Search: “inference”

SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs

model-releasesarxiv-cs-cv

arXiv:2608.25068v1 Announce Type: new Abstract: Depth pruning removes entire Transformer blocks to reduce the inference cost of large language models, but disrupts the hidden-state distributions expec

27 Aug 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models

DGX agent

arXiv:2608.26067v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a sin

model-releasesarxiv-cs-cv

27 Aug 2026

MoRF-AST: Calibrated Probabilistic Virtual Sensing for Structural Monitoring under Changing Operating Conditions

model-releasesarxiv-cs-lg

arXiv:2608.24531v1 Announce Type: cross Abstract: Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse

PonderPounce: A Pretrained MLLM as an Episode Context Engine for Robot Control

arXiv:2608.24115v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) can integrate long visual histories, reason under partial observability, and infer behavior from a few exampl

TurboT2VA: Fast Large-Scale Text-to-Video-Audio Generation via Score-Regularized Consistency Distillation

model-releasesarxiv-cs-cv

arXiv:2608.24674v1 Announce Type: new Abstract: Joint text-to-video-audio generation produces synchronized visual and acoustic content, but the long sampling trajectories and heterogeneous multimodal

Tutorials

ZODIAC: Zero-shot Octree-based Diffusion for Anatomical Completion

arXiv:2608.24422v1 Announce Type: new Abstract: Recovering the full 3D spine anatomy from intraoperative ultrasound is an ill-posed inverse problem, as the complete structure must be inferred from inc

tutorialsarxiv-cs-cv

A Latency-Aware Framework for Visuomotor Policy Learning on Industrial Robots

arXiv:2602.14255v2 Announce Type: replace Abstract: Industrial robots are increasingly deployed in construction and manufacturing tasks, where the deployment of end-to-end visuomotor policies is chall

safetyarxiv-cs-ro

DynaContext: Self-Improving Dynamic Contextualization of Optimized Prompts for Heterogeneous Parameter Extraction

arXiv:2608.22014v1 Announce Type: new Abstract: Automated prompt and skill optimization typically produces a single static instruction that is reused across inference instances until the next optimiza

LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization

arXiv:2608.21836v1 Announce Type: new Abstract: Large language models have become increasingly capable agents for low-level code and kernel optimization, but isolated kernel benchmarks provide only a

Power-Performance Characterization of TinyML Systems

arXiv:2608.21646v1 Announce Type: cross Abstract: TinyML systems are enabling machine learning (ML) inference at the edge. However, there is little quantitative analysis of such systems. This paper pr

Proxy reliance in large language model decisions is uncalibrated to predictive evidence

arXiv:2608.22887v1 Announce Type: new Abstract: Large language models (LLMs) are entering decisions in triage and lending, where task-relevant inference must be distinguished from impermissible proxy

The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction

arXiv:2608.22301v1 Announce Type: cross Abstract: Humans imitate at the level of intent: given a demonstration, we infer its goal and carry it out with whatever tools, objects, and layouts are at hand

VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting

arXiv:2603.14659v2 Announce Type: replace-cross Abstract: Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiabl

DiffVC-ONE: Diffusion-based Generative Video Compression with One-Step Video Diffusion Transformer

arXiv:2608.20515v1 Announce Type: new Abstract: Generative video compression can recover rich visual details at low bitrates, but simultaneously achieving high temporal consistency and low inference c

researcharxiv-cs-cv

Hardware

Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores

arXiv:2608.20725v1 Announce Type: cross Abstract: Convolution is a principal computational bottleneck in deep neural networks, and its efficiency depends on tight integration between algorithms and GP

hardwarearxiv-cs-cv

GeoExplain: Multimodal Reasoning based on Hierarchy of Visual Information in Street View

arXiv:2506.16633v3 Announce Type: replace-cross Abstract: Multimodal reasoning is a process of understanding, integrating and inferring information across different data modalities. It has recently at

Hidden Axis of Uncertainty: Latent-Posterior Alignment in Graph Neural Networks with Bayesian Output Layers

model-releasesarxiv-cs-lg

arXiv:2608.20758v1 Announce Type: new Abstract: Bayesian Neural Networks (BNNs) with Bayesian output layers provide a principled and tractable framework for quantifying predictive uncertainty, yet the

Agents

FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills

arXiv:2607.21596v2 Announce Type: replace Abstract: Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usu

agentsarxiv-cs-ai

21 Aug 2026

Learning how to Forget: Fine-tuning for Long-Context Sparse Attention

arXiv:2608.19920v1 Announce Type: new Abstract: A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer langua

safetyarxiv-cs-cl

21 Aug 2026

Local Ai

Write Once, Run Everywhere: The Axon DSL for Shape-Safe and Framework-Agnostic LLM Architectures

arXiv:2608.19889v1 Announce Type: new Abstract: The entire ecosystem of open-source language models effectively relies on a single platform. What if this platform was forced to shut down tomorrow? Imp

local-aiarxiv-cs-ai

21 Aug 2026

GEAR: Generative Expansion and Real Anchoring for Two-Stage Distillation of Tabular Foundation Models

arXiv:2608.18849v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong performance through in-context learning, but context-dependent inference imposes substantial latency and

researcharxiv-cs-lg

20 Aug 2026

Hardware

LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations

arXiv:2608.18503v1 Announce Type: new Abstract: The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significant energy and resourc

hardwarearxiv-cs-lg

20 Aug 2026

Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring

arXiv:2608.18341v1 Announce Type: cross Abstract: Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is constrained by power, latency, and deplo

20 Aug 2026

G-ReAct: Graph-Guided Deep Search via Structure-State Co-Evolution

arXiv:2608.01324v2 Announce Type: replace Abstract: Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing approaches

19 Aug 2026

Local Ai

When to Plan, When to Polish: Noise Level as a Granularity Axis for Diffusion Language Models

arXiv:2606.21802v2 Announce Type: replace Abstract: Standard tokenwise diffusion LMs keep training corruption and inference commitment at token granularity throughout denoising. At high noise, this le

local-aiarxiv-cs-cl

19 Aug 2026

Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL

arXiv:2608.15389v1 Announce Type: new Abstract: LLM-based Text-to-SQL progress is reported across heterogeneous benchmarks, backbones, and inference protocols, making cross-system comparison fragile.

CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs

arXiv:2608.14791v1 Announce Type: new Abstract: Abductive reasoning, often characterized as inference to the best explanation, is central to explanation under uncertainty, from everyday sense-making a

Efficient Audio-Visual Generation via Synchrony-Aware Cross-Modal Sparse Attention

arXiv:2608.15522v1 Announce Type: new Abstract: Recent audio-visual generation models can synthesize synchronized video and sound in a unified diffusion process, but their inference cost remains high

researcharxiv-cs-cv

Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning

arXiv:2608.14706v1 Announce Type: cross Abstract: Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedul

Probabilistic Circuits as Reasoning Machines in Artificial Intelligence (Part I)

arXiv:2608.16565v1 Announce Type: new Abstract: This cumulative habilitation thesis studies probabilistic circuits (PCs) as a powerful and tractable framework for reasoning and learning under uncertai

StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding

arXiv:2608.16320v1 Announce Type: new Abstract: Streaming video understanding demands direct responses from the causally observed prefix of an unfolding video. Existing systems add inference-time memo

safetyarxiv-cs-cv

Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning

model-releasesarxiv-cs-lg

arXiv:2608.16345v1 Announce Type: new Abstract: Pre-trained models (PTMs) provide a strong foundation for continual learning by offering stable representations that facilitate lightweight adaptation t

Wiola 13M, a Gated Spiral Attention Architecture for Parameter Efficient Small Language Models

arXiv:2608.14604v1 Announce Type: cross Abstract: Small language models in the ten to one hundred million parameter range are attractive for on device inference, rapid experimentation, and controlled

BiasTrace: Linking Reasoning Behaviours to Biased Outputs in LLMs

arXiv:2608.14161v1 Announce Type: new Abstract: LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in high-stakes applications. While prior work has mad

safetyarxiv-cs-ai

FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC

arXiv:2608.13774v1 Announce Type: new Abstract: Markov chain Monte Carlo (MCMC) requires only the ability to evaluate the likelihood, making it a common technique for inference in complex models. Howe

KV Cache Compression Through the Lens of Transform Coding

arXiv:2608.14191v1 Announce Type: cross Abstract: The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference. Existing quantization methods

QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction

arXiv:2608.13966v1 Announce Type: cross Abstract: As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aw

RGBX-Next: Towards Realistic Generative Rendering from G-Buffers

arXiv:2608.13929v1 Announce Type: new Abstract: Diffusion models have achieved impressive results in image, video, and streaming generation. However, compared to traditional 3D rendering, they still l

researcharxiv-cs-cv

From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks

arXiv:2608.12350v1 Announce Type: cross Abstract: The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cos

14 Aug 2026

Hardware

Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines

arXiv:2608.11770v1 Announce Type: new Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where

hardwarearxiv-cs-cv

Applications

Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting

arXiv:2608.12259v1 Announce Type: new Abstract: Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memor

applicationsarxiv-cs-lg

Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces

arXiv:2608.11354v1 Announce Type: new Abstract: Modern recommender systems treat observed actions as reliable proxies for user preferences, yet interactions often reflect exploration or comparison rat

safetyarxiv-cs-ai

Who Thinks Best Depends on How Long You Let Them: Budget-Dependent Rankings in LLM Evaluation

arXiv:2608.12150v1 Announce Type: new Abstract: Standard evaluation of large language models assumes stable model rankings across inference conditions. We challenge this assumption by varying the toke

Can Released LLM Vocabularies Support Token-Level Estimation of Hidden Corpora?

arXiv:2608.10690v1 Announce Type: new Abstract: Pretraining corpus composition shapes LLM capabilities, but it often remains hidden even when model weights are released. Prior work has inferred corpus

ImpactHO: Importance-Aware KV Cache Transfer for Multi-User Edge LLM Handover

arXiv:2608.10545v1 Announce Type: cross Abstract: Edge LLMs must preserve inference continuity when a user hands over between edge nodes, requiring key-value (KV) cache transfer to the target node. Ho

InSight-doc: Agentic Visual Perception for Long-Document Understanding

arXiv:2608.10628v1 Announce Type: cross Abstract: Long-document understanding often requires reasoning over many visually rich pages, making inference costly and prone to context rot. In this work, we

LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations

arXiv:2602.09924v4 Announce Type: replace-cross Abstract: Running LLMs with extended reasoning on every problem is expensive, but determining which inputs actually require additional compute remains c

safetyarxiv-cs-ai

Conversation as Measurement in Clinical Encounters: Observable Phase Structure, Partially Observable Patient State

arXiv:2608.08868v1 Announce Type: new Abstract: Many modern AI systems analyze conversational traces to infer aspects of human interaction and state, implicitly assuming that such information is recov