Search: “models”

Mechanistic Evidence for Spectral Structures in Prior-Data Fitted Networks

arXiv:2601.21731v2 Announce Type: replace Abstract: Prior-Data Fitted Networks (PFNs) enable amortized Bayesian inference in a single forward pass, yet their internal representations remain opaque. It

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Neural QAOA^{2}: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization

DGX agent

arXiv:2605.13072v1 Announce Type: cross Abstract: The quantum approximate optimization algorithm (QAOA) holds promise for combinatorial optimization but is constrained by limited qubits. While divide-

model-releasesarxiv-cs-ai

14 May 2026

On the Generalization of Knowledge Distillation: An Information-Theoretic View

arXiv:2605.13143v1 Announce Type: cross Abstract: Knowledge distillation is widely used to improve generalization in practice, yet its theoretical understanding remains elusive. In the standard distil

safetyarxiv-cs-lg

Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo

arXiv:2605.13807v1 Announce Type: cross Abstract: Neural-network quantum states have emerged as a powerful variational framework for quantum many-body systems, with recent progress often driven by mas

Path-independent Flow Matching for Multi-parameter Generative Dynamics

arXiv:2605.13487v1 Announce Type: new Abstract: Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formulation is no

PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records

arXiv:2601.09636v2 Announce Type: replace Abstract: While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more

Predicting Channel Closures in the Lightning Network with Machine Learning

arXiv:2605.12759v1 Announce Type: new Abstract: The Lightning Network (LN) is a second-layer protocol for Bitcoin designed to enable fast and cost-efficient off-chain transactions. Channels in the LN

Profit Maximization in Bilateral Trade against a Smooth Adversary

arXiv:2605.12664v1 Announce Type: cross Abstract: Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, who wish to trade a good. We study this problem

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations

arXiv:2605.12813v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across many tasks but remain vulnerable to hallucinations, motivating the need for realistic a

researcharxiv-cs-ai

Reducing cross-sample prediction churn in scientific machine learning

arXiv:2605.13826v1 Announce Type: new Abstract: Scientific machine learning reports predictive performance. It does not report whether the same prediction would survive a different draw of training da

Rethinking Graph Convolution for 2D-to-3D Hand Pose Lifting

model-releasesarxiv-cs-cv

arXiv:2605.13604v1 Announce Type: new Abstract: Graph convolutional networks (GCNs) are widely used for 3D hand pose estimation, where the hand skeleton is encoded as a fixed adjacency graph. We revis

ReTool-Video: Recursive Tool-Using Video Agents with Meta-Augmented Tool Grounding

arXiv:2605.13228v1 Announce Type: cross Abstract: Video understanding requires active evidence seeking, motivating tool-augmented video agents for temporal reasoning, cross-modal understanding, and co

Revealing Interpretable Failure Modes of VLMs

arXiv:2605.12674v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in safety-critical applications because of their broad reasoning capabilities and ability to general

safetyarxiv-cs-ai

Tutorials

Revisit What You See: Revealing Visual Semantics in Vision Tokens to Guide LVLM Decoding

arXiv:2506.09522v3 Announce Type: replace-cross Abstract: Large Vision Language Models (LVLMs) achieve strong performance across multimodal tasks by integrating visual perception with language underst

tutorialsarxiv-cs-ai

RTLC -- Research, Teach-to-Learn, Critique: A three-stage prompting paradigm inspired by the Feynman Learning Technique that lifts LLM-as-judge accuracy on JudgeBench with no fine-tuning

arXiv:2605.13695v1 Announce Type: cross Abstract: LLM-as-a-judge is now the default measurement instrument for open-ended generation, but on the public JudgeBench benchmark even strong instruction-tun

Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection

arXiv:2312.04110v2 Announce Type: replace-cross Abstract: The COVID-19 pandemic has exerted a profound impact on the global economy and continues to exact a significant toll on human lives. The COVID-

Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts

arXiv:2605.12890v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-written text. W

Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs

arXiv:2603.24002v2 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) for high-dimensional and high-order partial differential equations (PDEs) are primarily constrained by the

Teaching and Learning under Deductive Errors

arXiv:2605.13384v1 Announce Type: new Abstract: Most models of machine teaching and learning assume the learner makes no errors in its internal deductive inference. However, humans and large language

ToolWeave: Structured Synthesis of Complex Multi-Turn Tool-Calling Dialogues

arXiv:2605.12521v1 Announce Type: cross Abstract: Multi-turn tool calling is essential for LLMs to function as autonomous agents, yet synthesizing the training data required for these capabilities rem

Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations

arXiv:2605.13761v1 Announce Type: new Abstract: AI-driven flood digital twins demand fast hydrodynamic surrogates for ensemble forecasting and observation assimilation. Yet even GPU-accelerated two-di

TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking

arXiv:2605.12587v1 Announce Type: new Abstract: Dense 3D tracking from monocular video is fundamental to dynamic scene understanding. While recent 3D foundation models provide reliable per-frame geome

safetyarxiv-cs-cv

TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation

arXiv:2605.13433v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising paradigm that replaces fragmented, scenario-specific architectures with unified Transformer-

Universal Representation of Generalized Convex Functions and their Gradients

arXiv:2509.04477v3 Announce Type: replace-cross Abstract: A wide range of optimization problems can often be written in terms of generalized convex functions (GCFs). When this structure is present, it

Useful Memories Become Faulty When Continuously Updated by LLMs

arXiv:2605.12978v1 Announce Type: new Abstract: Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated

Watermarking Should Be Treated as a Monitoring Primitive

arXiv:2605.13095v1 Announce Type: cross Abstract: Watermarking is widely proposed for provenance, attribution, and safety monitoring in generative models, yet is typically evaluated only under adversa

safetyarxiv-cs-ai

When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling

arXiv:2605.13172v1 Announce Type: cross Abstract: Recent advances in agent and multi-agent systems have shown strong performance on tool use, reasoning, and collaborative tasks. However, existing benc

Applications

ZKBoost: Zero-Knowledge Verifiable Training for XGBoost

arXiv:2602.04113v3 Announce Type: replace-cross Abstract: Gradient boosted decision trees, particularly XGBoost, are among the most effective methods for tabular data. As deployment in sensitive setti

applicationsarxiv-cs-lg

3D Gaussian Splatting for Efficient Retrospective Dynamic Scene Novel View Synthesis with a Standardized Benchmark

model-releasesarxiv-cs-cv

arXiv:2605.12437v1 Announce Type: new Abstract: Retrospective novel view synthesis (NVS) of dynamic scenes is fundamental to applications such as sports. Recent dynamic 3D Gaussian Splatting (3DGS) ap

3DGS^3: Joint Super Sampling and Frame Interpolation for Real-Time Large-Scale 3DGS Rendering

model-releasesarxiv-cs-cv

arXiv:2605.11489v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time 3D rendering but faces challenges in efficiently scaling to ultra-dense scenes and high-re

A Proprioceptive-Only Benchmark for Quadruped State Estimation: ATE, RPE, and Runtime Trade-offs Between Filters and Smoothers

model-releasesarxiv-cs-ro

arXiv:2605.11674v1 Announce Type: new Abstract: We compare three state-of-the-art proprioceptive state estimators for quadruped robots: MUSE [1], the Invariant Extended Kalman Filter (IEKF) [2], and t

Tutorials

A Transfer Learning Evaluation of Deep Neural Networks for Image Classification

arXiv:2605.11989v1 Announce Type: new Abstract: Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by

tutorialsarxiv-cs-cv

AgentDisCo: Towards Disentanglement and Collaboration in Open-ended Deep Research Agents

arXiv:2605.11732v1 Announce Type: cross Abstract: In this paper, we present AgentDisCo, a novel Disentangled and Collaborative agentic architecture that formulates deep research as an adversarial opti

An Empirical Study of Automating Agent Evaluation

arXiv:2605.11378v1 Announce Type: new Abstract: Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive

Hardware

AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration -- Learning from Cheap, Optimizing Expensive

arXiv:2605.11518v1 Announce Type: cross Abstract: Effectively configuring scalable large language model (LLM) experiments, spanning architecture design, hyperparameter tuning, and beyond, is crucial f

hardwarearxiv-cs-cl

AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor

arXiv:2601.05752v3 Announce Type: replace Abstract: We introduce AutoMonitor-Bench, the first benchmark designed to systematically evaluate the reliability of LLM-based misbehavior monitors across div

Backbone-Equated Diffusion OOD via Sparse Internal Snapshots

arXiv:2605.11014v1 Announce Type: new Abstract: Fair comparison between diffusion-based OOD detectors is challenging, as conclusions can vary with backbone choice, corruption parameterization, and tes

Can a Single Message Paralyze the AI Infrastructure? The Rise of AbO-DDoS Attacks through Targeted Mobius Injection

arXiv:2605.11442v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of digit

safetyarxiv-cs-cl

CAST: Collapse-Aware multi-Scale Topology Fusion for Multimodal Coreset Selection

arXiv:2605.11705v1 Announce Type: new Abstract: The training of large multimodal models fundamentally relies on massive image-text datasets, which inevitably incur prohibitive computational overhead.

researcharxiv-cs-cv

Tutorials

CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives

arXiv:2605.12496v1 Announce Type: new Abstract: Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single sce

tutorialsarxiv-cs-cv

Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing

arXiv:2505.05665v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently demonstrated success in decision-making tasks including planning, control, and prediction, but thei

safetyarxiv-cs-cl

Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering

arXiv:2605.11145v1 Announce Type: cross Abstract: Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item s

safetyarxiv-cs-lg

Decomposing Evolutionary Mixture-of-LoRA Architectures: The Routing Lever, the Lifecycle Penalty, and a Substrate-Conditional Boundary

arXiv:2605.11153v1 Announce Type: new Abstract: We decompose an evolutionary mixture-of-LoRA system on a from-scratch ~150M-parameter widened-D substrate (D=1536, V=32000; D/V approx 0.048; the 'widen

Deep Probabilistic Unfolding for Quantized Compressive Sensing

arXiv:2605.11475v1 Announce Type: new Abstract: We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to

researcharxiv-cs-cv

Delightful Gradients Accelerate Corner Escape

arXiv:2605.11908v1 Announce Type: new Abstract: Softmax policy gradient converges at O(1/t), but its transient behavior near sub-optimal corners of the simplex can be exponentially slow. The bottlenec

Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning

arXiv:2605.12122v1 Announce Type: cross Abstract: Unlearning specific concepts in text-to-image diffusion models has become increasingly important for preventing undesirable content generation. Among

researcharxiv-cs-cv

Applications

Does Head Pose Correction Improve Biometric Facial Recognition?

arXiv:2512.03199v2 Announce Type: replace Abstract: Biometric facial recognition models often demonstrate significant decreases in accuracy when processing real-world images, often characterized by po

applicationsarxiv-cs-cv

Applications

Dual-Temporal LSTM with Hybrid Attention for Airline Passenger Load Factor Forecasting: Integrating Intra-Flight and Inter-Flight Booking Dynamics

arXiv:2605.11569v1 Announce Type: cross Abstract: Accurate short-term demand forecasting is crucial to airline revenue management, yet most existing systems fail to meet this need because current mode

applicationsarxiv-cs-lg