datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
fluidgym-datafluidgym-experiments
FluidGym Experiments
Paper | GitHub | Documentation
FluidGym is a standalone, fully differentiable benchmark suite for reinforcement learning (RL) in active flow control (AFC). Built entirely in PyTorch on top of the GPU-accelerated PICT solver, it provides standardized evaluation protocols and diverse environments for systematic comparison of control methods.
This repository contains the training and test datasets with results for all experimental runs presented in the paper.… See the full description on the dataset page: https://huggingface.co/datasets/safe-autonomous-systems/fluidgym-experiments.Execution-Finality-Security-for-Agentic-AI-Autonomous-Systems-Cloud-Payments-Telecom-OS-and-Robo
Dataset Description
The architecture addresses a structural gap in modern AI and autonomous systems: the separation between computation and external consequence. Existing protocols and controls (identity, access control, encryption, logging, policy engines) govern movement, authentication, and recording of data. They do not, by themselves, make the transition from a generated act to an externally effective act a protected technical precondition.
This dataset provides a clean… See the full description on the dataset page: https://huggingface.co/datasets/sangamdas/Execution-Finality-Security-for-Agentic-AI-Autonomous-Systems-Cloud-Payments-Telecom-OS-and-Robo.FINALITY-CHECKPOINT-REGISTRY-FOR-AI-AUTONOMOUS-SYSTEMS
DAS Protocols: Execution Finality Architecture for AI & Critical Infrastructure
Complete Technical README & FAQ
Executive Summary
The DAS Protocols (Distributed Attestation & Scoping) provide a pre-effectuation verification architecture that prevents unauthorized or out-of-scope operations from becoming consequence-bearing before validation occurs. Unlike post-event auditing or advisory permissions, this architecture makes operations structurally… See the full description on the dataset page: https://huggingface.co/datasets/sangamdas/FINALITY-CHECKPOINT-REGISTRY-FOR-AI-AUTONOMOUS-SYSTEMS.