Search: “inference”

CausalGuard: Conformal Inference under Graph Uncertainty

arXiv:2605.21928v1 Announce Type: new Abstract: Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph

23 May 2026

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Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidence

DGX agent

arXiv:2605.22300v1 Announce Type: cross Abstract: Scientific evidence often spans instruments, databases, and disciplines, so no single source records the full phenomenon. This makes it difficult to d

model-releasesarxiv-cs-lg

23 May 2026

PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data

arXiv:2511.06943v3 Announce Type: replace-cross Abstract: Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the c

20 May 2026

Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models

arXiv:2605.19663v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are becoming the cornerstone of high-level reasoning for robotic automation, enabling robots to parse natural language com

20 May 2026

Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators

arXiv:2601.20888v3 Announce Type: replace-cross Abstract: We study sampling from posterior distributions in Bayesian linear inverse problems where A, the parameters to observables operator, is computa

19 May 2026

Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting

arXiv:2605.17091v1 Announce Type: new Abstract: Scientific forecasting typically relies on direct state prediction, an approach that grows brittle under data scarcity, extended horizons, non-stationar

19 May 2026

RAP: Runtime Adaptive Pruning for LLM Inference

arXiv:2505.17138v5 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at language understanding and generation, but their enormous computational and memory requirements hinder d

19 May 2026

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection

arXiv:2510.05676v2 Announce Type: replace Abstract: Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud

18 May 2026

Agents

When Should an AI Workflow Release? Always-Valid Inference for Black-Box Generate-Verify Systems

arXiv:2605.12947v1 Announce Type: cross Abstract: LLM-enabled AI workflows increasingly produce outputs through iterative generate-evaluate-revise loops. Each iteration can improve the candidate, but

agentsarxiv-cs-ai

14 May 2026

Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference

arXiv:2510.05497v5 Announce Type: replace-cross Abstract: Large-scale Mixture of Experts (MoE) Large Language Models (LLMs) have recently become the frontier open-weight models, achieving remarkable m

13 May 2026

Agents

SkillGen: Verified Inference-Time Agent Skill Synthesis

arXiv:2605.10999v1 Announce Type: new Abstract: Skills are a promising way to improve LLM agent capabilities without retraining, while keeping the added procedure reusable and controllable. However, h

agentsarxiv-cs-lg

13 May 2026

Attention-Mamba: A Mamba-Enhanced Multi-Scale Parallel Inference Network for Medical Image Segmentation

arXiv:2402.02286v4 Announce Type: replace-cross Abstract: U-shaped architectures have long dominated the field of medical image segmentation, while Transformers are widely employed for modeling long-r

Local Ai

Merlin: Deterministic Byte-Exact Deduplication for Lossless Context Optimization in Large Language Model Inference

arXiv:2605.09990v1 Announce Type: new Abstract: Data-intensive applications, ranging from large-scale retrieval systems to advanced data pipelines, are increasingly bottlenecked by the processing of h

local-aiarxiv-cs-cl

PMCTS: Particle Monte Carlo Tree Search for Principled Parallelized Inference Time Scaling

arXiv:2605.08982v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement through search with increasing popularity for real world applications. D

safetyarxiv-cs-lg

Sketch-and-Verify: Structured Inference-Time Scaling via Program Sketching

arXiv:2605.08658v1 Announce Type: cross Abstract: SKETCHVERIFY is a within-tier cost-performance policy, not a universal accuracy improvement. The operational question: a practitioner stuck with a sma

SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference

arXiv:2605.08151v1 Announce Type: cross Abstract: LLM serving platforms are increasingly deployed as multi-model cloud systems, where user demand is often long-tailed: a few popular large models recei

applicationsarxiv-cs-ai

SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference

arXiv:2605.08835v1 Announce Type: new Abstract: The expansion of Artificial Intelligence-generated content service requires diffusion model serving to simultaneously achieve high throughput and low ta

researcharxiv-cs-ai

Inference Time Causal Probing in LLMs

arXiv:2605.07631v1 Announce Type: new Abstract: Causal probing methods aim to test and control how internal representations influence the behavior of generative models. In causal probing, an intervent

11 May 2026

Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference

arXiv:2605.07354v1 Announce Type: cross Abstract: The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditiona

researcharxiv-cs-cv

11 May 2026

BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference

arXiv:2605.04087v1 Announce Type: cross Abstract: Optimization over the Stiefel manifold St(p,d), the set of p imes d column-orthonormal matrices, is fundamental in statistics, machine learning, and s

7 May 2026

Do Multimodal RAG Systems Leak Data? A Comprehensive Evaluation of Membership Inference and Image Caption Retrieval Attacks

arXiv:2601.17644v3 Announce Type: replace-cross Abstract: The growing adoption of multimodal Retrieval-Augmented Generation (mRAG) pipelines for vision-centric tasks (e.g., visual QA) introduces impor

applicationsarxiv-cs-ai

7 May 2026

Hardware

Massively Parallel Exact Inference for Hawkes Processes

arXiv:2604.01342v2 Announce Type: replace Abstract: Multivariate Hawkes processes are a widely used class of self-exciting point processes, but maximum likelihood estimation naively scales as O(N^2) i

hardwarearxiv-cs-lg

7 May 2026

CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation

arXiv:2605.00930v1 Announce Type: cross Abstract: In this work, we introduce CellxPert, a scalable multimodal foundation model that unifies single-cell and spatial multi-omics within a common represen

researcharxiv-cs-ai

6 May 2026

Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality

arXiv:2605.01907v1 Announce Type: cross Abstract: We study clustered multitask learning in a semiparametric setting where tasks share a latent cluster structure in their target parameters but exhibit

Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference

arXiv:2605.02739v1 Announce Type: new Abstract: Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execu

researcharxiv-cs-ro

Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference

arXiv:2605.01579v1 Announce Type: cross Abstract: Empirical causal claims depend on many analyst decisions, from selecting covariates to choosing estimators. Existing robustness tools summarize how re

Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.01662v1 Announce Type: new Abstract: Large vision-language models (VLMs) have advanced multimodal tasks such as video question answering (QA). However, VLMs face the challenge of selecting

Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes

arXiv:2604.27394v1 Announce Type: cross Abstract: Conditional Average Treatment Effect (CATE) estimation in practice demands three properties simultaneously: heterogeneous effects au(x), calibrated un

1 May 2026

Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference

model-releasesarxiv-cs-cl

arXiv:2604.26294v1 Announce Type: new Abstract: We present tensor and sequence parallelism (TSP), a parallel execution strategy that folds tensor parallelism and sequence parallelism onto a single dev

30 Apr 2026

From Syntax to Emotion: A Mechanistic Analysis of Emotion Inference in LLMs

arXiv:2604.25866v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in emotionally sensitive human-AI applications, yet little is known about how emotion recognition is

researcharxiv-cs-cl

29 Apr 2026

Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach

arXiv:2604.24280v1 Announce Type: new Abstract: We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment acti

safetyarxiv-cs-lg

28 Apr 2026

HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference

arXiv:2604.22293v1 Announce Type: cross Abstract: Lookup-table (LUT) based neural networks can deliver ultra-low latency and excellent hardware efficiency on FPGAs by mapping arithmetic operations dir

applicationsarxiv-cs-lg

27 Apr 2026

PreMoE: Proactive Inference for Efficient Mixture-of-Experts

arXiv:2505.17639v3 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for depl

27 Apr 2026

MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference

arXiv:2604.21026v1 Announce Type: new Abstract: Deploying large language models to heterogeneous hardware is often constrained by memory, not compute. We introduce MCAP (Monte Carlo Activation Profili

24 Apr 2026

Propensity Inference: Environmental Contributors to LLM Behaviour

arXiv:2604.21098v1 Announce Type: new Abstract: Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned b

researcharxiv-cs-ai

24 Apr 2026

Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models

arXiv:2604.21193v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual ina

applicationsarxiv-cs-ai

24 Apr 2026

Efficient Multi-Cohort Inference for Long-Term Effects and Lifetime Value in A/B Testing with User Learning

arXiv:2604.20777v1 Announce Type: new Abstract: In streaming platforms churn is extremely costly, yet A/B tests are typically evaluated using outcomes observed within a limited experimental horizon. E

23 Apr 2026

Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics

arXiv:2601.17647v2 Announce Type: replace-cross Abstract: Quantifying the causal relationship between sea ice thickness and sea surface height (SSH) is essential for understanding the mechanisms drivi

22 Apr 2026

Product-of-Experts Training Reduces Dataset Artifacts in Natural Language Inference

arXiv:2604.19069v1 Announce Type: cross Abstract: Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlatio

22 Apr 2026

Two-dimensional early exit optimisation of LLM inference

arXiv:2604.18592v1 Announce Type: cross Abstract: We introduce a two-dimensional (2D) early exit strategy that coordinates layer-wise and sentence-wise exiting for classification tasks in large langua

22 Apr 2026

Chronax: A Jax Library for Univariate Statistical Forecasting and Conformal Inference

arXiv:2604.16719v1 Announce Type: new Abstract: Time-series forecasting is central to many scientific and industrial domains, such as energy systems, climate modeling, finance, and retail. While forec

applicationsarxiv-cs-lg

21 Apr 2026

Combining Bayesian and Frequentist Inference for Laboratory-Specific Performance Guarantees in Copy Number Variation Detection

arXiv:2604.14305v1 Announce Type: cross Abstract: Targeted amplicon panels are widely used in oncology diagnostics, but providing per-gene performance guarantees for copy number variant (CNV) detectio

applicationsarxiv-cs-lg

17 Apr 2026

Hardware

LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers

arXiv:2509.23638v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models face memory and PCIe latency bottlenecks when deployed on commodity hardware. Offloading expert weights to CPU memor

hardwarearxiv-cs-lg

17 Apr 2026

Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference

arXiv:2604.13634v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by letting draft tokens bypass full verification, but conventional frameworks suffer from fre

researcharxiv-cs-cl

16 Apr 2026

Counterfactual Peptide Editing for Causal TCR--pMHC Binding Inference

arXiv:2604.13256v1 Announce Type: new Abstract: Neural models for TCR-pMHC binding prediction are susceptible to shortcut learning: they exploit spurious correlations in training data -- such as pepti

16 Apr 2026

Deliberative Alignment is Deep, but Uncertainty Remains: Inference time safety improvement in reasoning via attribution of unsafe behavior to base model

arXiv:2604.09665v1 Announce Type: cross Abstract: While the wide adoption of refusal training in large language models (LLMs) has showcased improvements in model safety, recent works have highlighted

14 Apr 2026

FastSHADE: Fast Self-augmented Hierarchical Asymmetric Denoising for Efficient inference on mobile devices

model-releasesarxiv-cs-cv

arXiv:2604.10275v1 Announce Type: new Abstract: Real-time image denoising is essential for modern mobile photography but remains challenging due to the strict latency and power constraints of edge dev

14 Apr 2026

Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference

arXiv:2604.08971v1 Announce Type: new Abstract: Edge devices increasingly run multimodal sensing pipelines that must remain accurate despite fluctuating power budgets and unpredictable sensor dropout.