Search: “models”

Don't Retrieve, Generate: Prompting LLMs for Synthetic Training Data in Dense Retrieval

arXiv:2504.21015v4 Announce Type: replace-cross Abstract: Training effective dense retrieval models typically relies on hard negative (HN) examples mined from large document corpora using methods such

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DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imaging with Quantum Detectors

DGX agent

arXiv:2605.10185v1 Announce Type: cross Abstract: Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensi

researcharxiv-cs-ai

12 May 2026

Efficient LLM Collaboration via Planning

arXiv:2506.11578v4 Announce Type: replace Abstract: Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achie

local-aiarxiv-cs-ai

FORTIS: Benchmarking Over-Privilege in Agent Skills

arXiv:2605.09163v1 Announce Type: new Abstract: Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This

Medical Model Synthesis Architectures: A Case Study

arXiv:2605.09716v1 Announce Type: new Abstract: Medicine is rife with high-stakes uncertainty. Doctors routinely make clinical judgments and decisions that juggle many fundamental unknowns, like predi

applicationsarxiv-cs-ai

Merlin: Deterministic Byte-Exact Deduplication for Lossless Context Optimization in Large Language Model Inference

arXiv:2605.09990v1 Announce Type: new Abstract: Data-intensive applications, ranging from large-scale retrieval systems to advanced data pipelines, are increasingly bottlenecked by the processing of h

local-aiarxiv-cs-cl

MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration

arXiv:2605.08566v1 Announce Type: new Abstract: Chemical imaging enables label-free visualization of cells, tissues and living systems while providing direct biochemical information that is difficult

researcharxiv-cs-cv

Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansions

arXiv:2605.08275v1 Announce Type: cross Abstract: Conventional MRI reconstruction methods treat images and coil sensitivities as discrete objects, leading to high memory demands and limited structural

researcharxiv-cs-cv

Muninn: Your Trajectory Diffusion Model But Faster

arXiv:2605.09999v1 Announce Type: new Abstract: Diffusion-based trajectory planners can synthesize rich, multimodal robot motions, but their iterative denoising makes online planning and control prohi

safetyarxiv-cs-ro

Tutorials

Predicting 3D structure by latent posterior sampling

arXiv:2605.10830v1 Announce Type: new Abstract: The remarkable achievements of both generative models of 2D images and neural field representations for 3D scenes present a compelling opportunity to in

tutorialsarxiv-cs-cv

Qwen-Image-2.0 Technical Report

model-releasesarxiv-cs-cv

arXiv:2605.10730v1 Announce Type: new Abstract: We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a si

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data

arXiv:2602.12606v2 Announce Type: replace Abstract: Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relat

Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model

arXiv:2311.03600v3 Announce Type: replace Abstract: Robots capable of learning from demonstration (LfD) must exhibit stability while executing learned motion skills. To be effective in the real world,

applicationsarxiv-cs-ro

Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse

arXiv:2601.11042v2 Announce Type: replace-cross Abstract: Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for para

The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning

arXiv:2605.08746v1 Announce Type: new Abstract: In training a neural network with gradient descent (GD), each iteration induces a linear operator that governs first-order updates to a model's internal

Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction

arXiv:2605.08633v1 Announce Type: cross Abstract: Earth observation is becoming one of the largest data-producing activities in science, yet current pipelines still treat compression as a storage and

researcharxiv-cs-cv

Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI

arXiv:2605.08137v1 Announce Type: cross Abstract: Weight pruning is widely advocated for deploying Large Language Models on resource-constrained IoT and edge devices, yet its impact on model fairness

What's the plan? Metrics for implicit planning in LLMs and their application to rhyme generation and question answering

arXiv:2601.20164v2 Announce Type: replace-cross Abstract: Prior work suggests that language models, while trained on next token prediction, show implicit planning behavior: they may select the next to

Adaptive Memory Decay for Log-Linear Attention

arXiv:2605.06946v1 Announce Type: cross Abstract: Sequence models face a fundamental tradeoff between memory capacity and computational efficiency. Transformers achieve expressive context modeling at

ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning

arXiv:2602.01003v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a key training step for improving mathematical reasoning in large language models (LLMs), but it often

Fast Byte Latent Transformer

arXiv:2605.08044v1 Announce Type: cross Abstract: Recent byte-level language models (LMs) match the performance of token-level models without relying on subword vocabularies, yet their utility is limi

How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study

arXiv:2605.05340v2 Announce Type: replace-cross Abstract: As Vision-Language Models (VLMs) are increasingly deployed as autonomous cognitive cores for embodied assistants, evaluating their privacy awa

Tutorials

HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs

arXiv:2506.12362v3 Announce Type: replace-cross Abstract: Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., n

tutorialsarxiv-cs-ai

Improved Model-based Reinforcement Learning with Smooth Kernels

arXiv:2605.07218v1 Announce Type: new Abstract: For continuous state-action space scenarios, classical reinforcement learning (RL) theory predominantly focuses on low-rank Markov decision processes (M

researcharxiv-cs-lg

Agents

Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems

arXiv:2605.07768v1 Announce Type: cross Abstract: We investigate interactive trajectory planning subject to uncertainty in the decisions of surrounding agents. To control the ego-agent, we aim to firs

agentsarxiv-cs-lg

Narrow Secret Loyalty Dodges Black-Box Audits

arXiv:2605.06846v1 Announce Type: cross Abstract: Recent work identifies secret loyalties as a distinct threat from standard backdoors. A secret loyalty causes a model to covertly advance the interest

NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection

model-releasesarxiv-cs-cv

arXiv:2508.01248v4 Announce Type: replace Abstract: The rapid progress of generative models, such as GANs and diffusion models, has facilitated the creation of highly realistic images, raising growing

Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy

arXiv:2605.06820v1 Announce Type: cross Abstract: Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy workflows, but models trained on adult data often

local-aiarxiv-cs-ai

Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding

arXiv:2605.07575v1 Announce Type: cross Abstract: Proactive streaming video understanding requires Video-LLMs to decide when to respond as a video unfolds, a task where existing methods often fall sho

safetyarxiv-cs-ai

Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control

arXiv:2605.07182v1 Announce Type: new Abstract: Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiri

Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles

arXiv:2512.03454v4 Announce Type: replace-cross Abstract: Interpreting natural-language commands to localize target objects is critical for autonomous driving (AD). Existing visual grounding (VG) meth

local-aiarxiv-cs-ai

Budgeted LoRA: Distillation as Structured Compute Allocation for Efficient Inference

arXiv:2605.04341v1 Announce Type: cross Abstract: We study distillation for large language models under explicit compute constraints, with the goal of producing student models that are not only cheape

Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs

model-releasesarxiv-cs-cv

arXiv:2605.04903v1 Announce Type: cross Abstract: Large language models (LLMs) show strong potential for neural architecture generation, yet existing approaches produce complete model implementations

FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detection

arXiv:2605.04882v1 Announce Type: new Abstract: Automated glaucoma detection is critical for preventing irreversible vision loss and reducing the burden on healthcare systems. However, ensuring fairne

safetyarxiv-cs-cv

Low-Latency Quasi-Static Modeling of UAV Tether Aerodynamics

arXiv:2512.22588v3 Announce Type: replace Abstract: One of the main limitations of multirotor UAVs is their short flight time due to battery constraints. A practical solution for continuous operation

applicationsarxiv-cs-ro

OPENJ: A Conceptual Framework for Open-Source Digital Human Modeling and Ergonomic Assessment in a CAD Environment

arXiv:2605.04270v1 Announce Type: cross Abstract: Industrial workplace challenges range from musculoskeletal disorders -- a leading cause of occupational injury -- to suboptimal workstation layouts, i

researcharxiv-cs-ro

The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences

arXiv:2605.05080v1 Announce Type: new Abstract: We administer 45 validated psychometric questionnaires to 50 large language models (LLMs) to identify the dimensions along which LLMs differ psychometri

researcharxiv-cs-cl

AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognition

arXiv:2605.03590v1 Announce Type: new Abstract: Recent large language models (LLMs) show strong speech recognition and translation capabilities for high-resource languages. However, African languages

CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing

arXiv:2605.02910v1 Announce Type: cross Abstract: Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative

EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage

arXiv:2605.03998v1 Announce Type: new Abstract: Emergency department triage assigns patients an acuity score that determines treatment priority, and clinical evidence documents persistent gender dispa

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs

arXiv:2605.03964v1 Announce Type: new Abstract: Training machine learning interatomic potentials (MLIPs) for reactive chemistry is often bottlenecked by the high cost of quantum chemical labels and th

researcharxiv-cs-lg

Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery

arXiv:2605.02110v1 Announce Type: new Abstract: Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients upload manipulated updates to degrade the performance of the global

researcharxiv-cs-lg

Beyond Perplexity: Character Distribution Signatures and the MDTA Benchmark for AI Text Detection

arXiv:2605.01647v1 Announce Type: new Abstract: Training-free AI text detection methods primarily rely on model log-probabilities, achieving strong performance through approaches like Binoculars and D

Binary Rewards and Reinforcement Learning: Fundamental Challenges

arXiv:2605.02375v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard approach for improving reasoning in language models, yet models trained with

safetyarxiv-cs-lg

Confident, Calibrated, or Complicit: Safety Alignment and Ideological Bias in LLM Hate Speech Detection

arXiv:2509.00673v2 Announce Type: replace Abstract: We investigate the efficacy of Large Language Models (LLMs) in detecting implicit and explicit hate speech, examining how models with minimal safety

safetyarxiv-cs-cl

Distributional Causal Mediation via Conditional Generative Modeling

arXiv:2605.01765v1 Announce Type: cross Abstract: Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional ch

applicationsarxiv-cs-lg

FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training

arXiv:2605.02337v1 Announce Type: cross Abstract: Federated Learning (FL) has gained significant attention in distributed machine learning by enabling collaborative model training across decentralized

Heterogeneous Model Fusion for Privacy-Aware Multi-Camera Surveillance via Synthetic Domain Adaptation

arXiv:2605.02169v1 Announce Type: new Abstract: We propose HeroCrystal, a novel privacy-preserving framework for multi-camera domain-adaptive object detection, addressing challenges such as data priva

local-aiarxiv-cs-cv