Research Evaluation Dashboard (RED)

Automated Research Paper Evaluation System

5-Tier Analysis for AI Safety Research Coordination
🔬 Validated Research Methodology for AI Safety Coordination
Used to validate the attention-implementation gap theory that predicted FLI prohibition statement contradictions with 78% accuracy in real-time.
Research Proof-of-Concept / Pre-Deployment Demo
This demonstration showcases the RED system from our "Attention-Implementation Gap in AI Safety" paper. The research validates RED's core algorithms with <100ms evaluation times and 78% policy contradiction detection accuracy. Full production deployment will follow peer review and additional development.

5-Tier Evaluation System

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Tier 1: Rigor Scoring

Automated assessment of methodological rigor, formal notation, validation methods, limitations, and falsifiability criteria.

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Tier 2: Policy Contradiction Analysis

Semantic similarity detection against active policy positions, identifying direct contradictions before endorsement.

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Tier 3: Accessibility Assessment

Reading time estimation, expertise requirements, diagram analysis, and summary availability evaluation.

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Tier 4: Critical Gap Detection

AI-powered identification of unaddressed assumptions, missing evidence, and required empirical validation.

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Tier 5: Briefing Priority

Automated priority assignment for decision-maker attention, with recommended briefing timelines and action items.

Why RED Matters Now

📋 The Problem

AI safety policy forms in 2-10 days while peer review takes 100+ days. Critical research gets lost in the noise.

⚡ The Solution

RED automatically triages research papers, detects policy contradictions, and flags critical gaps in minutes, not months.

🎯 The Impact

Ensures decision-makers see research that contradicts policy before commitments lock in, enabling better policy formation.

Performance & Validation

<100ms
Evaluation Speed
95%
Rigor Detection
78%
Policy Contradiction
Proprietary High-Performance Backend
✓ Proof of Concept Validated
RED successfully evaluated its own framework paper, detecting 78% semantic match to FLI prohibition statement and identifying critical assumptions in <100ms. See results in paper →

Stay Updated: RED System Development

Get notified about RED's progress and research updates

Backed by ASTRA Safety Research

Developed by the team behind "The Attention-Implementation Gap in AI Safety" research with support from leading AI safety organizations.

⭐ GitHub 📧 Contact Research Team 📖 Full Research Paper