Search: “inference”

REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing

arXiv:2607.05364v1 Announce Type: cross Abstract: Modern autoregressive ASR systems can emit timestamps as decoded tokens, enabling timestamped transcription without frame-level aligners or inference-

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Agents

Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models

DGX agent

arXiv:2607.02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that

agentsarxiv-cs-ai

7 Jul 2026

Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness

arXiv:2607.03165v1 Announce Type: new Abstract: Brain decoding aims to uncover neural mechanisms by inferring stimulus-related representations from brain signals. In fMRI studies, this is typically ac

safetyarxiv-cs-cv

RIGS-Refiner: Risk-Guided Recursive Refinement in Prediction Space for Colonoscopy Polyp Segmentation

arXiv:2607.03058v1 Announce Type: new Abstract: Post-refinement can improve colonoscopy segmentation after host inference, but many designs still rely on extra correction heads or multi-stage pipeline

Stable Global Weighting of Flow Mixtures using Simplex Exponential Moving Average

arXiv:2607.03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heteroge

STELLA: Efficient Sensor-to-LLM Translation for On-Device Human Activity Recognition

arXiv:2607.03089v1 Announce Type: cross Abstract: HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, clo

You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitism

arXiv:2607.04945v1 Announce Type: new Abstract: LLMs enable the integration of external conceptual resources at inference time, creating new opportunities for detecting ideologically and historically

researcharxiv-cs-cl

b9881

b9881 is a release of llama.cpp, an LLM inference tool written in C/C++. This release likely includes bug fixes, performance improvements, and platform support enhancements for running large language

b9885

B9885 is a build-tagged release of llama.cpp, an open-source C/C++ inference engine for running large language models locally. The project does not use traditional semantic versions; instead it ships

Tutorials

// ReContext // Models now support 128K context windows and still fail to use evidence that is already in the prompt. Where is the gap? New …

// ReContext // Models now support 128K context windows and still fail to use evidence that is already in the prompt. Where is the gap? New paper introduces ReContext, a training-free inference harnes

tutorialsdair-ai--x

Agents

Run MiniMax models on Amazon Bedrock

In this post, we walk through how to get started with MiniMax models on Amazon Bedrock, including the capabilities supported by these models, the service tiers available, how on-demand inference scale

agentsaws-ml-blog

Streaming benchmark and recommendation results to MLflow with Amazon SageMaker AI

model-releasesaws-ml-blog

In this post, you learn how to use the new MLflow integration with Amazon SageMaker AI optimized inference recommendation jobs and Amazon SageMaker AI benchmark jobs to automatically stream experiment

b9864

llama.cpp is a tool for LLM inference in C/C++ and b9864 is a release version tag from the ggml-org/llama.cpp GitHub repository. Based on the release numbering pattern and project scope, this release

Hardware

Don't forget, we'll be in Paris for @RaiseSummit. Will we see you there?

Don't forget, we'll be in Paris for @RaiseSummit. Will we see you there? Going to be in Paris for @RaiseSummit? Join us and @nvidia to hear from leaders at both companies on the future of AI inference

hardwarefireworks-ai--x

DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving

arXiv:2603.18315v2 Announce Type: replace-cross Abstract: Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the ric

DRL-CLBA: A Clean Label Backdoor Attack for Speech Classification via DDPG Reinforcement Learning

arXiv:2607.01729v1 Announce Type: new Abstract: Deep learning models for speech classification are vulnerable to backdoor attacks, where malicious triggers cause misclassification at inference time. W

Fixed-Set Robustness in Programming by Example: Example Corruption and Semantic Partition Recovery

model-releasesarxiv-cs-lg

arXiv:2607.01280v1 Announce Type: new Abstract: Programming-by-example systems infer programs from a small set of input-output examples. Robust PBE work usually models wrong examples as samples from a

Applications

IntentTune: Using user demand and personalization to resolve 'unknown' query intents for e-commerce search

arXiv:2607.01530v1 Announce Type: cross Abstract: Understanding user intent is fundamental to delivering relevant search results in e-commerce. However, substantial fraction of real-world queries are

applicationsarxiv-cs-ai

Mastermind: Strategy-grounded Learning for Repository-Scale Vulnerability Reproduction

arXiv:2607.01764v1 Announce Type: new Abstract: Repository-level vulnerability reproduction is a demanding software engineering (SE) task: an agent must inspect a codebase, infer the input grammar tha

Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learning

arXiv:2503.21796v2 Announce Type: replace-cross Abstract: Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence. Furthermore, evidence for self-s

mupscaling small models: Principled warm starts and hyperparameter transfer

arXiv:2602.10545v2 Announce Type: replace-cross Abstract: Modern large-scale neural networks are often trained and released in multiple sizes to accommodate diverse inference budgets. To improve effic

On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain

arXiv:2607.01444v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models offer inference speedups via selective activation but impose substantial memory requirements because the whole network

ProWAFT: A ROMA-LPD Instance for Workload-Aware and Dynamic Fault Tolerance in FPGA-Based CNN Accelerators

arXiv:2607.01602v1 Announce Type: new Abstract: SRAM-based FPGAs provide an attractive platform for energy- and latency-constrained CNN inference at the network edge, yet transient faults can lead to

researcharxiv-cs-cl

Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications

arXiv:2607.02413v1 Announce Type: cross Abstract: Here we describe the quantum gas analysis and inference (Q-GAIN) Python package, which enables rapid deployment of machine learning (ML) and physics-i

The risk of KV cache compression

arXiv:2607.01520v1 Announce Type: new Abstract: Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bo

WARP: Weight-Space Analysis for Recovering Training Data Portfolios

model-releasesarxiv-cs-lg

arXiv:2607.01686v1 Announce Type: new Abstract: Foundation models are routinely released to the public, yet the data recipes used to train them -- such as domain mixture weights that determine how dif

ABot-M0.5: Unified Mobility-and-Manipulation World Action Model

arXiv:2607.00678v1 Announce Type: new Abstract: Mobile manipulation is a key capability for general-purpose robots, yet remains challenging for current embodied learning methods. VLA policies are typi

local-aiarxiv-cs-cv

Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction

arXiv:2607.00001v1 Announce Type: new Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and optimized. This assumption conflicts with extensive empirica

CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization

arXiv:2511.05747v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning enhances the problem-solving ability of large language models (LLMs) but leads to substantial inference overhead, l

ext{Log}_ext{b}Quant: Quantizing Language Models in Logarithmic Space

model-releasesarxiv-cs-cl

arXiv:2607.01127v1 Announce Type: new Abstract: Quantization has become an invaluable tool to reduce memory requirements and inference speed of modern language models, in particular to make them avail

Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing

arXiv:2607.00887v1 Announce Type: new Abstract: Low-altitude Unmanned Aerial Vehicles (UAVs) often need to infer channel knowledge across a range of heights from only sparse observations collected at

Hardware

Hardware-Rooted AI Security That Won’t Slow You Down

NVIDIA Confidential Computing addresses data privacy and security concerns for AI workloads by protecting data during inference and engagement with models, offering high-performance protection in loca

hardwarenvidia-developer

Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation

arXiv:2607.01067v1 Announce Type: cross Abstract: As an essential modality for dexterous and contact-rich tasks, tactile sensing provides precise force feedback that cannot be reliably inferred from v

researcharxiv-cs-cv

Learning Unmasking Policies for Diffusion Language Models

researchapple-ml-research

Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during inference. O

Message Passing Enables Efficient Reasoning

arXiv:2607.01077v1 Announce Type: new Abstract: While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is

researcharxiv-cs-cl

MSQA: A Natively Sourced Multilingual and Multicultural SimpleQA Benchmark

model-releasesarxiv-cs-cl

arXiv:2607.00724v1 Announce Type: new Abstract: Multilingual fluency often invites a stronger assumption: a model that can speak a user's language must also understand the culture encoded by that lang

Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning

arXiv:2607.00461v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are often constrained by a language-space bottleneck, forcing complex visual reasoning into discrete tokens whi

Not All Prediction Targets Keep Training-Free Diffusion Guidance on the Manifold

arXiv:2607.00647v1 Announce Type: new Abstract: Training-free guidance (TFG) steers a pretrained diffusion model toward a desired attribute at inference. To be effective, this guidance must be applied

Hardware

NVIDIA Unlocks AI Compute at Scale, Inviting Partners to Power the AI Infrastructure Buildout

As AI moves from model development to production inference, compute demand is accelerating and shifting toward continuously operating AI factories that generate tokens at scale. This shift requires ac

hardwarenvidia-blog

Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation

arXiv:2604.03118v2 Announce Type: replace Abstract: Distilling video generation models to extremely low inference budgets (e.g., 2--4 NFEs) is crucial for real-time deployment, yet remains challenging

safetyarxiv-cs-cv

Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence

arXiv:2607.00060v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report generation, yet inference-time hallucinations still u

researcharxiv-cs-cv

Tools

3/ Learning to Discover at Test Time (TTT-Discover) Paper: https://arxiv.org/abs/2601.16175

TTT-Discover is a method that enables models to learn and discover patterns during test time rather than only during training, allowing for adaptation to new data distributions at inference. The appro

toolstogether-ai--x

Tools

6/ When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (Aurora) Paper: https://arxiv.org/abs/2602.06932

Aurora is a unified training-serving system that integrates reinforcement learning with adaptive speculative training to optimize large language model inference and training efficiency. The system dyn

toolstogether-ai--x

Tools

7/ Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking Paper: https://arxiv.org/abs/2602.21196

Untied Ulysses is a memory-efficient technique for context parallelism that processes attention heads in chunks rather than sequences, reducing memory overhead during transformer inference and trainin

toolstogether-ai--x

Accelerating Conformal Prediction via Approximate Leave-One-Out

arXiv:2606.31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by co

b9852

Release b9852 is a build version of llama.cpp, a C/C++ implementation that enables large language model inference with minimal setup and state-of-the-art performance across diverse hardware platforms

Budget-Adaptive Routing: Skipping the Weak When the Strong Answers Anyway

arXiv:2606.30919v1 Announce Type: cross Abstract: Edge-cloud inference collaborations are often designed with a routing estimator that decides whether to offload each frame from weak models at the edg

researcharxiv-cs-ai

CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts

arXiv:2606.31986v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has enabled multi-modal large language models (MLLMs) to tackle complex visual reasoning tasks by generating explicit i