Search: “inference”

Distributed Sparse Interventions in Language Models

arXiv:2607.07128v1 Announce Type: new Abstract: Language models perform a wide range of tasks at varying levels of abstraction with the capacity to flexibly infer tasks from context, execute multiple

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Final Checkpoints Are Not Enough: Analyzing Latent Reasoning Faithfulness Along Training Trajectories

DGX agent

arXiv:2607.06648v1 Announce Type: cross Abstract: Latent reasoning methods perform multi-step inference entirely in the model's continuous hidden states, promising more compact and efficient reasoning

researcharxiv-cs-cl

9 Jul 2026

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting

arXiv:2607.06623v1 Announce Type: cross Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online. Labeled data are sc

PeTeR: Post-Training Robustification of Probabilistic Circuits

arXiv:2607.07671v1 Announce Type: new Abstract: Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However,

Physical activities enable scalable foundation modelling for broad-spectrum health prediction

arXiv:2607.06954v1 Announce Type: new Abstract: Wearable and mobile sensing technologies have demonstrated strong potential for health inference; however, most sensor models are designed for specific

applicationsarxiv-cs-lg

Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production

arXiv:2607.07052v1 Announce Type: cross Abstract: AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously sol

STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning

arXiv:2607.06629v1 Announce Type: new Abstract: Brain age -- the age inferred from a physiological recording -- is an emerging biomarker whose deviation from chronological age tracks neurological and

The Key to Going Linear: Analysis-Driven Transformer Linearization

model-releasesarxiv-cs-lg

arXiv:2607.07706v1 Announce Type: new Abstract: The quadratic cost of causal self-attention severely bottlenecks long-context transformer inference. While numerous post hoc linearization pipelines exi

b9909

llama.cpp is an open-source software library that performs inference on various large language models , and the project ships continuous build-tagged releases rather than traditional semantic versioni

b9913

b9913 is a build-tagged release from llama.cpp, an open-source software library that performs inference on various large language models. The project does not use traditional semantic versions; instea

b9916

b9916 is a release of llama.cpp, an open-source C/C++ implementation for LLM inference . The release represents part of the project's rapid development cycle, with binaries available for multiple plat

b9923

b9923 is a release build of llama.cpp, an open-source C/C++ project for LLM inference with minimal setup and state-of-the-art performance on various hardware . The specific b9923 build includes binary

b9925

Release b9925 of llama.cpp is a version update for the LLM inference in C/C++ project. This build is part of the project's frequent release cycle, which can publish multiple releases in a single day ,

b9929

The search results don't provide specific details about release b9929. Based on the available information and the context that llama.cpp releases frequently with tagged versions, b9929 is a specific b

b9931

Based on available information, b9931 is a release of llama.cpp, which is an open-source project for large language model inference in C/C++. As a commit-based release from the ggml-org/llama.cpp repo

Bridging Diffusion Pruning and Step Distillation with Teacher-Aligned Repair

arXiv:2607.06335v1 Announce Type: new Abstract: Diffusion models generate high-quality images, but their inference cost comes from two sources: large denoising networks and repeated denoising steps. E

safetyarxiv-cs-cv

Hardware

C4N, now GA: Delivering cloud’s highest per vCPU network and block storage I/O for x86 workloads

As organizations scale modern workloads — from high-throughput databases and network/security appliances to real-time analytics and AI/ML inference — network and block storage performance can quickly

hardwaregoogle-cloud-ai

CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries

arXiv:2607.05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.e., the ability to infer and adapt to a user's i

applicationsarxiv-cs-ai

Hardware

DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression

arXiv:2607.06523v1 Announce Type: new Abstract: Long-context language model inference is increasingly limited by the memory bandwidth and capacity required to store key-value caches, yet existing comp

hardwarearxiv-cs-ai

Agents

From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b

arXiv:2607.06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidenc

agentsarxiv-cs-ai

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

arXiv:2512.23236v4 Announce Type: replace-cross Abstract: Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key syst

Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models

model-releasesarxiv-cs-cv

arXiv:2607.05533v1 Announce Type: new Abstract: Computational pathology foundation models (PFMs) have advanced whole-slide image analysis. However, their size and inference cost hinder local deploymen

Hardware

Open, convenient and predictable: Introducing Provisioned Throughput

Provisioned Throughput gives you reserved inference capacity for frontier open models like MiniMax M3 and GLM-5.2. Token-based pricing, a 99% uptime SLA, and up to 90% lower cost than proprietary APIs

hardwaretogether-ai-blog

Tools

Read our blog: https://www.together.ai/blog/provisioned-throughput

Together AI announced provisioned throughput capabilities, a feature that allows users to reserve dedicated model inference capacity at predictable costs. This offering enables organizations to optimi

toolstogether-ai--x

This is a big update that I’m excited to finally share. We launched our first dev platform for AI media in 2020, offering a simple API and a…

applicationscristobal-valenzuela--x

This is a big update that I’m excited to finally share. We launched our first dev platform for AI media in 2020, offering a simple API and a diverse set of models. Back then, we built a powerful SDK a

A Decomposable Probe for Few-Step Diffusion Models: Prompt, Latent, and Score Selectivity across Backbone Families and Distillation Paradigms

arXiv:2607.03256v1 Announce Type: new Abstract: Few-step distilled diffusion students cut text-to-image inference from ~50 to 1-8 network evaluations, but the quality gap is usually summarised by a si

researcharxiv-cs-cv

A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition

arXiv:2204.02803v2 Announce Type: replace-cross Abstract: Sign language recognition from monocular video or 2D pose sequences is challenging, both because 3D information must be inferred from 2D obser

applicationsarxiv-cs-ai

AI-RAN on NPUs: Baseband Processing Without Baseband Chips

arXiv:2607.04224v1 Announce Type: cross Abstract: AI-RAN aims to unify artificial intelligence and radio access network workloads on a shared compute substrate. While this paradigm has so far been dem

APeB: Benchmarking Personalization Ability of Large Language Model Agents

arXiv:2607.03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract

ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs

arXiv:2601.07475v2 Announce Type: replace-cross Abstract: The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. Howe

b9895

Release b9895 of llama.cpp includes a fix for speculative inference out-of-bounds read in ngram-map on prompt shrink, with ~2x performance gains in PP_Speed for FP32, Q4_0 and Q8_0 models. The release

Best-of-Better-N: Generating Pre-Aligned Responses with In-Context Learning

arXiv:2607.03453v1 Announce Type: cross Abstract: Inference-time alignment methods, such as Best-of-N, offer a flexible alternative to training-based alignment by using reward models to select high-qu

CARD: Cross-component Audio Representation Distillation for Encoder-Free Audio Captioning

arXiv:2607.04619v1 Announce Type: cross Abstract: Modern automated audio captioning systems pair a frozen audio encoder with a large language model (LLM) via a trainable projector, incurring the encod

researcharxiv-cs-cl

Constrained Flow Matching via Lagrangian Dual Flows

arXiv:2607.04513v1 Announce Type: cross Abstract: Flow matching is a powerful tool for generative modeling, but emerging applications in robotics, planning, and physics require inference-time constrai

Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation

arXiv:2607.04809v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning. However, t

Distribution Matching Distillation Meets Reinforcement Learning

arXiv:2511.13649v5 Announce Type: replace Abstract: Distribution Matching Distillation (DMD) facilitates efficient inference by distilling multi-step diffusion models into few-step variants. Concurren

researcharxiv-cs-cv

DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation

arXiv:2607.05147v1 Announce Type: new Abstract: Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel dra

Facing US export controls, China's DeepSeek plans to make its own chips

model-releasesars-technica

Chinese AI startup DeepSeek is developing its own inference chips to reduce Nvidia dependence and break U.S. export controls, as China moves to protect its AI ecosystem. DeepSeek has been quietly deve

Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine

arXiv:2607.03787v1 Announce Type: new Abstract: Accurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery. Here, we introduce Open Drug Discovery Engin

researcharxiv-cs-ai

Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST

arXiv:2607.04241v1 Announce Type: cross Abstract: Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible

Agents

HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better

arXiv:2607.04884v1 Announce Type: new Abstract: We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, informati

agentsarxiv-cs-cv

Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data

model-releasesarxiv-cs-lg

arXiv:2607.04647v1 Announce Type: cross Abstract: Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but exist

Learning 3D Affordances for Blade Insertion in Cluttered Stowing

model-releasesarxiv-cs-cv

arXiv:2607.02549v1 Announce Type: new Abstract: Many manipulation tasks require reasoning about free-space affordances: discovering volumes where an extended rigid tool can safely navigate, complement

Agents

LLMoxie: Exploring Agentic AI for Scientific Software Development

arXiv:2607.02703v1 Announce Type: cross Abstract: In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a Lite

agentsarxiv-cs-ai

MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration

arXiv:2211.10872v2 Announce Type: replace Abstract: Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability t

researcharxiv-cs-cv

PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction

arXiv:2607.02881v1 Announce Type: new Abstract: Long-horizon behavior prediction aims to infer a user's next action based on a lengthy historical sequence, playing a crucial role in artificial intelli

researcharxiv-cs-cl

QiVC-Net: Quantum-Inspired Variational Convolutional Network, with Application to Biosignal Classification

model-releasesarxiv-cs-lg

arXiv:2511.05730v2 Announce Type: replace Abstract: In this paper, a learning framework is introduced which incorporates principles of probabilistic inference, variational optimization, and geometry-p

Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models

arXiv:2607.05199v1 Announce Type: new Abstract: Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited