Does the farthest-point spread still beat random draws when every chooser is scored on the same held-out designs?

What it found

A check on eda-adaptpick, which scored each chooser on the designs it had not chosen. Here each of the four families is split five times into a pool (60%) and a fixed test set (40%); every chooser picks k pool designs from features only and all are scored on the same test set. The spread still beats random draws for the linear model at every k (4.8% against 6.0% at k = 5, weight 5; 2.9% against 5.2% at k = 8) but the margin is 20–45% at 5 to 8 designs, not the earlier halving, and for the linear model it wins in 50–80% of the 20 (family, split) cells rather than all. Picking the smallest designs stays worst. Stylised: 133 small designs, a Yosys cell-count target, no place and route, no significance test.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Algorithmic chip-design loops

• Does a cell-count surrogate used as a screen find a chip design’s Pareto front with fewer syntheses than random search or TPE?A cell-count surrogate used as a screen in the design-search loop: 4 spread syntheses plus a log-linear fit reach 0.990 of the true front's hypervolume at 12 syntheses against 0.860 for random and 0.878 for TPE, of which about +0.05 comes from free cycles/error objectives (stylised, one 80-design space).
• How much error in a cheap objective can design-search proposers tolerate before they stop beating random search?With Gaussian error on the objectives a proposer sees, TPE's budget-30 lead over random falls from +0.037 to +0.006 (gone at error 0.8 of the objective range), NSGA-II and the genetic method lose theirs by 0.1, annealing is never better (stylised, synthetic noise, one 80-design space).
• Does the ranking of design-search proposers survive a change of hypervolume reference point or quality indicator?TPE stays best under five hypervolume reference points, recall, IGD+ and epsilon, but its lead over random at budget 30 ranges from +0.045 to +0.002 with the reference point (stylised, one 80-design space).
• How much of a surrogate screen’s lead over random search comes from the free objectives?A random search that uses the free cycles/error objectives reaches 0.901 of the true front's hypervolume at 12 syntheses (random 0.860, screen 0.990), so +0.04 of the screen's lead is the free objectives and +0.09 the cell-count surrogate (stylised, one 80-design space).
• Can a cost model trained on other chip-design families screen a new family’s Pareto front with a handful of syntheses?A Pareto screen whose cell-count model was trained only on other design families reaches 0.94 to 0.98 of the true front's hypervolume with 3 Yosys syntheses (random about 0.65), though the model's absolute error is large; ranking is what matters (stylised, MAC and FIR spaces of 27 designs).
• Does the best ABC recipe for one design transfer to another?All 400 ABC pass sequences on eight small SKY130-mapped designs: one design's best recipe scores a mean 1.31× the other's optimum, and a transferred warm start helps only one design (stylised, toy designs, no place and route).

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Other research topics

Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.