Search: “inference”

Compiled AI: Deterministic Code Generation for LLM-Based Workflow Automation

arXiv:2604.05150v2 Announce Type: replace-cross Abstract: We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which wor

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Model Releases

COSI-Lab: Conference Living Lab for Modeling Multi-Perspective Multimodal Social Intention

DGX agent

arXiv:2607.28649v1 Announce Type: cross Abstract: COSI-Lab presents a multimodal, multi-sensor dataset of an interdisciplinary scientific workshop containing 32 academics at an international conferenc

model-releasesarxiv-cs-ai

3 Aug 2026

DeepSeek V4-Flash (284B MoE) at 33 tok/s single / 68 tok/s aggregate on 2× RTX 3090 + a used quad-Xeon DDR4 server — full config

model-releasesr-localllama

Ran DeepSeek V4-Flash-0731 — the full official checkpoint, not a re-quant — on commodity used hardware. Sharing because I couldn't find anyone else publishing Ampere results for this engine. Why bothe

Deformable Medical Image Registration with KAN-based Implicit Neural Representations

arXiv:2509.22874v2 Announce Type: replace Abstract: Deformable image registration (DIR) is central to medical image analysis, supporting spatial alignment for longitudinal studies and multi-modal fusi

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Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification

arXiv:2607.28907v1 Announce Type: cross Abstract: Deep joint source--channel coding (Deep JSCC) enables visual semantic transmission by mapping inputs directly to channel symbols and task outputs, but

safetyarxiv-cs-cv

Tutorials

DualDiT: A Conditional Dual-Output Diffusion Transformer for Joint OCT Image and Segmentation Mask Generation

arXiv:2607.29337v1 Announce Type: cross Abstract: Background and Objective: Generating realistic medical images with anatomically accurate segmentation masks helps address the shortage of annotated da

tutorialsarxiv-cs-ai

DuET: Dual Expert Trajectories for Diffusion Image Editing

arXiv:2606.13303v2 Announce Type: replace Abstract: Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent sour

researcharxiv-cs-cv

Evidence-Type Competition: When Can Interventional Data Teach Language Models Causal Direction?

arXiv:2607.29484v1 Announce Type: new Abstract: Interventional data is widely regarded as the gold standard for teaching models causal reasoning. We test this assumption in a fully controlled syntheti

Evolving language compositionality in a frequency-structured meaning space

arXiv:2607.29642v1 Announce Type: new Abstract: The iterated learning model was introduced to investigate language evolution: the way in which the characteristic properties of human languages have bee

FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents

arXiv:2607.28945v1 Announce Type: new Abstract: Synthetic tabular data is increasingly used in privacy-preserving data sharing, data augmentation, and to mitigate downstream classifier bias. State-of-

safetyarxiv-cs-lg

Tutorials

FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution

arXiv:2607.29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands

tutorialsarxiv-cs-ai

From Code Review to Code Critique: Intent, Drift, and Spotlight for AI-Generated Diffs at Scale

arXiv:2607.29516v1 Announce Type: cross Abstract: AI coding agents are generating code at volumes that exceed the capacity of traditional peer review. At the same time, existing AI code review tools o

agentsarxiv-cs-ai

HenTwin: A Multimodal Digital Twin Framework for Longitudinal Biological State Monitoring in Laying Hens

arXiv:2607.28652v1 Announce Type: cross Abstract: Early-life monitoring in laying hens remains constrained by fragmented single-modality sensing and the absence of formal system-level state representa

Local Ai

HERO: History-Enriched Rollout Training for Long-Horizon Autoregressive Neural Operators

arXiv:2607.29135v1 Announce Type: cross Abstract: Neural operators provide fast surrogates for time-dependent partial differential equations (PDEs) by applying a learned evolution operator recursively

local-aiarxiv-cs-ai

Implicit Reasoning for Large Language Model-based Generative Recommendation

arXiv:2606.14142v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world kn

safetyarxiv-cs-ai

In-situ Autoguidance: Eliciting Self-Correction in Diffusion Models

arXiv:2510.17136v2 Announce Type: replace Abstract: The generation of high-quality, diverse, and prompt-aligned images is a central goal in image-generating diffusion models. The popular classifier-fr

safetyarxiv-cs-lg

Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning

arXiv:2607.29211v1 Announce Type: new Abstract: Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-soun

LARA: Lightweight Adapters in the Residual Stream for Composable Adaptation and Alignment

model-releasesarxiv-cs-lg

arXiv:2607.28669v1 Announce Type: new Abstract: We present LARA (Lightweight Additive Residual Adaptation), a method for efficient adaptation that operates in the residual stream of a frozen model rat

Applications

Learning Manifolds in High-D Point Embedding for Anisotropic Surface Approximation from Unstructured Point Clouds

arXiv:2607.28855v1 Announce Type: cross Abstract: Dense 3D sensors in various real-world fields produce point clouds that are geometrically redundant for real-time processing. In this paper, we propos

applicationsarxiv-cs-cv

M3MAD-Bench: Multi-Dimensional Evaluation of Multi-Agent Debate Across Domains and Modalities

arXiv:2601.02854v2 Announce Type: replace Abstract: As an agent-level reasoning and coordination paradigm, Multi-Agent Debate (MAD) orchestrates multiple agents through structured debate to improve an

Meshy T2: Fast Native Mesh Generation with Flow Matching

arXiv:2607.28675v1 Announce Type: cross Abstract: Polygonal meshes are the standard surface representation of modern 3D pipelines, and generating high-quality meshes with artist-style topology is esse

researcharxiv-cs-cv

Mirror Learning

arXiv:2607.28737v1 Announce Type: cross Abstract: We investigate imitation learning through the lens of third-person observation and propose a framework for mirror learning: acquiring actionable polic

safetyarxiv-cs-cv

MMShopBench: A Real-Log Benchmark for Multimodal, Multi-Turn Shopping Agents

arXiv:2607.29002v1 Announce Type: new Abstract: Online shoppers increasingly turn to AI shopping assistants, using images and multi-turn dialogue to express and refine product needs that are difficult

MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation

arXiv:2607.29479v1 Announce Type: new Abstract: Text-to-molecule generation is typically formulated as a one-shot sequence generation problem, where a model directly maps target descriptions to molecu

researcharxiv-cs-lg

NeSyFS: A Neuro-symbolic Fast-Slow Thinking Framework for LLM Agent under Partial Observability

arXiv:2607.28942v1 Announce Type: new Abstract: Recently Large Language Models (LLMs) have been increasingly deployed as autonomous agents in applications such as self-reflection, retrieval-augmented

agentsarxiv-cs-ai

Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination

arXiv:2607.28947v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to solve complex problems by searching over program space, offering a general paradigm for scientific

agentsarxiv-cs-lg

Paris: A Decentralized Trained Open-Weight Diffusion Model

model-releasesarxiv-cs-lg

arXiv:2510.03434v3 Announce Type: replace-cross Abstract: We present Paris, the first publicly released diffusion model pre-trained entirely through decentralized computation. Paris demonstrates that

Patch-Based 3D Variational Autoencoder for Super-Resolution of Turbulent Channel Flow

arXiv:2507.22082v2 Announce Type: replace-cross Abstract: Direct numerical simulation (DNS) accurately resolves all spatio-temporal scales of wall-bounded turbulence but becomes prohibitively expensiv

Hardware

Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds

arXiv:2607.28994v1 Announce Type: cross Abstract: High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit p

hardwarearxiv-cs-ai

“poorly evidenced sensationalism makes for bad policymaking, politics, philanthropy and much else.” beautiful coda for a weekend in which pe…

“poorly evidenced sensationalism makes for bad policymaking, politics, philanthropy and much else.” beautiful coda for a weekend in which people went completely nuts without even asking for a control

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PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction

arXiv:2607.29378v1 Announce Type: new Abstract: Large language models (LLMs) generate text by auto-regressively sampling the next token. This inherently leads to a many-to-many mapping between prompts

SAGP: Semantic Affordance-Guided Grasp Planning via Coarse-Zone VLM Reasoning

arXiv:2607.29374v1 Announce Type: new Abstract: Geometry-based grasp planners ensure physically valid grasps but ignore functional semantics, often generating grasps that are antipodal and collision-f

safetyarxiv-cs-ro

SciToolAgent-Evo: An Ontology-Aware Self-Evolving Agent for Open-World Scientific Tool Acquisition

arXiv:2607.28692v1 Announce Type: new Abstract: Large language model (LLM) agents have been increasingly adopted in scientific research for organizing and invoking specialized computational tools. How

SEDR-Seq2P: A Lightweight Dilated Residual Sequence-to-Point Network for Multi-Task Industrial NILM

arXiv:2607.28693v1 Announce Type: cross Abstract: Industrial NILM remains challenging because measurement noise and widespread concurrent machine operation reduce the generalization of models tuned on

SERUM: State Extraction and Refinement for User Modeling

arXiv:2607.29181v1 Announce Type: cross Abstract: Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow. However, building these mode

agentsarxiv-cs-ai

Local Ai

Small Is Enough: Per-User Style Rewriting of AI-Edited Text via LoRA Adapters

arXiv:2607.29238v1 Announce Type: cross Abstract: InMyStyle is a privacy first, single user system that adapts small language models to rewrite AI-edited text towards an individual user's writing styl

local-aiarxiv-cs-ai

So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection

model-releasesarxiv-cs-cv

arXiv:2505.18660v5 Announce Type: replace Abstract: Recent advances in AI-powered generative models have enabled the creation of increasingly realistic synthetic images, posing significant risks to in

Applications

ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts

arXiv:2607.28993v1 Announce Type: cross Abstract: World Action Models (WAMs) have emerged as a promising paradigm by jointly modeling robot actions and future visual dynamics. However, their reliance

applicationsarxiv-cs-cv

STAGE: STyle-controllable Action GEneration for personalized autonomous driving

arXiv:2607.29517v1 Announce Type: new Abstract: Driving style refers to the behavioral preferences that drivers maintain during driving, shaped by their diverse experiences, habits, and needs, and is

safetyarxiv-cs-ro

TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion

arXiv:2607.29459v1 Announce Type: cross Abstract: Large-scale multivariate time series from heterogeneous IoT sensors demand accurate long-term forecasting for resource scheduling and predictive maint

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale

arXiv:2607.14144v2 Announce Type: replace Abstract: The Platonic Representation Hypothesis (PRH) holds that as models scale, representations of heterogeneous networks converge toward a shared model of

Token-Level Diagnosis of Sycophancy in LLMs with Attribution-Guided Steering

arXiv:2607.28906v1 Announce Type: new Abstract: Sycophancy refers to the tendency for large language models (LLMs) to match user beliefs at the cost of factual correctness, thereby undermining model r

Tokenizer Transplantation: Mitigating Autoregressive Collapse in Edge-Efficient Bengali ASR

arXiv:2607.09598v2 Announce Type: replace Abstract: Lightweight speech recognition models are critical for edge deployment, yet highly optimized architectures like Moonshine often fail on morphologica

Hardware

TokTier: Exact Stateful Tokenization for Agentic LLM Serving

arXiv:2607.29678v1 Announce Type: new Abstract: LLM serving systems cache prompt KV state, yet most front ends still re-tokenize the full request text on every call. The cost lands on coding agents, w

hardwarearxiv-cs-cl

Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents

arXiv:2607.29254v1 Announce Type: new Abstract: AI agents extend large language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential

safetyarxiv-cs-ai

Applications

Towards Automated Initial Probe Placement in Transthoracic Teleultrasound Using Human Mesh and Skeleton Recovery

arXiv:2603.11257v2 Announce Type: replace Abstract: Cardiac and lung ultrasound are technically demanding because operators must identify patient-specific intercostal acoustic windows and then navigat

applicationsarxiv-cs-cv

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation

arXiv:2601.22546v2 Announce Type: replace-cross Abstract: The recent advancements in Large Language Models (LLMs) have attracted interest in exploring their in-context learning abilities and chain-of-

Training-Free Entity-Level Few-Shot Segmentation of Remote Sensing Images with Advection Refinement

arXiv:2607.29278v1 Announce Type: new Abstract: Existing cross-domain few-shot segmentation approaches suffer from high training costs due to source-domain episodic training and pixel-wise dense predi

researcharxiv-cs-cv