Open framework for confidential AI
FIBO is a SOTA, first open-source, JSON-native text-to-image model built for controllable, predictable, and legally safe image generation.
Neural Network Verification Software Tool https://www.verivital.com Documentation:
Sagar is a Python-based command-line virtual assistant for CSE students and cybersecurity learners. It supports single-line and multi-line commands to open trusted websites, play curated music links, and answer questions using an AI model—designed for safe automation, learning, and terminal-first exploration.
The course provides guidance on best practices for prompting and building applications with the powerful open commercial license models of Llama 2.
Local-first AI context guardrails for coding agents. Seal secrets into reversible tokens so agents keep working, define repo policy in one .offsend.yml, and enforce it with editor hooks, git hooks, and CI. macOS & Linux.
Safety harness for autonomous AI agents: Spec-driven AI factory. Use with any agentic CLI. Language-agnostic. Safe by design.
Security firewall and supply-chain verifier for Claude Code / Codex / Agent skills and MCPs.
AAAI 2025 Tutorial on AI Safety
Safety-Constrained Reinforcement Learning for Assistive Robot Navigation
Official implementation of "Uncertainty-Guided Semi-Supervised Learning for Safe Medical Image Classification".
SOEA-Plus (PDEMC): 3-task biomedical metacognition benchmark evaluating LLM metacognitive control across 2 frontier models on 300 real PubMed examples. Reveals the Control Collapse Gap
Evaluate high school math reasoning in LLMs with baseline and Chain-of-Thought (CoT) prompts. Includes confidence calibration metrics, JSON output parsing, and reliability analysis.
Heike — The deterministic runtime for reliable AI agents. No more prompt roulette. 侍
Safe hierarchical embodied-AI framework with multi-layer safety filtering and formal recovery — IEEE TENSYMP 2026
Production-Grade LLM Alignment Engine (TruthProbe + ADT)
A governed world-model evidence layer for AI agents: simulate bounded scenarios, track assumptions, score prediction-vs-reality error, and produce human-reviewable execution evidence.
Computable metric for autonomous internal dynamics in neural systems (CCI + MRC-C)
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