Dynamic Pre-Formulation Clarification for Interactive Optimization

📦 Dataset: OR-Clarify on Hugging Face

“Buy low, sell high” sounds simple—but can newly purchased stock be sold immediately? Must a fixed amount remain in storage at the end? Guessing these rules can mean optimizing the wrong problem. InterOPT asks targeted questions before formulation, while OR-Clarify evaluates whether agents uncover the missing requirements.

Core contributors (equal contribution): Sihan Ge, Yichen Lin, and Chenyu Zhou.
Paper authors: Sihan Ge, Yichen Lin, Chenyu Zhou, Jianghao Lin, Tao Yao, and Dongdong Ge.
Corresponding authors: Jianghao Lin and Tao Yao. Hosted by AIOR-Research.

💬 A short example

Business request: Plan purchases and sales over several periods to maximize profit, within a fixed warehouse capacity.

Two selected turns from a Choice (MC-D) InterOPT run, shortened and anonymized; intervening turns are omitted.

Agent: What happens to unsold stock at the end of the plan?

A. Discard it with no value.
B. Sell it at the final-period price.
C. Value it at the purchase price and add it to profit.
D. None of the above — I'd like to explain in my own words.

User: D. We must retain exactly the required quantity as ending inventory. It stays in storage, rather than being sold, discarded, or counted as profit.

The agent also checks resale timing:

Agent: Can newly purchased stock be sold in the same period?

A. Yes, it is available for sale immediately.
B. No, sales can only come from stock held at the start of the period.
C. Only if starting inventory cannot meet demand.
D. None of the above — I'd like to explain in my own words.

User: B.

These answers add two concrete constraints: no same-period resale and an exact ending-inventory target.

🧠 How it works

Dynamic Gap Search tracks formulation gaps across turns. Gap-Guided Action Search uses them to guide the next question or declare readiness. Both stages work from the public brief and conversation.

🚀 Quick start

Set up

Use a Python 3.11+ environment. The commands below use Bash (Linux, macOS, or Git Bash on Windows).

git clone https://github.com/AIOR-Research/InterOpt.git
cd InterOpt
python -m pip install -r requirements.txt
cp -n .env.example .env

Edit .env: set DEEPSEEK_API_KEY and DEEPSEEK_BASE_URL, and choose model identifiers supported by your endpoint for the *_MODEL entries. Choice runners load this file automatically. Running the example calls hosted LLM APIs and may incur charges.

Run one case

This is a single-case smoke test: it confirms the full InterOPT pipeline runs end to end before you spend time and API budget on the complete evaluation.

python experiments/interopt/run_parallel.py \
  --toml_dirs data --case_ids 001 --interaction_modes mc_d \
  --k 1 --max_turns 20 --max_concurrency 1 \
  --gap_search_prompt_path experiments/interopt/prompts/prompt-ledger-gap-search.md \
  --agent_prompt_supplements experiments/interopt/prompts/agent_ledger_supplement.md \
  --selector_prompt_supplements experiments/interopt/prompts/selector_ledger_supplement.md \
  --output_dir runs/quickstart

Keep the three prompt arguments: they configure the full two-stage method.

Check the result

When the run finishes, open interopt_eval_report.md for the summary, then transcript.md to inspect the public conversation:

runs/quickstart/
├── interopt_eval_report.md
├── summary_interopt_eval.json
└── mc_d/generic_agent/run_01/orclarify_001/
    ├── transcript.md
    └── judge_result.json

The single case checks the workflow. For a full Choice InterOPT evaluation, use the 100-case, K=5 command.

🧪 Benchmark and experiments

OR-Clarify contains 100 TOML cases and 178 hidden requirements. The data card explains the case fields and the information available to each role. Choice uses structured answer options; Open uses free-form answers.

Figure 2 from the paper. From complete problems to clarification cases and transcript-based evaluation. Click the image to enlarge.

The packaged methods are linked below. Runners expose their command-line options through --help.

Method	Choice	Open
InterOPT	Guide	Runner
w/o Stage 1	Guide	Runner
w/o Stage 2	Guide	Runner
MC / MC-D	Guide	—
ReadyGate	Guide	—
ORPilot adapter	—	Guide
T-P-P adapter	—	Guide

For the Open InterOPT runners, export the variables from .env into your shell before execution; these runners read the process environment directly. ORPilot and T-P-P cover only the adapted interaction stages described in their ORPilot scope and T-P-P scope notes.

📊 Results at a glance

Figure 4 from the paper. Curves average five runs per case, with shaded bands showing 95% case-level bootstrap confidence intervals. Compare methods within each protocol.

Under Choice, InterOPT reaches higher recovery while asking more questions. Open shows a different ranking, with ORPilot ahead of InterOPT in endpoint recovery.

Read the scores.

• All-Slot Exact — the share of runs that recover every hidden requirement.
• Core Exact — the same all-or-nothing test, but only over the P0/P1 requirements (the 94 cases that have them).
• Avg Q — the average number of independently answerable (atomic) questions per run. A single turn may contain several such questions.
• Avg Turns — the average number of agent turns per run, including the final READY_TO_MODEL action when present. That stopping turn adds no questions.

📖 Citation

If you use OR-Clarify or InterOPT in your work, please cite:

@misc{ge2026askoptimize,
  title={Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization},
  author={Sihan Ge and Yichen Lin and Chenyu Zhou and Jianghao Lin and Tao Yao and Dongdong Ge},
  year={2026},
  eprint={2609.05258},
  archivePrefix={arXiv},
  primaryClass={math.OC},
  url={https://arxiv.org/abs/2609.05258}
}