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Research · Results · Experiment

Probe the decision, not the goal.

An interpretability report on Pythagoras-Prover-4B. Read at the moment it chooses a tactic, the prover's next move is linearly encoded mid-network — far beyond what the proof text alone implies.

17 September 2026 · Model Pythagoras-LM/Pythagoras-Prover-4B · revision aa05cf9a

Headline

The tactic is decided before it is written.

0.867next tactic, read from the prover's activations at the mid-proof decision state (layer 20, 5 classes, chance 0.20)
0.643a general text encoder given the identical proof-so-far. The prover's edge is +0.224
0.950math domain, read from the theorem statement at layer 10 (6 classes, chance 0.167)
99%of steered lines flip to exact when pushed along one direction at layer 16; equal-energy null directions flip 16–18%

We froze a trained Lean theorem prover and asked what its activations make linearly readable, with no gradient and no generation. Read from the theorem statement, the prover is barely better than a general text encoder at predicting the tactic: the goal predicts the tactic, and any encoder sees the goal. Read at the decision state — the position just before a tactic is emitted, mid-proof — the prover carries its choice well above the surface, rising with depth through layers 8–20. A single direction in the residual stream then causally steers that choice for exact, simp and have, and fails honestly for apply and rw.

Evidence label: experiment. One prover, small curated sets (480 domain problems, 495 statements, 750 decision points), leave-one-out readouts with a shuffled-label control at chance. Read-only except the steering panel. Two earlier headlines in this line were wrong and were corrected in the open; the corrections are part of the report.

The model

What was measured, on what.

ModelPythagoras-LM/Pythagoras-Prover-4B on Hugging Face — a Qwen3-4B derivative, 36 layers, hidden size 2560, run dense in fp32
Pinned revisionaa05cf9a86cd1bc5af16935ab8f2190f4a1e62b8
CorpusThe QED Lean-proof corpus; tactic labels come from the winning, compiled proofs in the proven ledger
MethodOption G: probe, classify and complete a concept family on frozen activations. Whitened linear head, dimensionality reduction and mean fit inside each leave-one-out fold
BaselinesSurface literal (TF-IDF), a general text encoder (BGE-large) on the same text, and a shuffled-label control
CertificationAt the located sites the canonical frame fits at 100% energy and passes byte-equal replay; a doctored bin is rejected

The model is public. The probe reads it; it does not change it.

Open on Hugging Face