Search: “inference”

TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge

arXiv:2608.22716v1 Announce Type: cross Abstract: Despite their remarkable success, machine learning models, particularly in vision applications, are alarmingly vulnerable to a range of security threa

safetyarxiv-cs-ai

25 Aug 2026

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Agents

Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understanding

DGX agent

arXiv:2608.22429v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relianc

agentsarxiv-cs-ai

25 Aug 2026

Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation

arXiv:2608.21243v1 Announce Type: cross Abstract: Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combin

local-aiarxiv-cs-ai

Agents

AgentDecarbonizer: Carbon-Aware Execution for AI Agents

arXiv:2608.20566v1 Announce Type: new Abstract: AI agents extend large language models from single prompt-response interactions to long-running, goaldirected workflows that issue many model calls, inv

agentsarxiv-cs-lg

Hardware

AI with Authority, from Application to Silicon

arXiv:2608.21356v1 Announce Type: cross Abstract: For sixty years, machine verification has been a major cost overhead, affordable only for exceptional artifacts. Here we report that generative AI inv

hardwarearxiv-cs-ai

Auditable by Construction: An Ontology-Driven Framework for Trustworthy LLM Analytics in Enterprise Finance

arXiv:2608.20661v1 Announce Type: new Abstract: Enterprise adoption of large language models in finance is constrained less by fluency than by trust: in Financial Planning and Analysis (FP&A) and othe

applicationsarxiv-cs-ai

COEC: Calibrated Orthogonal-Equivalence Compensation for Structured Pruning of Large Language Models

model-releasesarxiv-cs-lg

arXiv:2608.21142v1 Announce Type: new Abstract: Structured pruning reduces the size and inference cost of large language models (LLMs) by removing weight columns, but the resulting output error can de

Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation

arXiv:2608.20804v1 Announce Type: cross Abstract: Temporal Knowledge Graph (TKG) extrapolation seeks to infer future facts from time-varying relational histories. Recent diffusion-based approaches imp

researcharxiv-cs-ai

DreamBench-SWE: A Multi-Session Memory-Hygiene Benchmark for Software Agents

arXiv:2608.20664v1 Announce Type: new Abstract: DreamBench-SWE is a multi-session benchmark for software-agent memory hygiene in which later software tasks depend on non-inferable evidence from earlie

Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models

arXiv:2608.20988v1 Announce Type: cross Abstract: Quantization of Large Language Models (LLMs) is often hindered by the sensitivity of the self-attention mechanism to discretization errors. We identif

local-aiarxiv-cs-ai

Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models

arXiv:2608.21247v1 Announce Type: new Abstract: Token compression has become a key technique for reducing the inference cost of large foundation models, with approaches such as token pruning and KV-ca

LiLiCorr: Lightweight Likelihood Correlation of Parallel Drafts for Speculative Decoding

model-releasesarxiv-cs-cl

arXiv:2608.20530v1 Announce Type: new Abstract: Speculative decoding accelerates language-model inference by drafting future tokens that the target model verifies in parallel. A diffusion-style block

Perseus: Interactive Time Series Segmentation with Sparse Supervision via Stateful Memory

arXiv:2510.09930v2 Announce Type: replace-cross Abstract: Real-world systems, ranging from industrial manufacturing to wearable healthcare, generate multivariate time series with hierarchical states r

applicationsarxiv-cs-ai

Rethinking Expressivity and Efficiency in Test-Time Training

arXiv:2608.21308v1 Announce Type: new Abstract: Test-Time Training (TTT) enables long-context processing via continuous weight updates during inference, but current methods struggle to balance the exp

StruProKGR: A Structural and Probabilistic Framework for Sparse Knowledge Graph Reasoning

arXiv:2512.12613v2 Announce Type: replace Abstract: Sparse Knowledge Graphs (KGs) are commonly encountered in real-world applications, where knowledge is often incomplete or limited. Sparse KG reasoni

applicationsarxiv-cs-cl

When to Ponder: Adaptive Compute Allocation for Code Generation via Test-Time Training

arXiv:2601.00894v2 Announce Type: replace-cross Abstract: Large language models apply uniform computation to all inputs, regardless of difficulty. We propose PonderTTT, a gating strategy using the TTT

researcharxiv-cs-cl

Block3D: Efficient Text-to-3D Generation via Block-Wise Diffusion

arXiv:2608.19567v1 Announce Type: new Abstract: While text-to-3D generation has advanced rapidly, achieving high geometric fidelity at low inference cost remains challenging. Existing text-to-3D metho

researcharxiv-cs-cv

CalcSeg: Confidence-aware 3D Latent Context Curriculum Learning For Myocardial Scar Segmentation From Single-Stack LGE-CMRs

arXiv:2608.20305v1 Announce Type: new Abstract: Myocardial scar segmentation from single-stack late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging has been a longstanding and clinica

Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization

arXiv:2608.20281v1 Announce Type: cross Abstract: Large language models often fail to answer questions about a bounded document collection when the source documents are not retrieved at inference time

Multi-Method Causal Evidence Synthesis: Ranking Candidate Drivers by Convergent Cross-Method Evidence from Observational Data

arXiv:2608.20187v1 Announce Type: cross Abstract: Practitioners inferring causality from observational data usually rely on a single method and treat its output as causal truth. Recent tools select an

Multi-Source Wasserstein Distributionally Robust Graph Learning

arXiv:2608.19914v1 Announce Type: new Abstract: Network topology inference from graph signals is central to graph signal processing with applications in neuroscience, sensor, and social networks. In p

safetyarxiv-cs-lg

Robust Incomplete Multimodal Sentiment Analysis via Iterative Proxy Correction

arXiv:2608.19971v1 Announce Type: new Abstract: Multimodal sentiment analysis aims to infer affective states by integrating language, visual, and acoustic cues. However, real-world multimodal inputs a

applicationsarxiv-cs-cl

Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment

model-releasesarxiv-cs-cl

arXiv:2608.19825v1 Announce Type: cross Abstract: Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI s

Truncate Bad, Upweight Good: BoN-Style Distillation via Rank-Based Classification

arXiv:2608.19748v1 Announce Type: cross Abstract: Inference-time selection methods, such as Best-of-N, improve generation by sampling a pool of candidates and selecting the top-ranked completion accor

safetyarxiv-cs-ai

A Real-Time Tsetlin Machine-based Non-intrusive Load Monitoring System on MCUs

arXiv:2608.18780v1 Announce Type: new Abstract: Non-Intrusive Load Monitoring (NILM) systems estimate individual appliance energy consumption from a single aggregate meter, without requiring separate

local-aiarxiv-cs-lg

DyG^2T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer

arXiv:2608.18498v1 Announce Type: new Abstract: Modeling object dynamics from limited visual observations is a fundamental problem for enabling accurate motion trajectory prediction in embodied intera

applicationsarxiv-cs-cv

Hardware

Entropy-Constrained Adaptive Stochastic Quantization

arXiv:2608.18147v1 Announce Type: cross Abstract: Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input wh

hardwarearxiv-cs-ai

Subgroup performance analysis of adaptation strategies for chest X-ray foundation models

arXiv:2608.19078v1 Announce Type: new Abstract: Foundation models are increasingly adapted for downstream medical imaging tasks, yet the influence of the chosen adaptation strategy on subgroup fairnes

Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks

arXiv:2608.17352v1 Announce Type: new Abstract: Distributed Denial-of-Service (DDoS) attacks threaten network availability, requiring a cognitive detection process that senses traffic, infers intent,

Composing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields

arXiv:2608.18004v1 Announce Type: new Abstract: Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-ba

Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements

model-releasesarxiv-cs-lg

arXiv:2509.00203v3 Announce Type: replace Abstract: Parameterized partial differential equations (PDEs) underpin the mathematical modeling of complex systems in diverse domains, including engineering,

KAN We Flow? Advancing Robotic Manipulation with 3D Flow Matching via KAN & RWKV

arXiv:2602.01115v3 Announce Type: replace-cross Abstract: Diffusion-based visuomotor policies excel at modeling action distributions but are inference-inefficient, since recursively denoising from noi

safetyarxiv-cs-cv

Agents

REChart: Reasoning-Efficient Chart Editing with Large Reasoning Models

arXiv:2608.17414v1 Announce Type: new Abstract: Chart editing requires inferring and modifying visualization code from a reference chart image based on an editing instruction, challenging fine-grained

agentsarxiv-cs-cv

Revisiting WEASEL 2.0: Reproduction, Sensitivity, and an Adaptive Ensemble-Size Rule

arXiv:2608.18021v1 Announce Type: new Abstract: WEASEL 2.0 is a dictionary-based time series classifier that combines dilated sliding windows with a randomised hyperparameter ensemble and a fixed-size

Tutorials

Understanding the Surprising Generalization Properties of Tabular Foundation Models

arXiv:2608.17957v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) increasingly rely on in-context learning, where a model receives labelled examples at inference time and predicts label

tutorialsarxiv-cs-lg

Anatomy of a Quantized Agent: VRAM Stability and Forecasting in Code-Synthesis Agentic Workloads

arXiv:2608.15117v1 Announce Type: new Abstract: Analytical models of peak VRAM consumption for LLM inference decompose memory into weight-storage, KV-cache, and activation terms parameterized by step

Dynamic Multi-Byte Prediction With Hierarchical Language Models

arXiv:2608.15454v1 Announce Type: new Abstract: Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization.

researcharxiv-cs-ai

Generalised Transportability via Causal Abstractions

arXiv:2608.15645v1 Announce Type: new Abstract: Transporting a causal conclusion from a source study population to a target one is a fundamental problem in causal inference. The theory of transportabi

GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling

arXiv:2501.18898v4 Announce Type: replace Abstract: Generating full-body human gestures based on speech signals remains challenges on quality and speed. Existing approaches model different body region

applicationsarxiv-cs-cv

Guaranteed Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group

arXiv:2608.15520v1 Announce Type: new Abstract: A multimodal system may begin inference holding only some of its inputs and may acquire the rest at a cost. With adaptive acquisition, the policy determ

safetyarxiv-cs-lg

HTTM: Head-wise Temporal Token Merging for Faster VGGT

arXiv:2511.21317v2 Announce Type: replace Abstract: The Visual Geometry Grounded Transformer (VGGT) marks a significant leap forward in 3D scene reconstruction, as it is the first model that directly

local-aiarxiv-cs-cv

Inferential Evaluation of Surrogate-Derived Models under Covariate Shift

arXiv:2608.15783v1 Announce Type: cross Abstract: In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are

safetyarxiv-cs-lg

Information Geometry of Message Passing

arXiv:2608.15922v1 Announce Type: cross Abstract: We show that the natural-gradient stationary condition of variational inference has an edge-local form on a Forney-style factor graph. We start from t

Low Cost Two-Stage Fabric Defect Detection at the Edge

arXiv:2608.14727v1 Announce Type: new Abstract: Fabric inspection in the garment industries of low-income economies remains largely manual, and commercial vision systems are priced beyond most small a

NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption

arXiv:2608.16038v1 Announce Type: cross Abstract: Post-hoc Graph Neural Network (GNN) explainers commonly follow a Perturb-Query paradigm, inferring the importance of graph elements based on queried p

researcharxiv-cs-ai

Paired Exact-Reset Evaluation of a Prediction-Derived Medium-to-Full World-Model Cascade

arXiv:2608.14650v1 Announce Type: new Abstract: Existing adaptive-inference and world-action-model systems use cheap-stage outputs or predicted futures to allocate additional computation. We study a n

RAPAC-DP: Response-Aligned Pending-Action Compensation for Diffusion Policies under Delayed Execution

model-releasesarxiv-cs-ro

arXiv:2608.15924v1 Announce Type: new Abstract: Cloud-side inference gives imitation-learning policies access to greater computational resources, but communication and computation delays can degrade c

Revisiting Classifier-Free Guidance Methods in Latent Diffusion Models

arXiv:2608.16786v1 Announce Type: new Abstract: Inference-time quality-enhancement methods are an effective and widely adopted means of improving diffusion models without expensive retraining. We stud

safetyarxiv-cs-cv