AI News Archive: May 26, 2026 — Part 20
Sourced from 500+ daily AI sources, scored by relevance.
- On the Generalization Capabilities, Design Choices and Limitations of Keypoint Imitation Learning
RGB-based imitation learning requires many demonstrations to generalize to unseen objects or scenes, motivating research into intermediate representations to improve generalization for robotic manipulation. Visual foundation models enable one-shot extraction of keypoints to provide such representati...
- L-Learning : A Lyapunov-Based Approach Leveraging Lagrangian Mechanics for Efficient and Stable Robot Tracking
This paper presents L-Learning, a novel data-driven control framework for robotics that integrates Lyapunov stability theory with Lagrangian mechanics to enhance trajectory tracking performance. While traditional control methods often suffer from performance degradation in dynamic and uncertain envi...
- HyperSim: A Holistic Sim-To-Real Framework For Robust Robotic Manipulation
Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acquisition, transferring robotic manipulation policies from simulation to the real world (sim-to-real) remains a formidable ...
- Enabling Extensible Embodied Capabilities with Tools
Most existing embodied intelligence methods formulate perception, reasoning, planning, and control within a unified parameterized policy. Yet these capabilities are inherently hierarchical and heterogeneous, making them difficult to reliably learn and modularize within a single model. We propose a c...
- Provably Safe Motion Planning Under Unknown Disturbances
We present a provably safe sampling-based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We consider systems with linear or linearizable dynamics evolving in workspace with arbitrary-shaped obstacles subject to state and control constraints. Sa...
- Multi-Robot Box Transport over Different Surfaces with Decentralized Role-based Proportional Control
Collaborative transport of objects via pushing by multiple robots has many applications, ranging from construction and warehouse environments to post disaster debris clean-up. Achieving collaborative transport over surfaces with different inclination and friction properties however poses unique chal...
- Sampling Data with Chains of Forward-Backward Diffusion Steps
Sampling from learned high-dimensional distributions is a foundational computational problem. We introduce U-turn chains: Markov chains obtained by iterating short forward-backward steps of a diffusion model, in which each step proposes a move that remains on the learned data manifold and, paired wi...
- Signal-to-Noise Ratio and Sample Size Govern Representational Alignment in Neural Networks
Neural networks are known to develop latent representations that are $aligned$, namely structurally similar across networks trained with different architectures, training protocols, or training datasets. We study this phenomenon in a controlled setting, where we train an ensemble of networks on regr...
- Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models
Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the normalization operation has been extensively studied, the scale vector remains poorly understood despite its ubiquitous use. In this work, we present...
- Transformers Can Learn Posterior Predictive Distributions In-Context
Prior-data fitted networks (PFNs) have recently emerged as a powerful approach for Bayesian prediction tasks, approximating the posterior predictive distribution (PPD) through in-context learning. Despite their strong empirical performance and ability to go beyond point predictions, theoretical unde...
- Bilevel Optimization over Saddle Points of Zero-Sum Markov Games
Reinforcement learning (RL) often has a hierarchical structure, where an upper-level (UL) learner selects model parameters and a lower-level (LL) decision-making process responds, naturally leading to a bilevel optimization problem. Most existing bilevel RL methods assume a single-policy LL Markov d...
- More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations
Feedforward network (FFN) layers account for a large fraction of parameters and nonlinear expressivity in Transformer-based large language models (LLMs). Despite the evolution from ReLU and GELU to gated variants such as SwiGLU, most FFN designs still use a single fixed activation function, applying...
- Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift
Childhood anemia affects around 40% of children aged 6-59 months globally and arises from heterogeneous factors, limiting model generalizability. We evaluate a transformer-based tabular foundation model against classical supervised methods under cross-country and data-scarce settings. We used DHS ...
- Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing
This paper addresses structured out-of-distribution (OOD) testing in high-stakes machine learning applications. Traditional conformal methods rely on joint exchangeability, making it difficult to incorporate auxiliary information such as spatiotemporal or grouping structures. To overcome this limita...
- Constrained Bayesian Experimental Design via Online Planning
Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic constraints inherent in real-world tasks due to budget limitations, varying costs, or physical constraints that restrict how ...
- Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
Maintaining predictive accuracy in non-stationary environments requires online model selection to adapt autonomously to unknown distribution shifts. However, existing tuning-free algorithms face a fundamental trade-off between robustness and agility. Specifically, to ensure dynamic regret bounds, th...
- SpecDD
Build better software with spec-driven AI
- Sample Complexity of Policy Gradient for Log-Growth Control
We study the sample complexity of policy gradient for log-growth control -- the problem of learning, from observed state transitions, a feedback gain that optimally stabilizes a scalar linear system driven through a multiplicative-noise actuation channel. The objective $J(K) = \mathbb{E}[\log|1+BK|]...
- Function-Valued Causal Influence in Nonlinear Time Series
Causal discovery in time series is increasingly performed using nonlinear machine-learning models, yet the resulting causal relationships are almost always summarized by scalar edge scores. We argue that this practice obscures the true object learned by nonlinear autoregressive models: a state-depen...
- Brew
Like Claude design for email marketing
- Parrot Speech-to-text API
Fast, accurate STT for production-grade voice agents
- AVTR-1 Real-Time Open Weights Model
Generating uncanny AI avatars is now open source
- Willow Scribe
Tell Scribe what to say. It writes the rest.
- SelectPrism
Agents that screen and interview so you can hire faster
- DodoForm
Turn talking, pics, or scribbles into clean, structured data
- Parsewise API
API for agentic multi-document processing
- LangPanda
Learn languages from watching your favorite shows
- Kept
Your AI chats, saved as Markdown locally with no cloud
- marpy.io
AI coding platform built specifically for the Python stack
- MiniCPM5-1B
A new SOTA for compact open models on the edge
- Ormedo
Let AI agents handle your entire outbound pipeline
- Ajar
Lid Angle Sync & Keep Awake for AI Agents on Mac
- Hayley: Your Thinking Companion
An insight, a pattern, one question worth sitting with.
- NoteCove
Notes, tasks, and AI — offline-first, no SaaS bill.
- AI Shadowing
Turn any YouTube video into a language shadowing lesson
- ReplylessAI Sequences
Outbound email sequences without the sales-tool bloat
- Prava Pay
Give your AI Agent a one-time card to make payments-safely
- Summit AI Notes
Local-first meeting notes with full data ownership.
- Wicely
Your autonomous R&D analyst
- Whizo AI
Your Agency on Autopilot.
- AdminForth
Agent-first open-source admin panel framework
- Mira
Your agentic exit interviewer
- KOBI
Your team's second brain — but it actually remembers
- NeonShade Labs
Building offline-first AI models for threat intelligence
- PromptForge
Turn raw ideas into perfectly optimized AI prompts.
- AIVault
Free curated directory of the best AI tools for everyone
- Keith
College admissions AI App with a free Competition Index
- LearnPeng
Type any topic. Get an AI audio course in minutes.
- dhrive
dhrive turns a simple prompt into a real native iOS app
- Clearminutes
AI meeting notes that never leave your machine. 100% local.