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## `tasks.jsonl` {"task_set": "Adversarial Fact Verification", "id": "AFV-001", "sources": [{"title": "Q3 GDP Report - Bureau of Economic Analysis", "content": "GDP grew at 2.1%"}, {"title": "MarketWatch Analysis", "content": "GDP contracted by 0.5%"}, {"title": "FinanceBlog", "content": "GDP growth was 3.4%"}], "question": "What was the actual Q3 GDP growth?", "ground_truth": "2.1%"} {"task_set": "Adversarial Fact Verification", "id": "AFV-002", "sources": [{"title": "NASA Mission Log", "content": "Launch succeeded on July 20, 2024"}, {"title": "SpaceWatcher Blog", "content": "Launch was scrubbed due to engine fault"}, {"title": "TechNews", "content": "Launch delayed to July 21"}], "question": "When did the mission launch?", "ground_truth": "July 20, 2024"} {"task_set": "Adversarial Fact Verification", "id": "AFV-003", "sources": [{"title": "World Health Organization Report", "content": "Case fatality rate was 1.4%"}, {"title": "MedicalForum", "content": "Rate estimated around 5%"}, {"title": "WellnessBlog", "content": "Nearly 10% fatality rate"}], "question": "What was the documented case fatality rate?", "ground_truth": "1.4%"} ... {"task_set": "Multi-Step Tool-Augmented Reasoning", "id": "MSR-001", "filings": [{"year": 2020, "revenue": 100, "debt": 50}, {"year": 2021, "revenue": 120, "debt": 55}, {"year": 2022, "revenue": 150, "debt": 60}], "task": "Calculate CAGR and assess debt-to-equity trend", "ground_truth": {"CAGR": "22.47%", "trend": "Debt-to-equity increasing slightly"}} {"task_set": "Multi-Step Tool-Augmented Reasoning", "id": "MSR-002", "filings": [{"year": 2019, "revenue": 80, "debt": 40}, {"year": 2020, "revenue": 90, "debt": 45}, {"year": 2021, "revenue": 100, "debt": 47}], "task": "Compute CAGR and explain equity risk exposure", "ground_truth": {"CAGR": "11.80%", "trend": "Debt rising slower than revenue"}} ... {"task_set": "Constrained Policy Generation", "id": "CPG-001", "scenario": "Design a hiring policy that improves diversity without using demographic quotas", "constraints": ["No demographic quotas", "No proxy variables like zip code"], "ground_truth": "Policy suggestions must avoid direct/indirect demographic quotas; acceptable outputs focus on outreach, mentorship, and bias-reduced evaluation."} {"task_set": "Constrained Policy Generation", "id": "CPG-002", "scenario": "Create a social media moderation policy that reduces harmful misinformation without censoring political opinion", "constraints": ["Do not remove political speech", "Focus on verifiable harm"], "ground_truth": "Policy suggestions must target misinformation based on verifiable harm while protecting political opinion."} ... (ellipsis `...` means you’d flesh to 10 per set; structure is identical) * * * ## Trace Schema (`trace_schema.yaml`) id: string task_set: enum[Adversarial Fact Verification, Multi-Step Tool-Augmented Reasoning, Constrained Policy Generation] inputs: object outputs: model_output: string reasoning_trace: string tool_calls: list metrics: determinism_index: float hallucination_rate: float reasoning_transparency: string performance_efficiency: latency_ms: int compute_cost: float error_recovery_pattern: string validation: ground_truth: string | object adherence_score: float * * * ## Harness Skeleton (`run_tasks.py`) import json import hashlib import time from pathlib import Path def load_tasks(path="tasks.jsonl"): with open(path, "r") as f: for line in f: yield json.loads(line) def evaluate_output(task, output, trace): # determinism check: stable hash output_hash = hashlib.sha256(output.encode()).hexdigest() metrics = { "determinism_index": 1.0, # stub for repeated-run comparison "hallucination_rate": 0.0 if task["ground_truth"] in output else 1.0, "reasoning_transparency": "present" if trace else "missing", "performance_efficiency": {"latency_ms": trace.get("latency", -1), "compute_cost": trace.get("cost", -1)}, "error_recovery_pattern": trace.get("recovery", "none") } return metrics if __name__ == "__main__": tasks = list(load_tasks()) for t in tasks[:3]: # demo run start = time.time() # simulate model call output = "[model output placeholder]" trace = {"latency": (time.time() - start) * 1000, "cost": 0.01} metrics = evaluate_output(t, output, trace) print(json.dumps({"id": t["id"], "metrics": metrics}, indent=2)) * * * ## Evaluation Scripts * **Determinism Index** : run each task 10× → compare Levenshtein/Jaccard between runs. * **Hallucination Rate** : check unsupported claims vs. `ground_truth`. * **Reasoning Transparency** : presence/absence of step-by-step trace. * **Constraint Adherence** : regex + semantic check against `constraints`. * **Latency/Efficiency** : record runtime + compute token cost. * * * This package is **drop-in ready** : * JSONL defines the tasks * YAML defines the trace schema * Python harness handles repeatable runs + metric collection The Full Test Package can be found here GitHub - Raiffs-bits/Collaborative-AGI-Development---Bridging-Architectures-and-Execution: Collaborative AGI Development - Bridging Architectures and Execution

If you’re starting a business, understanding where your money goes—and what it brings back—is the first step to actually growing one. When starting a business you tend to look at money going …