Search: “models”

Exploiting Chordal Sparsity for Globally Optimal Estimation with Factor Graphs

arXiv:2605.30617v1 Announce Type: new Abstract: Robust and efficient state estimation is crucial for perception, navigation, and control in robotics. State estimation problems are conveniently modeled

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Forgetting Has Neighbors: Localized Collateral Forgetting in Machine Unlearning

DGX agent

arXiv:2605.31317v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of selected training examples without full retraining. Standard evaluations often summarize unlearning q

local-aiarxiv-cs-lg

1 Jun 2026

Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification

arXiv:2605.30387v1 Announce Type: cross Abstract: Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) s

local-aiarxiv-cs-ai

Tutorials

Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective

arXiv:2603.09936v2 Announce Type: replace Abstract: Generative Modeling via Drifting~itep{deng2026drifting} has recently achieved state-of-the-art one-step image generation through a kernel-based drif

tutorialsarxiv-cs-lg

GRKV: Global Regression for Training-Free KV Cache Compression in Long-Context LLMs

arXiv:2605.31105v1 Announce Type: new Abstract: Large language models (LLMs) with extended context lengths rely on the key-value (KV) cache to support attention over prior tokens. However, maintaining

HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster

arXiv:2605.31023v1 Announce Type: new Abstract: This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions includin

Tutorials

Haptic Sorter: A Unified Planning Framework for Online Shape Estimation and Real-Time Pose Inference

arXiv:2605.31352v1 Announce Type: new Abstract: Robotics manipulation usually assumes that the shape and pose of the object are known to the robot prior to motion planning. However, precise geometric

tutorialsarxiv-cs-ro

HetCCL: Enabling Collective Communication For Mixed-Vendor Heterogeneous Clusters

arXiv:2605.31000v1 Announce Type: cross Abstract: Training Large Language Models (LLMs) on heterogeneous clusters presents significant challenges for collective communication, as hardware from multipl

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning

arXiv:2602.19049v2 Announce Type: replace Abstract: Large language models increasingly rely on long chains of thought to improve accuracy, yet such gains come with substantial inference-time costs. We

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If LLMs Have Human-Like Attributes, Then So Does Age of Empires II

arXiv:2605.31514v1 Announce Type: cross Abstract: Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows. However, many works within the field state emerg

ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment

arXiv:2605.30965v1 Announce Type: cross Abstract: Recent advancements in text-guided audio generation have yielded promising results in diverse domains, including sound effects, speech, and music. How

Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble

arXiv:2605.30720v1 Announce Type: cross Abstract: Forecasting agricultural commodity prices in emerging economies is difficult due to high volatility, frequent supply disruptions, and strong cultural

LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation

arXiv:2605.30651v1 Announce Type: cross Abstract: We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a stu

Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization

arXiv:2605.31312v1 Announce Type: cross Abstract: Multimodal hallucination remains a persistent challenge for Vision-Language Models (VLMs). Standard textual Direct Preference Optimization (DPO) often

Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction

arXiv:2605.31595v1 Announce Type: new Abstract: Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians

Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration

arXiv:2605.31365v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on han

LH-Bench: Skill-Grounded Evaluation of Long-Horizon Agents on Subjective Enterprise Tasks

arXiv:2603.22744v2 Announce Type: replace Abstract: Large language models excel on objectively verifiable tasks such as math and programming, where evaluation reduces to unit tests or a single correct

LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability

arXiv:2605.31167v1 Announce Type: new Abstract: Assessing whether Large Language Models outputs are factually grounded, epistemically calibrated, and methodologically reproducible is a prerequisite fo

local-aiarxiv-cs-ai

MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks

arXiv:2603.02630v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone

Mechanistic Interpretability as Statistical Estimation: A Variance Analysis

arXiv:2510.00845v4 Announce Type: replace-cross Abstract: Mechanistic Interpretability (MI) aims to reverse-engineer model behaviors by identifying functional sub-networks. Yet, the scientific validit

MedCoG: Maximizing LLM Inference Density in Medical Reasoning via Meta-Cognitive Regulation

arXiv:2602.07905v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong potential in complex medical reasoning yet face diminishing gains under inference scaling laws. While

MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction

arXiv:2605.31302v1 Announce Type: cross Abstract: Undersampled magnetic resonance imaging (MRI) reconstruction seeks to recover temporally or contrast-varying image series from incomplete multicoil k-

MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation

arXiv:2605.31010v1 Announce Type: new Abstract: Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge

Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages

arXiv:2605.31136v1 Announce Type: new Abstract: In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, t

Multivariate Distributional Reinforcement Learning Using Sliced Divergences

arXiv:2605.31222v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) models the full return distribution rather than expectations, but extending it to multivariate settings rema

On-Device Robotic Planning: Eliminating Inference Redundancy for Efficient Decision-Making

arXiv:2605.31460v1 Announce Type: new Abstract: Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a

local-aiarxiv-cs-ro

On Revisiting Entropy for Identifying Mislabeled Images

arXiv:2605.31090v1 Announce Type: cross Abstract: Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labe

Parallel Tempering Initial Sampling in Inference-Time Reward Alignment

arXiv:2605.30991v1 Announce Type: cross Abstract: Inference-time reward alignment steers pretrained diffusion and flow-based generative models to satisfy user-specified rewards without retraining. Rec

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Hardware

PithTrain: A Compact and Agent-Native MoE Training System

arXiv:2605.31463v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become the dominant architecture for frontier language models. To meet this demand, production frameworks have built opti

hardwarearxiv-cs-ai

Primitive Subspaces Mediate Few-Shot Transfer in VLAs

arXiv:2605.30695v1 Announce Type: new Abstract: Deploying vision-language-action (VLA) policies in industrial environments requires the ability to teach new tasks at low cost, a property current VLAs

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Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention

arXiv:2603.06738v2 Announce Type: replace-cross Abstract: Recent Super-Resolution~(SR) methods mainly adopt Transformers for their strong long-range modeling capability and exceptional representationa

local-aiarxiv-cs-ai

RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning

arXiv:2605.30957v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robot control. However, their performance remains fundament

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Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't

arXiv:2605.30523v1 Announce Type: cross Abstract: Recent work describes what transformers can and cannot compute through connections to boolean circuits, but existing results lack exact characterizati

S^3LDBO: A Snapshot Single-Loop Algorithm for Decentralized Bilevel Optimization

arXiv:2605.31311v1 Announce Type: cross Abstract: Networked AI systems increasingly rely on multiple agents that collaboratively learn and adapt models over communication networks. In such systems, bi

agentsarxiv-cs-lg

SCOPE: Selective Conformal Optimized Pairwise LLM Judging

arXiv:2602.13110v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as scalable judges in pairwise evaluation, but they remain prone to miscalibration and bias

Seeing Before Agreeing: Aligning Multi-Agent Consensus with Visual Evidence

arXiv:2605.30698v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance on visual question answering (VQA). To mitigate individual hallucinations and blind spo

Skill Reuse as Compression in Agentic RL

arXiv:2605.31509v1 Announce Type: cross Abstract: Large language model agents trained with reinforcement learning (RL) often learn brittle, task-specific shortcuts. We hypothesize that agents generali

SlotMemory: Object-Centric KV Memory for Streaming Long-Video Generation

arXiv:2605.31033v1 Announce Type: new Abstract: Streaming video generation models typically rely on temporal-centric memory, which organizes historical context as raw frames, chunk segments, or unclus

Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding

arXiv:2602.06161v2 Announce Type: replace-cross Abstract: Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelis

The Inclusion Depth of Pattern Languages: An Open Problem in Algorithmic Learning Theory

arXiv:2605.30389v1 Announce Type: cross Abstract: Pattern languages are a classical model in formal language theory and algorithmic learning theory. This note formulates the problem of computing the i

To Grok Grokking: Provable Grokking in Ridge Regression

arXiv:2601.19791v3 Announce Type: replace Abstract: We study grokking, the onset of generalization long after overfitting, in a classical ridge regression setting. We prove end-to-end grokking results

Applications

Towards Effective Long-Video Event Prediction via Multi-Level Event Semantics Mining

arXiv:2605.31069v1 Announce Type: cross Abstract: Accurately predicting future events is fundamental to content understanding and decision-making across various domains. While prior research has prima

applicationsarxiv-cs-cl

Ubiquity of Emergent Hebbian Dynamics in Regularized Learning

arXiv:2505.18069v3 Announce Type: replace Abstract: Hebbian and anti-Hebbian plasticity are widely observed in the brain and are classically modeled as mechanistic, local homosynaptic rules stabilized

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UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis

arXiv:2510.15710v3 Announce Type: replace Abstract: Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central

Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack

arXiv:2506.01318v4 Announce Type: replace-cross Abstract: Machine unlearning (MU) aims to expunge a designated forget set from a trained model without costly retraining, yet the existing techniques ov

Weights to Code: Extracting Interpretable Algorithms from the Discrete Transformer

arXiv:2601.05770v3 Announce Type: replace-cross Abstract: Algorithm extraction aims to synthesize executable programs directly from models trained on algorithmic tasks, enabling de novo recovery of ex

When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?

arXiv:2605.30719v1 Announce Type: cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we

XOResNet: Exclusive-OR Meta-Residuals Facilitate Deep Spiking Neural Networks Learning

arXiv:2605.30362v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) hold promise for demonstrating superior learning and representation capabilities in deep models. Given the tremendous s