Search: “models”

EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation

arXiv:2605.23493v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has gained wide attraction as an LLM post-training paradigm due to its effectiveness in improving capabilities without intr

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Emotion Recognition in Sign Language Conversation

DGX agent

arXiv:2605.23328v1 Announce Type: new Abstract: Emotion Recognition in Conversation is a core component of affective computing, while current resources of sign language emotion datasets primarily focu

applicationsarxiv-cs-cl

25 May 2026

Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries

arXiv:2605.23854v1 Announce Type: new Abstract: Bradley-Terry-Luce (BTL) model estimation is a well-established strategy to rank a collection of items given a dataset of pairwise comparisons. Although

Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation

arXiv:2605.23191v1 Announce Type: new Abstract: Scaling recommendation models is a central challenge in recommender systems. Recently, RankMixer has emerged as an effective solution, operating on a un

Industry

Figure AI had a livestream of their robots sorting packages 24/7 for 8 days straight. These aren't staged demos anymore.

Figure AI livestreamed three humanoid robots (F.03 models) autonomously sorting packages for over 24 hours continuous operation without failure, extending their original 8-hour target . The robots sor

industryr-chatgpt

Geo-Align: Video Generation Alignment via Metric Geometry Reward

arXiv:2605.23903v1 Announce Type: new Abstract: Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rel

safetyarxiv-cs-cv

GlowGS: Generative Semantic Feature Learning for 3D Gaussian Splatting in Nighttime Glow Scenes

arXiv:2605.23602v1 Announce Type: new Abstract: Existing 3DGS methods effectively render high-quality novel views in clear-day scenes. However, they struggle with night scenes, particularly in glow re

researcharxiv-cs-cv

GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks

arXiv:2602.11629v2 Announce Type: replace Abstract: Graph Prompt Learning (GPL) has recently emerged as a promising paradigm for downstream adaptation of pre-trained graph models, mitigating the misal

applicationsarxiv-cs-lg

Agents

GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agents

arXiv:2602.00979v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as educational agents for automatic short answer grading (ASAG) in real-world education

agentsarxiv-cs-ai

Human Decision-Making with Persuasive and Narrative LLM Explanations

arXiv:2605.23867v1 Announce Type: cross Abstract: Large language models (LLMs) have the potential to aid and improve human decision-making in classification tasks, not only by providing fairly accurat

Instance-Optimal Estimation with Multiple LLM Judges on a Budget

arXiv:2605.23362v1 Announce Type: new Abstract: Evaluating large language models increasingly relies on LLM-as-a-judge protocols, but such evaluations remain costly: different judges have different pr

LaMo: Self-Supervised Latent Motion Priors for Physical Realism in Video Generation

arXiv:2605.23878v1 Announce Type: new Abstract: Modern video generators produce visually compelling clips but still struggle with physical and motion consistency, limiting their use as reliable world

researcharxiv-cs-cv

LLM Code Smells: A Taxonomy and Detection Approach

arXiv:2605.22976v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and abilit

Agents

MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection

arXiv:2605.23723v1 Announce Type: new Abstract: Large language model agents increasingly rely on persistent memory to store past interactions, retrieve relevant demonstrations, and improve long-horizo

agentsarxiv-cs-ai

Multimodal Distribution Matching for Vision-Language Dataset Distillation

arXiv:2605.23482v1 Announce Type: cross Abstract: Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increas

Occlusion-Aware Physics-Semantic Keyframe Selection for Robust Video Editing

arXiv:2605.23192v1 Announce Type: new Abstract: Video editing has recently achieved remarkable progress with diffusion-based generative models, enabling diverse object-level manipulations from natural

researcharxiv-cs-cv

Tutorials

PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction

arXiv:2605.23478v1 Announce Type: cross Abstract: Accurate crop yield prediction is crucial for sustainable agriculture and global food security. While existing methods are predominantly developed for

tutorialsarxiv-cs-ai

pi_0-EqM: Equilibrium Matching for Closed-Loop Vision-Language-Action Control

arXiv:2605.23128v1 Announce Type: new Abstract: Currently, Vision-Language-Action (VLA) models have become the most adopted paradigm for robotic manipulation for its great potential for task generaliz

safetyarxiv-cs-ro

PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels

arXiv:2605.22856v1 Announce Type: cross Abstract: Channel foundation models assume access to fully observed channels, an assumption that fails in deployment. We introduce PilotWiMAE, a self-supervised

RAG-Pull: Turning Retrieval into a Code-Injection Channel via Invisible Unicode Perturbations

arXiv:2510.11195v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminatin

ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload

arXiv:2605.11215v2 Announce Type: replace-cross Abstract: Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient t

Robust OT-Guided Generative Residual Domain Adaptation for Bike-Sharing Demand Prediction under Temporal Domain Shift

arXiv:2605.23115v1 Announce Type: new Abstract: Bike-sharing models trained on historical station-hour data may degrade when deployed in later years because travel patterns change over time. This pape

Agents

RS2AD-LiDAR: End-to-End Autonomous Driving LiDAR Data Generation from Roadside Sensor Observations

arXiv:2605.23406v1 Announce Type: new Abstract: End-to-end autonomous driving solutions, which directly process multimodal sensory data and output fine-grained control commands, have gradually become

agentsarxiv-cs-cv

Safe Reinforcement Learning with Preference-based Constraint Inference

arXiv:2603.23565v2 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be com

SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety

arXiv:2605.05704v2 Announce Type: replace-cross Abstract: Recent advances in foundation models have transformed LLMs from passive conversational systems into autonomous agents capable of reasoning and

Sample-wise Targeted Adversarial Attacks on Test-time Adaptation

arXiv:2605.23411v1 Announce Type: cross Abstract: Test-time adaptation (TTA) effectively counters distribution shifts but exposes models to adversarial manipulation via the unlabeled test stream. Exis

safetyarxiv-cs-cv

Scaling-Aware Adapter for Structure-Grounded LLM Reasoning

arXiv:2602.02780v3 Announce Type: replace Abstract: Large language models (LLMs) are enabling reasoning over 2D and 3D structures, yet existing methods remain modality-specific and typically compress

Self-Improving In-Context Learning

arXiv:2605.23180v1 Announce Type: new Abstract: We propose to improve in-context learning (ICL) by optimizing the continuous embeddings of a fixed few-shot prompt at test time. The key observation is

researcharxiv-cs-cl

Solving the Aircraft Disassembly Scheduling Problem

arXiv:2605.23592v1 Announce Type: new Abstract: Dismantling aircrafts reaching their end of life is a complex endeavour that is necessary in terms of sustainability but yields small income margins for

Spatio-Temporal Forecasting of Retaining Wall Deformation: Mitigating Error Accumulation via Multi-Resolution ConvLSTM Stacking Ensemble

arXiv:2603.10453v2 Announce Type: replace Abstract: This study proposes a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) ensemble framework that leverages diverse temporal input reso

Tutorials

Steered Generation via Gradient-Based Optimization on Sparse Query Features

arXiv:2605.23040v1 Announce Type: new Abstract: Latent steering exploits internal representations of Large Language Models (LLMs) to guide generation, yet interventions on dense states can entangle di

tutorialsarxiv-cs-lg

UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries

arXiv:2507.23372v2 Announce Type: replace Abstract: Emotional understanding and generation are often treated as separate tasks, yet they are inherently complementary and can mutually enhance each othe

safetyarxiv-cs-cv

Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes

arXiv:2507.05064v4 Announce Type: replace-cross Abstract: Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalabili

applicationsarxiv-cs-lg

VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation

arXiv:2602.07399v2 Announce Type: replace Abstract: Vision--Language--Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations r

clarifying: the issue is that alignment instructions and don’t pass, and the emotional weight that some people attach to LLMs can cause chal…

Gary Marcus discusses how alignment instructions in large language models often fail to work as intended, and argues that the emotional attachment some people develop toward LLMs can create additional

safetygary-marcus--x

24 May 2026

@GaryMarcus @hendrycks @Yoshua_Bengio We recently tested all of the major LLMs with tic-tac-toe, modified chess, and a novel game -- even th…

@GaryMarcus @hendrycks @Yoshua_Bengio We recently tested all of the major LLMs with tic-tac-toe, modified chess, and a novel game -- even the top models all failed: illegal moves, claiming a win when

safetygary-marcus--x

24 May 2026

Algorithm Design and Stronger Guarantees for the Improving Multi-Armed Bandits Problem

arXiv:2511.10619v2 Announce Type: replace Abstract: The improving multi-armed bandits problem is a formal model for allocating effort under uncertainty, motivated by scenarios such as investing resear

Hardware

AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems

arXiv:2605.21560v1 Announce Type: new Abstract: Deploying neural networks on microcontroller units (MCUs) is critical for edge intelligence but remains challenging due to tight memory, storage, and co

hardwarearxiv-cs-lg

Local Ai

CacheClip: Accelerating RAG with Effective KV Cache Reuse

arXiv:2510.10129v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems suffer from severe time-to-first-token (TTFT) bottlenecks due to long input sequences. Existing KV cach

local-aiarxiv-cs-lg

Tutorials

Conditional Entropy of Heat Diffusion on Temporal Networks

arXiv:2605.21514v1 Announce Type: cross Abstract: Many complex systems can be modeled by temporal networks, whose organization often evolves through distinct structural phases. Detecting the change po

tutorialsarxiv-cs-lg

Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization

arXiv:2605.22749v1 Announce Type: new Abstract: Modern smart grids rely on dense measurement infrastructures, communication links, and intelligent field devices. Although this improves supervision and

Local Ai

Detecting Atypical Clients in Federated Learning via Representation-Level Divergence

arXiv:2605.22266v1 Announce Type: new Abstract: Federated learning enables collaborative training across distributed clients with heterogeneous data, but such heterogeneity often leads to unstable upd

local-aiarxiv-cs-lg

Event-Aware Prompt Learning for Dynamic Graphs

arXiv:2510.11339v2 Announce Type: replace Abstract: Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph le

applicationsarxiv-cs-lg

Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift

arXiv:2605.21552v1 Announce Type: new Abstract: Confidence calibration for classification models is vital in safety-critical decision-making scenarios and has received extensive attention. General con

safetyarxiv-cs-lg

Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration

arXiv:2512.11587v2 Announce Type: replace Abstract: Even for the gradient descent (GD) method applied to neural network training, understanding its optimization dynamics, including convergence rate, i

Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks

arXiv:2605.21502v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are increasingly used to model biological systems, yet the reliability of post-hoc explanation methods for recovering mea

Harnesses for Inference-Time Alignment over Execution Trajectories

arXiv:2605.21516v1 Announce Type: new Abstract: Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance

safetyarxiv-cs-lg

Industry

How AI stacks are rewriting the rules of business

The shift from on-premises computing to software as a service changed the technology model and forced information technology organizatios to modernize how it builds, buys and operates software. It als

industrysiliconangle