Search: “inference”

SoK: Where Do Flow Labels Come From? Auditing Label Provenance in Encrypted Traffic Benchmarks

model-releasesarxiv-cs-lg

arXiv:2609.02140v1 Announce Type: cross Abstract: Encrypted traffic classification infers semantics beyond the flow record from transport-layer observables, and supervised training rests on labels tha

3 Sept 2026

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Local Ai

A Study of Hidden-State Optimization Order in Predictive Coding Networks

DGX agent

arXiv:2609.00686v1 Announce Type: cross Abstract: Local learning methods offer an alternative to end-to-end backpropagation, but their unstructured local objectives can produce weak feature learning i

local-aiarxiv-cs-ai

2 Sept 2026

Applications

Autoresearch for Marketplace Catalogs: From Legacy Forms to AI-Native Matching

arXiv:2609.00274v1 Announce Type: new Abstract: Two-sided service marketplaces are moving from deterministic request-form intake to AI-native probabilistic matching, enabled by large language models (

applicationsarxiv-cs-ai

CaRL-EM: Cost-Aware Reinforcement Learning for Entity Matching with LLMs

arXiv:2609.01195v1 Announce Type: new Abstract: Entity matching (EM) requires fine-grained contextual understanding and domain knowledge. Recent work shows that large language models (LLMs) can serve

Dependency-Aware Chain-of-Thought Compression for Financial Reasoning

arXiv:2609.00413v1 Announce Type: new Abstract: Chain of thought prompting improves complex reasoning, but its long intermediate traces create substantial inference cost and hinder practical deploymen

Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning

arXiv:2609.01449v1 Announce Type: new Abstract: Diffusion models and recursive reasoners are both iterative, but they carry information across iterations differently. We add a persistent hidden state

researcharxiv-cs-lg

EEG-AS: Instance-Level Foundation Model Selection for EEG Foundation Models via Behavior Reconstruction

arXiv:2609.00653v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a non-invasive technique for measuring neural activity and has been widely used in neuroscience applications. Recent a

From Base Rollouts to RL Reasoning: A Budgeted Search Perspective

arXiv:2609.01274v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning, but how these gains relate to inference-time decoding and searc

OCGQuant: Outlier-Companion Grouping for NVFP4 Quantization

arXiv:2609.00066v1 Announce Type: cross Abstract: NVFP4 is an efficient microscaling format for low-bit inference, but activation outliers can still degrade quantization accuracy within NVFP4 blocks.

Position: Privacy Is a Claim, Not a Property of Synthetic Data

arXiv:2609.01273v1 Announce Type: new Abstract: Synthetic data has become a common component of machine learning research. While widely adopted, its use in privacy-sensitive contexts has quietly shift

researcharxiv-cs-lg

SFAD: Speculative Factuality-Aware Decoding

arXiv:2609.00796v1 Announce Type: new Abstract: As one of the most critical challenges in large language models, contextual faithfulness directly determines their reliability in knowledge-intensive ap

Some Emotions Run Deeper: Layer-wise Probing and Causal Intervention in Large Language Models

arXiv:2609.01279v1 Announce Type: cross Abstract: Emotion is expressed in text along a wide spectrum, from surface lexical cues to inferences entangled with content. Most layer-wise analyses of emotio

Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection

model-releasesarxiv-cs-cv

arXiv:2603.02974v2 Announce Type: replace Abstract: DINO models provide rich patch-level representations that have recently enabled strong performance in unsupervised anomaly detection (UAD). Most exi

VIBE-Bench: Evaluating Personalized Large Language Models When Profiles Don't Mean Preferences

arXiv:2609.00921v1 Announce Type: new Abstract: Personalized Large Language Models (PLLMs) aim to tailor responses to individual users, where a central challenge is preference reasoning: inferring que

Applications

Vision Is Not Overhead: One-Pass Block Drafting for Lossless Speculative Decoding in Vision-Language Models

arXiv:2609.00355v1 Announce Type: new Abstract: Speculative decoding accelerates generation without changing its output, yet on vision-language models (VLMs) it has been caught in a self-defeating cyc

applicationsarxiv-cs-ai

Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment

arXiv:2609.00055v1 Announce Type: cross Abstract: Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference, limiting their utility without task-sp

safetyarxiv-cs-ai

A Universal Context-Reuse Layer for Cross-Model KV Sharing

arXiv:2608.30963v1 Announce Type: cross Abstract: Modern large language model (LLM) serving systems increasingly operate over repeated or shared context, yet each model typically performs its own pref

Adaptive teachers for amortized samplers

arXiv:2410.01432v3 Announce Type: replace Abstract: Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized de

safetyarxiv-cs-lg

ALTSTEER: Selective Safety Steering for Moving Beyond Hard Refusals to Constructive Alternatives

arXiv:2608.30197v1 Announce Type: new Abstract: Safety alignment is essential for deploying large language models, requiring systems to prevent harmful compliance while preserving helpfulness on benig

Capability-Stratified Degradation in Ternary Language Models

arXiv:2608.28809v1 Announce Type: new Abstract: Extreme low-bit inference offers a route toward smaller models and constrained deployment. Ternary language models restrict weights to {-1,0,+1}, approa

Controlling Refusal Behavior of LLMs via Stiefel-Constrained Rotation Steering

arXiv:2608.30986v1 Announce Type: cross Abstract: Activation steering has emerged as a lightweight approach for controlling model refusal at inference time. A growing line of research explores trainab

DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark

arXiv:2608.30413v1 Announce Type: new Abstract: Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of ne

Designing an Auditable LLM-Supported Workflow for Qualitative Thematic Analysis

arXiv:2608.30543v1 Announce Type: new Abstract: Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological tr

local-aiarxiv-cs-ai

EVAR: Evidence-Validated Hypothesis Admission for Budget-Aware Narrative Reasoning

arXiv:2608.29835v1 Announce Type: new Abstract: Large language models (LLMs) often produce fluent but weakly grounded conclusions when reasoning over non-interactive, long-form narratives. A central f

researcharxiv-cs-cl

Applications

Federated Personalization of Early-Exit Networks

arXiv:2601.10015v2 Announce Type: replace Abstract: Personalized Federated Learning (PFL) excels at tailoring client-specific models, which is particularly critical for decentralized and heterogeneous

applicationsarxiv-cs-lg

FreeFuse: Multi-Subject LoRA Fusion via Adaptive Token-Level Routing at Test Time

arXiv:2510.23515v3 Announce Type: replace Abstract: This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRA

local-aiarxiv-cs-cv

GarmentWeaver: Schema-Aware Structured Synthesis for Multimodal Sewing Patterns

arXiv:2608.30550v1 Announce Type: new Abstract: Multimodal Sewing pattern generation aims to infer executable sewing patterns from design cues such as sketches and textual descriptions. As an interpre

local-aiarxiv-cs-ai

GramLoop: Training-Free Gram-Gated Replay for Robust Dense Prediction

model-releasesarxiv-cs-cv

arXiv:2608.29113v1 Announce Type: new Abstract: We aim to improve frozen DINOv3 dense-prediction models under distribution shift by adding inference computation inside the visual backbone, without cha

Improving Image-to-Image Translation via a Rectified Flow Reformulation

arXiv:2603.20186v2 Announce Type: replace Abstract: In this work, we propose Image-to-Image Rectified Flow Reformulation (I2I-RFR), a practical plug-in reformulation that recasts standard I2I regressi

researcharxiv-cs-cv

Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting

arXiv:2608.28666v1 Announce Type: new Abstract: Video-language models (VLMs) remain brittle on tasks that require tracking events over time and grounding answers in specific spatial regions. We propos

researcharxiv-cs-cv

JPO: Juris Policy Optimization for Structured Legal Reasoning in Criminal Judgment Prediction

arXiv:2608.29616v1 Announce Type: new Abstract: Criminal judgment prediction requires models to infer statutory articles, charges, and sentencing outcomes from case facts. Unlike standard classificati

Agents

Last Step Matters: Early Uncertainty Cannot Predict Failure in Long-Horizon Agents

arXiv:2608.29685v1 Announce Type: new Abstract: Early failure prediction is important for long-horizon agents, as it enables timely intervention and can reduce inference and tool-use costs. Uncertaint

agentsarxiv-cs-lg

Memory-Efficient Training-Free Acceleration of Diffusion Transformers with BaryCache

arXiv:2608.28670v1 Announce Type: new Abstract: Diffusion Transformers achieve high-fidelity image and video generation, but their iterative sampling remains expensive, for each denoising step require

local-aiarxiv-cs-cv

Tutorials

Mitigating Over-Optimization in PRM-Guided Search in Mathematical Reasoning by Optimizing the Guide

arXiv:2608.30051v1 Announce Type: new Abstract: Process reward models (PRMs) provide dense step-level guidance for search-based reasoning, enabling inference-time compute to be allocated toward promis

tutorialsarxiv-cs-ai

Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval

arXiv:2608.30553v1 Announce Type: cross Abstract: Generative Retrieval (GR) has emerged as a promising paradigm by mapping queries directly to Semantic IDs (SIDs) with powerful representation capabili

safetyarxiv-cs-ai

Read the Room, Read the Image: Understanding Indirect Speech Acts in Multimodal Visual Contexts

arXiv:2608.30270v1 Announce Type: new Abstract: Indirect speech acts (ISAs) require pragmatic reasoning over context, as directive intent can- not be inferred from surface form alone. Prior text-based

Agents

Rethinking Language's Role in Efficient VLA for Autonomous Vehicles: Toward Smarter, Trustworthy Driving

arXiv:2608.30144v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are reshaping autonomous driving (AD) by unifying perception, reasoning, and control through language, enabling sema

agentsarxiv-cs-ro

ReTrace: Rejected-Trajectory Conditioning for Speculative Decoding

arXiv:2608.29748v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive language model inference by having a lightweight draft model propose multiple candidate tokens, which ar

local-aiarxiv-cs-cl

Selection, Representation, and Execution in Sparse Fourier Neural Operators

model-releasesarxiv-cs-lg

arXiv:2608.30070v1 Announce Type: new Abstract: Sparse representations are often expected to make models smaller and also reduce inference cost. For Fourier Neural Operators (FNOs), these objectives a

SingProbe Technical Report

arXiv:2608.30703v1 Announce Type: cross Abstract: Runtime guardrails are essential for reliable large language model (LLM) deployment, yet existing approaches typically rely on independent, external m

TopoCompress: Long Context Compression via Graph-Wired Semantic Trajectories

arXiv:2608.30811v1 Announce Type: new Abstract: Long-context compression is essential for reducing the cost and latency of large language model inference. However, existing methods can fragment import

Towards an Expressivity-Normalized Energy-Demand Comparison of ANNs and SNNs

arXiv:2608.29869v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are often regarded as energy-efficient alternatives to artificial neural networks (ANNs), yet their advantage depends cri

researcharxiv-cs-lg

Towards Cognitive Process-Aware Proactive Writing Support

arXiv:2608.30424v1 Announce Type: cross Abstract: Large language models can support writing, but existing tools require users to explicitly articulate prompts-particularly burdensome in creative writi

Training-Free Hidden-State Refinement for Flow-Matching Image Generators

arXiv:2608.29160v1 Announce Type: cross Abstract: We aim to improve frozen flow-matching image generators by adding inference computation inside the denoiser, without changing model weights or the out

Universal Redundancies in Time Series Foundation Models

arXiv:2602.01605v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need for

safetyarxiv-cs-lg

Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment

arXiv:2608.29635v1 Announce Type: new Abstract: We study unsupervised hypergraph alignment, where the goal is to infer node correspondences between two hypergraphs using only structural information, w

safetyarxiv-cs-lg

When Models Hear What They Expect: Diagnosing Prosodic Heuristics in Multimodal Sarcasm Detection

arXiv:2608.30204v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) process speech and text jointly, yet whether they exploit prosodic cues for pragmatic inference or rely on surf

Ladders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation