Search: “inference”

Closing the Context Gap: Activation Alignment for Tabular In-Context Learning

arXiv:2610.06679v1 Announce Type: new Abstract: Tabular foundation models perform in-context learning (ICL) by conditioning predictions on labeled training examples provided as context. Unlike traditi

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Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging

DGX agent

arXiv:2610.06726v1 Announce Type: new Abstract: Electroencephalography (EEG) offers millisecond temporal resolution, but inferring underlying neural sources is a severely ill-posed spatial inverse pro

local-aiarxiv-cs-lg

6 Oct 2026

DriftSR: One-Step Real-World Image Super-Resolution via Distribution Drifting

arXiv:2610.04819v1 Announce Type: new Abstract: One-step real-world image super-resolution (Real-ISR) offers efficient inference, but recovering realistic and perceptually rich details often relies on

local-aiarxiv-cs-cv

Dynamic Time Step Prediction in Inverse Heat Dissipation for Blur-Like Image Restoration Tasks

arXiv:2610.03942v1 Announce Type: new Abstract: When using diffusion models to target image restoration problems, diffusion inversion is typically employed to retain relevant image information from th

First-Order Steering: Translating Weight Adaptation into Activation Steering

arXiv:2610.04283v1 Announce Type: new Abstract: Activation steering exploits interpretable directions in the residual stream to enable inference-time manipulation of model behavior. Composing steering

safetyarxiv-cs-lg

Grounded in Time: A Multi-Source Dataset and Benchmark for Temporal Grounding in Robotic Manipulation

model-releasesarxiv-cs-cv

arXiv:2610.04255v1 Announce Type: cross Abstract: Robotic manipulation often requires inferring task-relevant states from past interactions when the current observation alone is insufficient to determ

Learning Discriminative Geometry for Drifting Models

arXiv:2610.04703v1 Announce Type: cross Abstract: Recently proposed Drifting Models shift iterative distribution refinement from inference to training, enabling effective one-step generation. However,

Tutorials

Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D Foundation Models

arXiv:2610.06813v1 Announce Type: new Abstract: Recent progress in 3D foundation models has enabled rapid 3D reconstruction and camera calibration by leveraging learned 3D priors from vast amount of s

tutorialsarxiv-cs-cv

LiFT: Loop Flow Transformers

arXiv:2610.05538v1 Announce Type: new Abstract: We introduce Loop Flow Transformers (LiFT), a family of looped generative models that scales computation by repeatedly applying a shared Diffusion Trans

Hardware

Rethinking Streaming-Perception Evaluation on Heterogeneous Edge Platforms

arXiv:2610.05578v1 Announce Type: cross Abstract: Multi-camera streaming perception is increasingly deployed on heterogeneous edge platforms shared with co-resident workloads, yet accelerator placemen

hardwarearxiv-cs-cv

StageVLN: Spatial and Trajectory Auxiliary Guidance for Efficient Vision-Language Navigation

model-releasesarxiv-cs-cv

arXiv:2610.05664v1 Announce Type: new Abstract: Vision-and-Language Navigation (VLN) policies increasingly benefit from strong semantic priors provided by large vision-language models (VLMs). However,

Applications

Synthetic-to-Real ViT-Based Pose Estimation of a Noncooperative UAV

arXiv:2610.04335v1 Announce Type: new Abstract: Remote pose estimation of noncooperative Unmanned Aerial Vehicles (UAVs) from imagery is critical, as they cannot be influenced or instrumented in advan

applicationsarxiv-cs-cv

Towards causal effect estimation with learned instrument representations

arXiv:2602.10370v3 Announce Type: replace-cross Abstract: Instrumental variable (IV) methods mitigate bias from unobserved confounding in observational causal inference but rely on the availability of

safetyarxiv-cs-lg

Triggering Generalist Reasoning via Predictive Uncertainty for Dual-System VLA

arXiv:2610.05025v1 Announce Type: new Abstract: Dual-system Vision-Language-Action (VLA) models improve real-time robotic control by pairing a slow, reasoning-capable generalist with a fast specialist

A Generative Model of Complex Networks Using Graphons and Neural Inverse Operators

arXiv:2610.02439v1 Announce Type: new Abstract: Generative graph models are central to understanding and simulating complex networks. However, existing approaches have complementary strengths and limi

Dynamic LLM Routers are Often Misguided

arXiv:2610.02762v1 Announce Type: new Abstract: Dynamic LLM routers promise to cut inference costs by sending each query to the cheapest model that can answer it correctly. We analyze six commercial r

Exact Memory-Time Optimization for Prefix-Cached Language Model Serving

arXiv:2610.02766v1 Announce Type: new Abstract: Retaining language-model prefix states trades recomputation against storage time. Optimizing each cached block independently can overcount savings: a re

One Photon, Many Worlds: Posteriors and Predictions with Single-Photon Cameras

arXiv:2610.02675v1 Announce Type: cross Abstract: Single-photon avalanche diode (SPAD) cameras operate fundamentally differently from conventional cameras due to their photon-counting nature. Each fra

Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles

arXiv:2509.10756v4 Announce Type: replace-cross Abstract: We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providin

What Does a Token Cost? A Mixture-of-Agents Measurement of Sufficient Per-Token Compute

arXiv:2610.02491v1 Announce Type: new Abstract: Large language models spend the same amount of computation on every token they generate, regardless of how difficult each token is to produce. Methods s

Capturing In-Context Learning Dynamics with Task Operators

arXiv:2610.01054v1 Announce Type: cross Abstract: In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requir

Does Scaling Reinforcement Learning Really Require More Training?

arXiv:2610.01133v1 Announce Type: cross Abstract: Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference. We show that a completed RL training history can yield

From Discovery to Decision: Finite-Budget Recoverability in LLM Voting

arXiv:2610.01014v1 Announce Type: new Abstract: Voting over multiple LLM responses is a common primitive in test-time scaling and ensemble inference. Collecting more responses can expand the candidate

In-context Learning of Single-index Targets: Comparing Kernel and Feature Learners

arXiv:2610.01712v1 Announce Type: new Abstract: In-context learning (ICL) enables a pretrained model to infer a task from demonstrations without updating its parameters. While much of the existing the

Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Models

arXiv:2610.00864v1 Announce Type: cross Abstract: In this paper, we study how to achieve one-step action generation in Robotic Foundation Models (RFMs), aiming to overcome the high inference latency o

local-aiarxiv-cs-ai

Large Language Bayes Is Not Reparameterisation-Invariant

arXiv:2610.00265v1 Announce Type: new Abstract: Large Language Bayes (LLB) answers an informal modelling question by sampling candidate probabilistic programs from a language model, running approximat

MapLightning: Online Vectorized HD Map Construction with 1D Map Tokens

model-releasesarxiv-cs-cv

arXiv:2610.01905v1 Announce Type: new Abstract: Online vectorized HD map construction is essential for scaling safe autonomous driving and requires accurate, real-time inference. Prior methods typical

Applications

Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports

arXiv:2603.11546v2 Announce Type: replace Abstract: Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multi

applicationsarxiv-cs-lg

MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs

arXiv:2610.01434v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) incur substantial inference costs when processing long visual-textual sequences. While existing operation com

One Basis to Animate Them All: Gaussian Blendshape Distillation for Real-Time Avatars

arXiv:2610.02207v1 Announce Type: cross Abstract: 3D Gaussian avatars support fast rendering, however, their real-time animation is often challenged by the costly neural inference. We address this bot

local-aiarxiv-cs-ai

Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization

arXiv:2610.00558v1 Announce Type: new Abstract: While Mixture-of-Experts (MoE) models effectively scale model capacity through sparse activation, their deployment is often bottlenecked by prohibitive

Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces

arXiv:2610.00751v1 Announce Type: cross Abstract: Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks. These inclu

TRACE: Trajectory Return Attribution and Contrastive Erasure for Multi-Turn Safety

arXiv:2610.01323v1 Announce Type: new Abstract: Safety-aligned large language models (LLMs) often refuse a harmful request but comply once the same goal is spread over several turns. Preference object

safetyarxiv-cs-ai

When Do Attention-Head Ablations Support Causal Claims? Projection-Level Confounds, Floor Effects, and Matched Controls

arXiv:2610.00373v1 Announce Type: new Abstract: Attention-head ablation, zeroing a head and measuring the resulting change in task performance, is a common method for inferring which components of a l

Acceleration of Diffusion Language Model through Discrete Average Generator

arXiv:2609.38364v1 Announce Type: cross Abstract: Discrete diffusion models and flow matching have emerged as powerful frameworks for generative modeling over discrete state spaces, yet efficient few-

Tutorials

Blackboard Intelligence Can Surpass Autoregressive on Globally Constrained Problems

arXiv:2609.38806v1 Announce Type: cross Abstract: Next-token prediction has driven remarkable progress in large language models, yet a growing body of evidence suggests that they can struggle on probl

tutorialsarxiv-cs-cl

Cascadia: A Control-Plane-Free Alternative to Hyperconverged AI Infrastructure

arXiv:2609.38697v1 Announce Type: cross Abstract: We present Cascadia, a system for serving large language models on fleets of commodity Intel AIPCs using their CPU, integrated-GPU, and NPU resources.

Feature-Aware Token Attack for Compression-Triggered Stealthy Failures in Large Vision-Language Models

arXiv:2609.39134v1 Announce Type: new Abstract: Visual-token compression improves the efficiency of large vision-language models, but can expose failures that full-token evaluation misses. We study ad

Fork-dLLM: Avoiding the Flexibility Trap in Diffusion Language Models

arXiv:2609.39859v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have shown strong potential for faster inference through parallel token generation when combined with confidenc

safetyarxiv-cs-lg

GraphMAS: A Systematic Benchmark of Multi-Agent Coordination for Graph Learning

arXiv:2609.39777v1 Announce Type: new Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph lea

Mitigating Memorization In Language Models

arXiv:2410.02159v3 Announce Type: replace-cross Abstract: Language models (LMs) can 'memorize' information, i.e., encode training data in their weights in such a way that inference-time queries can le

local-aiarxiv-cs-ai

Prompt2Skill: Unsupervised Skill Optimization From Natural Language Instructions

arXiv:2609.38593v1 Announce Type: cross Abstract: Skills are external artifacts that Large Language Models (LLMs) consume at inference time to improve their performance on specialized domains by incor

Tutorials

Abductive World Modeling via Causal Representation Learning

arXiv:2609.36985v1 Announce Type: cross Abstract: The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly r

tutorialsarxiv-cs-ai

ARC-KV: Amortizing Anchor Search for Reconstruction-Based KV Cache Compaction

arXiv:2609.36835v1 Announce Type: new Abstract: Long-context large language model inference is bottlenecked by KV caches that grow linearly with sequence length. This burden is especially severe for l

Beyond Symmetric Agents: Cognitive Diversity and Multi-Agent Debate in Small Language Models

arXiv:2609.35875v1 Announce Type: new Abstract: Multi-agent debate (MAD) reportedly improves reasoning and factuality over single-model inference, but prior work treats agents as symmetric peers, leav

Applications

Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment

arXiv:2501.18157v2 Announce Type: replace-cross Abstract: Building reliable speech systems often requires combining multiple modalities, like audio and visual cues. While such multimodal solutions fre

applicationsarxiv-cs-cv

Estimating the Causal Effects of T Cell Receptors

arXiv:2410.14127v2 Announce Type: replace-cross Abstract: A central question in human immunology is how a patient's T cell receptors impacts disease. Here, we introduce a method to infer the causal ef

Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling

arXiv:2609.38104v1 Announce Type: new Abstract: Power-sharpened sampling is an inference-time alternative to reinforcement-learning (RL) post-training for enhancing reasoning in large language models