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arXiv:2606.01339v1 Announce Type: cross Abstract: Long-term time-series forecasting needs models that are accurate yet efficient enough for commodity hardware. Lightweight linear forecasters are remar

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Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference

DGX agent

arXiv:2603.12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an i

safetyarxiv-cs-lg

2 Jun 2026

From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation

arXiv:2606.02167v1 Announce Type: new Abstract: Engineers designing production systems need to verify that a given layout supports all required production sequences. Automated planning techniques can

From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction

arXiv:2606.00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving. Recent studies have incorporated safety risk into trajec

From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models

arXiv:2606.00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robot

From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users

arXiv:2606.00728v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability.

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures

model-releasesarxiv-cs-ai

arXiv:2602.04861v2 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (P

From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data

arXiv:2606.02268v1 Announce Type: new Abstract: Geometric analysis fundamentally distinguishes between extit{extrinsic} and extit{intrinsic} perspectives. The dominant paradigm in current 3D represent

Local Ai

From Features to Actions: Explainability in Traditional and Agentic AI Systems

arXiv:2602.06841v4 Announce Type: replace Abstract: Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate

local-aiarxiv-cs-ai

From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization

arXiv:2603.00021v2 Announce Type: replace Abstract: Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-rang

From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphs

arXiv:2508.01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs. While standard evaluations often fix th

From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data

arXiv:2606.00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most exi

From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression

arXiv:2606.02559v1 Announce Type: cross Abstract: Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted

From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning

model-releasesarxiv-cs-lg

arXiv:2510.03690v4 Announce Type: replace Abstract: Real-world graph datasets often arise from mixtures of populations, where graphs are generated by multiple distinct underlying distributions. In thi

From Noise to Control: Parameterized Diffusion Policies

arXiv:2606.00336v1 Announce Type: new Abstract: We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embed

From Noise to Order: Learning to Rank via Denoising Diffusion

arXiv:2602.11453v2 Announce Type: replace-cross Abstract: In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approac

From Outliers to Errors: Auditing Pali-to-English LLM Translations with Multi-Reference Adjudication

model-releasesarxiv-cs-cl

arXiv:2606.01136v1 Announce Type: new Abstract: Single-score translation metrics can conflate legitimate variation with error, a problem especially acute for classical languages where multiple defensi

From Performance to Viability: A Bootstrap Framework for Latent-Space Representation Learning in Adaptive Biological Systems

arXiv:2606.01374v1 Announce Type: new Abstract: Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct o

From question to model. The public equity investing plugin for Codex.

This post describes OpenAI's public equity investing plugin for Codex, which enables users to convert investment questions into analytical models through natural language processing. The plugin likely

model-releasesopenai--x

From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets

arXiv:2606.00202v1 Announce Type: cross Abstract: Standard machine learning pipelines often admit many near-optimal models. These 'Rashomon sets' pose a range of challenges and opportunities for uncer

From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning

arXiv:2606.01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback. Existing offline

From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction

model-releasesarxiv-cs-lg

arXiv:2511.12081v2 Announce Type: replace-cross Abstract: Despite massive investments in scale, deep models for click-through rate (CTR) prediction often exhibit rapidly diminishing returns -- a stark

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model

model-releasesarxiv-cs-ai

arXiv:2512.05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with

From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2603.04828v2 Announce Type: replace Abstract: Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly

From 'Weak' Signals to Strong Models: Preference Delta Aggregation with LoRA Merging

arXiv:2606.00357v1 Announce Type: new Abstract: Training strong large language models (LLMs) requires high-quality supervision, which is often scarce. Recent work shows that paired preference data fro

From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection

arXiv:2602.03018v2 Announce Type: replace Abstract: Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes al

From Zero to Hero: Training-Free Custom Concept Spawning in World Models

arXiv:2606.02575v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for interactive video generation, allowing users to navigate dynamically generated envir

FROST-STA: Frozen Dense Features for the Ego4D Short-Term Object Interaction Anticipation

arXiv:2606.00694v1 Announce Type: new Abstract: Short-term anticipation in egocentric video requires more than recognizing the current scene: a system must infer which object the camera wearer will co

safetyarxiv-cs-cv

Fundamental bounds on efficiency-confidence trade-off for transductive conformal prediction

arXiv:2509.04631v2 Announce Type: replace Abstract: Transductive conformal prediction addresses the simultaneous prediction for multiple data points. Given a desired confidence level, the objective is

FVSpec: Real-World Property-Based Tests as Lean Challenges

model-releasesarxiv-cs-ai

arXiv:2606.01008v1 Announce Type: cross Abstract: We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks. We first scrape 11,039 property-based tes

FW-NKF: Frequency-Weighted Neural Kalman Filters

arXiv:2606.02251v1 Announce Type: cross Abstract: Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch

G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs

model-releasesarxiv-cs-lg

arXiv:2606.01873v1 Announce Type: new Abstract: LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embed

GABI: Geometry-Aware Boundary Integration for Spacecraft Segmentation

model-releasesarxiv-cs-cv

arXiv:2606.00886v1 Announce Type: new Abstract: Accurate segmentation is crucial for autonomous spacecraft, as it directly affects downstream tasks related to 3D situational awareness. The harsh illum

Industry

@garrytan Model, application, and infrastructure layers trying to commoditize each other

This post discusses how the model, application, and infrastructure layers of AI are competing to commoditize each other, suggesting a dynamic where each layer is attempting to move upstream or downstr

industrysonya-huang--x

Tutorials

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs

arXiv:2606.01660v1 Announce Type: new Abstract: Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop

tutorialsarxiv-cs-lg

GateKD: Confidence-Gated Closed-Loop Distillation for Robust Reasoning

arXiv:2605.13136v2 Announce Type: replace Abstract: Distilling multi-step reasoning abilities from large language models (LLMs) into compact student models remains challenging due to noisy rationales,

GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics

arXiv:2606.02424v1 Announce Type: cross Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images

GeistBERT: Breathing Life into German NLP

arXiv:2506.11903v5 Announce Type: replace Abstract: Advances in transformer-based language models have highlighted the benefits of language-specific pre-training on high-quality corpora. In this conte

Gemini Spark is the most impressive and terrifying AI experience I’ve had yet

model-releasesthe-verge-ai

According to every product demo from the last four years, planning a trip is a killer use case for AI. Just tell it where you're going, they all promise, and your chatbot / agent / other buzzword will

General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling

arXiv:2606.00110v1 Announce Type: new Abstract: Achieving robust generalization from limited data is a central challenge in embodied intelligence. Prevailing methods fail by regressing absolute coordi

Generalization Limits in Vehicle Re-Identification

arXiv:2606.01981v1 Announce Type: new Abstract: Vehicle re-identification focuses on retrieving images of the same vehicle from a gallery given a query image. Upon closer inspection of commonly used d

Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime

arXiv:2510.06028v3 Announce Type: replace Abstract: This paper provides data-dependent bounds on the expected error of the Gibbs algorithm in the overparameterized interpolation regime, where low trai

Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetry

arXiv:2511.01064v3 Announce Type: replace-cross Abstract: Variational inference (VI) approximates a target density p by the best match q in a family of tractable distributions. The best variational ap

Local Ai

Generate a complete storyboard from a single prompt, then animate it in Seedance 2.0 using a reference image. This ComfyUI workflow uses LLM…

Generate a complete storyboard from a single prompt, then animate it in Seedance 2.0 using a reference image. This ComfyUI workflow uses LLMs to expand prompts, create storyboard scenes, and format ev

local-aicomfyui--x

Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation

arXiv:2606.00514v1 Announce Type: cross Abstract: Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distribu

Agents

Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey

arXiv:2606.00136v1 Announce Type: cross Abstract: The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffecti

agentsarxiv-cs-ai

Generative AI and Sales Productivity: Field Experiments in Online Retail

arXiv:2510.12049v4 Announce Type: replace-cross Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomize

Tutorials

Generative Diffusion Priors for 3D Mapping of the Dark Universe

arXiv:2606.00803v1 Announce Type: cross Abstract: Reconstructing the three-dimensional distribution of dark matter from weak-lensing observations is a central but highly ill-posed inverse problem in c

tutorialsarxiv-cs-cv