Search: “model-releases”

LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing

arXiv:2507.00029v2 Announce Type: replace-cross Abstract: Recent attempts to combine low-rank adaptation (LoRA) with mixture-of-experts (MoE) for multi-task adaptation of Large Language Models (LLMs)

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Low-Rank Adapters Initialization via Gradient Surgery for Continual Learning

DGX agent

arXiv:2605.12752v1 Announce Type: new Abstract: LoRA is widely adopted for continual fine-tuning of Large Language Models due to its parameter efficiency, modularity across tasks, and compatibility wi

model-releasesarxiv-cs-lg

14 May 2026

Many-Shot CoT-ICL: Making In-Context Learning Truly Learn

arXiv:2605.13511v1 Announce Type: cross Abstract: In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates.

Mechanistic Evidence for Spectral Structures in Prior-Data Fitted Networks

arXiv:2601.21731v2 Announce Type: replace Abstract: Prior-Data Fitted Networks (PFNs) enable amortized Bayesian inference in a single forward pass, yet their internal representations remain opaque. It

MedCore: Boundary-Preserving Medical Core Pruning for MedSAM

arXiv:2605.13688v1 Announce Type: new Abstract: Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large fo

MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows

arXiv:2603.24649v2 Announce Type: replace Abstract: Medical imaging benchmarks often evaluate VLMs on pre-selected 2D images, slices, crops, or patches, making evaluation closer to visual recognition.

MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving

arXiv:2605.12624v1 Announce Type: cross Abstract: Autonomous driving has progressed from modular pipelines toward end-to-end unification, and Vision-Language-Action (VLA) models are a natural extensio

Mixed neural posterior estimation for simulators with discrete and continuous parameters

arXiv:2605.13551v1 Announce Type: new Abstract: Neural Posterior Estimation (NPE) enables rapid parameter inference for complex simulators with intractable likelihoods. NPE trains an inference network

MMCL-Bench: Multimodal Context Learning from Visual Rules, Procedures, and Evidence

arXiv:2605.12703v1 Announce Type: cross Abstract: We introduce MMCL-Bench, a benchmark for multimodal context learning: learning task-local rules, procedures, and empirical patterns from visual or mix

MobiBench: Multi-Branch, Modular Benchmark for Mobile GUI Agents

arXiv:2512.12634v3 Announce Type: replace Abstract: Mobile GUI Agents, AI agents capable of interacting with mobile applications on behalf of users, have the potential to transform human computer inte

Multi-Armed Sampling Problem and the End of Exploration

arXiv:2507.10797v2 Announce Type: replace Abstract: This paper introduces the framework of multi-armed sampling, which serves as the sampling counterpart to the optimization problem of multi-armed ban

N-vium: Mixture-of-Exits Transformer for Accelerated Exact Generation

arXiv:2605.13190v1 Announce Type: cross Abstract: Improving the inference efficiency of autoregressive transformers typically means reducing FLOPs per token, usually through approximations that degrad

Negation Neglect: When models fail to learn negations in training

arXiv:2605.13829v1 Announce Type: cross Abstract: We introduce Negation Neglect, where finetuning LLMs on documents that flag a claim as false makes them believe the claim is true. For example, models

Neural QAOA^{2}: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization

arXiv:2605.13072v1 Announce Type: cross Abstract: The quantum approximate optimization algorithm (QAOA) holds promise for combinatorial optimization but is constrained by limited qubits. While divide-

Neurosymbolic Auditing of Natural-Language Software Requirements

arXiv:2605.13817v1 Announce Type: cross Abstract: Natural-language software requirements are often ambiguous, inconsistent, and underspecified; in safety-critical domains, these defects propagate into

No One Knows the State of the Art in Geospatial Foundation Models

arXiv:2605.12678v1 Announce Type: new Abstract: Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, a

OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation

arXiv:2605.13815v1 Announce Type: new Abstract: LiDAR scene generation is increasingly important for scalable simulation and synthetic data creation, especially under diverse sensing conditions that a

Orthrus: Memory-Efficient Parallel Token Generation via Dual-View Diffusion

arXiv:2605.12825v1 Announce Type: cross Abstract: We introduce Orthrus, a simple and efficient dual-architecture framework that unifies the exact generation fidelity of autoregressive Large Language M

OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention

arXiv:2605.13473v1 Announce Type: new Abstract: Linear attention and state-space models offer constant-memory alternatives to softmax attention, but often struggle with in-context associative recall.

PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interface

arXiv:2605.13297v1 Announce Type: new Abstract: Periodic crystals repeatedly instantiate similar local coordination motifs across translated cells and chemically related structures, but current equiva

PanoWorld: Towards Spatial Supersensing in 360^irc Panorama World

arXiv:2605.13169v1 Announce Type: cross Abstract: Multimodal large laboratory models (MLLMs) still struggle with spatial understanding under the dominant perspective-image paradigm, which inherits the

Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo

arXiv:2605.13807v1 Announce Type: cross Abstract: Neural-network quantum states have emerged as a powerful variational framework for quantum many-body systems, with recent progress often driven by mas

Path-independent Flow Matching for Multi-parameter Generative Dynamics

arXiv:2605.13487v1 Announce Type: new Abstract: Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formulation is no

Pattern-Enhanced RT-DETR for Multi-Class Battery Detection

arXiv:2605.13670v1 Announce Type: new Abstract: Accurate and efficient battery detection is increasingly important for applications in electronic waste recycling, industrial quality control, and autom

PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media

arXiv:2605.12525v1 Announce Type: cross Abstract: Current emotion analysis in social media is predominantly author-centric, failing to capture the subjective nature of emotional responses across diver

Persona-Model Collapse in Emergent Misalignment

arXiv:2605.12850v1 Announce Type: cross Abstract: Fine-tuning large language models on narrow data with harmful content produces broadly misaligned behavior on unrelated prompts, a phenomenon known as

PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records

arXiv:2601.09636v2 Announce Type: replace Abstract: While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more

Phasor Memory Networks: Stable Backpropagation Through Time for Scalable Explicit Memory

arXiv:2605.13370v1 Announce Type: new Abstract: For over a decade, explicit memory architectures like the Neural Turing Machine have remained theoretically appealing yet practically intractable for la

PhysEditBench: A Protocol-Conditioned Benchmark for Dense Physical-Map Prediction with Image Editors

arXiv:2605.13493v1 Announce Type: new Abstract: Can general-purpose image editors predict physical maps from a single RGB image? General-purpose image editors differ from standard task-specific dense-

Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles

arXiv:2605.12803v1 Announce Type: new Abstract: Detecting concept drift in high-speed data streams remains challenging, particularly when models must operate on unlabeled data and avoid false alarms c

Plan Before You Trade: Inference-Time Optimization for RL Trading Agents

arXiv:2605.12653v1 Announce Type: cross Abstract: Reinforcement learning agents for portfolio management are typically trained and deployed as static policies, with no mechanism for using price foreca

PolySHAP: Extending KernelSHAP with Interaction-Informed Polynomial Regression

arXiv:2601.18608v3 Announce Type: replace Abstract: Shapley values have emerged as a central game-theoretic tool in explainable AI (XAI). However, computing Shapley values exactly requires 2^d game ev

Predicting Channel Closures in the Lightning Network with Machine Learning

arXiv:2605.12759v1 Announce Type: new Abstract: The Lightning Network (LN) is a second-layer protocol for Bitcoin designed to enable fast and cost-efficient off-chain transactions. Channels in the LN

PreFIQs: Face Image Quality Is What Survives Pruning

arXiv:2605.13396v1 Announce Type: new Abstract: Face Image Quality Assessment (FIQA) evaluates the utility of a face image for automated face recognition (FR) systems. In this work, we propose PreFIQs

Preserve-Then-Quantize: Balancing Rank Budgets for Quantization Error Reconstruction in LLMs

arXiv:2602.02001v2 Announce Type: replace-cross Abstract: Quantization Error Reconstruction (QER) reduces accuracy loss in Post-Training Quantization (PTQ) by approximating weights as mathbf{W} approx

Probing Persona-Dependent Preferences in Language Models

arXiv:2605.13339v1 Announce Type: cross Abstract: Large language models (LLMs) can be said to have preferences: they reliably pick certain tasks and outputs over others, and preferences shaped by post

Procedural Refinement by LLM-driven Algorithmic Debugging for ARC-AGI-2

arXiv:2603.20334v2 Announce Type: replace-cross Abstract: In high-complexity abstract reasoning, a system must infer a latent rule from a few examples or structured observations and apply it to unseen

Query-Conditioned Test-Time Self-Training for Large Language Models

arXiv:2605.13369v1 Announce Type: cross Abstract: Large language models (LLMs) are typically deployed with fixed parameters, and their performance is often improved by allocating more computation at i

Qwen-Image-VAE-2.0 Technical Report

arXiv:2605.13565v1 Announce Type: new Abstract: We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fide

RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation

arXiv:2605.13542v1 Announce Type: new Abstract: Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states u

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

arXiv:2510.13999v3 Announce Type: replace-cross Abstract: Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create signi

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning

arXiv:2507.01908v3 Announce Type: replace Abstract: Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple

Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering

arXiv:2605.12961v1 Announce Type: new Abstract: Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowle

Reducing cross-sample prediction churn in scientific machine learning

arXiv:2605.13826v1 Announce Type: new Abstract: Scientific machine learning reports predictive performance. It does not report whether the same prediction would survive a different draw of training da

Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models

arXiv:2605.13587v1 Announce Type: cross Abstract: Near-infrared spectroscopy (NIRS) is rapid and non-destructive, but reliable calibration still depends heavily on spectral preprocessing. In routine p

Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective

arXiv:2605.13597v1 Announce Type: new Abstract: Graph neural networks (GNNs) have emerged as a fundamental tool for learning from graph-structured data, achieving strong performance across a wide rang

Rethinking Graph Convolution for 2D-to-3D Hand Pose Lifting

arXiv:2605.13604v1 Announce Type: new Abstract: Graph convolutional networks (GCNs) are widely used for 3D hand pose estimation, where the hand skeleton is encoded as a fixed adjacency graph. We revis

ReTool-Video: Recursive Tool-Using Video Agents with Meta-Augmented Tool Grounding

arXiv:2605.13228v1 Announce Type: cross Abstract: Video understanding requires active evidence seeking, motivating tool-augmented video agents for temporal reasoning, cross-modal understanding, and co