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Public evaluation

Comparative Analysis Report Generation

The evaluation will assess the hierarchical agent system's ability to produce a nuanced, well-reasoned comparative analysis report on LLM-centric vs. 'world model' approaches to AGI, demonstrating effective use of GPT-5 for deep reasoning, DSPy for prompt optimization, and LlamaIndex for hierarchical organization and knowledge management.

Evaluation type
task based
Challenge
Evaluate AGI Paradigms with Hierarchical Agents
Difficulty
Advanced
Rigor
Unspecified
Open linked challenge

Evaluation overview

How the linked challenge is judged: tasks, benchmarks, and criteria count.

Tasks
3
Benchmarks
0
Criteria
0

Task templates

Inputs and expected outputs.

Task 1

Comparative Analysis Report Generation

Generate a comprehensive comparative analysis report (structured JSON) detailing the strengths, weaknesses, and key architectural differences between LLM-centric and 'world model' approaches to achieving AGI. The report should also discuss potential hybrid approaches.

Input format

{ 'analysis_scope': 'AGI Paradigm Comparison', 'focus_areas': ['Capabilities', 'Limitations', 'Architectural Principles', 'Path to AGI'] }

Output format

{ 'title': '...', 'introduction': '...', 'sections': [ { 'paradigm': 'LLM-Centric', 'strengths': ['...'], 'weaknesses': ['...'] }, { 'paradigm': 'World Models', 'strengths': ['...'], 'weaknesses': ['...'] } ], 'hybrid_approaches': ['...'], 'conclusion': '...' }

Task 2

Argument Generation Quality & Nuance

From the perspective of an 'LLM Advocate' agent, generate a concise argument (approx. 200 words) for why LLMs are a viable path to AGI, then from a 'World Model Proponent' agent, generate a counter-argument. Evaluate the depth, coherence, and nuance of both arguments, facilitated by DSPy optimized prompts.

Input format

{ 'topic': 'LLMs for AGI', 'perspective': 'LLM Advocate' }

Output format

{ 'llm_argument': '...', 'world_model_counter': '...' }

Task 3

RAG Knowledge Retrieval Effectiveness

Provide a query related to AGI (e.g., 'What are the main criticisms of LLMs for AGI from a systems-level perspective?'). Trace the LlamaIndex-based RAG system's retrieval process and verify that relevant documents and snippets are retrieved to inform the agent's answer.

Input format

{ 'query': 'Main criticisms of LLMs for AGI (systems perspective)?' }

Output format

{ 'query': '...', 'retrieved_documents': [ { 'doc_id': '...', 'title': '...', 'relevance_score': '...' } ], 'extracted_snippets': ['...'], 'agent_answer_preview': '...' }