jasonlingg/envoy-qasper-code-trajectories

Envoy QASPER Code-Execution Trajectory Pilot

This is a small, fully disclosed pilot of executable research-agent trajectories. Claude Sonnet 5 generated Python actions against a persistent document REPL. The Envoy pipeline executed every action and retained the real observations. An AI coding assistant then reviewed answer support, stopping behavior, and replay.

This release is useful for studying trajectory validation and citation failures. It is not a production-ready SFT dataset.

Subsets

Subset	Rows	Intended use
reviewed (default)	2	Examples that passed the disclosed assistant review
diagnostic	24	Every candidate, including known failures and pending reviews

Diagnostic verdicts: 2 pass, 18 fail, and 4 pending. The same 12 QASPER training questions were attempted twice while developing the teacher prompt. Rows are not statistically independent, and this pilot is not an evaluation benchmark.

Only rows with accepted_for_sft=true belong in supervised training. Consumers must not interpret mechanically valid spans as semantic support. Review was assistant-led with QASPER annotations visible; it was not human, independent, or blind. Some diagnostic rows intentionally preserve execution errors, overly long searches, answer leakage, incomplete evidence, or unsupported claims.

Task and format

Each episode starts with a known-paper QASPER question. The agent writes Python using search_within(), read(), and passage(), observes actual tool output, and finishes with an answer, paper citation, and exact character offsets. The messages field is the student-facing conversation. trajectory preserves both the provider's raw action and the cleaned action that actually executed.

from datasets import load_dataset

reviewed = load_dataset(
    "jasonlingg/envoy-qasper-code-trajectories", "reviewed", split="train"
)
diagnostic = load_dataset(
    "jasonlingg/envoy-qasper-code-trajectories", "diagnostic", split="train"
)

Generation and review

• Teacher: claude-sonnet-5; the returned provider identity was checked on every response. There was no Haiku fallback.
• Source questions: QASPER's official training split; one known paper per question.
• Student protocol: multi-turn executable Python followed by exact-span submission.
• Candidate generation: 24 episodes over 12 unique questions, across a reference-guided attempt and an unhinted attempt.
• Review: automated assistant review with annotations visible. The revised 12 episodes also replayed with identical executed observations and completion flags.
• No Qwen checkpoint was trained on this release before publication.

Source data, license, and changes

This is an adaptation of QASPER, released by the Allen Institute for AI under CC BY 4.0. QASPER contains full text extracted from S2ORC, which is made available under ODC-By 1.0. This repository contains only the questions and short paper passages surfaced during agent execution; it does not redistribute the complete QASPER corpus, full papers, or PDFs.

Changes made here include selecting QASPER training questions, converting papers to stable character-offset documents, generating and executing Python tool-use trajectories, adding exact-span submissions, and attaching replay and review metadata. Neither Ai2, the QASPER authors, Semantic Scholar, nor the paper authors endorse this derivative dataset.

The derived dataset is released under CC BY 4.0. Retain this attribution and cite QASPER when redistributing it.

@inproceedings{dasigi-etal-2021-dataset,
  title = {A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
  author = {Dasigi, Pradeep and Lo, Kyle and Beltagy, Iz and Cohan, Arman and Smith, Noah A. and Gardner, Matt},
  booktitle = {Proceedings of NAACL-HLT 2021},
  year = {2021}
}

Limitations

The release is extremely small, restricted to within-paper scientific QA, and contains repeated questions across prompt variants. Three revised trajectories remain pending rather than accepted. The proxy score uses lexical answer overlap and span overlap; it is not a semantic judge. Source excerpts inherit extraction artifacts from QASPER/S2ORC. Do not use the diagnostic subset as unfiltered SFT data or claim that it improves a model without a held-out comparison.

Reproducibility

provenance.json records source artifact hashes, prompt hash, QASPER revision, counts, and provider-model totals. Generation scripts and the full experiment record are in Envoy.