Search: “inference”

Best-of-N TTS Evaluation is Confounded by ASR Family Alignment

arXiv:2607.08256v1 Announce Type: cross Abstract: Best-of-N (BoN) inference improves content consistency in zero-shot text-to-speech by selecting from N candidates with an automatic speech recognition

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Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion

DGX agent

arXiv:2607.08241v1 Announce Type: new Abstract: Deploying classifier-free guidance (CFG) diffusion models under real-world compute budgets requires quantization, yet existing post-training quantizatio

model-releasesarxiv-cs-cv

10 Jul 2026

Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation

arXiv:2607.07767v1 Announce Type: cross Abstract: Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametri

applicationsarxiv-cs-lg

DominoTree: Conditional Tree-Structured Drafting with Domino for Speculative Decoding

model-releasesarxiv-cs-cl

arXiv:2607.08642v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting several tokens and verifying them in parallel. Block-diffusion drafters such as DFlash produc

It Takes a MAESTRO To Prune Bad Experts

arXiv:2607.08601v1 Announce Type: new Abstract: Sparsely-activated Mixture-of-Experts (MoE) language models achieve remarkable inference efficiency by activating only a small fraction of parameters pe

safetyarxiv-cs-cl

Joint Bayesian Parameter and Model Order Estimation for Low-Rank Probability Mass Tensors

arXiv:2410.06329v4 Announce Type: replace-cross Abstract: Obtaining a reliable estimate of the joint probability mass function (PMF) of a set of random variables from observed data is a significant ob

Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition

arXiv:2607.08374v1 Announce Type: cross Abstract: Personality recognition has traditionally been constrained by theory-dependent formulations, where models are trained to fit predefined psychological

Less Is More: Reducing Token Counts Without Compromising Performance

arXiv:2506.15138v2 Announce Type: replace-cross Abstract: Tokenization directly affects the inference efficiency of large language models, since fragmented tokenization increases sequence length and g

researcharxiv-cs-ai

OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

arXiv:2607.08766v1 Announce Type: new Abstract: We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR v

safetyarxiv-cs-cv

Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems

arXiv:2607.08010v1 Announce Type: new Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference

agentsarxiv-cs-cl

Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows

arXiv:2607.08740v1 Announce Type: new Abstract: Large language model (LLM) applications increasingly use explicit workflows for tool use, retrieval, branching, checkpointing, and human approval. Exist

Local Ai

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

local-aiarxiv-cs-lg

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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-

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

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