The lab

Every experiment, full size.

Screen recordings from our test bench, each one an algorithm raced against a version that adapts. We kept the losses. They taught us more than the wins did.

Hybrid wins · 3Model wins · 1Rule wins · 5Mixed · 1
01 Rule wins

Pathfinding

Decision
Which gap in the wall to route through
Raced against
Dijkstra, A* (Manhattan), weighted A*, a no-model gap rule
Setup
Every searcher expands nodes at the same rate, in its real order. The model only picks the gaps; A* does the rest.
Result
About half the node expansions of A*, but paths 13.8% longer and 15.5 ms of model time against 0.16 ms for the whole A* search. A no-model gap rule does better.
02 Rule wins

Sudoku

Decision
Which cell to branch on
Raced against
MRV, fewest options first
Setup
The model sees the same four-cell shortlist as the no-model rule, written as words.
Result
MRV: 12/12 solved, 1.06 s total. Model: 4/12. The 1970s heuristic keeps its belt.
03 Hybrid wins

Snake

Decision
Next move
Raced against
Planner + cycle-safety shield
Setup
The planner labels every move with a verdict. The model picks one, and a cycle-safety shield can overrule it.
Result
Planner labels the moves, model picks, shield vetoes. The pattern that works.
04 Hybrid wins

Minesweeper

Decision
Which cell to click
Raced against
Solver verdicts + mine shield
Setup
Each option carries the solver's verdict. A shield blocks any cell that is a proven mine.
Result
16/20 boards with the shield, 13/20 without. The shield stepped in 8 times in 1,286 clicks.
05 Hybrid wins

Agent command gate

Decision
Allow, confirm or block
Raced against
Regex rules
Setup
Classification only, nothing is executed. Commands are a small hand-labeled set, some written after the regex was frozen.
Result
Regex alone waved 5/10 destructive commands through. Regex + model: zero.
06 Model wins

Tool router

Decision
Which tool gets the request
Raced against
Keyword rules
Setup
Hand-written requests routed to eight tools. The model generates no text, it only scores the options.
Result
83.7% vs 78.8%. On tricky phrasing, 72.5% vs 47.5%. 3.8 ms, zero tokens.
07 Rule wins

Cache eviction

Decision
Which key gets evicted
Raced against
LRU, LFU, Belady
Setup
The model picks a victim from the four least-recently-used keys, with no hint about which one LRU would evict.
Result
Matches LRU within 0.2 points. LFU still wins two traces at ~5,000× less cost per eviction.
08 Rule wins

Tetris

Decision
Which of four placements
Raced against
Placement heuristic
Setup
Both boards get the same pieces. The options are the planner's top four placements, with no 'best' label.
Result
Heuristic: 278 lines, never topped out. Model: 51 lines, topped out 5/5. D'oh.
09 Rule wins

Traffic signal

Decision
Keep green or switch
Raced against
Fixed-time, actuated, longest-queue
Setup
Every policy sees the same seeded arrivals. The model only gets the queues described in words.
Result
Longest-queue rule: 6.34 s mean wait. Model alone: 41.5 s. The model is still learning to read queues.
10 Mixed

Algorithm arena

Decision
Zero-shot vs trained classifiers
Raced against
SVM, LR, kNN, naive Bayes, keywords
Setup
Small hand-written data sets. Trained models are scored on out-of-fold predictions.
Result
Six data sets, significance tests on screen. 4 wins, 2 losses against the best trained model.

The model in these clips is Laya, an open decision model that runs on the device (about 4 ms a call on the Apple Neural Engine). The benchmarks are small and built by hand, so read them as examples rather than general accuracy numbers.

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