IssueSpec: Reproduction Data Bundle (CIKM 2026)

# IssueSpec — Reproduction Data Bundle Data artifacts for the CIKM 2026 paper *IssueSpec: A Framework for Structured Review-to-Issue Translation*. Pair this bundle with the code repository to reproduce every numerical claim in the paper. ## Contents ``` data/processed/ ├── verified_annotations.json 5,230-review verified anchor (lead-author labels) ├── rrgen_v5_training.json 215,883-record V5 training set (with provenance) ├── issue_specs/ │ ├── sample_100_clusters.json 100-cluster Stage-3 benchmark │ ├── specs_with_taxonomy.json LLM-with-taxonomy IssueSpecs │ ├── specs_free_form.json free-form baseline specs │ ├── specs_raw_summary.json raw-concatenation lower bound │ └── specs_human_written.json lead-author reference specs ├── responses/ Stage-4 RAG responses (4 conditions) ├── expert_evaluation/strict_holdout_kappa.json 490-gold κ progression + 307 held-out ├── experiments/ │ ├── exp2_human_eval.json Stage-4 human eval (400 ratings, 4 conditions) │ └── ablation_a5_results.json A5 no-RAG ablation ├── ablations/ │ ├── agentic_vs_vanilla_rag.json agentic-RAG feasibility study (n=10) │ └── a1b_repbased.json count-controlled cluster ablation ├── rlhf/head_to_head/metrics.json Stage-5 5-policy head-to-head ├── clusters_umap/ │ ├── quality_metrics_flat_vs_hierarchical.json cluster quality (Table 11) │ └── quality_metrics_summary.txt └── inter_annotator/agreement_summary.json 99-review 3-rater Krippendorff α ``` ## How to use 1. Clone the code repository: `git clone https://github.com/<ANON>/ReviewAgent` 2. Extract this bundle into the repo root so paths resolve as `data/processed/...` 3. Verify every paper number: ```bash python3 verify_paper_results.py ``` No GPU or API keys are needed for verification — every headline number is recomputed from these saved files. ## Models Classifier checkpoints (V1–V5 RoBERTa) are released separately on Hugging Face; see the code repository's `SETUP_GUIDE.md` §4.4. ## License All rights reserved