# PhysTax

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Ritvik Shrivastava](https://cerebralvalley.ai/u/ritvik_shriv13)
- **GitHub:** https://github.com/ritvik412/phystax
- **Demo video:** https://youtu.be/c9G5n33THqM
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3778

PhysTax: an AI scientist that sees only through SAM 3.1, seals its predictions before reality answers, and is graded by what actually happens.

It measures SAM 3.1 as a instrument and holds Muse Spark to sealed predictions across simulated physics, real video, and an open problem from Meta's own research.

Problem: AI-scientist benchmarks hand agents clean numbers, but real science, robotics and world-model evaluation measure through vision models. Nobody knows what those perception errors cost scientific conclusions, or whether the agent's reasoning is the real bottleneck.

Method: Muse Spark experiments in universes with hidden, non-textbook force laws (orbit a = -k r^-alpha, projectile drag |v|^beta, nonlinear springs). It sees experiments only through SAM 3.1 tracking, fits laws in a sandbox, then seals a prediction (law, constants, 80% intervals) as a SHA-256 hash in a hash-chained ledger before the test exists. A deterministic grader scores it; no LLM judges. Since the truth is known, every error splits exactly into measurement (camera) + design (experiments) + reasoning (law chosen).

Results (reproducible from cached runs):
1. The camera is not the bottleneck, reasoning is: over 18 Muse Spark episodes, through SAM the error is 0.009 measurement, 0.000 design, 0.226 reasoning (camera about 3%).
2. The agent's sealed law carries real physics (CMU Predictive Credit protocol): a fresh forecaster's error falls from 49.8% to 19.0% with it (true law: 3.6%); credit +20.4 pts, 95% CI 3.3-28.4.
3. SAM 3.1 as an instrument (432 tracks): 0.7-2.1 px interior error vs 85 px classical; 5-8x worse at borders; names found 4/12 times, taps/boxes always; box confidence predicts error (Spearman -0.61, 10,685 frames); correlated errors shrink textbook 80% interval coverage to 17-25% (our correction: 46-58%).
4. Open problem from Meta's research: Arslan & Muse Spark's ellipsoid-fitting theorem leaves the edge n/d^2 -> 1/4 open. We measured it by semidefinite programming and sealed four predictions before computing d = 30, 36, 44: Muse Spark's hit the 95% interval 3/3; our own conjecture d(d+1)/4 was falsified 3/3. Finite-size evidence, not a proof.
5. Real footage: on 7 IRIS benchmark pendulum clips (ECCV 2026), the exponential damping its ground truth assumes is rejected 7/7. A live webcam mode lets anyone tap an object; the scientist names the motion and seals the second half at a split fixed in advance.

Engineering: hash-chained ledgers, cached replayable SAM calls, budget guards, 40 CI tests, public results site, documented limitations, under $10 API spend.

Frontier: a first measured error budget for a segmentation foundation model used as a scientific instrument, with AI scientists held to falsifiable, pre-registered predictions across physics, real video and open mathematics. Next: verifiable-reward RL environments for scientist agents, error budgets for SAM-graded world-model benchmarks, open-weight Glimmer scientists, and real lab instruments.

## More from Meta Global AI Developer Hackathon

- [Prysm](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3775)
- [CodeVision](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3776)
- [Rust Ghost](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3777)
- [saavren](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3779)
- [MagicRayality](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3780)
- [Pegayaman Hack](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3781)

---

Markdown version of https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3778. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt