AI News Archive: May 20, 2026 — Part 21
Sourced from 500+ daily AI sources, scored by relevance.
- Emberis AI Decision Intelligence
Neuromorphic decision intelligence No RAG No hallucinations.
- IndeskAI
Support Faster, Serve Better, Keep Customers Coming Back
- Mindvaults
Turn expert knowledge into answers for your problem.
- Canonry
Track how AI cites you — agent-first, open source
- Architect Studio X
Design, validate, and evolve software architecture with AI
- NoShoot
The AI fashion & product photography studio
- AlphaProxy.AI
Create, code & research on autopilot – tasks truly done.
- Inkshift
Write in any client's voice, instantly.
- SubTGraph: Large-Scale Subterranean Environment Synthesis with Controllable Topological Variability for Robotic Autonomy Validation
Subterranean (SubT) environments have been a frontier for autonomous robotics, driven by the push for automation of mining operations and the interest in planetary exploration (Martian Lava Tubes). Due to the challenges involved in accessing real SubT environments, rigorous hardening of autonomy sta...
- Mobile UMI: Cross-View Diffusion Policy with Decoupled Kinematics for Mobile Manipulation
Mobile imitation learning on portable demonstration interfaces faces two coupled bottlenecks: locomotion-contaminated action labels and inference-induced execution latency on a continuously moving base. Recent wrist-mounted interfaces lower the cost of tabletop data collection, yet a single wrist vi...
- DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation
Language-conditioned manipulation policies typically process instructions and observations through shared network parameters. This task-state entanglement provides a pathway for observation leakage -- networks learn scene-to-action shortcuts that bypass language grounding entirely. DISC eliminates t...
- SmoCap: Unified Scale-Pose Canonicalization with Proxy-Mapped Trust-Region QP
Objective: Stage-wise workflows that separate model scaling and inverse kinematics can induce morphology-posture compensation, resulting in anatomically inconsistent yet numerically acceptable solutions, especially in weakly observed directions. We present SmoCap, a leakage-resistant canonicalizatio...
- Demo-JEPA: Joint-Embedding Predictive Architecture for One-shot Cross-Embodiment Imitation
Robotic imitation learning is often treated as reproducing demonstrated actions, but actions are inherently embodiment-specific. When demonstrations come from humans or robots with different morphology, kinematics, or action spaces, this action-centric view requires shared action spaces, heuristic r...
- VLA-REPLICA: A Low-Cost, Reproducible Benchmark for Real-World Evaluation of Vision-Language-Action Models
Vision-Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, but their real-world evaluation remains limited by a lack of accessible, reproducible, and consistent benchmarks. Simulation benchmarks fail to capture real-world complexity, while existing real-w...
- GaussianDream: A Feed-Forward 3D Gaussian World Model for Robotic Manipulation
Vision-language-action (VLA) policies have advanced language-conditioned robotic manipulation by transferring semantic priors from pretrained vision-language models to action generation. Yet, standard action-imitation training often provides limited explicit supervision for 3D geometry, dense visual...
- Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to new compositions of known skills without retraining. Modern generative policies model distributions over action trajectories alone, thus are unable to...
- Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination
Advanced Air Mobility (AAM) operations are expected to significantly increase aerial traffic in urban airspace, requiring autonomous traffic management systems to ensure collision-free operations in highly congested environments. In this paper, we propose a multi-agent coordination framework that us...
- $L^2$ over Wasserstein: Statistical Analysis for Optimal Transport
Optimal transport provides an inherently geometric and highly structured framework for studying spaces of probability measures, supplying a rich theoretical toolkit for contemporary statistics, machine learning, and generative modelling. In applications, however, the measures of interest are almost ...
- Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment
Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA under a federated learning setting, enabling collaborative fine-tuning across clients while preserving parameter efficiency. We focus on a highly het...
- Divide et Calibra: Multiclass Local Calibration via Vector Quantization
Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challenging: global approaches assume calibration errors are homogeneous across the latent space, while local methods often rely on latent-space dimensionalit...
- LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
Communication is a major bottleneck in distributed learning, especially in large-scale settings and in federated learning environments with slow links. Three standard ways to reduce this cost are communication compression, local training, and communication-computation overlap. Methods that combine t...
- Correcting Stochastic Update Bias in Preconditioned Language Model Optimizers
Preconditioned optimizers are central to language model training, but their stochastic update rules are usually treated as direct approximations to population preconditioned descent. We show that this view misses two finite-sample biases. First, the gradient and preconditioner are typically estimate...
- Interpretable Discriminative Text Representations via Agreement and Label Disentanglement
Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply. Existing discriminative representations often use anonymous embedding directions, while concept-bottleneck and LLM-assisted methods attach natural-...
- Webclaw
Turn any website into LLM-ready data
- The General Theory of Localization Methods
This paper proposes a general machine learning framework called the localization method, which is fundamentally built on two core concepts: localization kernels and local means -- key components that underpin the self-attention mechanism. To establish a rigorous theoretical foundation, the framework...
- Semiparametric Efficient Bilevel Gradient Estimation
Functional bilevel methods estimate a lower-level function and plug it into a hypergradient, but this plug-in gradient can retain first-order bias when the lower-level problem is learned nonparametrically. To remove this bias, we develop a semiparametric debiasing theory for population bilevel gradi...
- Conditioning Gaussian Processes on Almost Anything
Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We establish an explicit equivalence between GPs and a class of linear diffusion models, recasting predictive sampling as an ODE with closed-form Gaussian ...
- Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification
Random forests aggregate tree votes by simple majority, treating all trees as equally informative. We observe that the topological pattern along each tree's root-to-leaf decision path -- where and how often the dominant class label flips along it -- carries a signal of tree reliability that is explo...
- SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front
Scalarization is widely used in multi-objective optimization owing to its simplicity and scalability. In many applications, the goal is to generate solutions that represent diverse user preferences, ideally with uniform coverage of the Pareto front (PF). However, uniformly sampling scalarization wei...
- StoreClaw
Grow your store profits with agents that know how to sell
- mailX by mailwarm
Email deliverability toolkit for humans and AI agents
- Emdash
One app. Every coding agent. Open-source.
- Gemini Omni
Create anything from any input – starting with video
- Runtime
Sandboxed coding agents for everyone on your team
- Re_gent
Version Control for AI agent Activity
- Supercut for Agents
Permission-aware AI access to recordings and metadata
- Retina
Screen recorder w/ auto-zoom, smooth cursors, + AI graphics
- Viberia
Command AI agents like you're playing Civilization
- Owlish
Reduce support volume with AI agents trained on your docs
- GhostSnap
Multiple screenshots - Single paste - Auto compressed for AI
- Glia
Local-first AI memory bridge between browser chats and IDEs
- Contextberg
Turn your work into AI agent memory, served over MCP
- LayerProof Kraft
Co-write insightful long form content
- Multi-Claude
Run multiple Claude accounts side by side on your Mac
- Invenio
Local AI search for Mac video & photo libraries
- Google Pics
Boundless AI image generation meets precision editing
- Hiro
Your agentic security team from first commit to SOC 2
- Adrian
The open-source runtime security toolkit for your agents
- Nytivo
EU AI Act compliance for AI startups — without the law firm
- AgentPMO
Enterprise governance and compliance for AI agents