Search: “models”

SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting

arXiv:2605.12992v1 Announce Type: cross Abstract: Neural population models, which predict the joint firing of many simultaneously recorded neurons forward in time, are typically evaluated by a single

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SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification

DGX agent

arXiv:2605.13672v1 Announce Type: new Abstract: Few-shot classification (FSC) is widely used for learning from limited labeled data, yet most evaluations implicitly assume that target concepts are ind

model-releasesarxiv-cs-cv

14 May 2026

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STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition

arXiv:2605.13202v1 Announce Type: cross Abstract: Few-shot action recognition (FSAR) requires models to generalize to novel action categories from only a handful of annotated samples. Despite progress

safetyarxiv-cs-ai

Stylized Text-to-Motion Generation via Hypernetwork-Driven Low-Rank Adaptation

arXiv:2605.13333v1 Announce Type: cross Abstract: Text-driven motion diffusion models are capable of generating realistic human motions, but text alone often struggles to express fine-level nuances of

researcharxiv-cs-ai

Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks

arXiv:2605.13560v1 Announce Type: new Abstract: This work studies lung tumor growth prediction from sparse and irregular longitudinal computed tomography (CT) observations with measurement variability

VectorSmuggle: Steganographic Exfiltration in Embedding Stores and a Cryptographic Provenance Defense

arXiv:2605.13764v1 Announce Type: cross Abstract: Modern retrieval-augmented generation (RAG) systems convert sensitive content into high-dimensional embeddings and store them in vector databases that

ViDR: Grounding Multimodal Deep Research Reports in Source Visual Evidence

arXiv:2605.13034v1 Announce Type: new Abstract: Recent deep research systems have improved the ability of large language models to produce long, grounded reports through iterative retrieval and reason

When Diffusion Breaks Constraints: Sequential Autoregressive Generation with RL and MCTS

arXiv:2512.01242v3 Announce Type: replace-cross Abstract: Data-driven generative models excel in language and vision, but diffusion models often fail in constrained planning and design tasks, exhibiti

researcharxiv-cs-ai

Allegory of the Cave: Measurement-Grounded Vision-Language Learning

arXiv:2605.11727v1 Announce Type: cross Abstract: Vision-language models typically reason over post-ISP RGB images, although RGB rendering can clip, suppress, or quantize sensor evidence before infere

B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding

arXiv:2508.05269v2 Announce Type: replace Abstract: Understanding dynamic outdoor environments requires capturing complex object interactions and their evolution over time. LiDAR-based 4D point clouds

BARISTA: A Multi-Task Egocentric Benchmark for Compositional Visual Understanding

arXiv:2605.12074v1 Announce Type: new Abstract: Scene understanding is central to general physical intelligence, and video is a primary modality for capturing both state and temporal dynamics of a sce

Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm

arXiv:2605.12271v1 Announce Type: new Abstract: Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generat

Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters

arXiv:2605.11960v1 Announce Type: new Abstract: Vision Large Language Models (VLLMs) have achieved remarkable success in modern text-rich visual understanding. However, their perceptual robustness in

CORE: Cyclic Orthotope Relation Embedding for Knowledge Graph Completion

arXiv:2605.11159v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to automatically infer missing facts in multi-relational data by mapping entities and relations into continuous re

DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows

arXiv:2304.09479v5 Announce Type: replace Abstract: We introduce a novel approach to single-view face relighting in the wild, addressing challenges such as global illumination and cast shadows. A comm

Local Ai

Distributed Quantum Gaussian Processes for Multi-Agent Systems

arXiv:2602.15006v2 Announce Type: replace-cross Abstract: Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale re

local-aiarxiv-cs-lg

Efficient Bayesian Inference from Noisy Pairwise Comparisons

arXiv:2510.09333v2 Announce Type: replace-cross Abstract: Evaluating generative models is challenging because standard metrics often fail to reflect human preferences. Human evaluations are more relia

researcharxiv-cs-cv

Enriching and Controlling Global Semantics for Text Summarization

arXiv:2109.10616v2 Announce Type: replace Abstract: Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Ne

researcharxiv-cs-cl

ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?

arXiv:2605.11086v1 Announce Type: cross Abstract: AI agents are rapidly gaining capabilities that could significantly reshape cybersecurity, making rigorous evaluation urgent. A critical capability is

Finite Volume-Informed Neural Network Framework for 2D Shallow Water Equations: Rugged Loss Landscapes and the Importance of Data Guidance

arXiv:2605.11001v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are a simple surrogate-modelling paradigm for partial differential equations, but their standard strong-form re

Local Ai

FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers

arXiv:2512.07150v2 Announce Type: replace-cross Abstract: Deep generative models are powerful priors for imaging inverse problems, but training-free solvers for latent flow models face a practical fin

local-aiarxiv-cs-cv

Geometric Factual Recall in Transformers

arXiv:2605.12426v1 Announce Type: new Abstract: How do transformer language models memorize factual associations? A common view casts internal weight matrices as associative memories over pairs of emb

GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms

arXiv:2605.11117v1 Announce Type: new Abstract: Scientific discovery can be modeled as a sequence of probabilistic decisions that map physical problems to numerical solutions. Recent agentic AI system

Holder Policy Optimisation

arXiv:2605.12058v1 Announce Type: new Abstract: Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mappi

Tutorials

L2P: Unlocking Latent Potential for Pixel Generation

arXiv:2605.12013v1 Announce Type: new Abstract: Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohib

tutorialsarxiv-cs-cv

Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data

arXiv:2605.11881v1 Announce Type: new Abstract: The high-dimensional features extracted from large-scale unlabeled data via various pretrained models with diverse architectures are referred to as hete

Applications

M^4-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection

arXiv:2605.11760v1 Announce Type: new Abstract: The Segment Anything Model 2 (SAM2) has emerged as a foundation model for universal segmentation. Owing to its generalizable visual representations, SAM

applicationsarxiv-cs-cv

Machine Learning for neutron source distributions

arXiv:2605.12165v1 Announce Type: cross Abstract: In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation

researcharxiv-cs-lg

Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding

arXiv:2605.12185v1 Announce Type: new Abstract: Large language models accumulate extensive parametric knowledge through pre-training. However, knowledge conflicts occur when outdated or incorrect para

MULTI: Disentangling Camera Lens, Sensor, View, and Domain for Novel Image Generation

arXiv:2605.12134v1 Announce Type: new Abstract: Recent text-to-image models produce high-quality images, yet text ambiguity hinders precise control when specific styles or objects are required. There

Not How Many, But Which: Parameter Placement in Low-Rank Adaptation

arXiv:2605.12207v1 Announce Type: cross Abstract: We study the extit{parameter placement problem}: given a fixed budget of k trainable entries within the B matrix of a LoRA adapter (A frozen), does th

ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging

arXiv:2605.12419v1 Announce Type: new Abstract: Despite the rapid advancements in large language model (LLM) development, fine-tuning them for specific tasks often results in the catastrophic forgetti

researcharxiv-cs-cl

PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming

arXiv:2605.11427v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must downlo

Safety

Position: Universal Aesthetic Alignment Narrows Artistic Expression

arXiv:2512.11883v3 Announce Type: replace-cross Abstract: Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when 'anti-aesthetic' out

safetyarxiv-cs-cv

Applications

Prompting from the bench: Large-scale pretraining is not sufficient to prepare LLMs for ordinary meaning analysis

arXiv:2510.25356v2 Announce Type: replace Abstract: In the U.S. judicial system, a widespread approach to legal interpretation entails assessing how a legal text would be understood by an `ordinary' s

applicationsarxiv-cs-cl

Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function

arXiv:2602.02406v2 Announce Type: replace-cross Abstract: Data-driven algorithm design automates hyperparameter tuning, but its statistical foundations remain limited because model performance can dep

Revisiting Shadow Detection from a Vision-Language Perspective

arXiv:2605.11771v1 Announce Type: new Abstract: Shadow detection is commonly formulated as a vision-driven dense prediction problem, where models rely primarily on pixel-wise visual supervision to dis

Agents

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

arXiv:2605.12500v1 Announce Type: new Abstract: Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as disti

agentsarxiv-cs-cv

ShapeCodeBench: A Renewable Benchmark for Perception-to-Program Reconstruction of Synthetic Shape Scenes

arXiv:2605.11680v1 Announce Type: new Abstract: We introduce ShapeCodeBench, a synthetic benchmark for perception-to-program reconstruction: given a rendered raster image, a model must emit an executa

Targeted Tests for LLM Reasoning: An Audit-Constrained Protocol

arXiv:2605.11599v1 Announce Type: new Abstract: Fixed reasoning benchmarks evaluate canonical prompts, but semantically valid changes in presentation can still change model behavior. Studies of prompt

researcharxiv-cs-lg

The Price of Proportional Representation in Temporal Voting

arXiv:2605.11157v1 Announce Type: cross Abstract: We study proportional representation in the temporal voting model, where collective decisions are made repeatedly over time over a fixed horizon. Prio

Urban Risk-Aware Navigation via VQA-Based Event Maps for People with Low Vision

arXiv:2605.11782v1 Announce Type: new Abstract: Visual impairment affects hundreds of millions of people worldwide, severely limiting their ability to navigate urban environments safely and independen

A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility

model-releasesarxiv-cs-ai

arXiv:2605.09483v1 Announce Type: cross Abstract: In this (work in progress) paper, we present Bounded Pragmatic Listener (or BPL), a cognitively grounded Bayesian framework for modelling susceptibili

A Qualitative Test-Risk Mechanism for Scaling Behavior in Normalized Residual Networks

arXiv:2605.08297v1 Announce Type: cross Abstract: The scaling behavior, in which test performance often improves as model size and data increase, is a central empirical phenomenon in modern deep learn

researcharxiv-cs-ai

Safety

A Scalable Entity-Based Framework for Auditing Bias in LLMs

arXiv:2601.12374v2 Announce Type: replace-cross Abstract: Existing approaches to bias evaluation in large language models (LLMs) trade ecological validity for statistical control, relying either on ar

safetyarxiv-cs-ai

Action-Guided Attention for Video Action Anticipation

arXiv:2603.01743v2 Announce Type: replace Abstract: Anticipating future actions in videos is challenging, as the observed frames provide only evidence of past activities, requiring the inference of la

AdaPaD: Adaptive Parallel Deflation for PEFT with Self-Correcting Rank Discovery

arXiv:2605.10741v1 Announce Type: new Abstract: Fine-tuning large language models with LoRA requires choosing a rank r before training starts. Existing approaches either extract rank-1 components sequ

Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values

model-releasesarxiv-cs-ai

arXiv:2605.10365v1 Announce Type: new Abstract: Autonomous agents have rapidly matured as task executors and seen widespread deployment via harnesses such as OpenClaw. Safety concerns have rightly dra