assets/DEMO_SCRIPT.md

90-second demo video script — FATHOM

Target length: 90 seconds. Recording specs: 1080p / 30 fps / OBS Studio or Windows Game Bar (Win + G). Save as assets/demo.mp4 LOCALLY ONLY (do NOT commit large video files; upload to YouTube unlisted, link from README).

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Beat sheet

[0:00 – 0:10] TITLE CARD

On screen: Big text — "FATHOM — the first RL-trained Recursive Language Model. A 1.5B model that reads documents 50× larger than its context window."

Voiceover:

"FATHOM — the first RL-trained Recursive Language Model. A 1.5B model that reads documents 50× larger than its context window."

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[0:10 – 0:25] THE ENV

On screen: Browser → https://Pratham-math-fathom-env.hf.space/openapi.json. Scroll the OpenAPI page so the /reset, /step, /healthz endpoints are visible.

Voiceover:

"Our OpenEnv server gives the agent two tools — a sandboxed Python REPL and a recursive llm() call. It's a public Hugging Face Space; anyone can hit it."

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[0:25 – 0:45] THE REWARD

On screen: Cursor / VS Code with REWARD_AUDIT.md open. Scroll the table that lists the 5 attacks (masked-context, format-only, length gaming, recursion-spam, copy-question).

Voiceover:

"We hardened the verifier against five reward-hacking attacks before training. Every component is grep-verifiable. pytest -m reward_audit catches masked-context exploits, format-only attacks, and length gaming."

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[0:45 – 1:10] THE TRAINING

On screen: Browser → W&B run page → reward curve panel (composite reward over steps). Pause briefly on the rising curve.

Voiceover:

"Here's GRPO training Qwen 2.5 Coder 1.5B against the FATHOM env. Composite reward rises from baseline to a clean trained value across the run. The dashed line is an untrained Qwen baseline."

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[1:10 – 1:25] THE OUTCOME

On screen: Streamlit running at localhost:8501 OR a terminal showing python -m env.client --doc 200k.txt --question "...". Show the recursion tree visualization rendering the model's tool calls.

Voiceover:

"The trained model decomposes the long document, calls itself recursively, and answers correctly using only its 4K native context."

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[1:25 – 1:30] CLOSE

On screen: README.md with the Reproduce section visible — Colab link badge.

Voiceover:

"Full training reproducer in our Colab notebook. Code public on Hugging Face and GitHub. FATHOM."

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Recording checklist

• OBS or Game Bar set to 1080p / 30 fps / mic on
• Browser tabs pre-loaded so no waiting on page-loads during the take
• outputs/plots/reward_curve.png rendered + W&B run public BEFORE recording
• Streamlit viz/app.py already running on localhost:8501
• Single take preferred; if more, edit ruthlessly to ≤90s
• Upload to YouTube as Unlisted, copy URL into README + submission form
• Save the URL into assets/DEMO_URL.txt (one line) so the preflight can grep it

Plan-B if recording fails

The hackathon accepts mini-blog OR slides OR video. If recording falls through:

• Use assets/BLOG_DRAFT.md as the writeup and post on huggingface.co/blog
• OR build a 5-slide PDF deck (Canva) and save as assets/pitch.pdf
• Either route satisfies the storytelling requirement