Search: “inference”

Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?

arXiv:2610.08775v1 Announce Type: new Abstract: Large language models (LLMs) can solve many narrow tasks, but querying them separately for millions of related instances can be prohibitively expensive.

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Agents

Agentic Semantic Sensing for Resource-Adaptive AI-RAN

DGX agent

arXiv:2610.07829v1 Announce Type: new Abstract: Semantic sensing (SemS) acquires task-relevant information rather than reconstructing complete physical information. Existing SemS formulations typicall

agentsarxiv-cs-ai

7 Oct 2026

Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failure Modes in K-12 Math Tutoring Dialogue

arXiv:2610.08703v1 Announce Type: new Abstract: In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning a

researcharxiv-cs-cl

Local Ai

Algorithmically Aligned Neural Agglomerative Tree Construction

arXiv:2610.07271v1 Announce Type: new Abstract: Linkage algorithms for hierarchical clustering (HC) are a powerful and efficient framework for constructing clustering trees, yet it is often unclear wh

local-aiarxiv-cs-lg

An Empirical Fault Vulnerability Exploration of ReRAM-based Process-in-Memory CNN Accelerators

arXiv:2610.07029v1 Announce Type: cross Abstract: Resistive random-access memory (ReRAM)-based Processing-in-Memory (PIM) accelerator is a promising platform for processing massively memory intensive

Attenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuning

arXiv:2610.08118v1 Announce Type: new Abstract: Covariate-aware time-series foundation models (TSFMs) promise training-free what-if answers for instrumented plants: the change in output that a differe

AttSVD:Prompt-Adaptive Low-Rank KV Cache Compression via Attention-Guided SVD

arXiv:2610.06927v1 Announce Type: cross Abstract: The key-value (KV) cache of autoregressive transformers grows linearly with context length and dominates memory at long context. Most training-free re

BoT-Feedback: Grounding Multimodal Reasoning in Biomechanical Evidence for Explainable Human Action Feedback

model-releasesarxiv-cs-cv

arXiv:2610.06972v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual understanding and multimodal reasoning, yet they remain fun

Calibrated Answers About Randomized Trials From a 4-Billion-Parameter Open Model: A Registered Test and a License-Clean Release

model-releasesarxiv-cs-cl

arXiv:2610.07019v1 Announce Type: new Abstract: Fiorillo v0.5 is an open model that answers typed questions with a probability for each answer. Its main specialist reads a randomized trial's article,

Can LLM-assisted regularization increase forecast accuracy for migration flows in low data regimes?

arXiv:2610.07208v1 Announce Type: new Abstract: Predicting migration flows remains a significant challenge for traditional gravity-based forecasting models, which primarily rely on structured socio-ec

Can phenotypic activity be predicted without experimental readouts?

arXiv:2610.07997v1 Announce Type: new Abstract: Molecular encoders contrastively pretrained on paired molecule-morphology data, such as CLOOME and CellCLIP, have been proposed as cheap surrogates for

CHARTER: Auditing Reference Substitution in Hierarchical Compact-Evidence Evaluation for Computational Pathology

arXiv:2610.07843v1 Announce Type: new Abstract: In digital pathology, compact evidence is often used to explain or audit predictions made by whole-slide image multiple instance learning models. In hie

researcharxiv-cs-cv

CNet: A Complex-Valued Deep Learning Framework with Wirtinger Autodifferentiation and FFT--Hadamard Convolution

arXiv:2610.08592v1 Announce Type: new Abstract: CNet is a C++/CUDA framework for building and training deep complex-valued neural networks (CVNNs) and, more generally, for optimizing complex-valued fu

Co-Evolving Paths and Flows via Path-Flow Alignment

arXiv:2610.08717v1 Announce Type: new Abstract: We study path-flow alignment as a unified training objective for flow matching. Instead of fixing the interpolation path and learning only the velocity

safetyarxiv-cs-cv

Common-Mode Errors Limit Low-Timestep Deep Spiking Q-Networks

arXiv:2610.07808v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer sparse and event-driven computation, making them attractive for energy-constrained reinforcement learning (RL) on

COMPASS: Finding Where Reasoning Lives in Language Models

arXiv:2610.07469v1 Announce Type: new Abstract: Explicitly eliciting reasoning substantially improves LLM performance. Existing approaches require a predefined characterization of reasoning, whether t

Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning

arXiv:2610.07628v1 Announce Type: new Abstract: Out-of-distribution (OOD) generalization remains challenging for graph neural networks (GNNs), as graph distributions can vary substantially across time

Confidence Reasoning Graphs: Structured Confidence Estimation for LLM Agents

arXiv:2610.07948v1 Announce Type: new Abstract: When using an LLM agent in a consequential domain, making an informed decision about whether to trust its output or intervene requires calibrated confid

agentsarxiv-cs-ai

DAEDALUS: Bootstrapping Agent Memory from Self-Generated Tasks

arXiv:2610.08048v1 Announce Type: new Abstract: LLM agents often lack the operational knowledge to act reliably in new environments, as they must discover specific tool behaviors or environment conven

Data-Driven Personas for Survey Simulation: Insights into Simulation Alignment Across Data-Access Regimes

arXiv:2610.05828v2 Announce Type: replace Abstract: Large language models (LLMs) offer new opportunities for public opinion research by enabling early prediction of survey responses, potentially reduc

safetyarxiv-cs-ai

Data Fusion for Errors-in-Variables

arXiv:2610.07048v1 Announce Type: cross Abstract: We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an externa

safetyarxiv-cs-lg

Decoupling What from Where: How Should a Small GUI Grounding Model Receive the Action Type?

arXiv:2610.07444v1 Announce Type: cross Abstract: A GUI agent decides which action to take and where to take it; we ask how a small grounding model should receive the action type. Fine-tuning Qwen2-VL

agentsarxiv-cs-cv

Deep Defence on Wheels: A Dual Intrusion Detection System Architecture for Comprehensive In-Vehicle Network Security

arXiv:2610.07489v1 Announce Type: cross Abstract: Increasing connectivity to the outside world and the lack of inbuilt security mechanisms have made legacy intra-vehicular networks vulnerable to cyber

Applications

Do LLMs Act on What They Know? From Partner Representations to Cooperative Actions

arXiv:2610.08129v1 Announce Type: new Abstract: Cooperation with unfamiliar partners requires adapting to communication conventions that are not known in advance. We study this problem in a controlled

applicationsarxiv-cs-lg

Does the Model Use the Feature? Separating Steering from Mechanism in LLMs

arXiv:2610.07270v1 Announce Type: new Abstract: Internal features in LLMs are often interpreted as mechanisms when they track a concept and their manipulation changes a related behavior. Yet steering

Dynamic Budget Allocation for LLM Evaluation under Hard Resource Constraints

arXiv:2610.07362v1 Announce Type: new Abstract: We evaluate large language models (LLMs) in multi-turn interactions through their time-to-event: the number of interaction steps required to produce an

Tutorials

ElasticFit: Fit-Aware 3D Object Insertion via VLM Reasoning and Generative Adaptation

arXiv:2610.07460v1 Announce Type: cross Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location: the inserted object must also fit local geometry while pr

tutorialsarxiv-cs-ai

EnGRICH: Enhancing Generative Reward Modeling with Critiques from Humans

arXiv:2610.05370v2 Announce Type: replace Abstract: Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside

Enhancing Diffusion Language Models with Autoregressive Post-Training Weights

arXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders

ESP: Energy-Score Policy for One-Step Multimodal Action Generation

arXiv:2610.07696v1 Announce Type: new Abstract: Generative action models based on diffusion and flow matching have been increasingly adopted in vision-language-action (VLA) policies for their ability

safetyarxiv-cs-ro

Evaluating human-AI workflows for field research in viticulture

arXiv:2610.07669v1 Announce Type: cross Abstract: We assessed the value of two live human-AI interactions in a precision disease control project in California vineyards. The project tested whether 202

agentsarxiv-cs-ai

Event Cameras for Melt-Pool Monitoring in Additive Manufacturing: A Benchmark and a Cross-Machine Transfer Analysis

model-releasesarxiv-cs-cv

arXiv:2610.06973v1 Announce Type: new Abstract: Melt-pool monitoring is central to qualifying metal additive manufacturing (AM), yet no public event-camera benchmark exists for this domain. Event came

Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design

arXiv:2610.08367v1 Announce Type: new Abstract: Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity al

Tutorials

Explore ideas through interactive visuals that respond as you learn, try, and discover.

On October 7, 2026 OpenAI released a session titled “Explore ideas through interactive visuals that respond as you learn, try, and discover.” The webcast showcased real‑time visual tools that adapt to

tutorialsopenai--x

Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields

arXiv:2311.18029v2 Announce Type: replace-cross Abstract: The current trend in the literature on Time Series Classification is to develop increasingly accurate algorithms by combining multiple models

Local Ai

From Shared Demand Patterns to Local Uncertainty: Probabilistic Load Forecasting by Mixing Compact Adaptations

arXiv:2610.08538v1 Announce Type: cross Abstract: Probabilistic load forecasting has been widely studied for power-system operation and planning, but customer- and transformer-level forecasting introd

local-aiarxiv-cs-ai

GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion

arXiv:2602.08784v2 Announce Type: replace Abstract: Robust and accurate perception of dynamic objects and map elements is crucial for autonomous vehicles performing safe navigation in complex traffic

agentsarxiv-cs-ro

GeneICL: A Tabular Foundation Model for Bulk Transcriptomics

arXiv:2610.08694v1 Announce Type: new Abstract: Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature correl

Generalizable Dense Reward for Long-Horizon Robotic Tasks

model-releasesarxiv-cs-cv

arXiv:2604.00055v2 Announce Type: replace-cross Abstract: Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabiliti

GeoPID: Decomposing and Steering Visual Information in Vision-Language Models

arXiv:2610.08401v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have shown outstanding performance across diverse applications, they tend to under-use visual information a

GeoWM: Efficient Direct World Modeling in Explicit Geometry

arXiv:2610.07381v1 Announce Type: new Abstract: Modeling 3D scene geometry and its evolution over time is essential for autonomous driving and robotics. A common paradigm is to use world models to pre

agentsarxiv-cs-cv

Global Transport Couplings for Classifier-Free Guided Flows

arXiv:2610.07555v1 Announce Type: new Abstract: Optimal-transport couplings have been shown to reduce training variance in unconditional flow models, but their role in conditional generation remains u

Honesty over Accuracy: Trustworthy Language Models through Reinforced Hesitation

arXiv:2511.11500v3 Announce Type: replace Abstract: Modern language models fail a fundamental requirement of trustworthy intelligence: knowing when not to answer. Despite achieving impressive accuracy

How Qlik built grounded, enterprise-scale AI with Amazon Bedrock

Qlik built Qlik Answers on Amazon Bedrock to give its 40,000+ customers grounded, sourced answers across structured and unstructured enterprise data. Learn how a layered, multi-agent architecture with

agentsaws-ml-blog

Hub for Outliers, Spokes for Inliers: Uniform Latent Space Construction for Dual-Mismatched Semi-Supervised Learning

arXiv:2610.07610v1 Announce Type: new Abstract: Semi-supervised learning typically assumes that labeled and unlabeled data share an identical class distribution and label space. However, this setting

Local Ai

Hybrid Cross-Modal Attention Network for Early Breast Cancer Detection in Low-Resource Clinical Settings

arXiv:2610.07243v1 Announce Type: new Abstract: Breast cancer is the leading cause of cancer-related mortality among women in Sub-Saharan Africa, where delayed diagnosis results from limited radiology

local-aiarxiv-cs-cv

IdeaAnchor: Teaching LLMs to Turn Literature into Research Ideas

arXiv:2610.08781v1 Announce Type: cross Abstract: Scientific research often begins by synthesizing ideas from a set of related papers to identify gaps and formulate new directions. However, training l

Identifiability Analysis of Linear ODE Systems with Hidden Confounders

arXiv:2410.21917v3 Announce Type: replace-cross Abstract: The identifiability analysis of linear Ordinary Differential Equation (ODE) systems is a necessary prerequisite for making reliable causal inf