derive-budget-is-flexible-and-efficient
IN derived (depth 2)
The derive pipeline's budget allocation is both strategically flexible (three selection strategies: alphabetical truncation, random sampling, semantic clustering) and computationally efficient (linear accumulation with guaranteed floor of 5 per agent group), enabling callers to trade off reproducibility, diversity, and semantic coherence without performance penalty.
Summary
When the derive pipeline selects which beliefs to feed into each agent, callers can choose between three approaches — alphabetical cutoff for reproducibility, random sampling for variety, or semantic clustering for topical coherence — and switching between them costs nothing extra because the underlying budget math is simple linear allocation with a minimum of 5 slots per group. This means you can tune how belief selection works without worrying about performance tradeoffs or starving any agent of input.
Justifications
SL — Strategy flexibility (what to select) and computational efficiency (how fast selection runs) are independently established — combining them shows the system avoids the usual flexibility-performance tradeoff
Antecedents (all must be IN):
- derive-budget-three-strategies — `_build_beliefs_section` supports three budget strategies — alphabetical truncation (default), random sampling (`sample=True`), and semantic clustering (`cluster=True`) — all controlled by a single `max_beliefs` parameter.
- derive-budget-is-efficient-and-floor-bounded — The derive pipeline's per-agent budget allocation is both computationally efficient (O(N) linear accumulation, not quadratic) and representation-safe (each agent and local group guaranteed at least 5 belief slots), ensuring proportional allocation never starves minority agents.
Dependents
These beliefs depend on this one:
- derive-achieves-flexibility-with-reproducibility — The derive pipeline resolves the tension between strategic flexibility and deterministic reproducibility: three budget strategies (alphabetical truncation, random sampling, semantic clustering) provide diverse exploration approaches, while fixed-seed deterministic sampling and accurate proportional allocation ensure each strategy produces identical results across runs.