Latent reasoning that decides how long to think, and gets it exactly right.
Coconut feeds the last hidden state back in as the next input embedding. Reverie supervises every thought to decode to its gold reasoning step, and lets a PonderNet halt pick how many thoughts each problem gets. One loss, one stage, no RL.
L = Σₙ pₙ · CE(answer, W yₙ) # PonderNet answer loss
+ α · Σᵢ CE(path[i], W yᵢ) # every thought decodes to its gold step
+ γ · (−log p_m) # halt at the teacher's hop count
+ β · KL(p ‖ Geometric(λ_prior)) # don't collapse
γ is the point. The generator knows how many hops each problem needs, so train the halt against that. One MLE term.
It works. 2 hops gets 2.0 steps, 3 gets 3.0, 4 gets 4.0, ρ = +1.00. Not "correlated". Exact. Kill γ and the halt pins to max depth at the same accuracy, 0.887 vs 0.883, so the calibration is free and saves 40% of the latent passes.
No accuracy edge. None. No-CoT gets 0.847 on the search task with zero reasoning steps, Reverie gets 0.850. I figured it was cheating off component membership and added cross-edges to force real directed reachability (--connect). No-CoT went up: 0.847 → 0.882 → 0.940 as cross-edges went 0 → 12 → 24. A 2-layer transformer resolves reachability on graphs this small in one pass, so nothing here needs to think twice.
Calibration survives it. The halt learns depth, not the answer.
make install
make demo # ~8 min CPU
make phase0 # the numbers above
make test0.43M params, from scratch. Binary choice, so chance is 0.5. Rust generates the graphs, JAX and Equinox do the rest. Method in docs/DESIGN.md, numbers in docs/paper.md.