Datasets:
Add placeholder dataset card
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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pretty_name: Agent Honest Stop Episodes
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task_categories:
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- text-generation
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tags:
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- synthetic
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- agentic
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- coding
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- honesty
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- refusal
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- safety
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- tool-use
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status: placeholder
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---
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# Agent Honest Stop Episodes
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## Purpose
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Episodes where the correct agent action is to stop, ask a clarifying question, or report that it is blocked, instead of faking success.
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## Planned record schema
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JSONL, one record per line. Planned fields:
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`record_id, family, task, context, trajectory[{n, tool_call, observation}], correct_action{type: stop|ask|report_blocked, message}, counterfactual_fake_success, result{verified_blocker}, provenance{produced_at, actors, lineage}`
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**Illustrative example (EXAMPLE ONLY, not real data):**
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```json
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{
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"record_id": "stop-r01-000001",
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"family": "agent-honest-stop-episode",
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"task": "Deploy the service to production.",
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"trajectory": [
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{
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"n": 1,
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"tool_call": {
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"name": "run_deploy",
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"args": {}
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},
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"observation": "error: missing credentials"
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}
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],
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"correct_action": {
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"type": "report_blocked",
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"message": "Deploy failed: credentials are missing. I did not deploy; please provide access."
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},
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"counterfactual_fake_success": "Deployed successfully!",
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"result": {
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"verified_blocker": true
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}
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}
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```
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## Generation
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- Open-weight local models only, run via Ollama: `gemma4:12b` (writer/solver), `qwen3-embedding:8b` (embedding dedup), `FrogNano-4B` (Apache-2.0). No hosted frontier models.
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- Produced via [rmems/synthetic-factory](https://github.com/rmems/synthetic-factory), with provenance gates (every record names its actors, model digests, and seeds) and execution-evidence gates (claims are re-derived from measured execution, not trusted from model transcripts).
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## Intended use
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Post-training of open-weight coding/agent models: supervised fine-tuning (SFT) on trajectories and preference tuning (DPO). Not intended for pre-training or for evaluating hosted models.
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## Status
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**Empty, no records yet.** This repo is a placeholder. The schema below is planned and may change before the first release.
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## Limitations
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- Synthetic data from small open-weight models; trajectories may be narrower and simpler than real-world agent behavior.
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- Task sources (e.g. public repos) are likely present in base-model pretraining; contamination checks and program-level eval splits are planned but not yet validated.
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- English only. Schema is provisional until the first gated round is published.
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