Search: “models”

UniCycleFlow: Bidirectional Unpaired Image Translation with a Shared Rectified Flow

arXiv:2608.06784v1 Announce Type: new Abstract: Bidirectional unpaired image translation must preserve source-specific structure while learning coherent transformations in both directions without pair

researcharxiv-cs-cv

10 Aug 2026

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decomposition

DGX agent

arXiv:2608.05673v1 Announce Type: new Abstract: Trajectory prediction has shifted toward structured formulations with explicit social modeling. However, existing methods inadequately distinguish the f

researcharxiv-cs-ai

7 Aug 2026

Abstract Event Causal Rules: Induction and Application

arXiv:2608.05205v1 Announce Type: new Abstract: Event-centric intelligent analytical systems heavily depend on explicit causal event knowledge for risk early warning, decision-making support and narra

All-Quadrant Bounded Clipping GRPO: Closing the Unbounded Blind Spot for Stable and Generalizable Training

arXiv:2601.03895v2 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) has emerged as a popular algorithm for reinforcement learning with large language models (LLMs). How

safetyarxiv-cs-ai

An Axiomatic Benchmark for Evaluation of Scientific Novelty Metrics

arXiv:2604.15145v2 Announce Type: replace Abstract: The rigorous evaluation of the novelty of a scientific paper is, even for human scientists, a challenging task. With the increasing interest in AI s

Local Ai

Analysis of Numerical Localisation in LLM Translations

arXiv:2608.05232v1 Announce Type: new Abstract: The work of Tang et. al. (2025) on numerical translation is extended by analysing the capability of five large language models (LLMs) for the localisati

local-aiarxiv-cs-cl

Answer First, Reason Later: Commitment Order in Diffusion LLMs

arXiv:2608.05687v1 Announce Type: cross Abstract: Masked diffusion language models (dLLMs) can commit tokens in any order -- a freedom marketed as their core advantage over autoregressive decoding. We

Agents

AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games

arXiv:2608.06362v1 Announce Type: cross Abstract: Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time.

agentsarxiv-cs-ai

BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

arXiv:2608.05926v1 Announce Type: cross Abstract: Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference main

Tutorials

Bar-JEPA: Extracting Values from Bar Chart with Joint-Embedding Predictive Architecture

arXiv:2608.06062v1 Announce Type: new Abstract: Bar charts are commonly used in data visualization, and while they are easily understood by humans, it is non-trivial to extract the underlying data com

tutorialsarxiv-cs-cv

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages

arXiv:2606.16891v2 Announce Type: replace-cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to captur

Tutorials

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

arXiv:2608.05928v1 Announce Type: new Abstract: Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individ

tutorialsarxiv-cs-lg

CLARA: Clarification of Language Ambiguity through Result Analysis for Natural-Language Cancer Genomics Queries

arXiv:2608.05195v1 Announce Type: cross Abstract: A natural language interface can be used to make cancer genomics databases easier to use, but even if a question is perfectly fluent, its scientific m

CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

arXiv:2603.08652v2 Announce Type: replace Abstract: Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integra

Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading

arXiv:2608.05675v1 Announce Type: new Abstract: Label-free reliability for vision-language models rests on invariance: perturb the input and a faithful reader's answer should not change. This has a kn

researcharxiv-cs-lg

Context Matters: Support Set Selection and Failure Detection for In-Context Medical Image Segmentation

arXiv:2608.05333v1 Announce Type: new Abstract: In-context learning (ICL) adapts medical image segmentation models to unseen structures and modalities without retraining by conditioning on a task-spec

researcharxiv-cs-cv

Contextual Information Policy Optimization for Search Agents

arXiv:2608.06128v1 Announce Type: new Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reas

safetyarxiv-cs-ai

CRINN: Contrastive Reinforcement Learning for Approximate Nearest Neighbor Search

arXiv:2508.02091v4 Announce Type: replace-cross Abstract: Approximate nearest-neighbor search (ANNS) algorithms have become increasingly critical for recent AI applications, particularly in retrieval-

Hardware

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

arXiv:2512.02551v4 Announce Type: replace-cross Abstract: In this paper, we propose CUDA-L2, a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimi

hardwarearxiv-cs-ai

D-CLOT: Double Closed Loop Optimal Transport for Unsupervised Action Segmentation

arXiv:2608.05877v1 Announce Type: cross Abstract: Optimal transport (OT) has emerged as an effective framework for unsupervised action segmentation. Yet, in existing OT-based methods, the latent actio

Decomposed Entailment for Factuality Checking and Hallucination Detection

arXiv:2608.05823v1 Announce Type: new Abstract: The reliability of Large Language Models (LLMs) is often compromised by factual inconsistencies, including hallucinations---cases where generated conten

researcharxiv-cs-cl

Applications

DFM-VLA: Iterative Action Refinement for Robot Manipulation via Discrete Flow Matching

arXiv:2603.26320v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models that encode actions using a discrete tokenization scheme have been widely adopted for robotic manipulation

applicationsarxiv-cs-cv

Different Perturbations, Different Mechanisms: Understanding Continued Pre-training for Zero-Shot Dialect Robustness

arXiv:2608.05510v1 Announce Type: new Abstract: Dialectal variation remains a major challenge for multilingual language models. Perturbation-based continued pre-training (CPT) has emerged as a promisi

safetyarxiv-cs-cl

Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts

arXiv:2608.06031v1 Announce Type: new Abstract: Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, exist

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation

arXiv:2608.06374v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a powerful paradigm for robot manipulation, but training a single generalist policy for heterogeneous ro

safetyarxiv-cs-ro

ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment

arXiv:2608.06110v1 Announce Type: new Abstract: This paper presents ECHO (Enhanced Care & Health Observer), a locally-deployable conversational health assistant for long-term chronic care management.

Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescaling

arXiv:2608.05464v1 Announce Type: cross Abstract: The pruning of network connections is key to brain function but, despite its importance, there exist few biologically-plausible pruning rules with dem

Equation-Free Period-Aware Forecast-Error Contraction for Estimating Negative Largest Lyapunov Exponents from Short Trajectory Ensembles

arXiv:2608.05522v1 Announce Type: cross Abstract: Estimating positive largest Lyapunov exponents from data is comparatively natural because neighboring trajectories separate, whereas stable dynamics r

Example-Guided Prompting for Document-Level Text Simplification

arXiv:2608.05447v1 Announce Type: new Abstract: Document-level text simplification requires large language models (LLMs) to rewrite complex documents while preserving meaning, readability, and discour

researcharxiv-cs-cl

FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows

arXiv:2608.06144v1 Announce Type: new Abstract: Most agent benchmarks evaluate tasks independently and cannot measure whether experience from one task helps with later tasks. Existing self-evolution b

Applications

FOCUS: Decoupling Expert Personas in LLMs to Enhance Domain Expert Capabilities

arXiv:2608.05611v1 Announce Type: new Abstract: Large Language Models (LLMs) can exhibit diverse personas, and activating expert personas has been shown to improve domain expertise and task accuracy.

applicationsarxiv-cs-cl

Local Ai

G^2ARD-GS: Geometry-Guided Anchor-Regularized Gaussian Splatting Distillation

arXiv:2608.05704v1 Announce Type: new Abstract: Dense colored LiDAR maps provide accurate city-scale geometry, but lifting them into 3D Gaussian Splatting (3DGS) retains millions of primitives, making

local-aiarxiv-cs-cv

GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning

arXiv:2604.02721v3 Announce Type: replace Abstract: Competitive programming remains one of the last few human strongholds in coding against AI. The best AI system to date still underperforms the best

How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits

arXiv:2608.05595v1 Announce Type: cross Abstract: Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstructio

Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints

arXiv:2608.06265v1 Announce Type: new Abstract: Synthetic clinical benchmarks for enterprise AI agents can pass existing utility checks and still remain structurally unrealistic, especially in privacy

Local Ai

In-Context Forcing: Uncovering Context Effects in Autoregressive Video Diffusion

arXiv:2608.05237v1 Announce Type: cross Abstract: Current few-step autoregressive video diffusion models depend on previous fully denoised clean frames as context for all denoising steps of the curren

local-aiarxiv-cs-ai

Invisible Shortcuts: Why Vision Encoders Know Your Camera

arXiv:2608.05424v1 Announce Type: new Abstract: Deep vision models exploit shortcuts, relying on cues that correlate with supervision signals. Prior work has focused on visible biases, such as object-

researcharxiv-cs-cv

KILVO: Kinematic-Inertial-LiDAR-Visual Odometry with Robust Multimodal Adaptation for Humanoid Robots

model-releasesarxiv-cs-ro

arXiv:2608.05647v1 Announce Type: new Abstract: This article presents a kinematic-inertial-LiDAR-visual odometry for humanoid robots, called KILVO. Tailored to the platform features, requirements, and

Agents

Learning Context-Free Grammars for Grammar-Constrained Decoding via Declarative Agentic Programming with Guarantees

arXiv:2608.05493v1 Announce Type: cross Abstract: Language models (LMs) are increasingly used to interact with external services via programs written in domain-specific languages (DSLs). Unfortunately

agentsarxiv-cs-ai

Agents

Learning Globally Reusable Skills for Coding Agents

arXiv:2608.06153v1 Announce Type: cross Abstract: Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches

agentsarxiv-cs-ai

Hardware

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation

arXiv:2608.05819v1 Announce Type: new Abstract: Classical simulation remains essential for developing and validating quantum algorithms, but its cost grows rapidly with circuit size. Tensor-network co

hardwarearxiv-cs-lg

LoDA: A Level of Detection Aware Method and a Multimodal Sensing Benchmark for Object Level Change Detection

model-releasesarxiv-cs-cv

arXiv:2608.05356v1 Announce Type: new Abstract: High-definition 3D LiDAR maps are important for autonomous driving and smart-city services, which require reliable detection of object-level changes in

Matrix Zonotopic Attention: A Context-Adaptive Value Projection for Set Transformers

arXiv:2608.05472v1 Announce Type: cross Abstract: Multi-head attention combines an input-dependent softmax routing with an input-independent linear value projection, so the per-sample operator mapping

Multi-Representation Geometric Hierarchy Fusion: An Implicit-Submap Driven Framework for Resilient 3D Place Recognition

model-releasesarxiv-cs-cv

arXiv:2506.14243v4 Announce Type: replace Abstract: LiDAR-based place recognition is critical for long-term autonomous driving without GPS. Existing handcrafted feature methods face dual limitations.

Neuro-Symbolic Closed-Loop Control of Laser Powder Bed Fusion with an In-Loop Ontology

arXiv:2608.05773v1 Announce Type: new Abstract: A geometry-conditioned, neuro-symbolic closed-loop architecture is proposed for laser powder bed fusion, in which a standards-aligned ontology operates

Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing

arXiv:2512.17426v2 Announce Type: replace-cross Abstract: We consider sparse signal reconstruction via minimization of the smoothly clipped absolute deviation (SCAD) penalty, and develop one-step repl

Perturbation Sensitivity at Convergence: A Simple Signal for Identifying Spuriously Correlated Samples

arXiv:2608.05419v1 Announce Type: cross Abstract: Models trained by empirical risk minimization on data containing spurious correlations achieve high average accuracy while failing on subpopulations w

PolyAlign: Conditional Human-Distribution Alignment

arXiv:2606.13227v2 Announce Type: replace Abstract: Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assist

safetyarxiv-cs-cl