Search: “inference”

Real-Time Streamable Generative Speech Restoration with Flow Matching

arXiv:2512.19442v3 Announce Type: replace-cross Abstract: Diffusion-based generative models have greatly impacted the speech processing field in recent years, exhibiting high speech naturalness and sp

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation

DGX agent

arXiv:2604.19144v1 Announce Type: new Abstract: Recent years have witnessed growing interest in applying Large Reasoning Models (LRMs) to Machine Translation (MT). Existing approaches predominantly ad

model-releasesarxiv-cs-cl

22 Apr 2026

SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion

model-releasesarxiv-cs-ai

arXiv:2510.17925v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel at code-related tasks but often struggle in realistic software repositories, where project-specific APIs an

StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs

arXiv:2510.24738v2 Announce Type: replace-cross Abstract: Running offers substantial health benefits, but improper gait patterns can lead to injuries, particularly without expert feedback. While prior

local-aiarxiv-cs-lg

TEMPO: Scaling Test-time Training for Large Reasoning Models

arXiv:2604.19295v1 Announce Type: new Abstract: Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the r

TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation

arXiv:2604.19473v1 Announce Type: new Abstract: Generating high-quality videos from complex temporal descriptions that contain multiple sequential actions is a key unsolved problem. Existing methods a

Hardware

AdaCluster: Adaptive Query-Key Clustering for Sparse Attention in Video Generation

arXiv:2604.18348v1 Announce Type: new Abstract: Video diffusion transformers (DiTs) suffer from prohibitive inference latency due to quadratic attention complexity. Existing sparse attention methods e

hardwarearxiv-cs-cv

Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations

arXiv:2510.16458v2 Announce Type: replace Abstract: Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches a

safetyarxiv-cs-cl

Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records

arXiv:2507.20993v3 Announce Type: replace Abstract: We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These poli

Attraction, Repulsion, and Friction: Introducing DMF, a Friction-Augmented Drifting Model

arXiv:2604.18194v1 Announce Type: cross Abstract: Drifting Models [Deng et al., 2026] train a one-step generator by evolving samples under a kernel-based drift field, avoiding ODE integration at infer

local-aiarxiv-cs-cv

BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation

arXiv:2604.16514v1 Announce Type: new Abstract: Autoregressive vision-language models (VLMs) deliver strong multimodal capability, but their token-by-token decoding imposes a fundamental inference bot

Batch-Adaptive Causal Annotations

arXiv:2502.10605v3 Announce Type: replace-cross Abstract: Estimating the causal effects of interventions is crucial to policy and decision-making, yet outcome data are often missing or subject to non-

Cross-Family Speculative Decoding for Polish Language Models on Apple~Silicon: An Empirical Evaluation of Bielik~11B with UAG-Extended MLX-LM

model-releasesarxiv-cs-cl

arXiv:2604.16368v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by using a small draft model to propose k candidate tokens for a target model to verify. While effective

Dynamic Eraser for Guided Concept Erasure in Diffusion Models

arXiv:2604.16483v1 Announce Type: new Abstract: Concept erasure in Text-To-Image (T2I) diffusion models is vital for safe content generation, but existing inference-time methods face significant limit

Embedding Arithmetic: A Lightweight, Tuning-Free Framework for Post-hoc Bias Mitigation in Text-to-Image Models

model-releasesarxiv-cs-cv

arXiv:2604.18167v1 Announce Type: new Abstract: Modern text-to-image (T2I) models amplify harmful societal biases, challenging their ethical deployment. We introduce an inference-time method that reli

EvoComp: Learning Visual Token Compression for Multimodal Large Language Models via Semantic-Guided Evolutionary Labeling

arXiv:2604.17087v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language understanding tasks, yet their inference efficie

FireScope: Wildfire Risk Prediction with a Chain-of-Thought Oracle

model-releasesarxiv-cs-cv

arXiv:2511.17171v4 Announce Type: replace Abstract: Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer

Fully Analog Resonant Recurrent Neural Network via Metacircuit

arXiv:2604.17277v1 Announce Type: new Abstract: Physical neural networks offer a transformative route to edge intelligence, providing superior inference speed and energy efficiency compared to convent

local-aiarxiv-cs-lg

HorizonBench: Long-Horizon Personalization with Evolving Preferences

model-releasesarxiv-cs-cl

arXiv:2604.17283v1 Announce Type: new Abstract: User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent lif

How Much Cache Does Reasoning Need? Depth-Cache Tradeoffs in KV-Compressed Transformers

arXiv:2604.17935v1 Announce Type: new Abstract: The key-value (KV) cache is the dominant memory bottleneck during Transformer inference, yet little is known theoretically about how aggressively it can

Applications

How Training Data Shapes the Use of Parametric and In-Context Knowledge in Language Models

arXiv:2510.02370v3 Announce Type: replace Abstract: Large language models leverage both parametric knowledge acquired during pretraining and in-context knowledge provided at inference time. Crucially,

applicationsarxiv-cs-cl

Interpolating Discrete Diffusion Models with Controllable Resampling

model-releasesarxiv-cs-lg

arXiv:2604.17310v1 Announce Type: new Abstract: Discrete diffusion models form a powerful class of generative models across diverse domains, including text and graphs. However, existing approaches fac

Agents

MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation

arXiv:2604.18509v1 Announce Type: new Abstract: Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) to incorporate external knowledge at inference time. However, when

agentsarxiv-cs-cl

On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations

arXiv:2604.16518v1 Announce Type: new Abstract: Satellite constellations are transforming space systems from isolated spacecraft into networked, software-defined platforms capable of on-orbit percepti

safetyarxiv-cs-ro

Tutorials

Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces

arXiv:2604.17690v1 Announce Type: cross Abstract: Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization

tutorialsarxiv-cs-lg

Reducing Peak Memory Usage for Modern Multimodal Large Language Model Pipelines

arXiv:2604.16734v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have recently demonstrated strong capabilities in understanding and generating responses from diverse visual in

RefineStat: Efficient Exploration for Probabilistic Program Synthesis

arXiv:2509.01082v3 Announce Type: replace Abstract: Probabilistic programming offers a powerful framework for modeling uncertainty, yet statistical model discovery in this domain entails navigating an

RS-HyRe-R1: A Hybrid Reward Mechanism to Overcome Perceptual Inertia for Remote Sensing Images Understanding

arXiv:2604.17504v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training substantially improves remote sensing vision-language models (RS-VLMs). However, when handling complex remote

local-aiarxiv-cs-cv

Support Sufficiency as Consequence-Sensitive Compression in Belief Arbitration

arXiv:2604.16434v1 Announce Type: cross Abstract: When a system commits to a hypothesis, much of the evidential structure behind that commitment is lost to compression. Standard accounts assume that s

The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus

model-releasesarxiv-cs-cl

arXiv:2604.16913v1 Announce Type: cross Abstract: Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposa

ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding

arXiv:2510.00546v5 Announce Type: replace Abstract: Large Reasoning Models (LRMs) allocate substantial inference-time compute to Chain-of-Thought (CoT) reasoning, improving performance on mathematics,

researcharxiv-cs-cl

CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder

arXiv:2604.15611v1 Announce Type: cross Abstract: Latent diffusion models have emerged as powerful generative models in medical imaging, enabling the synthesis of high quality brain magnetic resonance

researcharxiv-cs-ai

Tutorials

Estimating Joint Interventional Distributions from Marginal Interventional Data

arXiv:2409.01794v2 Announce Type: replace-cross Abstract: In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum

tutorialsarxiv-cs-lg

Tutorials

Fusing Cellular Network Data and Tollbooth Counts for Urban Traffic Flow Estimation

arXiv:2604.15782v1 Announce Type: new Abstract: Traffic simulations, essential for planning urban transit infrastructure interventions, require vehicle-category-specific origin-destination (OD) data.

tutorialsarxiv-cs-lg

Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration

arXiv:2512.07173v4 Announce Type: replace Abstract: We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate the dynamic n

Agents

Just Type It in Isabelle! AI Agents Drafting, Mechanizing, and Generalizing from Human Hints

arXiv:2604.15713v1 Announce Type: cross Abstract: Type annotations are essential when printing terms in a way that preserves their meaning under reparsing and type inference. We study the problem of c

agentsarxiv-cs-ai

Repurposing 3D Generative Model for Autoregressive Layout Generation

model-releasesarxiv-cs-cv

arXiv:2604.16299v1 Announce Type: new Abstract: We introduce LaviGen, a framework that repurposes 3D generative models for 3D layout generation. Unlike previous methods that infer object layouts from

Spectral Tempering for Embedding Compression in Dense Passage Retrieval

arXiv:2603.19339v2 Announce Type: replace-cross Abstract: Dimensionality reduction is critical for deploying dense retrieval systems at scale, yet mainstream post-hoc methods face a fundamental trade-

local-aiarxiv-cs-ai

Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants

arXiv:2604.15727v1 Announce Type: new Abstract: Large language models exhibit systematic limitations in structured logical reasoning: they conflate hypothesis generation with verification, cannot dist

researcharxiv-cs-ai

Hardware

Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective

arXiv:2511.00739v3 Announce Type: replace Abstract: Agentic AI serving converts monolithic LLM-based inference to autonomous problem-solvers that can plan, call tools, perform reasoning, and adapt on

hardwarearxiv-cs-ai

Transformer Neural Processes - Kernel Regression

model-releasesarxiv-cs-ai

arXiv:2411.12502v4 Announce Type: replace-cross Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic p

VADF: Vision-Adaptive Diffusion Policy Framework for Efficient Robotic Manipulation

arXiv:2604.15938v1 Announce Type: new Abstract: Diffusion policies are becoming mainstream in robotic manipulation but suffer from hard negative class imbalance due to uniform sampling and lack of sam

safetyarxiv-cs-ro

Compressing Sequences in the Latent Embedding Space: K-Token Merging for Large Language Models

arXiv:2604.15153v1 Announce Type: new Abstract: Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self-attention scales quadratically

researcharxiv-cs-cl

DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule

model-releasesarxiv-cs-lg

arXiv:2602.22699v2 Announce Type: replace-cross Abstract: SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through m

Applications

From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill

arXiv:2510.08055v2 Announce Type: replace Abstract: Large Language Model (LLM) inference in production must meet stringent service-level objectives for both time-to-first-token (TTFT) and time-between

applicationsarxiv-cs-lg

MapSR: Prompt-Driven Land Cover Map Super-Resolution via Vision Foundation Models

arXiv:2604.14582v1 Announce Type: new Abstract: High-resolution (HR) land-cover mapping is often constrained by the high cost of dense HR annotations. We revisit this problem from the perspective of m

Mean Flow Policy Optimization

arXiv:2604.14698v1 Announce Type: new Abstract: Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL). However, their iterative generative

MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution

arXiv:2602.07529v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequ