Search: “persistence”

FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning

arXiv:2511.14715v3 Announce Type: replace Abstract: Federated learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who

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Agents

Focusing Influence Mechanism for Multi-Agent Reinforcement Learning

DGX agent

arXiv:2506.19417v2 Announce Type: replace Abstract: Cooperative multi-agent reinforcement learning (MARL) under sparse rewards remains fundamentally challenging because agents often fail to concentrat

agentsarxiv-cs-lg

13 May 2026

GRP: Goal-Reversed Prompting for Zero-Shot Evaluation with LLMs

arXiv:2503.06139v2 Announce Type: replace Abstract: Pairwise LLM-as-a-judge evaluation asks the judge to identify the better of two candidate answers. We study a one-line modification that asks for th

Interpreting Context-Aware Human Preferences for Multi-Objective Robot Navigation

arXiv:2603.17510v2 Announce Type: replace Abstract: Robots operating in human-shared environments must not only achieve task-level navigation objectives such as safety and efficiency, but also adapt t

safetyarxiv-cs-ro

Is Monotonic Sampling Necessary in Diffusion Models?

arXiv:2605.11773v1 Announce Type: new Abstract: Diffusion models generate samples by iteratively denoising a Gaussian prior, traversing a sequence of noise levels that, in every published sampler, dec

researcharxiv-cs-lg

KV-Fold: One-Step KV-Cache Recurrence for Long-Context Inference

arXiv:2605.12471v1 Announce Type: cross Abstract: We introduce KV-Fold, a simple, training-free long-context inference protocol that treats the key-value (KV) cache as the accumulator in a left fold o

Learning, Fast and Slow: Towards LLMs That Adapt Continually

arXiv:2605.12484v1 Announce Type: new Abstract: Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to a

Limits of Learning Linear Dynamics from Experiments

arXiv:2605.12010v1 Announce Type: new Abstract: Learning governing dynamics from data is a common goal across the sciences, yet it is only well-posed when the underlying mechanisms are identifiable. I

researcharxiv-cs-lg

MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware

arXiv:2605.05945v2 Announce Type: replace-cross Abstract: The recent advancement of Vision Language Action (VLA) models has driven a critical demand for large scale egocentric datasets. However, exist

researcharxiv-cs-cl

Pretraining Exposure Explains Popularity Judgments in Large Language Models

arXiv:2605.12382v1 Announce Type: new Abstract: Large language models (LLMs) exhibit systematic preferences for well-known entities, a phenomenon often attributed to popularity bias. However, the exte

safetyarxiv-cs-cl

Applications

Quantifying the Reconstructability of Astrophysical Methods with Large Language Models and Information Theory: A Case Study in Spectral Reconstruction

arXiv:2605.11154v1 Announce Type: cross Abstract: Modern astrophysical studies rely heavily on complex data analysis pipelines; however, published descriptions often lack the detail required for compu

applicationsarxiv-cs-lg

Local Ai

Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction

arXiv:2605.05749v2 Announce Type: replace Abstract: Dense 3D reconstruction from continuous image streams requires both accurate geometric aggregation and stable long-term memory management. Recent fe

local-aiarxiv-cs-cv

Real-Time Whole-Body Teleoperation of a Humanoid Robot Using IMU-Based Motion Capture with Sim2Sim and Sim2Real Validation

arXiv:2605.12347v1 Announce Type: new Abstract: Stable, low-latency whole-body teleoperation of humanoid robots is an open research challenge, complicated by kinematic mismatches between human and rob

researcharxiv-cs-ro

Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training

arXiv:2605.11134v1 Announce Type: new Abstract: Preference learning methods such as Direct Preference Optimization (DPO) are known to induce reliance on spurious correlations, leading to sycophancy an

StoicLLM: Preference Optimization for Philosophical Alignment in Small Language Models

arXiv:2605.11483v1 Announce Type: new Abstract: While large language models excel at factual adaptation, their ability to internalize nuanced philosophical frameworks under severe data constraints rem

Tacmap: Bridging the Tactile Sim-to-Real Gap via Geometry-Consistent Penetration Depth Map

arXiv:2602.21625v2 Announce Type: replace Abstract: Vision-Based Tactile Sensors (VBTS) are essential for achieving dexterous robotic manipulation, yet the tactile sim-to-real gap remains a fundamenta

safetyarxiv-cs-ro

The new era of SaMD: Why cloud infrastructure is the foundation for digital health in 2026

In the healthcare and life sciences industries, speed saves lives, but meeting regulatory requirements and other administrative burdens often pumps the brakes for manufacturers of software as a medica

safetygoogle-cloud-ai

The Price of Proportional Representation in Temporal Voting

arXiv:2605.11157v1 Announce Type: cross Abstract: We study proportional representation in the temporal voting model, where collective decisions are made repeatedly over time over a fixed horizon. Prio

U-STS-LLM A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputation

arXiv:2605.11735v1 Announce Type: new Abstract: The efficient operation of modern cellular networks hinges on the accurate analysis of spatio-temporal traffic data. Mastering these patterns is essenti

Understanding Sample Efficiency in Predictive Coding

arXiv:2605.11911v1 Announce Type: new Abstract: Predictive Coding (PC) is an influential account of cortical learning. Much of recent work has focused on comparing PC to Backpropagation (BP) to find w

Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery

model-releasesarxiv-cs-cv

arXiv:2605.11654v1 Announce Type: new Abstract: Cross-view geo-localization (CVGL), which matches an oblique drone view to a geo-referenced satellite tile, has emerged as a key alternative for autonom

When Does ell_2-Boosting Overfit Benignly? High-Dimensional Risk Asymptotics and the ell_1 Implicit Bias

arXiv:2605.06314v2 Announce Type: replace Abstract: Benign overfitting is well-characterized in ell_2 geometries, but its behavior under the ell_1 implicit bias of greedy ensembles remains challenging

A Single-Layer Model Can Do Language Modeling

arXiv:2605.10643v1 Announce Type: new Abstract: Modern language models scale depth by stacking layers, each holding its own state - a per-layer KV cache in transformers, a per-layer matrix in Mamba, G

researcharxiv-cs-cl

Agents

Accurate Trajectory Tracking with MPCC for Flapping-Wing MAVs

arXiv:2605.06042v2 Announce Type: replace Abstract: Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones, yet achieving precise autonomous control of bird-scal

agentsarxiv-cs-ro

AdaPaD: Adaptive Parallel Deflation for PEFT with Self-Correcting Rank Discovery

arXiv:2605.10741v1 Announce Type: new Abstract: Fine-tuning large language models with LoRA requires choosing a rank r before training starts. Existing approaches either extract rank-1 components sequ

Agents

Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery

arXiv:2605.08956v1 Announce Type: new Abstract: A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they alread

agentsarxiv-cs-ai

An Annotation Scheme and Classifier for Personal Facts in Dialogue

arXiv:2605.10339v1 Announce Type: new Abstract: The advancement of Large Language Models (LLMs) has enabled their application in personalized dialogue systems. We present an extended annotation scheme

BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models

arXiv:2605.08110v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the standard for fine-tuning large pre-trained models at reduced computational cost. However, its low-rank point

researcharxiv-cs-ai

Beyond source code: The files AI coding agents trust — and attackers exploit

model-releasesgoogle-cloud-ai

As AI coding agents become deeply embedded in developer workflows, defenders must evolve their definition of malicious files and rethink how to protect against them. Autonomous AI agents operate acros

Agents

Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN

arXiv:2605.10036v1 Announce Type: cross Abstract: As 6G evolves, the radio access network must transcend traditional automation to embrace agentic AI capable of perception, reasoning, and evolution. A

agentsarxiv-cs-ai

Calibrate, Don't Curate: Label-Efficient Estimation from Noisy LLM Judges

arXiv:2605.09702v1 Announce Type: cross Abstract: Multi-judge evaluation is increasingly used to assess LLMs and reward models, and the prevailing heuristic is to curate: keep the most accurate judges

researcharxiv-cs-cl

ColorConceptBench: A Benchmark for Probabilistic Color-Concept Understanding in Text-to-Image Models

arXiv:2601.16836v3 Announce Type: replace-cross Abstract: Text-to-image (T2I) models have advanced considerably in generating high-quality images from textual descriptions. However, their ability to a

Consolidation-Expansion Operator Mechanics:A Unified Framework for Adaptive Learning

arXiv:2605.09968v1 Announce Type: new Abstract: Every adaptive learning system must alternate between two operations: consolidating what it already knows and expanding into new evidence. We propose Co

researcharxiv-cs-lg

Continuous Latent Contexts Enable Efficient Online Learning in Transformers

arXiv:2605.09867v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a strong capacity for in-context learning: Given labeled examples, they can generate good predictions without par

Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation

arXiv:2605.09253v1 Announce Type: cross Abstract: While recent work in Reinforcement Learning with Verifiable Rewards (RLVR) has shown that a small subset of critical tokens disproportionately drives

safetyarxiv-cs-ai

Diagnosing Spectral Ceilings in Equivariant Neural Force Fields

arXiv:2605.08286v1 Announce Type: cross Abstract: We introduce a spectral-injection diagnostic for measuring which angular frequencies a trained equivariant force-field backbone preserves: inject a co

Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation

arXiv:2605.09315v1 Announce Type: new Abstract: Recent advances in LLM agents enable systems that autonomously refine workflows, accumulate reusable skills, self-train their underlying models, and mai

Done, But Not Sure: Disentangling World Completion from Self-Termination in Embodied Agents

arXiv:2605.08747v1 Announce Type: new Abstract: Standard embodied evaluations do not independently score whether an agent correctly commits to task completion at episode closure, a capacity we call te

EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies

arXiv:2602.09514v3 Announce Type: replace-cross Abstract: Long-horizon planning is widely recognized as a core capability of autonomous LLM-based agents; however, current evaluation frameworks suffer

EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments

arXiv:2506.08136v3 Announce Type: replace Abstract: We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The ben

EduStory: A Unified Framework for Pedagogically-Consistent Multi-Shot STEM Instructional Video Generation

arXiv:2605.09378v1 Announce Type: cross Abstract: Long-horizon video generation has advanced in visual quality, yet existing methods still struggle to maintain knowledge consistency and coherent pedag

Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging

arXiv:2605.10054v1 Announce Type: new Abstract: Deep neural networks for medical image diagnosis often achieve high predictive accuracy while relying on spurious or clinically irrelevant visual cues,

safetyarxiv-cs-cv

Local Ai

HyDRA Scorpion: A Cost-effective and Modular ROV for Real-Time Underwater Inspection, Intervention, and Object Detection

arXiv:2605.09093v1 Announce Type: new Abstract: A Remotely Operated Vehicle (ROV) is a tethered underwater robot used for tasks like inspection and intervention. While essential tools for underwater s

local-aiarxiv-cs-ro

IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs

arXiv:2605.10267v1 Announce Type: new Abstract: In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every par

Layer Collapse in Diffusion Language Models

arXiv:2605.06366v2 Announce Type: replace Abstract: Diffusion language models (DLMs) have recently emerged as competitive alternatives to autoregressive (AR) language models, yet differences in their

Tutorials

Learning Pure Quantum States in Any Dimension (Almost) Without Regret

arXiv:2605.09019v1 Announce Type: cross Abstract: We extend quantum state tomography with minimal cumulative disturbance, first investigated in [arXiv:2406.18370], to arbitrary finite-dimensional pure

tutorialsarxiv-cs-lg

LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selection

arXiv:2605.09518v1 Announce Type: new Abstract: Meta-learning for algorithm selection relies on a meta-dataset in which each row corresponds to a supervised learning dataset described by meta-features

M^3: Reframing Training Measures for Discretized Physical Simulations

arXiv:2605.08843v1 Announce Type: new Abstract: Neural surrogate models for physical simulations are trained on discretized samples of continuous domains, where the induced empirical measure leads to

safetyarxiv-cs-ai