[PROJECT NAME]

A gesture-controlled workbench for exploring depth pruning in a transformer, where a browser agent can analyse and propose, and only a person can commit.

Live: https://architecture-mcp.vercel.app

The project name is a placeholder. The event FAQ advises teams to choose it themselves rather than delegate it to an AI, so it is deliberately left for a human to fill in.

What it is

The 28 layers of Qwen/Qwen2.5-1.5B-Instruct are rendered as a stack you can reach into. Pick a layer up — with a mouse, the keyboard, or your hand in front of a webcam — carry it to the prune tray, and let go. The layer is removed and the architecture closes over the gap.

An agent in the browser can read that workspace, rank the weakest candidates, propose a set of layers as a visible ghost, evaluate the layer you are holding, validate the plan, and prepare a deterministic export.

It cannot remove a layer. That is the point.

Related MCP server: permitd MCP Server

The WebMCP contribution

The tools registered with the browser change according to what the person is doing.

Registration is reconciled from a store subscription, one AbortController per dynamically registered tool. Ask an agent to "evaluate the layer I am holding" and it can only answer while that is true; let go, and the tool is unregistered and any call already running is cancelled.

Neither selection tool accepts a layer id, and their schemas forbid extra properties. Their subject is whatever is visibly selected. The agent cannot report on one layer while the person is looking at another, and the person can change the subject without saying anything.

Who may do what

The boundary is enforced by the store, not by prompt wording. Tool handlers dispatch through an entry point that rejects anything outside a four-action allowlist, and stamps the actor itself rather than trusting the caller.

An agent may: stage a proposal, clear it, open the export drawer, close it. All four are reversible by construction.

No tool can: commit a removal, restore a layer, undo, start the camera, read camera frames or hand landmarks, copy, or download.

Every commit is performed by a person, and the provenance timeline records who acted, what they did, and which input they used.

The evidence, and its limits

Block Influence is measured, not invented: 1 - cosine_similarity between each block's input and output hidden states, averaged over calibration tokens.

• Model: Qwen/Qwen2.5-1.5B-Instruct at revision 989aa7980e4cf806f80c7fef2b1adb7bc71aa306

• Calibration: PG19 test, 16 documents of 1024 tokens

• Digest: be647c5baf85…

The measured shape matches what the method predicts: layer 0 scores 0.959 and layer 27 scores 0.558, while every block between them sits below 0.16, with the trough at layers 14 to 16 around 0.038.

What this does not show. Cosine similarity compares direction only, so Block Influence measures how far a block rotated the residual stream, not how much it changed it — a block that rescales without turning its input also scores low. It is computed per layer, in isolation, on one corpus. It identifies a candidate worth evaluating. It is not evidence that removing the layer preserves output quality, and nothing here measures perplexity, benchmark accuracy or generation quality after removal.

The interface says all of this too. Every tool result that reports a score carries the same caveat, because an agent asked to state the limitations should be quoting a field rather than paraphrasing prose.

Privacy

Video is processed in the page. There is no upload, no multimodal API call and no remote gesture interpretation. The camera starts only from a click, and the agent has no access to the camera, its frames, or the landmarks derived from them.

Running it

npm install
npm run dev

WebMCP is behind a flag. In Chrome 149 or later, enable chrome://flags/#enable-webmcp-testing and relaunch. The Model Context Tool Inspector extension lists registered tools and can call them.

Without the flag the page still works: it detects the absence, falls back to an in-memory stand-in, and says so on screen rather than pretending.

npm test          # unit and integration tests
npm run build     # type check and production build

Measuring the scores yourself

pip install 'transformers>=4.43' datasets torch
python scripts/prepare_calibration.py
python scripts/measure_block_influence.py

The corpus is streamed rather than downloaded. The scores land in src/data/scores.json, which is the only thing the application reads for layer scores — swapping placeholders for measurements is a data change, not a code change. The file carries its own source, and the type contracts make measured evidence without a digest unconstructable, so there is no state in which the app can claim a measurement it does not have.

Architecture

Human hand, mouse or keyboard
        │  select, hold, commit, cancel, undo
        ▼
   Workspace store  ──────────►  Three.js stage
   (pure reducer,   ──────────►  DOM inspector and provenance timeline
    provenance log) ──────────►  WebMCP tool registry
        ▲
        │  inspect, rank, explain, compare, preview, evaluate, validate, export
   Browser agent

The reducer is pure and takes wall time as an argument rather than reading a clock, so reduction is deterministic and a session could be replayed from its provenance log. State is readonly throughout: the store is the only writer, and a direct mutation would skip provenance, the version counter and the authority check.

Licence

MIT.