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 #37never observed
Folded chains
Current MCMC sample· 6 folds · total surprise 2.5
fold 0ask_arkher0.31 nats
fold 1observe0.17 nats
fold 2ask_arkher0.82 nats
fold 3observe0.17 nats
fold 4think0.82 nats
fold 5✓ pass0.23 nats
Real attempt #37 (passed)· 6 folds · total surprise 2.5
fold 0ask_arkher0.31 nats
fold 1observe0.17 nats
fold 2ask_arkher0.82 nats
fold 3observe0.17 nats
fold 4think0.82 nats
fold 5✓ pass0.23 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 2 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).