Search: “models”

RAG or Learning? Understanding the Limits of LLM Adaptation under Continuous Knowledge Drift in the Real World

arXiv:2604.05096v2 Announce Type: replace Abstract: Large language models (LLMs) acquire most of their knowledge during pretraining, which ties them to a fixed snapshot of the world and makes adaptati

Model Releases

ReConText3D: Replay-based Continual Text-to-3D Generation

DGX agent

arXiv:2604.13730v1 Announce Type: new Abstract: Continual learning enables models to acquire new knowledge over time while retaining previously learned capabilities. However, its application to text-t

model-releasesarxiv-cs-cv

16 Apr 2026

Hardware

Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes

arXiv:2506.09163v2 Announce Type: replace Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic process

hardwarearxiv-cs-lg

Towards Successful Implementation of Automated Raveling Detection: Effects of Training Data Size, Illumination Difference, and Spatial Shift

arXiv:2604.13322v1 Announce Type: new Abstract: Raveling, the loss of aggregates, is a major form of asphalt pavement surface distress, especially on highways. While research has shown that machine le

WAI-ANIMA 1.0 released

model-releasesr-stablediffusion

WAI-ANIMA 1.0 is a newly released Stable Diffusion checkpoint model from the WAI model family, likely combining elements of the WAI-Illustrious anime generation lineage with the Anima diffusion archit

Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks

arXiv:2604.13403v1 Announce Type: new Abstract: In-context learning (ICL) enables models to adapt to new tasks via inference-time demonstrations. Despite its success in large language models, the exte

safetyarxiv-cs-cv

ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs

arXiv:2512.09953v2 Announce Type: replace-cross Abstract: Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety require

safetyarxiv-cs-lg

A Dataset and Evaluation for Complex 4D Markerless Human Motion Capture

arXiv:2604.12765v1 Announce Type: new Abstract: Marker-based motion capture (MoCap) systems have long been the gold standard for accurate 4D human modeling, yet their reliance on specialized hardware

safetyarxiv-cs-cv

Agents

Coding-Free and Privacy-Preserving MCP Framework for Clinical Agentic Research Intelligence System

arXiv:2604.12258v1 Announce Type: cross Abstract: Clinical research involves labor-intensive processes such as study design, cohort construction, model development, and documentation, requiring domain

agentsarxiv-cs-ai

Cooperative Memory Paging with Keyword Bookmarks for Long-Horizon LLM Conversations

arXiv:2604.12376v1 Announce Type: cross Abstract: When LLM conversations grow beyond the context window, old content must be evicted -- but how does the model recover it when needed? We propose cooper

Local Ai

DiffusionPrint: Learning Generative Fingerprints for Diffusion-Based Inpainting Localization

arXiv:2604.12443v1 Announce Type: new Abstract: Modern diffusion-based inpainting models pose significant challenges for image forgery localization (IFL), as their full regeneration pipelines reconstr

local-aiarxiv-cs-cv

EDGE-Shield: Efficient Denoising-staGE Shield for Violative Content Filtering via Scalable Reference-Based Matching

arXiv:2604.06063v2 Announce Type: replace Abstract: The advent of Text-to-Image generative models poses significant risks of copyright violation and deepfake generation. Since the rapid proliferation

EgoEsportsQA: An Egocentric Video Benchmark for Perception and Reasoning in Esports

arXiv:2604.12320v1 Announce Type: cross Abstract: While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, i

Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge

model-releasesarxiv-cs-lg

arXiv:2604.12737v1 Announce Type: cross Abstract: While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs).

Fine-tuning Factor Augmented Neural Lasso for Heterogeneous Environments

model-releasesarxiv-cs-lg

arXiv:2604.12288v1 Announce Type: cross Abstract: Fine-tuning is a widely used strategy for adapting pre-trained models to new tasks, yet its methodology and theoretical properties in high-dimensional

Tutorials

Generating Effective CoT Traces for Mitigating Causal Hallucination

arXiv:2604.12748v1 Announce Type: new Abstract: Although large language models (LLMs) excel in complex reasoning tasks, they suffer from severe causal hallucination in event causality identification (

tutorialsarxiv-cs-cl

GUIDE: Guided Updates for In-context Decision Evolution in LLM-Driven Spacecraft Operations

arXiv:2603.27306v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been proposed as supervisory agents for spacecraft operations, but existing approaches rely on static prompt

safetyarxiv-cs-ai

iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception

model-releasesarxiv-cs-ro

arXiv:2410.09072v4 Announce Type: replace Abstract: Robotic perception models often fail when deployed in real-world environments due to out-of-distribution conditions such as clutter, occlusion, and

JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence

arXiv:2510.23538v2 Announce Type: replace Abstract: The scope of neural code intelligence is rapidly expanding beyond text-based source code to encompass the rich visual outputs that programs generate

KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning

arXiv:2603.21440v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge-intensive reasoning tasks.

Local Ai

Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness

arXiv:2604.12373v1 Announce Type: new Abstract: Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether larg

local-aiarxiv-cs-cl

Multi-region endpoints are available for Claude on Vertex AI

model-releasesgoogle-cloud-ai

Today, we’re announcing U.S. and EU multi-region endpoints for Claude on Vertex AI are available in public preview. By pooling capacity across multiple regions, these endpoints dynamically route reque

One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness

arXiv:2604.13006v1 Announce Type: cross Abstract: Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness when trivially constrained? We show

QuarkMedSearch: A Long-Horizon Deep Search Agent for Exploring Medical Intelligence

arXiv:2604.12867v1 Announce Type: new Abstract: As agentic foundation models continue to evolve, how to further improve their performance in vertical domains has become an important challenge. To this

SpecBound: Adaptive Bounded Self-Speculation with Layer-wise Confidence Calibration

arXiv:2604.12247v1 Announce Type: cross Abstract: Speculative decoding has emerged as a promising approach to accelerate autoregressive inference in large language models (LLMs). Self-draft methods, w

VISTA: Validation-Informed Trajectory Adaptation via Self-Distillation

arXiv:2604.12044v1 Announce Type: cross Abstract: Deep learning models may converge to suboptimal solutions despite strong validation accuracy, masking an optimization failure we term Trajectory Devia

Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding

arXiv:2604.12358v1 Announce Type: new Abstract: Recently, visual token pruning has been studied to handle the vast number of visual tokens in Multimodal Large Language Models. However, we observe that

researcharxiv-cs-cv

Adaptive Multi-Expert Reasoning via Difficulty-Aware Routing and Uncertainty-Guided Aggregation

arXiv:2604.10335v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong performance in math reasoning benchmarks, but their performance varies inconsistently across problems wi

researcharxiv-cs-cl

Applications

AI Achieves a Perfect LSAT Score

arXiv:2604.10034v1 Announce Type: new Abstract: This paper reports the first documented instance of a language model achieving a perfect score on an officially disclosed Law School Admission Test (LSA

applicationsarxiv-cs-ai

Attention-Guided Flow-Matching for Sparse 3D Geological Generation

arXiv:2604.09700v1 Announce Type: cross Abstract: Constructing high-resolution 3D geological models from sparse 1D borehole and 2D surface data is a highly ill-posed inverse problem. Traditional heuri

BiCLIP: Domain Canonicalization via Structured Geometric Transformation

arXiv:2603.08942v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities, yet adapting these models to specialized

CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration

arXiv:2509.17458v3 Announce Type: replace-cross Abstract: Text-to-image diffusion models, such as Stable Diffusion, can produce high-quality and diverse images but often fail to achieve compositional

Local Ai

Codex CLI with ollama as provider?

This r/ollama thread discusses using Ollama as a local model provider for OpenAI's Codex CLI, covering configuration and setup. Open models can be used with OpenAI's Codex CLI through Ollama, allowing

local-air-ollama

Applications

CraftGraffiti: Exploring Human Identity with Custom Graffiti Art via Facial-Preserving Diffusion Models

arXiv:2508.20640v2 Announce Type: replace Abstract: Preserving facial identity under extreme stylistic transformation remains a major challenge in generative art. In graffiti, a high-contrast, abstrac

applicationsarxiv-cs-cv

Data-Efficient Semantic Segmentation of 3D Point Clouds via Open-Vocabulary Image Segmentation-based Pseudo-Labeling

arXiv:2604.11007v1 Announce Type: new Abstract: Semantic segmentation of 3D point cloud scenes is a crucial task for various applications. In real-world scenarios, training segmentation models often f

Decomposing and Reducing Hidden Measurement Error in LLM Evaluation Pipelines

arXiv:2604.11581v1 Announce Type: new Abstract: LLM evaluations drive which models get deployed, which safety standards get adopted, and which research conclusions get published. Yet these scores carr

Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky

arXiv:2507.03336v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the

Do Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment

arXiv:2603.07462v2 Announce Type: replace Abstract: Determining whether AI systems process information similarly to humans is central to cognitive science and trustworthy AI. While modern AI models ca

safetyarxiv-cs-ai

Exploring Knowledge Purification in Multi-Teacher Knowledge Distillation for LLMs

arXiv:2602.01064v2 Announce Type: replace Abstract: Knowledge distillation has emerged as a pivotal technique for transferring knowledge from stronger large language models (LLMs) to smaller, more eff

researcharxiv-cs-cl

FashionMV: Product-Level Composed Image Retrieval with Multi-View Fashion Data

arXiv:2604.10297v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) retrieves target images using a reference image paired with modification text. Despite rapid advances, all existing met

Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis

arXiv:2502.19056v2 Announce Type: replace-cross Abstract: Fatigue modeling is essential for motion synthesis tasks to model human motions under fatigued conditions and biomechanical engineering applic

safetyarxiv-cs-lg

FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation

arXiv:2604.10678v1 Announce Type: new Abstract: Social bot detection is critical to the stability and security of online social platforms. However, current state-of-the-art bot detection models are la

From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences

arXiv:2604.10332v1 Announce Type: new Abstract: We present the progress of the GPT family from GPT-3 through GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o, GPT-4.1, and the GPT-5 family. Our work is comparative

Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds

arXiv:2604.11104v1 Announce Type: new Abstract: This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through

GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension

arXiv:2604.09923v1 Announce Type: new Abstract: Text-to-image (T2I) models, and their encoded biases, increasingly shape the visual media the public encounters. While researchers have produced a rich

safetyarxiv-cs-ai

[Help] Gemma 4 26B LoRA Training on 16GB VRAM: Loss decreases, but inference degenerates into loops (Masking vs. MoE?)

This Reddit thread discusses a user's experience attempting LoRA fine-tuning of the Gemma 4 26B-A4B model on a 16GB VRAM GPU, where training loss decreases normally but the resulting model degenerates

model-releasesr-ollama

Learning Robustness at Test-Time from a Non-Robust Teacher

arXiv:2604.11590v1 Announce Type: new Abstract: Nowadays, pretrained models are increasingly used as general-purpose backbones and adapted at test-time to downstream environments where target data are

LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning

arXiv:2502.14644v5 Announce Type: replace Abstract: Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine