Search: “inference”

Resonant Brane Splatting for Arbitrary-Scale Super-Resolution

arXiv:2606.29453v1 Announce Type: cross Abstract: Arbitrary-Scale Super-Resolution (ASR) reconstructs images at continuous magnification factors. Recent methods accelerate inference by replacing compu

researcharxiv-cs-ai

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Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs

DGX agent

arXiv:2511.10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive

researcharxiv-cs-ai

30 Jun 2026

Set-Inclusive Uncertainty Modeling for Robust Brain Tumor Segmentation

arXiv:2606.30374v1 Announce Type: cross Abstract: Multimodal MRI is essential for accurate brain tumor segmentation. However, acquiring all modalities at inference is often challenging in practice, wh

researcharxiv-cs-ai

Speculative Pre-Positioning: Decoding Stateful Sessions to the Next Decision Point Off the Critical Path

arXiv:2606.29565v1 Announce Type: new Abstract: A stateless inference server (vLLM, SGLang, TensorRT-LLM) idles between requests while the accelerator waits; a stateful session reclaims that idle time

Hardware

SwarmX: Agentic Scheduling for Low-Latency Agentic Systems

arXiv:2606.21401v2 Announce Type: replace-cross Abstract: Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters. Their infer

hardwarearxiv-cs-ai

Agents

TacEvo: Self-Evolving Architecture Discovery for Robotic Tactile Perception via LLM-Driven Quality-Diversity Search

arXiv:2606.30109v1 Announce Type: new Abstract: Vision-based tactile sensing converts contact-induced surface deformation into images, enabling robots to infer contact forces and fine surface textures

agentsarxiv-cs-ro

TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning

arXiv:2401.11512v2 Announce Type: replace-cross Abstract: Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL). These variables must

safetyarxiv-cs-ai

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives

arXiv:2510.06096v3 Announce Type: replace-cross Abstract: The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a gr

safetyarxiv-cs-cl

Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data

arXiv:2606.30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling b

safetyarxiv-cs-ai

Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel

arXiv:2606.30595v1 Announce Type: cross Abstract: Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless cha

Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting

arXiv:2606.27688v1 Announce Type: cross Abstract: In financial forecasting, predictive performance depends not only on which model is trained, but also on how the trained model is deployed. We study t

safetyarxiv-cs-ai

ET-SAM: Efficient Point Prompt Prediction in SAM for Unified Scene Text Detection and Layout Analysis

arXiv:2603.25168v2 Announce Type: replace Abstract: Previous works based on Segment Anything Model (SAM) have achieved promising performance in unified scene text detection and layout analysis. Howeve

researcharxiv-cs-cv

NLL-Guided Full-Attention Layer Selection for Training-Free Sliding-Window Adaptation

arXiv:2606.27791v1 Announce Type: cross Abstract: Hybrid attention models that mix full and sliding-window attention across layers offer a promising approach to efficient long-context inference, but t

researcharxiv-cs-ai

Local Ai

Perceptual 3D Simulation With Physical World Modeling

arXiv:2606.27575v1 Announce Type: new Abstract: Predicting how a scene will evolve after a desired 3D transformation from images is a central goal in vision, graphics, and robotics. Yet unlike ideal s

local-aiarxiv-cs-cv

ReFreeKV: Towards Threshold-Free KV Cache Compression

arXiv:2502.16886v4 Announce Type: replace-cross Abstract: To reduce memory consumption during LLM inference, a handful of methods have been proposed for KV cache pruning. While these techniques can ac

SpikeVLA: Vision-Language-Action Models with Spiking Neural Networks

arXiv:2606.27807v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a dominant paradigm for embodied intelligence. However, most existing approaches are built on large-scal

safetyarxiv-cs-ro

Hardware

The Context-Ready Transformer

arXiv:2606.27538v1 Announce Type: cross Abstract: We introduce the context-ready transformer, a new recurrent neural network architecture built from a D-layer transformer block that pre-contextualizes

hardwarearxiv-cs-ai

Unbiased Diffusion Variational Inversion via Principled Posterior Matching

arXiv:2605.25042v2 Announce Type: replace Abstract: Existing score-based methods for inverse problems often resort to approximate minimization of the KL divergence between the inversion distribution a

researcharxiv-cs-cv

VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing

arXiv:2606.28301v1 Announce Type: new Abstract: Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize dow

Local Ai

AnySimLite: A Lightweight Few-Shot Similarity Encoder for On-Device Speech-Adjacent Classification

arXiv:2606.26452v1 Announce Type: new Abstract: To minimize privacy concerns and inference latency on edge devices like smartphones, lightweight on-device models remain important for end-user applicat

local-aiarxiv-cs-cl

Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)

arXiv:2510.22517v3 Announce Type: replace-cross Abstract: Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex physical systems. We propose

applicationsarxiv-cs-lg

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

arXiv:2510.17459v3 Announce Type: replace-cross Abstract: In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary sy

hisao{}: A GPU-Native Parallel Optimizer for Multimodal Black-Box Functions via Convergence-Anticonvergence Oscillation

arXiv:2606.26164v1 Announce Type: new Abstract: Finding all modes of a multimodal black-box function is a fundamental challenge in optimization, Bayesian inference, and scientific computing. Existing

Learning Probabilistic Filters with Strictly Proper Scoring Rules

arXiv:2606.26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical

safetyarxiv-cs-lg

Learning to Recover Task Experts from a Multi-Task Merged Model

arXiv:2606.26902v1 Announce Type: new Abstract: Multi-task model merging aims to consolidate several task-specific experts into a unified model, yet static merging consistently suffers from parameter

Distill on a Diet: Efficient Knowledge Distillation via Learnable Data Pruning

arXiv:2606.25488v1 Announce Type: new Abstract: Knowledge Distillation (KD) is widely used to obtain compact models for efficient inference in resource-constrained environments. Yet the computational

Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration

arXiv:2606.24970v1 Announce Type: new Abstract: Pruning Large Language Models (LLMs) reduces memory and inference costs by removing parts of the network, producing smaller models that retain most of t

Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding

arXiv:2606.24957v1 Announce Type: new Abstract: While speculative decoding improves inference throughput for multi-batch long-context Large Language Models (LLMs), its efficiency is often limited by a

researcharxiv-cs-cl

Enhancing Brain MRI Anomaly Detection and Reasoning with ROI Rethink and Synthetic Data

model-releasesarxiv-cs-cv

arXiv:2606.25894v1 Announce Type: new Abstract: Medical vision-language models typically generate diagnoses through single-pass inference without indicating which image regions support their conclusio

Erased, but Not Gone: Output Forgetting Is Not True Forgetting

arXiv:2606.25001v1 Announce Type: new Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced logit-level membership inference. But if out

Geometry-Aware Online Scheduling for LLM Serving: From Theoretical Bound to System Practice

arXiv:2606.22327v2 Announce Type: replace Abstract: The explosive demand for interactive Large Language Model serving has highlighted the management of the Key-Value cache's dynamic memory footprint a

Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound

arXiv:2606.25978v1 Announce Type: cross Abstract: Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis sp

Physics-conforming Latent Twins

arXiv:2606.15053v2 Announce Type: replace Abstract: Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physi

Agents

Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models

arXiv:2606.25519v1 Announce Type: cross Abstract: Quantization is widely used to reduce the inference cost of large language models, but its effect on reasoning models is not fully captured by final-a

agentsarxiv-cs-lg

SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs

model-releasesarxiv-cs-cl

arXiv:2602.06566v3 Announce Type: replace-cross Abstract: Despite recent successes, test-time scaling -- i.e., dynamically expanding the token budget during inference as needed -- remains brittle for

Speculative Decoding at Temperature Zero: A Scoped Safety-Invariance Screen with a 48,072-Sample Expansion

arXiv:2606.25097v1 Announce Type: new Abstract: Speculative decoding accelerates inference by letting a draft model propose tokens for a target model to verify, raising a concrete safety question: at

safetyarxiv-cs-lg

TacVerse: A Multi-Sensor Dataset and Benchmark for Cross-Sensor Vision-Based Tactile Perception

model-releasesarxiv-cs-ro

arXiv:2606.25877v1 Announce Type: new Abstract: Vision-based tactile sensors (VBTSs) enable robots to infer contact geometry and force-related cues by imaging deformation through an internal camera, y

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

arXiv:2606.25989v1 Announce Type: new Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Exis

safetyarxiv-cs-cv

Toward Low-Latency Vision-Language Models with Doubly-Correct Predictions in Egocentric Visual Understanding

model-releasesarxiv-cs-cv

arXiv:2606.25160v1 Announce Type: cross Abstract: The rapid rise of Vision-Language Models (VLMs) in egocentric visual understanding has made low-latency inference in human-robot collaborative (HRC) t

What Does the Brain See? Multiview Neural Representations to Demystify the Brain-Visual Alignment

model-releasesarxiv-cs-cv

arXiv:2606.25718v1 Announce Type: new Abstract: Zero-shot visual decoding from electroencephalography (EEG) aims to infer visual semantics from non-invasive neural recordings, but remains challenging

Adversarial dynamical systems characterize when data-driven learning succeeds or fails

arXiv:2407.06312v2 Announce Type: replace-cross Abstract: Many systems resist analytical modeling, making data-driven inference of dynamics important. Yet data-driven methods can fail to converge or g

CAVEWOMAN: How Large Language Models Behave Under Linguistic Input and Output Compression

arXiv:2606.24083v1 Announce Type: cross Abstract: 'Talk short. Drop grammar. Save token.' This caveman style is widely promoted as a way to cut inference cost, but whether it actually saves anything d

DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection

arXiv:2606.24805v1 Announce Type: new Abstract: Stereo-based 3D object detection still faces two critical safety challenges: real-time performance and open-set generalization. Existing stereo 3D metho

safetyarxiv-cs-cv

Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching

arXiv:2606.24457v1 Announce Type: new Abstract: Recent advances in stereo matching have achieved remarkable accuracy, but often rely on large models, heavy computation, or additional foundation-model

applicationsarxiv-cs-cv

Natural Identifiers for Privacy and Data Audits in Large Language Models

arXiv:2606.24408v1 Announce Type: new Abstract: Assessing the privacy of large language models (LLMs) presents significant challenges. In particular, most existing methods for auditing differential pr

applicationsarxiv-cs-lg

P-MTP: Efficient Document Parsing via Multi-Token Prediction with Progressive Depth Scaling

arXiv:2606.24447v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have revolutionized document parsing by enabling end-to-end mapping from images to structured text, imposing a significant

researcharxiv-cs-cv

Sim-to-Real Betting on the E-Process: Bringing 'simulators' to anytime-valid confidence sequences

arXiv:2606.24038v1 Announce Type: new Abstract: This note describes an integration of the sim-to-real performance estimate with betting (from Chen et al.) and the safe anytime-valid inference (from Ra

researcharxiv-cs-ro

Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matching

arXiv:2606.24824v1 Announce Type: new Abstract: Modeling chaotic systems is crucial yet challenging. Inverse problems in chaotic dynamics, namely inferring initial conditions from final states, remain

applicationsarxiv-cs-ai