Search: “inference”

HLA-WM: Hybrid Linear Attention for Long-Horizon Video World Models

arXiv:2610.05739v1 Announce Type: new Abstract: Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full gener

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Hybrid-Basis Feature Forecasting for Diffusion Sampling Acceleration

DGX agent

arXiv:2610.05254v1 Announce Type: new Abstract: We propose Hybrid-Basis Feature Forecasting (HybridFF), a training-free, plug-and-play framework for accelerating diffusion sampling. To capture local s

local-aiarxiv-cs-cv

6 Oct 2026

KITINet: KInetic Theory Inspired Inter-Channel Information Exchange for Residual Networks

arXiv:2505.17919v2 Announce Type: replace Abstract: Residual connections are foundational to modern neural networks, yet the standard additive update provides no explicit mechanism for structured inte

Kolmogorov-Arnold Networks for Personal Context Recognition on ExtraSensory

arXiv:2610.05250v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) have attracted increasing attention in recent years, with applications across a wide range of AI tasks. In this paper

Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support

arXiv:2610.06355v1 Announce Type: cross Abstract: Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs). We present Lat

Learning Latent Protein Languages for Autoregressive Generation

arXiv:2610.03978v1 Announce Type: new Abstract: Autoregressive transformers remain comparatively weak for protein sequence and structure generation. We study the role of target representation: amino a

Learning Pareto Stationary Fronts via Single-Pass Backpropagation

arXiv:2610.06397v1 Announce Type: new Abstract: We propose MOSEL (Multi-Objective Stackelberg Efficient Learning), a framework for a posteriori multi-objective optimization (MOO) in deep neural networ

Learning to Learn a Language: in-context learning of natural language from a synthetic non-linguistic prior [R]

model-releasesr-machinelearning

Learning from data as we observe it is easy for humans, but most machine learning models have limited ability to learn from new data that they have not seen during training. Prior-fitted networks (the

Local AI World Model Part 2 - Deep NN to turn Images into Playable Characters, with prompt switching mid rollout

model-releasesr-localllama

Last time when I posted on this subReddit to share my work, the response almost made me cry because a tiny demo got so many people talking about this In the past 2 months Ive been training a new model

Monitorability Disposition in Large Reasoning Models

arXiv:2610.04914v1 Announce Type: cross Abstract: Monitoring the chain-of-thought (CoT) of large reasoning models (LRMs) is a common way to detect misbehavior in real-world practice. However, current

On architectural choices for interpretability and thermodynamic consistency in Physically Recurrent Neural Networks in the low-data regime

arXiv:2610.04067v1 Announce Type: new Abstract: In this paper, we unravel the effect of different decoder architectures on the interpretability of the latent space of the Physically Recurrent Neural N

Ontology Concept Overlap as a Training Signal: Knowledge-Grounded Reinforcement Learning for Clinical Question Answering

arXiv:2610.06360v1 Announce Type: cross Abstract: Reinforcement learning post-training for language models relies on two reward designs: human preferences (RLHF, DPO) and binary verifiers (RLVR). Clin

Periscope: Extending Frozen Language Models Beyond Their Context Window

arXiv:2610.04047v1 Announce Type: cross Abstract: A language model reads long text in one quadratic forward pass, stops at the context window, and loses accuracy with length before reaching it. We ask

Probability flow ODEs in score-based and reflected diffusion models

arXiv:2610.03846v1 Announce Type: cross Abstract: Probability-flow ordinary differential equations (PF-ODEs) are widely used as deterministic samplers for score-based diffusion models. Their usual jus

ProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2603.17850v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models equipped with Flow Matching (FM) action heads achieve state-of-the-art performance in complex robot manip

Protecting Sensitive Data in Image Synthesis via PAC-Private Adaptation for Diffusion Models

arXiv:2610.04038v1 Announce Type: new Abstract: Synthetic data are increasingly used as an alternative to sharing sensitive records. However, synthetic data generation does not guarantee privacy, as d

tutorialsarxiv-cs-lg

Proximal Causal Learning under Unmeasured Confounding

arXiv:2610.04519v1 Announce Type: new Abstract: Estimating treatment effects from observational data typically relies on the No Unmeasured Confounding Assumption (NUCA), which rarely holds in practice

Quantifying the Stability of Multi-Step Reasoning via Error Amplification

arXiv:2610.06404v1 Announce Type: new Abstract: We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algori

RealtimeWAM: One-Step Asynchronous World Action Models

arXiv:2610.06617v1 Announce Type: new Abstract: World Action Models (WAMs) incorporate visual representations from video generation backbones to guide action prediction. Recent efficient WAMs adopt Mi

local-aiarxiv-cs-cv

Reinforcement Learning on the Discrete Composition Channel of a Crystal Generator: Validated Gains and Reward Hacking

arXiv:2610.03880v1 Announce Type: new Abstract: Inverse materials design is a long-standing goal of computational materials discovery. Generative models for crystalline materials are typically trained

Representation-Space MMD for Diffusion Language Models

arXiv:2610.06648v1 Announce Type: cross Abstract: We introduce a post-training method for diffusion language models (DLMs) that minimizes Maximum Mean Discrepancy (MMD) between generated and reference

Retrieval-Based In-Context Learning: A Domain Adaptation Framework

arXiv:2610.05717v1 Announce Type: cross Abstract: In-context retrieval (ICR) is a retrieval-based form of in-context learning (ICL) in which demonstrations are retrieved from a source database based o

RFG: Self-Improving Diffusion Large Language Models with Reward-Free Guidance

arXiv:2509.25604v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) have shown strong reasoning capabilities, yet further improving them typically requires costly post-tr

SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems

arXiv:2610.04893v1 Announce Type: new Abstract: Industrial Internet of Things (IIoT) systems generate high-dimensional sensor telemetry from interconnected components, where faults can propagate acros

Taylor Representations for Model-Free RL in Networked MDPs

arXiv:2610.05456v1 Announce Type: cross Abstract: In Networked Markov Decision Processes, transition dynamics are often unknown and the state--action space grows rapidly with the number of agents. In

local-aiarxiv-cs-lg

To Learn is to Wander: Learning Across Graphs and Tasks with Random Walks

arXiv:2610.06694v1 Announce Type: new Abstract: Graph foundation models aim to transfer across graphs, feature spaces, relational schemas, and prediction tasks, yet existing approaches typically gener

tutorialsarxiv-cs-lg

Trajectory-Derived Confidence for Reliable, Resource-Aware Clinical Text-to-SQL Agents

arXiv:2610.04156v1 Announce Type: cross Abstract: LLM agents for clinical text-to-SQL applications reason autonomously over multiple steps but cannot assess whether their own reasoning or outputs can

Trajectory-Guided Tokenization of Complex CSI for Wi-Fi Sensing

arXiv:2610.06288v1 Announce Type: new Abstract: Wi-Fi channel state information (CSI) enables contactless presence detection and gesture recognition. Its high-dimensional complex-valued time series re

Applications

Turns out you don't need a frontier lab budget to build a solid decision classifier. Our Head of AI Developer Education @prof_oz fine-tuned …

applicationsfireworks-ai--x

Turns out you don't need a frontier lab budget to build a solid decision classifier. Our Head of AI Developer Education @prof_oz fine-tuned a 9B Jev-style classifier on Fireworks using plain SFT, publ

Ultrasound Operator Guidance Using World Modeling and Retrieval Based Action Planning

arXiv:2610.06008v1 Announce Type: cross Abstract: Ultrasound is widely used, but acquisition quality is heavily dependent on the operator's knowledge and expertise. With demand for examinations outpac

tutorialsarxiv-cs-cv

Agents

Vast uses tiered storage to ease AI agent memory demands

AI agent memory is creating new demands on infrastructure as agents run longer sessions and spread across the enterprise. Retaining that context and making it available when needed puts pressure on me

agentssiliconangle

VLA-ZO: Fast Zeroth-Order Adaptation for Vision-Language-Action Models

arXiv:2610.06271v1 Announce Type: new Abstract: Adapting vision-language-action (VLA) models to deployment-time distribution shifts is important for reliable robotic operation, but conventional first-

researcharxiv-cs-ro

Hardware

WAMJET: A Harness for World Action Model Acceleration

arXiv:2610.03797v1 Announce Type: new Abstract: World Action Models (WAMs) leverage pretrained video foundation models for robot manipulation, but their large backbones and video-action co-prediction

hardwarearxiv-cs-cv

Hardware

Why Telecom Operators Are Building Their AI Strategy on Open Models

Telecom operators are increasingly building their AI strategies on open models — and the reasons go beyond mere cost. Open models give telcos the ability to trust, control and customize AI across thei

hardwarenvidia-blog

All Work And No Play Makes Jack a Dull Boy: Understanding and Preventing Catastrophic Strategy Collapse in RLVR

arXiv:2610.02835v1 Announce Type: new Abstract: During post-training of large language models (LLMs) with Reinforcement Learning with Verifiable Rewards (RLVR), GRPO-style algorithms can exhibit sever

AvoKV-E: Payload-Aware KV Cache Eviction for Long Reasoning

arXiv:2610.03007v1 Announce Type: cross Abstract: Long-output reasoning shifts the KV-cache bottleneck from the fixed prompt to the generated trace. Existing reasoning-cache eviction methods largely t

Bounded Reachability & Jailbreak Detection via Contraction-Constrained State Space Models

arXiv:2610.02853v1 Announce Type: new Abstract: Safety heads are lightweight classifiers attached to pretrained language models for flagging harmful inputs before generation. Their empirical detection

Agents

Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents

arXiv:2610.02330v1 Announce Type: new Abstract: Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condit

agentsarxiv-cs-ai

CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache

arXiv:2610.02235v1 Announce Type: cross Abstract: Long-context decoding is increasingly constrained by key--value (KV) cache memory and bandwidth. Existing fixed-budget compression methods typically s

researcharxiv-cs-ai

DeepSeek V4.1 Flash is now available for training on both the Dedicated Training API and Managed Training surfaces! It's a strong base for a…

model-releasesfireworks-ai--x

DeepSeek V4.1 Flash is now available for training on both the Dedicated Training API and Managed Training surfaces! It's a strong base for agentic coding, terminal automation, and tool use. It's also

Designing the Future of User Feedback for Generative AI

model-releasesarxiv-cs-ai

arXiv:2610.02631v1 Announce Type: new Abstract: Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and feature

DNAlign: Dynamic Null-Space Safe Alignment for LLMs

arXiv:2610.02844v1 Announce Type: new Abstract: Ensuring the safe and reliable deployment of large language models (LLMs) remains a fundamental challenge. Existing safety alignment approaches either i

EviDent-CBCT: Evidence-Bottlenecked Report Generation from Dental CBCT under Non-Exhaustive Report Supervision

arXiv:2610.02375v1 Announce Type: cross Abstract: Dento-maxillofacial cone-beam CT (CBCT) reports may contain dozens of tooth-specific, anatomical, and spatial findings from a single 3D scan. Learning

local-aiarxiv-cs-ai

FastOPD: On-Policy Distillation for Lightweight VLA Deployment

arXiv:2610.02832v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) foundation models have scaled rapidly to enhance manipulation performance and generalizability, but this scaling incurs h

Geometry-Aware Time Reparameterization for Flow-Map Distillation

arXiv:2610.02427v1 Announce Type: cross Abstract: Flow-map distillation enables one- and few-step generation by learning finite-time transitions of a pretrained generative ODE. We investigate whether

tutorialsarxiv-cs-ai

How Causality Bridges the Semantic Gap

arXiv:2610.02594v1 Announce Type: cross Abstract: Numerical measurements capture how a system behaves, but often leave the meanings of its variables unspecified. Some variables are measured but never

Hypothesis-guided discovery of cognitive algorithms via program refinement

arXiv:2610.02523v1 Announce Type: new Abstract: Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods. Tra

researcharxiv-cs-ai

Agents

Inherit-MAS: Test-Time Evolution of Multi-Agent Systems through Workflow and Execution Inheritance

arXiv:2610.02396v1 Announce Type: cross Abstract: Multi-agent systems (MAS) built from large language models coordinate specialized agents to tackle complex tasks, but effective workflows are difficul

agentsarxiv-cs-ai