488v1 Announce Type: new Abstract: Despite rapid advances in large language models (LLMs), deploying and personalizing them on resource-constrained devices remains impractical due to high

Energy-Tweedie: Score meets Score, Energy meets Energy

model-releasesarxiv-cs-lg

arXiv:2512.23818v2 Announce Type: replace-cross Abstract: Denoising and score estimation are classically linked through Tweedie's formula, which relates the posterior mean under Gaussian noise to the

Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation

arXiv:2608.04906v1 Announce Type: new Abstract: This study introduces a diffusion-based framework for robust and accurate semantic segmentation of lumbar spine MRI scans from patients with low back pa

researcharxiv-cs-cv

Enhancing Trustworthy Clinical Diagnosis Decision-Making in Large Language Models via Etiology-Aware Attention Supervision

model-releasesarxiv-cs-cl

arXiv:2508.00285v2 Announce Type: replace Abstract: Objective: Large Language Models (LLMs) have demonstrated strong capabilities in medical text understanding and generation. However, their trustwort

Applications

Equitable System-Prompt Selection via Constrained Mixed-Strategy GroupDRO

arXiv:2608.04339v1 Announce Type: cross Abstract: Large language models are increasingly used for information seeking, yet semantically equivalent questions phrased in different ways can receive answe

applicationsarxiv-cs-ai

EuroExec: Frontier Language Models Fall Short of Expert Judgment on European Executive Decision Tasks

arXiv:2608.04549v1 Announce Type: cross Abstract: Frontier LLMs are increasingly put to use on open-ended complex questions, different in nature from the ones they are typically evaluated on. We dedic

Evaluating the Diagnostic Robustness of Vision-Language Models Under Visual and Textual Perturbations

arXiv:2608.04885v1 Announce Type: cross Abstract: Standard accuracy metrics for VLMs often mask significant reliability failures in sensitive domains. In this work, we utilize a histopathology-validat

safetyarxiv-cs-cl

Evaluating Theory of Mind in Reasoning Models: Robustness over Reasoning

arXiv:2608.04646v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong performance on Theory of Mind (ToM) tests, prompting debate about the nature and validity of the

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification

arXiv:2608.04899v1 Announce Type: new Abstract: Confidence estimation is essential when LLMs are used for classification, indicating when predictions can be trusted. However, common approaches such as

EviGraph: Evidence-Guided Autonomous Research Agents

arXiv:2608.04738v1 Announce Type: new Abstract: Autonomous research agents can generate hypotheses, execute experiments, and draft manuscripts, yet their outputs often contain unsupported claims and i

agentsarxiv-cs-ai

Local Ai

EvolveNet: Collaborative Harness Evolution for Agent Self-Improvement

arXiv:2608.04968v1 Announce Type: new Abstract: The capabilities of an LLM agent depend not only on its model but on the harness: the executable program that constructs context, invokes tools, verifie

local-aiarxiv-cs-lg

EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series

arXiv:2608.04368v1 Announce Type: new Abstract: Multimodal temporal data are inherently irregular and uneven in information density, yet most models rely on uniform discretization, leading to ineffici

Exact Model-Free Policy Iteration for Co-safe LTL Planning

arXiv:2608.05047v1 Announce Type: cross Abstract: This work studies model-free reinforcement learning for co-safe linear temporal logic (sc-LTL) objectives in finite Markov decision processes, which c

safetyarxiv-cs-ro

ExeCRE: Execution-Consistency Guided Reliability Estimation for Self-Correcting Code Generation

arXiv:2608.04439v1 Announce Type: cross Abstract: Large language models (LLMs) have made notable progress in code generation, but they still struggle on challenging tasks that require sophisticated al

Exo2EgoPose: Leveraging Exocentric Demonstrations for Vision-Language guided Egocentric 3D Hand Pose Forecasting

arXiv:2607.15890v2 Announce Type: replace Abstract: Perceiving multimodal cues and forecasting fine-grained actions from an egocentric (Ego) perspective is vital for applications like robot manipulati

researcharxiv-cs-cv

Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

arXiv:2608.04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control. However,

Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models

model-releasesarxiv-cs-cv

arXiv:2608.04404v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot manipulation by learning how the environment evolves beyond the current observation. However, existing approach

FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks

arXiv:2608.04073v1 Announce Type: cross Abstract: Personalized Federated Learning (PFL) has emerged as a promising solution for intrusion detection in heterogeneous IoT environments, as it can improve

Feasibility of Embedded Photoplethysmography Sensing in Short-Duration Tactile Interactions With Pocket-Sized Robots Using IMU- and Confidence-Based Filtering

arXiv:2608.04242v1 Announce Type: new Abstract: Ubiquitous companion robots offer a promising avenue for immediate anxiety relief in children, yet their effectiveness relies on the ability to monitor

researcharxiv-cs-ro

Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System

arXiv:2512.02567v2 Announce Type: replace-cross Abstract: The advent of strong generative AI has a considerable impact on various software engineering tasks such as code repair, test generation, or la

safetyarxiv-cs-ai

Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning

arXiv:2608.04771v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) excel on complex tasks through long chain-of-thought (CoT) reasoning, but their lengthy intermediate steps cause severe ov

safetyarxiv-cs-ai

FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents

arXiv:2608.04095v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unc

FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables

arXiv:2608.04077v1 Announce Type: new Abstract: Evaluating financial AI agents requires criteria aligned with real professional work. Existing rubric methods typically derive criteria from task prompt

FinReportBench: Measuring and Improving Institution-Grade Financial Report Generation

arXiv:2608.04374v1 Announce Type: cross Abstract: Large language models can produce fluent financial analysis, but fluency alone does not establish whether a report is suitable for institutional deliv