Search: “inference”

Branch and Bound for Relational Verification of Neural Networks

arXiv:2608.13118v1 Announce Type: new Abstract: Verification of neural networks against relational specifications, such as global robustness, is crucial for safety-critical applications of cyber-physi

safetyarxiv-cs-lg

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CASA: Content-Acoustic Speaking Assessment with Speech Encoder and Large Language Model

DGX agent

arXiv:2608.13101v1 Announce Type: new Abstract: Research on automatic speaking assessment (ASA) has increasingly adopted multimodal speech large language models to assess learners' speaking performanc

researcharxiv-cs-cl

14 Aug 2026

Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation

arXiv:2608.13391v1 Announce Type: new Abstract: Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation b

safetyarxiv-cs-cv

DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees

arXiv:2608.13524v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters furt

Difference-of-Convex Regularization for Graph Learning by Differentiable Programming

arXiv:2608.12757v1 Announce Type: cross Abstract: Laplacian-regularized minimization is fundamental in signal processing and machine learning, but is limited by the dense and ill-conditioned nature of

DiffGRM: Diffusion-based Generative Recommendation Model

arXiv:2510.21805v2 Announce Type: replace-cross Abstract: Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID) and predicts

Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization

arXiv:2608.13461v1 Announce Type: new Abstract: Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experienc

safetyarxiv-cs-lg

Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection

arXiv:2608.12441v1 Announce Type: cross Abstract: Deep learning detectors for anomalies in dynamic graphs have reached strong accuracy, yet they remain opaque: when an edge is flagged, the analyst rec

Efficient Image Restoration with State-Dependent Forward Diffusion

arXiv:2505.16733v3 Announce Type: replace Abstract: This paper proposes to perform image restoration through a state-dependent mean-reverting forward diffusion (FoD) process. In contrast to traditiona

ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval

arXiv:2608.12720v1 Announce Type: cross Abstract: While Large Language Model (LLM) agents increasingly rely on long-term memory for persistent interactions, the retrieval mechanisms governing this mem

Tutorials

Exponential quantum advantage for learning signals with a single qubit

arXiv:2608.13521v1 Announce Type: cross Abstract: Quantum technology has the potential to transform scientific discovery, but quantum advantages often require processing capabilities well beyond the r

tutorialsarxiv-cs-lg

FastThaiG2P: Lightning-fast Thai Grapheme-to-phoneme Conversion for Voice Agent Pipelines

model-releasesarxiv-cs-cl

arXiv:2608.12814v1 Announce Type: new Abstract: FastThaiG2P provides sub-millisecond Thai grapheme-to-phoneme conversion for text-to-speech pipelines (International Phonetic Alphabet and Kokoro-TTS co

Applications

GENADA: efficient generative time series adversarial attack framework

arXiv:2608.12535v1 Announce Type: new Abstract: Deep learning models are widely used for time series analysis in domains such as healthcare, finance, energy systems, and environmental monitoring. Howe

applicationsarxiv-cs-lg

Genetic Fuzzy System-based Control for Final Approach of Spacecraft Rendezvous and Proximity Operations

arXiv:2608.12760v1 Announce Type: cross Abstract: In-space servicing has been receiving great attention to extend the operation of spacecraft with defective components. This requires rendezvous and pr

researcharxiv-cs-ro

Genetic Fuzzy System-Based Multi-Robot Coordination for Planetary Missions

arXiv:2608.12755v1 Announce Type: new Abstract: This paper proposes a decentralized approach for a multi-robot system (MRS) using a genetic fuzzy system to perform a collaborative object transportatio

researcharxiv-cs-ro

Heterogeneity-Aware Belief Synchronization for Semantic Communication in AI-Native 6G Networks

arXiv:2608.13394v1 Announce Type: cross Abstract: 6G networks will not be serving as communication infrastructures only; rather, they are expected to evolve into intelligent systems, where thousands o

local-aiarxiv-cs-ai

HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models

arXiv:2608.12821v1 Announce Type: new Abstract: Large language models (LLMs) remain vulnerable to harmful requests and jailbreak attacks. Parameter-efficient safety alignment methods based on prompt t

History-informed Lagrangian Neural Networks

arXiv:2608.13215v1 Announce Type: new Abstract: Forecasting the long-horizon evolution of mechanical systems from position-only observations is a pivotal yet difficult task, as hidden velocities and t

HounsWorld: A Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation

model-releasesarxiv-cs-cv

arXiv:2608.12904v1 Announce Type: new Abstract: Clinical intelligence requires estimating a patient's underlying condition from incomplete observations rather than learning isolated mappings from scan

How Do VLMs Behave When Blind or Misled? Behavioral Evaluation of VLMs on Scientific Figures

arXiv:2608.13267v1 Announce Type: cross Abstract: Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in

Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology

arXiv:2608.13518v1 Announce Type: cross Abstract: Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recov

researcharxiv-cs-cv

Into the ORBIT for Time Series: Training Regimes for Foundation Models

arXiv:2608.13262v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous c

LLMs Are Not Good Strategists, Yet Memory-Enhanced Agency Boosts Reasoning

arXiv:2608.12626v1 Announce Type: cross Abstract: Strategic reasoning in Large Language Models (LLMs) within long-horizon environments is often limited by inconsistent subgoals. In these settings, fin

local-aiarxiv-cs-ai

LLMs Know the Constraint But Do Not Use It: Activation Bottlenecks in Pragmatic Constraint Reasoning

arXiv:2608.12321v1 Announce Type: cross Abstract: When a salient surface cue competes with an implicit feasibility constraint, LLMs often fail -- but aggregate accuracy conflates genuine constraint in

Localize, Then Reason: Visual Latent Structural Reasoning for Molecular Properties and Edits

arXiv:2608.13244v1 Announce Type: new Abstract: Local chemical perception and property reasoning are both essential for understanding how molecular structure determines properties. Current LLM-based c

local-aiarxiv-cs-cl

LongEarth-R1: Benchmarking and Aligning Vision-Language Models for Long-Horizon Earth Observation Reasoning

arXiv:2608.13344v1 Announce Type: new Abstract: Long-horizon Earth observation reasoning requires models to organize multi-stage geographic evolution, localize spatial changes, detect temporal anomali

LoRA-Diffusion: Parameter-Efficient Fine-Tuning via Low-Rank Trajectory Decomposition

model-releasesarxiv-cs-cl

arXiv:2608.12328v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods such as LoRA have transformed the adaptation of large autoregressive language models, enabling task-specific cus

MAG: MAnifold Guided Semi-Supervised Multi-modal In-Context Learning

arXiv:2608.12724v1 Announce Type: new Abstract: Few-shot in-context learning (ICL) with multi-modal large language models (MLLMs) enables task adaptation without parameter updates, but its performance

MARCH: Scaling Recurrent Memory with Content-Routed State Anchors

arXiv:2608.12435v1 Announce Type: new Abstract: Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length. This flexibility, howeve

MatchMiner-AI: Open-source, Privacy-preserving Cancer Clinical Trial Matching using Artificial Intelligence

arXiv:2412.17228v4 Announce Type: replace Abstract: Background: Clinical trials are essential to advancing cancer treatments, but fewer than 10% of adults with cancer enroll in therapeutic trials. Ope

researcharxiv-cs-ai

Mimicry without understanding: the origins of decision bias in large language models

arXiv:2608.12339v1 Announce Type: cross Abstract: Large Language models (LLMs) were found to be susceptible to a host of social, affective, and cognitive biases. We examined two mechanisms through whi

Mr3D-VL: A generalist vision language foundation model for Multiparametric 3D Magnetic Resonance Imaging

arXiv:2608.12689v1 Announce Type: cross Abstract: Multi-parametric magnetic resonance imaging (mpMRI) is a cornerstone for brain tumor diagnosis and treatment, yet current AI models face critical limi

RulerNet: Learning Perspective-Invariant Ruler Representations for Robust Image Scale Estimation

arXiv:2507.07077v2 Announce Type: replace Abstract: Accurately converting pixel measurements into absolute real-world dimensions remains a fundamental challenge in computer vision, limiting progress i

local-aiarxiv-cs-cv

Rules or Character? Scaling Laws for AI Safety Design

arXiv:2608.13345v1 Announce Type: new Abstract: Artificial Intelligence (AI) safety systems combine character shaping (e.g., Reinforcement Learning from Human Feedback [RLHF], Constitutional AI), whic

SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization

arXiv:2608.13538v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are proposed to extract numerous features from large language model (LLM) representations, yet explaining these features stil

researcharxiv-cs-cl

Scaling Representation Diversity: Modulated Attention and Reconstructive Regularization for Visual Grounding

model-releasesarxiv-cs-cv

arXiv:2608.12748v1 Announce Type: new Abstract: Referring Expression Comprehension (REC) is commonly studied under dataset-specific fine-tuning, resulting in specialist models with limited cross-datas

SCOPE: Subspace Clustering with Online Per-Head Top-K Estimation for Sparse Video Attention

arXiv:2608.12780v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) incur quadratic self-attention cost over spatiotemporal tokens. Existing training-free sparse attention methods often cons

researcharxiv-cs-cv

Shortest-Path Decomposition for Foliage-Robust 3D Tree Modeling and Above-Ground Biomass Estimation from Point Clouds

model-releasesarxiv-cs-cv

arXiv:2506.15577v2 Announce Type: replace Abstract: Estimating above-ground biomass (AGB) from terrestrial laser scanning (TLS) via quantitative structural models (QSMs) is highly accurate under leaf-

Sign Language Video Synthesis via Loss-Guided Multi-Expert GANs

arXiv:2608.13368v1 Announce Type: cross Abstract: This preliminary technical report presents a framework for sign language video synthesis using a loss-guided multi-expert Generative Adversarial Netwo

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

arXiv:2608.13341v1 Announce Type: cross Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Convent

Agents

Sovereign by necessity? Frontier AI export controls, cyber security, and the limits of national AI capability

arXiv:2608.13272v1 Announce Type: new Abstract: A small number of firms based in two states produce the most capable frontier AI models. The governments of those states have shown both the legal power

agentsarxiv-cs-ai

StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems

arXiv:2608.13317v1 Announce Type: new Abstract: Large language model based multi-agent systems usually communicate in text, i.e., using discrete tokens. However, text introduces a discrete bottleneck.

Steering the Language Axis: From Linear Decodability to Causal Control

arXiv:2608.12334v1 Announce Type: cross Abstract: Despite the impressive multilingual capabilities of Large Language Models, the latent dynamics dictating language selection remain poorly understood.

Taking Qwen3.5-9B quants to SOTA. New lineup incoming :)

Hey Folks, Some of you saw my Muse-Glimmer-30B SOTA line this week. I recently went all in, got a few more techniques hooked up to my pipeline, and re-quantized the 3.5 9B. And oh boy, did it demolish

local-air-localllama

Applications

The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning

arXiv:2608.12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences. It re

applicationsarxiv-cs-lg

Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice

arXiv:2608.12962v1 Announce Type: new Abstract: Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models. In this setti

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization

model-releasesarxiv-cs-cl

arXiv:2608.12953v1 Announce Type: new Abstract: Structured pruning is a promising approach for compressing large language models (LLMs), yet existing methods rely heavily on greedy heuristics that pro

Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration

arXiv:2505.02787v3 Announce Type: replace Abstract: Current color fundus image registration approaches are limited, among other things, by the lack of labeled data, which is even more significant in t

researcharxiv-cs-cv