Search: “inference”

STS: Efficient Sparse Attention with Speculative Token Sparsity

model-releasesarxiv-cs-cl

arXiv:2605.15508v1 Announce Type: cross Abstract: The quadratic complexity of attention imposes severe memory and computational bottlenecks on Large Language Model (LLM) inference. This challenge is p

18 May 2026

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Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation

DGX agent

arXiv:2605.15388v1 Announce Type: new Abstract: Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although

safetyarxiv-cs-lg

18 May 2026

A Regret Perspective on Online Multiple Testing

arXiv:2605.13916v1 Announce Type: cross Abstract: Online Multiple Testing (OMT), a fundamental pillar of sequential statistical inference, traditionally evaluates the False Discovery Rate (FDR) and st

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Agentic Systems as Boosting Weak Reasoning Models

arXiv:2605.14163v1 Announce Type: new Abstract: Can a committee of weak reasoning-model calls reach the performance of much stronger models? We study verifier-backed committee search as inference-time

CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening

arXiv:2602.04473v2 Announce Type: replace Abstract: Recently, diffusion models have brought novel insights to pan-sharpening and notably boosted fusion precision. However, most existing models perform

Do Coding Agents Understand Least-Privilege Authorization?

arXiv:2605.14859v1 Announce Type: cross Abstract: As coding agents gain access to shells, repositories, and user files, least-privilege authorization becomes a prerequisite for safe deployment: an age

DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction

arXiv:2605.14104v1 Announce Type: new Abstract: Inferring spatially resolved gene expression from histology images offers a cost-effective complement to spatial transcriptomics (ST). However, existing

Hardware

Falkor-IRAC: Graph-Constrained Generation for Verified Legal Reasoning in Indian Judicial AI

arXiv:2605.14665v1 Announce Type: new Abstract: Legal reasoning is not semantic similarity search. A court judgment encodes constrained symbolic reasoning: precedent propagation, procedural state tran

hardwarearxiv-cs-ai

HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts

arXiv:2605.13997v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) layers route tokens through a handful of experts, and learning-free compression of these layers reduces inference cost

Image Restoration via Diffusion Models with Dynamic Resolution

arXiv:2605.14267v1 Announce Type: cross Abstract: Diffusion models (DMs) have exhibited remarkable efficacy in various image restoration tasks. However, existing approaches typically operate within th

Mistletoe: Stealthy Acceleration-Collapse Attacks on Speculative Decoding

arXiv:2605.14005v1 Announce Type: new Abstract: Speculative decoding has become a widely adopted technique for accelerating large language model (LLM) inference by drafting multiple candidate tokens a

local-aiarxiv-cs-cl

Performance-Driven Policy Optimization for Speculative Decoding with Adaptive Windowing

arXiv:2605.14978v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by having a lightweight draft model propose speculative windows of candidate tokens for parallel verifica

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SEDiT: Mask-Free Video Subtitle Erasure via One-step Diffusion Transformer

arXiv:2605.14894v1 Announce Type: new Abstract: Recent breakthroughs in video diffusion models have significantly accelerated the development of video editing techniques. However, existing methods oft

local-aiarxiv-cs-cv

TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering

arXiv:2601.21656v3 Announce Type: replace Abstract: Clustering tabular data is a fundamental yet challenging problem due to heterogeneous feature types, diverse data-generating mechanisms, and the abs

applicationsarxiv-cs-lg

Amortized Neural Clustering of Time Series based on Statistical Features

arXiv:2605.13128v1 Announce Type: cross Abstract: This paper introduces an algorithm-agnostic approach to feature-based time series clustering via amortized neural inference. By training neural networ

researcharxiv-cs-lg

Color Constancy in Hyperspectral Imaging via Reduced Spectral Spaces

arXiv:2605.13306v1 Announce Type: new Abstract: Illuminant estimation aims to infer scene illumination from image measurements despite intrinsic ambiguities between surface reflectance and lighting. M

Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations

arXiv:2605.13790v1 Announce Type: cross Abstract: Partial differential equations (PDEs) are fundamental for modeling complex natural and physical phenomena. In many real-world applications, however, o

GenCape: Structure-Inductive Generative Modeling for Category-Agnostic Pose Estimation

arXiv:2605.13151v1 Announce Type: new Abstract: Category-agnostic pose estimation (CAPE) aims to localize keypoints on query images from arbitrary categories, using only a few annotated support exampl

local-aiarxiv-cs-cv

KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving

arXiv:2605.13734v1 Announce Type: cross Abstract: LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggre

Language Model Networks: Supervision-Efficient Learning through Dense Communication

arXiv:2505.12741v2 Announce Type: replace Abstract: Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time reasoning

Learning a Continue-Thinking Token for Enhanced Test-Time Scaling

arXiv:2506.11274v2 Announce Type: replace-cross Abstract: Test-time scaling has emerged as an effective approach for improving language model performance by utilizing additional compute at inference t

Learning Transferable Latent User Preferences for Human-Aligned Decision Making

arXiv:2605.12682v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications. While they are efficient in certain tasks, LLMs often stru

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MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs

arXiv:2506.12876v2 Announce Type: replace Abstract: The rapid scaling of large language models~(LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, s

safetyarxiv-cs-lg

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

N-vium: Mixture-of-Exits Transformer for Accelerated Exact Generation

arXiv:2605.13190v1 Announce Type: cross Abstract: Improving the inference efficiency of autoregressive transformers typically means reducing FLOPs per token, usually through approximations that degrad

Procedural Refinement by LLM-driven Algorithmic Debugging for ARC-AGI-2

arXiv:2603.20334v2 Announce Type: replace-cross Abstract: In high-complexity abstract reasoning, a system must infer a latent rule from a few examples or structured observations and apply it to unseen

Quantifying Potential Observation Missingness in Inverse Reinforcement Learning

arXiv:2605.12831v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-maki

applicationsarxiv-cs-lg

Query-Conditioned Test-Time Self-Training for Large Language Models

arXiv:2605.13369v1 Announce Type: cross Abstract: Large language models (LLMs) are typically deployed with fixed parameters, and their performance is often improved by allocating more computation at i

Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning

arXiv:2605.13153v1 Announce Type: new Abstract: Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniforml

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

researcharxiv-cs-lg

Test-time Sparsity for Extreme Fast Action Diffusion

arXiv:2605.13316v1 Announce Type: new Abstract: Action diffusion excels at high-fidelity action generation but incurs heavy computational costs owing to its iterative denoising nature. Despite current

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The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models

arXiv:2605.12940v1 Announce Type: cross Abstract: Probabilistic Circuits (PCs) are deep generative models that support exact and efficient probabilistic inference. Yet in autoregressive language model

Tutorials

Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems

arXiv:2605.13470v1 Announce Type: new Abstract: Recent advances in AI have been primarily driven by large-scale neural architectures that excel at function approximation, rather than by tailored induc

tutorialsarxiv-cs-lg

Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks

arXiv:2605.13560v1 Announce Type: new Abstract: This work studies lung tumor growth prediction from sparse and irregular longitudinal computed tomography (CT) observations with measurement variability

When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation

arXiv:2605.13067v1 Announce Type: cross Abstract: As end-to-end robotic policies are progressively deployed in the real world to solve real tasks, they face a gap between the training and inference co

Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty

model-releasesarxiv-cs-cl

arXiv:2605.11436v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring a

Allegory of the Cave: Measurement-Grounded Vision-Language Learning

model-releasesarxiv-cs-cl

arXiv:2605.11727v1 Announce Type: cross Abstract: Vision-language models typically reason over post-ISP RGB images, although RGB rendering can clip, suppress, or quantize sensor evidence before infere

CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating

arXiv:2605.11723v1 Announce Type: new Abstract: In this paper, we propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models. During inference, it

local-aiarxiv-cs-cv

CORE: Cyclic Orthotope Relation Embedding for Knowledge Graph Completion

arXiv:2605.11159v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to automatically infer missing facts in multi-relational data by mapping entities and relations into continuous re

Agents

DORA: Dynamic Online Reinforcement Agent for Token Merging in Vision Transformers

arXiv:2605.11683v1 Announce Type: new Abstract: Vision Transformers (ViTs) incur significant computational overhead due to the quadratic complexity of self-attention relative to the token sequence len

agentsarxiv-cs-cv

Dynamic Execution Commitment of Vision-Language-Action Models

arXiv:2605.11567v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models predominantly adopt action chunking, i.e., predicting and committing to a short horizon of consecutive low-level act

applicationsarxiv-cs-cv

Hardware

Efficient Remote KV Cache Reuse with GPU-native Video Codec

arXiv:2602.09725v3 Announce Type: replace-cross Abstract: Remote KV cache reuse fetches KV cache for identical contexts from remote storage, avoiding recomputation, accelerating LLM inference. While i

hardwarearxiv-cs-lg

Agents

Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets

arXiv:2602.01682v2 Announce Type: replace Abstract: We study online inverse linear optimization, also known as contextual recommendation, where a learner sequentially infers an agent's hidden objectiv

agentsarxiv-cs-lg

Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer

arXiv:2605.11414v1 Announce Type: new Abstract: While traditional time-series classifiers assume full sequences at inference, practical constraints (latency and cost) often limit inputs to partial pre

Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes

arXiv:2605.11179v1 Announce Type: cross Abstract: Many three-dimensional spatial fields are anisotropic, with directions of rapid and slow variation that need not align with the coordinate axes. Stand

researcharxiv-cs-lg

Learning Ego-Centric BEV Representations from a Perspective-Privileged View: Cross-View Supervision for Online HD Map Construction

arXiv:2605.12218v1 Announce Type: new Abstract: Bird's-eye-view (BEV) representations derived from multi-camera input have become a central interface for online high-definition (HD) map construction.

More Than Meets the Eye: A Semantics-Aware Traffic Augmentation Framework for Generalizable Website Fingerprinting

arXiv:2605.11402v1 Announce Type: new Abstract: Deep learning-based website fingerprinting has emerged as an effective technique for inferring the websites users visit. Although existing methods achie

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OTT-Vid: Optimal Transport Temporal Token Compression for Video Large Language Models

arXiv:2605.11803v1 Announce Type: new Abstract: As Video Large Language Models (Video-LLMs) scale to longer and more complex videos, their inference cost grows rapidly due to the large volume of visua