applications

In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models

arXiv:2605.23908v1 Announce Type: new Abstract: We are in the midst of large-scale industrial and academic efforts to automate the processes of scientific, technological and creative production throug

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Infinite context windows seem to present a very large problem to using AI. Today's models already leak too much old information into current…

DGX agent

Infinite context windows seem to present a very large problem to using AI. Today's models already leak too much old information into current responses, a distraction that is part of why they are cogni

applicationsethan-mollick--x

26 May 2026

Introduction to the Analysis of Probabilistic Decision-Making Algorithms

arXiv:2508.21620v2 Announce Type: replace Abstract: Decision theories offer principled methods for making choices under various types of uncertainty. Algorithms that implement these theories have been

Invariant-Based Weight Sharing for Message Passing

arXiv:2605.25750v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) are a powerful framework for learning representations of graph-structured domains. However, weights in MPNNs act

Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation

arXiv:2605.24647v1 Announce Type: new Abstract: Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interactio

Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)

arXiv:2605.22093v2 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modellin

L2IR: Revealing Latent Intent in Graph Fraud Detection

arXiv:2605.26040v1 Announce Type: new Abstract: Graph fraud detection has long depended on Graph Neural Networks (GNNs) to propagate and aggregate information across relational data. A critical obstac

Lake Detection and Water Quality Estimation in Sentinel-2 Data

arXiv:2605.24515v1 Announce Type: new Abstract: With climate change and increasing human pressure on natural landscapes, inland water resources are becoming progressively scarcer, more vulnerable, and

Language Movement Primitives: Grounding Language Models in Robot Motion

arXiv:2602.02839v3 Announce Type: replace Abstract: Enabling robots to perform novel manipulation tasks from natural language instructions remains a fundamental challenge in robotics, despite signific

applicationsarxiv-cs-ro

Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction

arXiv:2605.25581v1 Announce Type: new Abstract: Single-cell perturbation prediction aims to infer how cells respond to unseen interventions and to achieve out-of-distribution (OOD) generalization, pro

LETS Forecast: Learning Embedology for Time Series Forecasting

arXiv:2506.06454v2 Announce Type: cross Abstract: Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future predict

LWM-CDE: A Representation Space for Wireless Data Reasoning and Transferability

arXiv:2605.24077v1 Announce Type: cross Abstract: Machine learning deployments in real-world wireless communication tasks face significant generalization challenges due to location and environment-spe

Manifold-Constrained MPPI: Real-Time Sampling-Based Control Under Hard Constraints

arXiv:2605.24813v1 Announce Type: new Abstract: Sampling-based model predictive control methods, such as Model Predictive Path Integral (MPPI), offer derivative-free optimization and robustness in com

applicationsarxiv-cs-ro

MASt3R-Nav: WayPixel Navigation in Relative 3D Maps

arXiv:2605.24111v1 Announce Type: cross Abstract: Visual navigation ability is strongly tied to its underlying representation of the world. Unlike classical 3D maps that require globally-consistent ge

MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification

arXiv:2605.24961v1 Announce Type: new Abstract: Medical time series are central to healthcare, enabling continuous monitoring and supporting timely clinical decisions. Despite recent progress, existin

Membership Inference Attacks on Tokenizers of Large Language Models

arXiv:2510.05699v4 Announce Type: replace-cross Abstract: Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these a

Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations

arXiv:2502.01397v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have been proposed as a tool for learning sparse matrix preconditioners, which are key components in accelerating

Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random

arXiv:2605.25439v1 Announce Type: new Abstract: Missing data frequently arises across diverse domains, including time-series and image domains. In the real world, missing occurrences often depend on t

Nano Banana Pro vs. GPT Image 2 Nano Banana Pro wins on photorealism, 4K output, 14 reference image slots for product/scene consistency, and…

Nano Banana Pro vs. GPT Image 2 Nano Banana Pro wins on photorealism, 4K output, 14 reference image slots for product/scene consistency, and live web search for real-world accuracy. GPT Image 2 wins o

applicationscomfyui--x

Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noise

arXiv:2509.15543v2 Announce Type: replace Abstract: Existing decentralized stochastic optimization methods assume the lower-level loss function is strongly convex and the stochastic gradient noise has

NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models

arXiv:2601.10457v3 Announce Type: replace Abstract: Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency productio

Overcoming 'Physics Shock' in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference

arXiv:2605.24106v1 Announce Type: cross Abstract: Rapid and accurate flood extent mapping from Remote Sensing data, such as Synthetic Aperture Radar (SAR), is critical for operational disaster respons

PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting

arXiv:2605.25554v1 Announce Type: new Abstract: As a core task in intelligent transportation systems, traffic forecasting plays a critical role in urban traffic management. Accurate traffic forecastin

Positivity in classical enumerative geometry: a case study in synchronized AI-assisted mathematics

arXiv:2605.25271v1 Announce Type: cross Abstract: We study the symmetric polynomial prod_{alphain A_{n,d}}igl(1+alpha_1 x_1+dots+alpha_n x_nigr) where A_{n,d}:={alphainZ_{ge 0}^n:|alpha|=d}, which is

PQDT: Pseudo-Query Dual Transformer for Robust Point Cloud Restoration

arXiv:2605.25127v1 Announce Type: cross Abstract: Point clouds are a fundamental 3D representation in computer vision, enabling a wide range of perception tasks. However, real-world point clouds often

Prism: A Plug-in Reproducible Infrastructure for Scalable Multimodal Continual Instruction Tuning

arXiv:2605.26110v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve versatility by reformulating diverse tasks into a unified instruction-following framework via instruc

Private Adaptive Covariance Estimation via Gaussian Graphical Models

arXiv:2605.24295v1 Announce Type: new Abstract: We propose PACE-GGM, a data-adaptive differentially private method for covariance estimation that concentrates its privacy budget on the most informativ

QUIVER: A Formal Framework for Quantifying Perturbation Propagation and Bifurcation in Compound AI Systems

arXiv:2605.23956v1 Announce Type: new Abstract: Compound AI systems that chain multiple LLM calls into directed computation graphs are now the dominant architecture for production AI. Although these a

RCTs & Human Uplift Studies: Methodological Challenges and Practical Solutions for Frontier AI Evaluation

arXiv:2603.11001v2 Announce Type: replace-cross Abstract: Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar

Restock is production ready.

Cohere announced that Restock, their production-ready system, has reached a stable release milestone. The announcement indicates the technology is now available for enterprise deployment and real-worl

applicationscohere--x

RoboHitch: Learning Visual Affordance from Disordered Keypoints for Hitch Knots Tying

arXiv:2605.24394v1 Announce Type: new Abstract: Robotic manipulation of deformable linear objects (DLOs) presents significant challenges due to complex dynamics and frequent self-occlusions. Existing

applicationsarxiv-cs-ro

Saturating Scaling Laws for Equational Discovery: A Phenomenology of Growth Dynamics in Three Toy Substrates with Two Real-World Replications

arXiv:2605.23983v1 Announce Type: new Abstract: We investigate growth dynamics in deterministic equational discovery substrates. Across three toy domains (arithmetic, boolean, higher-order list; n=592

Scan documents with your iPhone 📱, digitalize it with LlamaParse ✍️ We now support parsing HEIC formats natively in LlamaParse (along with …

Scan documents with your iPhone 📱, digitalize it with LlamaParse ✍️ We now support parsing HEIC formats natively in LlamaParse (along with 50+ other formats, incl. PDF, Word, Powerpoint, HTML) Try it

applicationsjerry-liu--x

SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack

arXiv:2605.24958v1 Announce Type: cross Abstract: Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especia

SeqRoute: Global Budget-Aware Sequential LLM Routing via Offline Reinforcement Learning

arXiv:2605.25424v1 Announce Type: cross Abstract: Existing LLM routing frameworks treat queries as independent events, neglecting the sequential nature of real-world user sessions constrained by globa

Simulating Human Memory with Language Models

arXiv:2605.25680v1 Announce Type: cross Abstract: Language models are increasingly being deployed as user simulators, but their memory is far more reliable than that of real users. To measure this gap

Software Engineer Roles @SakanaAILabs https://sakana.ai/careers/#software-engineer-enterprise

Sakana AI Labs is recruiting software engineers for enterprise-focused roles, with details available on their careers page. The position likely involves developing and maintaining software infrastruct

applicationsdavid-ha--x

SPACE: Unifying Symmetric and Asymmetric Routing Problems for Generalist Neural Solver

arXiv:2605.24484v1 Announce Type: new Abstract: Generalist neural routing solvers have shown great potential in solving diverse vehicle routing problems (VRPs) with a unified model. However, existing

StrTransformer: Source-Wise Structured Transformers for Unsupervised Blind Source Recovery

arXiv:2605.25648v1 Announce Type: cross Abstract: This paper proposes StrTransformer, a source-wise structured Transformer framework for blind source recovery and branch-wise latent modeling. Instead

Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents

arXiv:2605.24366v1 Announce Type: new Abstract: Large Language Models (LLMs) have been widely adopted in conversational applications. However, their reliance on parametric knowledge limits reliability

Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions

arXiv:2605.24452v1 Announce Type: cross Abstract: Legal NLP benchmarks evaluate models on randomly split data, implicitly assuming that legal language is stationary. We test this assumption by fine-tu

The closes thing is METR's very tentative update in February 2026, but the data is not high quality enough to know much of anything. https:/…

applicationsethan-mollick--x

The closes thing is METR's very tentative update in February 2026, but the data is not high quality enough to know much of anything. https://metr.org/blog/2026-02-24-uplift-update/#other-means-of-meas

The Meme Is the Message: Generative Memesis and AI Visuals in the 2024 USA Presidential Elections

arXiv:2411.00934v2 Announce Type: replace-cross Abstract: Visual content on social media has become increasingly influential in shaping political discourse and civic engagement, but it also limits par

This is our mission. The timeline for achieving this shrinks significantly if we can 10-100x the useful output of our engineering and manufa…

This is our mission. The timeline for achieving this shrinks significantly if we can 10-100x the useful output of our engineering and manufacturing teams. AI and robotics make this possible. This can

applicationselon-musk--x

Towards a Universal Causal Reasoner

arXiv:2605.24873v1 Announce Type: cross Abstract: Despite the importance of causal reasoning, training LLMs to reason causally remains underexplored. Existing data efforts mostly focus on benchmarking

Transformers over-extend what humans underlearn: the case of Spanish L-shaped morphome

arXiv:2507.21556v3 Announce Type: replace Abstract: The cognitive reality of irregular morphological patterns has been debated for decades: do speakers extend them to novel forms, or are they lexical

Translators as Invisible Teachers of AI: Copyright, Translation Memory, and the Political Economy of Linguistic Data

arXiv:2605.24842v1 Announce Type: new Abstract: This paper examines how the labour of translators has been transformed into foundational data capital for the age of artificial intelligence (AI). Trans

Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

arXiv:2011.10254v3 Announce Type: replace-cross Abstract: Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all