Search: “inference”

arXiv:2604.27942v1 Announce Type: new Abstract: Collective intelligence emerges across biological, physical, and artificial systems without central coordination, yet a unifying principle governing suc

local-aiarxiv-cs-ai

1 May 2026

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Context as Prior: Bayesian-Inspired Intent Inference for Non-Speaking Agents with a Household Cat Testbed

DGX agent

arXiv:2604.27445v1 Announce Type: new Abstract: Many agents in real-world environments cannot reliably communicate their goals through language, including household pets, pre-verbal infants, and other

applicationsarxiv-cs-cv

1 May 2026

EdgeFM: Efficient Edge Inference for Vision-Language Models

arXiv:2604.27476v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained

hardwarearxiv-cs-cv

1 May 2026

Predictive Multi-Tier Memory Management for KV Cache in Large-Scale GPU Inference

arXiv:2604.26968v1 Announce Type: cross Abstract: Key-value (KV) cache memory management is the primary bottleneck limiting throughput and cost-efficiency in large-scale GPU inference serving. Current

hardwarearxiv-cs-ai

1 May 2026

Compute Aligned Training: Optimizing for Test Time Inference

arXiv:2604.24957v1 Announce Type: new Abstract: Scaling test-time compute has emerged as a powerful mechanism for enhancing Large Language Model (LLM) performance. However, standard post-training para

safetyarxiv-cs-lg

Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing

arXiv:2602.11786v2 Announce Type: replace Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety through breadth-oriented evaluation acr

Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference

arXiv:2604.24911v1 Announce Type: new Abstract: Machine Learning is becoming more prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predicti

PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference

model-releasesarxiv-cs-cl

arXiv:2604.24971v1 Announce Type: cross Abstract: We present PolyKV, a system in which multiple concurrent inference agents share a single, asymmetrically compressed KV cache pool. Rather than allocat

GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval

arXiv:2604.23779v1 Announce Type: cross Abstract: The semantic gap between colloquial user queries and professional legal documents presents a fundamental challenge in Legal Case Retrieval (LCR). Exis

applicationsarxiv-cs-ai

Inference of Online Newton Methods with Nesterov's Accelerated Sketching

arXiv:2604.23436v1 Announce Type: cross Abstract: Reliable decision-making with streaming data requires principled uncertainty quantification of online methods. While first-order methods enable effici

Tutorials

Inverting Foundation Models of Brain Function with Simulation-Based Inference

arXiv:2604.23865v1 Announce Type: cross Abstract: Foundation models of brain activity promise a new frontier for in silico neuroscience by emulating neural responses to complex stimuli across tasks an

tutorialsarxiv-cs-ai

On-Device Vision Training, Deployment, and Inference on a Thumb-Sized Microcontroller

model-releasesarxiv-cs-cv

arXiv:2604.23012v1 Announce Type: cross Abstract: This paper presents a complete, end-to-end on-device vision machine learning pipeline, comprising data acquisition, two-layer CNN training with Adam o

POPI: Personalizing LLMs via Optimized Natural Language Preference Inference

arXiv:2510.17881v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. W

SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems

model-releasesarxiv-cs-ai

arXiv:2604.23905v1 Announce Type: cross Abstract: Threat modeling for cyber-physical systems (CPS) remains a largely manual exercise. This project presents SMSI (System Model Security Inference), a hy

Toward Robust and Efficient ML-Based GPU Caching for Modern Inference

arXiv:2509.20979v2 Announce Type: replace Abstract: In modern GPU inference, cache efficiency remains a major bottleneck, and heuristic policies such as extsc{LRU} can perform far worse than the offli

hardwarearxiv-cs-lg

27 Apr 2026

Membership Inference for Contrastive Pre-training Models with Text-only PII Queries

arXiv:2603.14222v2 Announce Type: replace-cross Abstract: Contrastive pretraining models such as CLIP and CLAP, serve as the ubiquitous perceptual backbones for modern multimodal large models, yet the

safetyarxiv-cs-ai

23 Apr 2026

TTKV: Temporal-Tiered KV Cache for Long-Context LLM Inference

arXiv:2604.19769v1 Announce Type: cross Abstract: Key-value (KV) caching is critical for efficient inference in large language models (LLMs), yet its memory footprint scales linearly with context leng

23 Apr 2026

Adaptive Slicing-Assisted Hyper Inference for Enhanced Small Object Detection in High-Resolution Imagery

arXiv:2604.19233v1 Announce Type: new Abstract: Deep learning-based object detectors have achieved remarkable success across numerous computer vision applications, yet they continue to struggle with s

researcharxiv-cs-cv

22 Apr 2026

Assessing VLM-Driven Semantic-Affordance Inference for Non-Humanoid Robot Morphologies

arXiv:2604.19509v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding human-object interactions, but their application to robotic sys

safetyarxiv-cs-ro

22 Apr 2026

Bayesian Event-Based Model for Disease Subtype and Stage Inference

arXiv:2512.03467v2 Announce Type: replace Abstract: Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a

applicationsarxiv-cs-lg

22 Apr 2026

Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference

arXiv:2507.14808v3 Announce Type: replace-cross Abstract: Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing

applicationsarxiv-cs-lg

Emergent Structured Representations Support Flexible In-Context Inference in Large Language Models

arXiv:2602.07794v3 Announce Type: replace Abstract: Large language models (LLMs) exhibit emergent behaviors suggestive of human-like reasoning. While recent work has identified structured conceptual r

researcharxiv-cs-cl

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

arXiv:2604.16648v1 Announce Type: new Abstract: In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via i

From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models

arXiv:2603.04592v3 Announce Type: replace Abstract: Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in d

applicationsarxiv-cs-cl

HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference

arXiv:2601.13684v2 Announce Type: replace Abstract: The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long-context tasks. Existing static compression methods

researcharxiv-cs-cl

LayerCache: Exploiting Layer-wise Velocity Heterogeneity for Efficient Flow Matching Inference

model-releasesarxiv-cs-cv

arXiv:2604.16492v1 Announce Type: new Abstract: Flow Matching models achieve state-of-the-art image generation quality but incur substantial inference cost due to iterative denoising through large Tra

RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design

arXiv:2604.17175v1 Announce Type: new Abstract: We introduce RosettaSearch, an inference-time multi-objective optimization approach for protein sequence optimization. We use large language models (LLM

Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation

arXiv:2411.05472v2 Announce Type: replace Abstract: The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-en

Theory of Mind in Action: The Instruction Inference Task in Dynamic Human-Agent Collaboration

model-releasesarxiv-cs-cl

arXiv:2507.02935v2 Announce Type: replace Abstract: Successful human-agent teaming relies on an agent being able to understand instructions given by a (human) principal. In many cases, an instruction

Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion

arXiv:2511.14178v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies st

safetyarxiv-cs-ro

Towards Faster Language Model Inference Using Mixture-of-Experts Flow Matching

arXiv:2604.15009v1 Announce Type: cross Abstract: Flow matching retains the generation quality of diffusion models while enabling substantially faster inference, making it a compelling paradigm for ge

Analog Optical Inference on Million-Record Mortgage Data

arXiv:2604.13251v1 Announce Type: new Abstract: Analog optical computers promise large efficiency gains for machine learning inference, yet no demonstration has moved beyond small-scale image benchmar

16 Apr 2026

Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting

arXiv:2604.13253v1 Announce Type: new Abstract: Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with asymptotic coverage guarantees for time series under distributio

safetyarxiv-cs-lg

16 Apr 2026

Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes

arXiv:2506.09163v2 Announce Type: replace Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic process

hardwarearxiv-cs-lg

16 Apr 2026

Active Imitation Learning for Thermal- and Kernel-Aware LFM Inference on 3D S-NUCA Many-Cores

arXiv:2604.11948v1 Announce Type: new Abstract: Large Foundation Model (LFM) inference is both memory- and compute-intensive, traditionally relying on GPUs. However, the limited availability and high

safetyarxiv-cs-lg

Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference

model-releasesarxiv-cs-ai

arXiv:2604.12168v1 Announce Type: cross Abstract: The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance, transport

Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling

arXiv:2604.12806v1 Announce Type: new Abstract: Inferring latent interaction structures from observed dynamics is a fundamental inverse problem in many-body interacting systems. Most neural approaches

applicationsarxiv-cs-lg

ProbeLogits: Kernel-Level LLM Inference Primitives for AI-Native Operating Systems

arXiv:2604.11943v1 Announce Type: cross Abstract: An OS kernel that runs LLM inference internally can read logit distributions before any text is generated -- and act on them as a governance primitive

Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices

model-releasesarxiv-cs-ai

arXiv:2512.06443v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed on edge devices. To meet strict resource constraints, real-world deployment has pushed

Discrete Bayesian Sample Inference for Graph Generation

arXiv:2511.03015v2 Announce Type: replace Abstract: Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their dis

Iterative Inference-time Scaling with Adaptive Frequency Steering for Image Super-Resolution

arXiv:2512.23532v2 Announce Type: replace Abstract: Diffusion models have become a leading paradigm for image super-resolution (SR), but existing methods struggle to guarantee both the high-frequency

researcharxiv-cs-cv

Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

arXiv:2602.19509v3 Announce Type: replace-cross Abstract: We observe that LLM cascading and routing implicitly solves an anytime computation problem -- a class of algorithms, well-studied in classical

Reasoning as Data: Representation-Computation Unity and Its Implementation in a Domain-Algebraic Inference Engine

arXiv:2604.10908v1 Announce Type: new Abstract: Every existing knowledge system separates storage from computation. We show this separation is unnecessary and eliminate it. In a standard triple is_a(A

Local Ai

Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learning

arXiv:2509.16606v5 Announce Type: replace-cross Abstract: In networked multi-agent reinforcement learning (Networked-MARL), decentralized agents must act under local observability and constrained comm

local-aiarxiv-cs-lg

13 Apr 2026

CSAttention: Centroid-Scoring Attention for Accelerating LLM Inference

arXiv:2604.08584v1 Announce Type: cross Abstract: Long-context LLMs increasingly rely on extended, reusable prefill prompts for agents and domain Q&A, pushing attention and KV-cache to become the domi

hardwarearxiv-cs-ai

13 Apr 2026

Local Ai

QCFuse: Query-Centric Cache Fusion for Efficient RAG Inference

arXiv:2604.08585v1 Announce Type: cross Abstract: Cache fusion accelerates generation process of LLMs equipped with RAG through KV caching and selective token recomputation, thereby reducing computati

local-aiarxiv-cs-ai

13 Apr 2026

Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference

model-releasesarxiv-cs-cl

arXiv:2604.07394v1 Announce Type: cross Abstract: The quadratic computational complexity of standard attention mechanisms presents a severe scalability bottleneck for LLMs in long-context scenarios. W

10 Apr 2026

See the Forest for the Trees: Loosely Speculative Decoding via Visual-Semantic Guidance for Efficient Inference of Video LLMs