Datasets:
Formats:
parquet
Languages:
English
Size:
1M - 10M
Tags:
agents
multi-agent-systems
agent-traces
workload-characterization
execution-traces
data-centric-ai
License:
Download examples/quickstart.py from AgentsSci/IEEE2026_BigData_MAS-4-Science-Matching: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
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https://huggingface.co/datasets/AgentsSci/IEEE2026_BigData_MAS-4-Science-Matching/resolve/main/examples/quickstart.py
- Command line
-
hf download hf://datasets/AgentsSci/IEEE2026_BigData_MAS-4-Science-Matching/examples/quickstart.py
-
curl -L -o quickstart.py https://huggingface.co/datasets/AgentsSci/IEEE2026_BigData_MAS-4-Science-Matching/resolve/main/examples/quickstart.py
2.14 kB
| #!/usr/bin/env python3 | |
| """Minimal SciAgentTrace download, trace lookup, and matched analysis.""" | |
| from itertools import islice | |
| from datasets import load_dataset | |
| REPO_ID = "AgentsSci/scientific-agent-protocol-traces" | |
| MATCHED_PROTOCOLS = { | |
| "baseline_llm", | |
| "single_agent", | |
| "per", | |
| "broadcast", | |
| } | |
| ACTOR_MODELS = {"gemma_4_31b", "gpt_oss_120b"} | |
| def main() -> None: | |
| trajectories = load_dataset(REPO_ID, "trajectories", split="test") | |
| matched = load_dataset(REPO_ID, "matched_outcomes", split="test") | |
| comparison = trajectories.filter( | |
| lambda row: row["protocol_id"] in MATCHED_PROTOCOLS | |
| and row["actor_model_id"] in ACTOR_MODELS | |
| and row["slice_id"] | |
| in {"competition_math_4181", "text_only", "llm_strict"} | |
| ) | |
| frame = comparison.to_pandas() | |
| summary = ( | |
| frame.groupby(["actor_model_id", "benchmark_id", "protocol_id"]) | |
| .agg( | |
| trajectories=("trajectory_uid", "size"), | |
| accuracy=("final_correct", "mean"), | |
| median_elapsed_seconds=("wall_time_seconds", "median"), | |
| ) | |
| .reset_index() | |
| ) | |
| print(summary.to_string(index=False)) | |
| print(f"matched problem/model groups: {len(matched):,}") | |
| paired = matched.filter( | |
| lambda row: row["actor_model_id"] == "gemma_4_31b" | |
| and row["benchmark_id"] == "omnimath2" | |
| ).to_pandas() | |
| baseline_rate = paired["baseline_correct"].mean() | |
| single_rate = paired["single_correct"].mean() | |
| print( | |
| "Gemma 4 31B on OmniMath2, paired Single-Agent Iterative minus " | |
| f"Direct Baseline: {100 * (single_rate - baseline_rate):.2f} percentage points" | |
| ) | |
| example = comparison[0] | |
| trajectory_uid = example["trajectory_uid"] | |
| message_stream = load_dataset( | |
| REPO_ID, "messages", split="test", streaming=True | |
| ) | |
| trace = islice( | |
| (row for row in message_stream if row["trajectory_uid"] == trajectory_uid), | |
| 20, | |
| ) | |
| print(f"\nfirst events for {trajectory_uid}:") | |
| for event in trace: | |
| print(event["sequence_index"], event["role"], event["content"][:160]) | |
| if __name__ == "__main__": | |
| main() | |