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Gated Graph Attention Networks with Learnable Temperature

arXiv:2605.29803v1 Announce Type: new Abstract: Graph attention networks learn neighbor importance through data-dependent coefficients, but standard layers lack explicit control over unreliable featur

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Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence

DGX agent

arXiv:2605.30093v1 Announce Type: new Abstract: Foundation features from self-supervised vision models and text-to-image diffusion models have proven effective for semantic correspondence estimation.

tutorialsarxiv-cs-cv

29 May 2026

Has @AnthropicAI completely given up on making API usage reasonably-priced? Following the token-usage changes recently they announced variou…

tutorialsjeremy-howard--x

Has @AnthropicAI completely given up on making API usage reasonably-priced? Following the token-usage changes recently they announced various updates to *subscription* usage to make it more reasonable

Having Grok Build sub-agents to iterate several ideas for me on data loading, batching, inference, and writing results to files for dense da…

Having Grok Build sub-agents to iterate several ideas for me on data loading, batching, inference, and writing results to files for dense datasets before I went to sleep. It gave me a nice summary of

tutorialselon-musk--x

HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens

arXiv:2512.15133v2 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented

How massive bonuses for Samsung's employees in the memory division have sparked debate over how companies and governments should share profits from the AI boom (Bloomberg)

Bloomberg: How massive bonuses for Samsung's employees in the memory division have sparked debate over how companies and governments should share profits from the AI boom — Payouts at Samsung have rai

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Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

arXiv:2605.29184v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by i

Knowing What to Solve Before How: Preplan Empowered LLM Mathematical Reasoning

arXiv:2605.30245v1 Announce Type: new Abstract: Current plan-based reasoning methods improve large language models (LLMs) by inserting a planning stage before execution, giving rise to the question ri

Large Depth Completion Model from Sparse Observations

arXiv:2605.30115v1 Announce Type: new Abstract: This work presents the Large Depth Completion Model (LDCM), a simple, effective, and robust framework for single-view metric depth estimation with spars

Latent Terms: Dense Retrievers Contain Trivially Extractable BM25-ready Zipfian Vocabularies

arXiv:2605.29384v1 Announce Type: cross Abstract: We propose Latent Terms, a method revealing that models trained for dense retrieval, whether single- or multi-vector, learn representations that can t

Learn from A Rationalist: Distilling Intermediate Interpretable Rationales

arXiv:2601.22531v2 Announce Type: replace-cross Abstract: Because of the pervasive use of deep neural networks (DNNs), especially in high-stakes domains, the interpretability of DNNs has received incr

Learnable Assessment Skills for LLM-based Automated Scoring: Rubric Construction via Iterative Optimization

arXiv:2605.29274v1 Announce Type: new Abstract: LLM-based automated scoring approaches near-human performance, but scaling to new tasks remains bottlenecked by the per-item human configuration of upst

Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control

arXiv:2605.29144v1 Announce Type: new Abstract: Robotics Wire Arc Additive Manufacturing (WAAM) is governed by complex and nonlinear process dynamics coupling thermal field to the build geometry. The

Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning

arXiv:2605.28990v1 Announce Type: new Abstract: Functional magnetic resonance imaging (fMRI) is a powerful tool for investigating human brain function. However, the high cost of data acquisition and t

Learning to Feel Materials from Multisensory Tactile Data via Interpretable Models

arXiv:2605.29572v1 Announce Type: new Abstract: Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual r

Make LLM Learn to Synthesize from Streaming Experiences through Feedback

arXiv:2605.29940v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted for synthetic data generation, significantly reducing annotation costs. However, most existing stu

MEMENTO: Leveraging Web as a Learning Signal for Low-Data Domains

arXiv:2605.29795v1 Announce Type: new Abstract: Real-world tasks often lack large labeled datasets, motivating extensive work on learning in low-data regimes. However, existing approaches such as few-

MonoDuo: Using One Robot Arm to Learn Bimanual Policies

arXiv:2605.29298v1 Announce Type: new Abstract: Bimanual coordination is essential for many real-world manipulation tasks, yet learning bimanual robot policies is limited by the scarcity of bimanual r

Multi-level Collaborative Distillation Meets Global Workspace Model: A Unified Framework for OCIL

arXiv:2508.08677v2 Announce Type: replace-cross Abstract: Online Class-Incremental Learning (OCIL) enables models to learn continuously from non-i.i.d. data streams. Since samples of the data streams

My MLSys keynote on AI writing systems code got more interest than I expected. The recording will take a while, so in the finest tradition o…

tutorialsjeremy-howard--x

My MLSys keynote on AI writing systems code got more interest than I expected. The recording will take a while, so in the finest tradition of AI labs sharing blog posts, we’re starting the Core Automa

Neural Logistic Bandits

arXiv:2505.02069v2 Announce Type: replace Abstract: We study the problem of neural logistic bandits, where the main task is to learn an unknown reward function within a logistic link function using a

NeuROK: Generative 4D Neural Object Kinematics

arXiv:2605.30347v1 Announce Type: new Abstract: Data-driven approaches have revolutionized 3D vision, enabling transformers to effectively reconstruct and generate static 3D objects. However, generati

Not All Inputs Are Valid: Towards Open-Set Video Moment Retrieval Using Language

arXiv:2605.29812v1 Announce Type: new Abstract: Video Moment Retrieval (VMR) targets to retrieve the specific moment corresponding to a sentence query from an untrimmed video. Although recent works ha

OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild

arXiv:2511.08423v3 Announce Type: replace Abstract: A truly universal AI-Generated Image (AIGI) detector must simultaneously generalize across diverse generative models and varied semantic content. Cu

One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them

arXiv:2605.28839v1 Announce Type: new Abstract: Knowledge editing methods such as ROME and MEMIT update factual associations in transformer models by modifying MLP weights. While evaluated mainly by o

Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild

arXiv:2504.12512v2 Announce Type: replace Abstract: Mobile manipulation robots are continuously advancing, with their grasping capabilities rapidly progressing. However, there are still significant ga

PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation

arXiv:2605.28867v1 Announce Type: cross Abstract: Generating high-quality time-series data is challenging because real-world signals often exhibit multimodal patterns and multiscale dynamics, includin

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

This guide introduces torch.profiler, PyTorch's built-in profiling tool for analyzing model performance and identifying computational bottlenecks. It covers the basics of using the profiler to measure

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Reasoning that Travels: Dissecting How Chain-of-Thought Transfers Across Models

arXiv:2605.28913v1 Announce Type: new Abstract: Large reasoning models (LRMs) often generate extensive chain-of-thought (CoT) traces before producing a final answer. As explicit textual artifacts, the

Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations

arXiv:2602.01456v2 Announce Type: replace-cross Abstract: Joint-Embedding Predictive Architectures (JEPA) learn view-invariant representations and admit projection-based distribution matching for coll

Robust Cross-Domain Generalization Using Unlabeled Target Data with Source-Domain Supervision

arXiv:2605.29122v1 Announce Type: new Abstract: It is often desirable to generalize medical imaging AI models trained with dense annotations to data acquired from different ultrasound scanners or clin

Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation

arXiv:2602.21565v2 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific d

Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals

arXiv:2605.29098v1 Announce Type: new Abstract: Reconstructing object geometry from radio frequency (RF) signals is fundamentally challenging due to the lensless imaging nature of RF sensing, which le

SRUG: Shadow-Guided Relightable Urban Scene with Generation Model

arXiv:2605.24700v2 Announce Type: replace Abstract: Creating relightable urban scenes from images or videos is widely useful but highly ill-posed. Urban environments are typically unbounded and extend

Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AI

arXiv:2605.29240v1 Announce Type: new Abstract: AI-augmented classrooms generate rich teacher and student feedback before graded outcomes become available, yet these signals can be difficult to transl

The forgotten developer who saved JavaScript...

This Fireship video explores the forgotten developer or pivotal historical figure who played a critical role in saving or advancing the JavaScript language during a crucial period of its development.

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There are still a few spots left for @hwchase17 + @traversal_ai founder @_anish_agarwal's technical fireside chat in NYC on June 2nd. Learn …

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There are still a few spots left for @hwchase17 + @traversal_ai founder @_anish_agarwal's technical fireside chat in NYC on June 2nd. Learn how Traversal builds, ships and improves their agents. Enjoy

Thoughts-as-Planning: Latent World Models for Chain-of-Thoughts Optimization via Reinforcement Planning

arXiv:2605.28842v1 Announce Type: cross Abstract: The success of large language models (LLMs) across diverse NLP tasks has elevated the importance of reasoning chain optimization as a critical step in

Tip on Grok Build + Grok 4.3 VLM One of my critical tasks is to keep Grok VLM in the loop. Throwing a default system prompt usually yields p…

Tip on Grok Build + Grok 4.3 VLM One of my critical tasks is to keep Grok VLM in the loop. Throwing a default system prompt usually yields poor results due to lack of context. Here is how to scale: -

tutorialselon-musk--x

Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection

arXiv:2605.29455v1 Announce Type: new Abstract: Multi-modal industrial anomaly detection typically relies on separate models for each product category, fundamentally limiting practical scalability. Wh

What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies

arXiv:2603.02082v2 Announce Type: replace Abstract: Children's acquisition of filler-gap dependencies has been argued by some to depend on innate grammatical knowledge, while others suggest that the d

When Models Learn to Ask Why: Adaptive Causal Reasoning for Trustworthy Medical Vision-Language Models

arXiv:2603.23085v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have enabled interpretable medical diagnosis by integrating visual perception with linguistic reasoning. Yet, existing

Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention

arXiv:2605.29548v1 Announce Type: new Abstract: Larger models learn tasks smaller models do not. What drives this phenomenon? We develop a simple phenomenological argument that power-law scaling alrea

Zero-shot CT Super-Resolution using Diffusion-based 2D Projection Priors and Signed 3D Gaussians

arXiv:2508.15151v3 Announce Type: replace-cross Abstract: Computed tomography (CT) is important in clinical diagnosis, but acquiring high-resolution (HR) CT is constrained by radiation exposure risks.

Adaptive Sampling and Clipping for Private Worst-Case Group Optimization

arXiv:2602.10820v2 Announce Type: replace Abstract: A central requirement for the acceptance of machine learning methods for human-centric tasks is that they should be fair, in the sense that they sho

Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting

arXiv:2605.27668v1 Announce Type: cross Abstract: Probabilistic forecasting estimates the likelihood of uncertain future events. To improve LLM forecasting, existing methods typically learn from binar

Automatic Pruning Discovery for Large Language Models

arXiv:2511.15390v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance on a wide range of tasks, hindering real-world deployment due to their massive siz

Banger paper from Harvard. AutoScientists drops the central planner entirely. Agents interpret shared experimental data, self-organize aroun…

Banger paper from Harvard. AutoScientists drops the central planner entirely. Agents interpret shared experimental data, self-organize around promising directions, evaluate proposals before resource a

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