Search: “inference”

PE-OPSD: Internalizing Prompt Enhancement into Flow-matching Models via On-Policy Self-Distillation

arXiv:2609.36638v1 Announce Type: cross Abstract: Text-to-image users often provide concise and underspecified prompts, whereas generative models benefit from detailed textual conditions for reliable

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Periodic Weak Spots: Phase Sensitivity from Chunked KV-Cache Compression

DGX agent

arXiv:2609.36322v1 Announce Type: cross Abstract: Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer

model-releasesarxiv-cs-ai

30 Sept 2026

PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units

arXiv:2603.15106v2 Announce Type: cross Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requir

Reactive Real-Time Flow Policies via Asynchronous Distribution Alignment

arXiv:2609.36540v1 Announce Type: new Abstract: Generalist robot policies such as vision-language-action models (VLAs) have achieved remarkable generalization, but their inference delays can conflict

safetyarxiv-cs-ro

Local Ai

Reducing compounded round trips with aggressively structured compaction.

Ollama’s new limits and max plans are worrying me, so I’ve been going harder on my harness. (Thanks to everyone who’s signed up for the waitlist so far.) Wanted to share how my v1.10.0 will dramatical

local-air-ollama

Reliability Testing of Medical Model Performance under Distributed Deployment

model-releasesarxiv-cs-ai

arXiv:2609.36525v1 Announce Type: cross Abstract: Distributed inference has become an indispensable part of deploying medical models under practical latency, memory, and throughput constraints. Althou

ReLOBGen: Replayable Limit Order Book Message Generation

arXiv:2609.35867v1 Announce Type: new Abstract: We propose ReLOBGen, a method for generating limit order book (LOB) messages that are replayable by construction. Replayability is required for closed-l

Representation Dynamics Reveal Semantic Saliency and Similarity for Visual Token Pruning in MLLMs

arXiv:2609.36916v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) incur high inference latency from long visual token sequences. Existing pruning methods commonly use attention

RS-OPSD: Reliable Privileged On-Policy-Self-Distillation for Ultra-High-Resolution Remote Sensing VQA

arXiv:2609.38072v1 Announce Type: new Abstract: Ultra-high-resolution (UHR) remote sensing visual question answering (VQA) requires models to resolve small visual evidence within extremely large image

SEED: Self-Speculative Decoding via Implicit Encoder-Decoder

arXiv:2609.36590v1 Announce Type: new Abstract: Self-speculative decoding accelerates large language model (LLM) inference by drafting tokens from the target model itself, but faces a sharp tradeoff b

researcharxiv-cs-cl

TReVS: Integrating Textual Relevance and Visual Saliency for Efficient Vision-Language Model Token Pruning

arXiv:2609.37581v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) excel at visual understanding and reasoning but often incur substantial inference costs due to the large number of visua

researcharxiv-cs-ai

Triadic Linear Attention: Three-Dimensional Recurrent States for Long-Context Sequence Modeling

model-releasesarxiv-cs-cl

arXiv:2609.36529v1 Announce Type: cross Abstract: Recurrent neural networks (RNNs) compress the historical context into a memory state of fixed size, thus allowing for constant-time inference. The mem

UltraMatch: Transport Path Routing for Ultra-Fast and Memory-Efficient Image Matching

arXiv:2609.36980v1 Announce Type: new Abstract: Despite recent advances in accuracy and efficiency, coarse matching remains an indispensable yet costly stage in existing semi-dense matchers due to den

Vulnerability Discovery and Exploitation Trends in the AI Era

model-releasesgoogle-cloud-ai

Written by: Robin Grunewald, Supriya Mazumdar, Kelli Vanderlee Introduction Google Threat Intelligence Group (GTIG) examines vulnerability disclosure and exploitation statistics to evaluate the impact

Local Ai

Where Should Physics Enter a Molecular Crystal Generator?

arXiv:2609.36398v1 Announce Type: cross Abstract: Generative models make molecular crystal structure prediction fast, but their samples still exhibit geometric and packing violations. Physics can be i

local-aiarxiv-cs-ai

WISE-ATTA: When to Ask for Labels in Budgeted Active Test-Time Adaptation

arXiv:2609.37687v1 Announce Type: new Abstract: Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively querying

researcharxiv-cs-ai

WUSH-KV: KV Cache Quantization with Data-Adaptive Transforms

arXiv:2609.38121v1 Announce Type: new Abstract: KV cache memory and bandwidth costs grow with context length and batch size, which limits efficient long-context inference. To address this bottleneck,

A Spectral Theory of Compositional Learning

arXiv:2609.33708v1 Announce Type: new Abstract: How does compositional reasoning emerge during learning? We address this question by mathematically analyzing the learning dynamics of deep linear netwo

Allspark: Weak to Strong Transfer via Alternating Chain of Thought

arXiv:2609.32913v1 Announce Type: new Abstract: Recent progress in frontier models has renewed interest in large-scale reinforcement learning (RL), but the cost of generating large-model rollouts make

Beyond Gradient Flow: Identifiability and Recovery from Distribution Snapshots

arXiv:2609.35060v1 Announce Type: new Abstract: Inferring dynamics from snapshots of evolving distributions is fundamentally underdetermined: the Fokker-Planck equation constrains the drift F only thr

Industry

Bring near-Astra intelligence to everyday work with GPT-6.1 Sol on Amazon Bedrock

GPT‑6.1 Sol is now generally available on Amazon Bedrock, providing enhanced reasoning for coding, computer use, and professional workflows. It runs on a high‑performance inference engine optimized fo

industryaws-ml-blog

CAESAR: Clustering via Autonomous Embedding-Space Agglomerative Reorganization

arXiv:2609.32570v1 Announce Type: new Abstract: Clustering algorithms that operate on nearest-neighbor graphs, such as FINCH (First Integer Neighbor Clustering Hierarchy), depend heavily on the qualit

Cartridges++: KV Cache Compression without Off-Context Derailment

arXiv:2609.35621v1 Announce Type: new Abstract: Serving long documents to a Large Language Model (LLM) repeatedly is expensive: computations grow with context length, and the memory footprint of the k

Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting

arXiv:2609.34638v1 Announce Type: new Abstract: Test-time adaptation (TTA) is a promising paradigm for handling distribution shift in time-series forecasting (TSF), where models adapt at inference tim

EffiPair: Improving the Efficiency of LLM-generated Code with Differential Execution Feedback

arXiv:2604.05137v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can generate functionally correct programs that differ substantially in execution efficiency. Existing inference-

Ego-Forge: Text and Geometric-Attention Free Exo-to-Egocentric Video Generation

arXiv:2609.35368v1 Announce Type: new Abstract: Exo-to-egocentric video generation aims to synthesize what a person sees from their own viewpoint given third-person footage and a target head trajector

Tutorials

Evolving Dexterous Robots from Scratch

arXiv:2609.33101v1 Announce Type: cross Abstract: Little is known about how to manually design agents capable of dexterous manipulation. Some design principles have been inferred from close examinatio

tutorialsarxiv-cs-lg

FlexLoop: Depth-Elastic Looped Policies for Adaptive Test-Time Computation in Deep RL

arXiv:2609.34488v1 Announce Type: new Abstract: Looped architectures scale computation by reusing the same parameters across recurrent steps, and recent work shows that they substantially improve deep

safetyarxiv-cs-lg

GeoShrink: Accelerating Diffusion Transformers with Two Lines of Code

arXiv:2609.33723v1 Announce Type: new Abstract: Diffusion transformers incur substantial inference cost through repeated model evaluations along a sampling trajectory. We introduce GeoShrink, a traini

GradLev: Token-Parallel Test-Time Training Via Costate Prediction

arXiv:2609.34174v1 Announce Type: new Abstract: Test-time training (TTT) allows a model to improve its predictions at inference time by updating weights after every observed token. However, sequential

HapticWorld: an Interactive World Simulator with Real-time Torque Feedback

arXiv:2609.31924v1 Announce Type: new Abstract: Contact-rich manipulation depends on force sensing that is hard to infer from visual signals alone, both for collecting demonstrations and for training

safetyarxiv-cs-ro

Harmonizing Spectral Evolution in Conditional Flow Matching for TTS

arXiv:2609.34431v1 Announce Type: new Abstract: Conditional Flow Matching (CFM) models for text-to-speech (TTS) suffer from incoherent frequency evolution during inference. While similar spectral imba

How Much Imprecision is Enough Imprecision in my Classifier? A Practical Elicitation Procedure

arXiv:2609.33352v1 Announce Type: new Abstract: Set-valued classifiers, whether derived from precise probabilities and an adapted cost function, from convex sets with a robust inference mechanism, or

Agents

ICMAPE: In-Context Multiagent Pure Exploration

arXiv:2609.33986v1 Announce Type: new Abstract: In some multi-agent systems, the quantity to be optimized is not an externally specified reward but the information acquired about unknown properties of

agentsarxiv-cs-lg

Learning with Object-centric Representations of Tactile Interactive Perception for Robot Manipulation

arXiv:2609.33235v1 Announce Type: new Abstract: Implicit object properties that are difficult to directly infer from vision, such as material, container contents, or softness, can be revealed through

safetyarxiv-cs-ro

Logic Gate Networks and Lookup Table Networks as Lightweight Hardware Classifiers for Inter-patient ECG Arrhythmia Classification

arXiv:2609.32854v1 Announce Type: new Abstract: Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) offer a promising approach for very low power inference due to their us

Loop Dropout: Regularizing Shared Updates in Looped Language Models

arXiv:2609.34218v1 Announce Type: new Abstract: Looped language models separate computational depth from parameter count by repeatedly applying the same transformer block. Adapting these models requir

Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation

arXiv:2609.32839v1 Announce Type: cross Abstract: Recommender systems rely heavily on heterogeneous behavioral feedback to infer user preference. Although abundant, these signals are imperfect measure

Mycelium: A Generalizable Cross-Grid Multi-Task Model for Electrical Distribution Systems

arXiv:2609.33120v1 Announce Type: new Abstract: Electrical distribution grid operations require inference across heterogeneous networks from sparse, noisy, and incomplete time series measurements. In

Nudgeability: Reasoning Models Follow Confidence Signals Without Tracking Their Own Competence

arXiv:2609.34572v1 Announce Type: cross Abstract: Reasoning language models that can call tools must decide during inference whether to answer unaided or delegate. Any self-reflection mechanism for th

Optimizing H-Graph Hybridization for Diffusion-Guided RRT

arXiv:2609.32897v1 Announce Type: new Abstract: Sampling-based motion planners guided by diffusion models produce high-quality trajectories in a single run, yet the stochastic diversity available at i

Local Ai

Output-aware Residual Stream Pruning for Large Language Models

arXiv:2609.35579v1 Announce Type: new Abstract: Residual stream pruning methods reduce inference cost by shrinking the model's hidden dimension, but existing approaches typically choose these dimensio

local-aiarxiv-cs-lg

PI-NOMT: Physics-Informed Neural Optimal Mass Transport for Brain Fluid Dynamics

arXiv:2609.33857v1 Announce Type: new Abstract: Recovering hidden transport mechanisms from sparse spatiotemporal observations is a fundamental inverse problem in scientific machine learning. In brain

Quantization-Aware Pre-Training with Constrained Empirical Weight Distribution

arXiv:2609.32659v1 Announce Type: new Abstract: Quantization-Aware Pre-Training (QAPT) can increase the inference efficiency of DNNs, but a problematic behaviour known as rounding boundary weight osci

Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning

arXiv:2609.32836v1 Announce Type: cross Abstract: Radiomap blind prediction aims to infer radiomaps from observable representations of the propagation environment and base station configuration withou

safetyarxiv-cs-lg

ReDrive: Shaping Representations with World Modeling for End-to-End Driving

arXiv:2609.33854v1 Announce Type: new Abstract: Driving policies require capabilities of scene understanding and future evolution prediction. To achieve this goal, current end-to-end models typically

Relevance Does Not Imply Applicability: Experience Activation for Personal GUI Agents

arXiv:2609.33304v1 Announce Type: new Abstract: Personal Graphical User Interface (GUI) agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recu

Resolution as a First-Class Decision: Task-Conditioned Routing for Efficient Multimodal Large Language Models

arXiv:2609.34942v1 Announce Type: new Abstract: The inference efficiency of Multimodal Large Language Models (MLLMs) is severely constrained by massive visual token sequences induced by high-resolutio