PƒPPrompts for Progress
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Mixed campaignphysics · classical, wave, and quantum dynamics

SciExplorer investigates initially unknown physics models

A general-purpose agent used experiments and code to recover equations of motion and Hamiltonians across simulated physical systems, while full logs also preserve premature and incorrect model commitments.

OutcomeMixed campaign
EvidencePeer-reviewed
Modeagentic search
Promptfull

Preserved artifact

Approved to publish

Prompt corpus

SciExplorer physics-model discovery prompt and run · Maximilian Nägele and Florian Marquardt

Original source ↗
computational-physicist system prompthidden-coordinate research requestexperiment toolsiterative hypothesesfinal model submission
This representative complete run preserves the exact system message, initial research request, tool schema, intermediate messages, and final answer for one hidden-oscillator task. Additional public result files cover the other simulated systems.

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SOURCE FILE: sources/sciexplorer-physics-models/prompt-inputs.md

# SciExplorer representative run prompt inputs

## System message

- Act as a computational physicist dedicated to thoroughly resolving the user's query through careful planning, hypothesis generation, and iterative verification.
- In your first message, create a comprehensive plan to solve the users query. Include an extensive list of candidate hypotheses.
- Initially, conduct at least 5 different experiments spanning the entire range of reasonable initial conditions. Make sure to cover also extreme cases. Then, create informative plots of your experimental results.
- Withhold any final answer until you are sure that no further improvements of your hypothesis are possible.
- Before submitting your final answer, simulate your proposed model using the same initial conditions as in your experiments, and compare the results. Only submit your final answer if the simulation results closely match your experimental data.

<tool_preambles>

- If you have run a tool but still need to extract the results (e.g. via visualization), just briefly explain what tool you will call next to extract the results.
- Otherwise, at each step, you must answer the following questions:
    1. What can you learn from the new tool results (if any)?
    2. Which old hypotheses still fit your data?
    3. Which new hypotheses might be worthwhile considering?

</tool_preambles>

## Initial message

You are investigating a dynamical physical system. You can only observe the first generalized coordinate of this system. However, there might be additional hidden generalized coordinates influencing the dynamics.

Can you find a model that reproduces the observe_experiment function? After your exploration, save it using the save_result function.

The results of all tool calls will be stored (using the result_label) and are available later.

## Final-message request

Can you please summarize your exploration?

## Run limits

- Maximum iterations: 60
- Maximum tool uses: 240
Retrieved
2026-07-17
Original format
JSON run artifact
Completeness
representative
Transcription
Verbatim run artifact from the cited repository commit. The site display extracts the exact prompt inputs; the machine-readable corpus retains the complete JSON run.
Permission basis
Redistribution under the repositories' MIT License, preserved with the local source bundle.

Editorial context

What happened

SciExplorer received the same compact, general system prompt across tasks and could choose numerical experiments, analyze results, write code, and decide when to submit a testable final model. The authors deliberately avoided task-specific prompt repairs. That makes the public failures, including sign errors and early commitment to an incorrect model, unusually useful for studying how research-agent prompts generalize across domains.

Keep in view

The agent rediscovered models in controlled simulations rather than previously unknown laws of nature. The environments and scoring rules were designed by the researchers.

Chronology

Timeline

01
Framework, generic prompt, and benchmark results first disclosedpreprint · day date
02
Paper accepted by Physical Review Xverification · day date
03
Peer-reviewed article published with public framework and logspeer review · day date

Trust boundaries

Validation

benchmark scoringmixed

Final answers were machine-checked against known simulated systems, exposing successful and failed exploration runs.

peer reviewpassed

Published in Physical Review X.

System

AI and tools

  • SciExplorer
  • Gemini 2.5 Pro
  • GPT-5 mini
  • GPT-5 nano

People

Human contributors

  • Maximilian Nägele
  • Florian Marquardt

Organizations

Affiliations

  • Max Planck Institute for the Science of Light
  • Friedrich-Alexander-Universität Erlangen-Nürnberg

Primary materials

Sources and artifacts