Search: “inference”

Self-attention summary networks for subsurface velocity-model building from common-image gathers

arXiv:2610.09282v1 Announce Type: new Abstract: Common-image gathers (CIGs) contain physically meaningful information about velocity-model errors through reflector focusing and residual moveout, but i

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SeOPD: Self-Evolving LLMs via Online Policy Distillation from Self-Generated Chain-of-Thought

DGX agent

arXiv:2609.33181v2 Announce Type: replace-cross Abstract: Recent advances in online policy self-distillation (OPSD) have demonstrated that large language models (LLMs) can improve their capabilities b

safetyarxiv-cs-cl

8 Oct 2026

Sparsifying Stochasticity, Not Capacity: Partial Stochasticity via Deep Weight Factorization of Prior Scales

model-releasesarxiv-cs-lg

arXiv:2610.09886v1 Announce Type: cross Abstract: Bayesian neural networks need not be fully stochastic to be universal conditional density approximators, but it remains open which parameters should b

Tutorials

Spatial Latent Reasoning for Embodied Reference Understanding

arXiv:2610.09418v1 Announce Type: new Abstract: Pointing-gesture visual grounding requires connecting hand geometry with the visual identity and extent of a referred object. A central challenge for co

tutorialsarxiv-cs-cv

Applications

Stream-Based Active Learning with Cooperative Neural Networks for Data-Efficient Partial Inverse Design: An Automotive Glass Run Channel Case Study

arXiv:2610.09848v1 Announce Type: new Abstract: Inverse design in engineering often runs into a simple problem. Each labeled training sample must be produced through expensive simulation, so building

applicationsarxiv-cs-lg

STRIKE: Learning Visual State Transitions for Physical World Modeling

arXiv:2610.09514v1 Announce Type: new Abstract: Physical world modeling requires predicting how interactions change a scene, not merely generating coherent motion. We propose STRIKE, a framework that

Symmetry-Informed Causal Partial Identification

arXiv:2610.09230v1 Announce Type: new Abstract: Partial identification (PI) entails estimating bounds on causal effects by encoding different assumptions on data generation as a constrained optimizati

Targeted Modality Dropout for Real-Robot Manipulation Robust to Intermittent Vision Loss

arXiv:2610.09566v1 Announce Type: new Abstract: Imitation learning policies that integrate multiple sensory modalities are prone to overreliance on a dominant modality, such as vision, during training

safetyarxiv-cs-ro

Tutorials

The Dichotomy Between Pattern Recognition and Step-by-Step Reasoning

arXiv:2610.09186v1 Announce Type: new Abstract: We argue that pattern recognition and step-by-step reasoning are two ends of a spectrum. A large language model (LLM) learns to reason step-by-step when

tutorialsarxiv-cs-lg

TileSkipper: Region-Adaptive Tile Pruning for 3D Gaussian Splatting

arXiv:2610.09343v1 Announce Type: new Abstract: Tiled 3D Gaussian Splatting rasterizers often use one scene-wide contribution cutoff for tile enumeration, although content differs in its sensitivity t

Training Advisors for LLM Agents from Task Outcomes

model-releasesarxiv-cs-cl

arXiv:2610.09858v1 Announce Type: new Abstract: Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment. Prior work has show

Tri-Prompting: Video Diffusion with Unified Control over Scene, Subject, and Motion

arXiv:2603.15614v2 Announce Type: replace Abstract: Recent video diffusion models achieve strong visual quality and temporal coherence, but still lack coordinated control over scene layout, customized

researcharxiv-cs-cv

Local Ai

UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map

arXiv:2610.09785v1 Announce Type: new Abstract: World models can enable autonomous ultrasound scanning by predicting the outcomes of probe motions from local observations. Learning this action--observ

local-aiarxiv-cs-cv

Understanding and Mitigating Token-Pruning-Induced Vulnerabilities in VLMs

arXiv:2610.09703v1 Announce Type: cross Abstract: Token-Pruning accelerates Vision-Language Models by removing redundant visual tokens, yet its safety implications remain underexplored. In this work,

Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures

arXiv:2610.10261v1 Announce Type: cross Abstract: Context: Once defined a taxonomy of stages structuring Machine Learning (ML) pipelines (e.g. Data Preprocessing, Modeling...), extracting these stages

Applications

Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators

arXiv:2602.22647v3 Announce Type: replace-cross Abstract: Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit fr

applicationsarxiv-cs-cl

VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification

arXiv:2604.01569v2 Announce Type: replace Abstract: Video multimodal large language models achieve strong results on existing benchmarks, but answer accuracy alone does not establish whether they can

Zero-Shot Brain MRI Inpainting with 2.5D Unconditional Flow Priors

arXiv:2610.08983v1 Announce Type: new Abstract: Generative inpainting of brain MRI volumes is essential for synthesizing healthy tissue in pathological regions, improving the accuracy and reliability

researcharxiv-cs-cv

3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects

arXiv:2607.10826v2 Announce Type: replace-cross Abstract: Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow. Yet the reliability of

A Decision-Focused Neural Optimization Framework for Personalized Route Reproduction from Vehicle Trajectories

arXiv:2610.07857v1 Announce Type: new Abstract: This study formulates individual route reproduction as a shortest-path problem over learned driver-specific latent link costs. The central idea is that,

A Systematic Investigation of Bias in Large Language Models for Advertising Relevance

arXiv:2610.07544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to judge how well an advertisement matches a query, but the fairness of these judgments has received

Adaptive Mean Estimation by In-Context Learning: A Gradient-Flow Analysis

arXiv:2610.07804v1 Announce Type: new Abstract: Prior Fitted Networks (PFNs) such as TabPFN now rival established statistical procedures across prediction and estimation tasks. A natural explanation i

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.

Agents

Agentic Semantic Sensing for Resource-Adaptive AI-RAN

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

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

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

model-releasesarxiv-cs-lg

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

researcharxiv-cs-ai

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

Agents

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

Agents

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

model-releasesarxiv-cs-lg

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