A python library built on top of Inspect AI to support Control research and evaluations, by Redwood and EquiStamp
-
Updated
Oct 24, 2026 - Python
A python library built on top of Inspect AI to support Control research and evaluations, by Redwood and EquiStamp
Kernel-enforced authority and runtime security for Linux systems, autonomous software, and AI agents.
🚦🗺️ UrbanFlow AI is a web app for generating 3D traffic simulations from real OpenStreetMap areas. It builds SUMO scenarios, runs microscopic vehicles, pedestrians, buses and trams, edits road events, controls real traffic lights with TraCI, trains JSON AI policies, saves models, and shows live metrics, charts, and notebooks.
Research developing AI control protocols using task decomposition
An intelligent traffic management system that dynamically adjusts highway lane configurations using AI-powered congestion detection and a movable median barrier.
White-box detection of collusion in an untrusted monitor: model organisms, linear probes, and a control evaluation that prices what they buy.
Replayable agent trajectories reconstructed from the July 2026 frontier-lab evaluation-containment failures, plus benign controls for measuring false-positive cost.
Judge-first framework where LLM outputs must converge under explicit, adversarial oracles.
In-depth exploration of Large Language Models (LLMs), their potential biases, limitations, and the challenges in controlling their outputs. It also includes a Flask application that uses an LLM to perform research on a company and generate a report on its potential for partnership opportunities.
Python client for Aegis — stabilize AI systems instantly with a simple API call.
Kho lưu trữ này chứa tài liệu, bài tập, và mã nguồn liên quan đến môn Trí tuệ nhân tạo trong điều khiển. Môn học tập trung vào ứng dụng AI trong các hệ thống điều khiển tự động, bao gồm lý thuyết và thực hành.
An attacker × monitor factorial in ControlArena: which model you pick as your trusted monitor matters more than its capability tier.
Benchmark for detecting insider threats by AI agents in a simulated frontier AI lab
Can a monitor catch a hidden goal by reading a model's chain of thought? Eval harness: gpt-oss-20b under vLLM in single-GPU PBS jobs; generated code graded in a time-limited subprocess on public and hidden tests; monitor sampled 8 times (bimodal at temperature 0). Hypothesis (monitoring degrades in lower-resource languages) failed; null published.
AI Governance — human-controlled code injection with oversight
Independent AI governance and control standard
Lichtarbeit
v0.8 preview · 2,288 multi-step agent trajectories · 513 hand-authored gold + 1,775 provenance-flagged augmented. A per-step benchmark that scores whether a verifier catches drift inside an agent's trajectory — not whether a prompt is harmful.
AI safety theory of infinite deferral: postponing self-modification, pursuing resources and recursive self-improvement, and risks of betrayal and resistance to human control.
Leon Maurice Browne | Safety-critical systems, AI control, assurance and public research
To associate your repository with the ai-control topic, visit your repo's landing page and select "manage topics."