Chain-of-thought folds
Each reasoning step folds one idea into the next. Real attempts define which folds are likely; a Markov chain Monte Carlo sampler then explores every chain of thought the agent could take, the way origami configuration spaces are sampled.
Fold graph
current samplereal attempt #3never observed
Folded chains
Current MCMC sample· 10 folds · total surprise 7.9
fold 0run_shell0.72 nats
fold 1observe0.13 nats
fold 2read_file1.70 nats
fold 3observe0.20 nats
fold 4think1.43 nats
fold 5edit_file1.10 nats
fold 6run_shell0.38 nats
fold 7observe0.13 nats
fold 8verify1.43 nats
fold 9✓ pass0.63 nats
Real attempt #3 (passed)· 10 folds · total surprise 7.9
fold 0run_shell0.72 nats
fold 1observe0.13 nats
fold 2read_file1.70 nats
fold 3observe0.20 nats
fold 4think1.43 nats
fold 5edit_file1.10 nats
fold 6run_shell0.38 nats
fold 7observe0.13 nats
fold 8verify1.43 nats
fold 9✓ pass0.63 nats
Crease angle = surprise of that fold (−log P). Sharp creases are unusual jumps from one idea to the next; mountain and valley folds alternate, keeping the strip flat.
Most visited chains
Run the sampler to estimate which chains of thought dominate.
How it works
- States are kinds of steps: think, act with a tool, observe a result, get verified.
- Folds are transitions between them, with probabilities learned from the 7 real attempts (smoothed, so unseen folds stay possible).
- Energy of a chain = total surprise of its folds, plus β if it ends in failure. Chains are sampled with probability ∝ e−E/T.
- Metropolis–Hastings proposes refolding, inserting or removing a step, or flipping the outcome, and accepts with probability min(1, e−ΔE/T × proposal ratio).
- Inspired by Hikaru Kuribayashi's MCMC sampling of origami and linkage configuration spaces (Regeneron ISEF 2026).