| """Execute a thinking flow (Def 4.2): a DAG over thinking operators run in |
| topological order. Generate seeds the pool; Divergent branches into a candidate |
| pool; Convergent collapses the pool by selecting the best; the other operators |
| transform the current best idea. e(y | G; x_t, x_r) returns the final idea. |
| """ |
| from __future__ import annotations |
| from . import operators as ops |
|
|
|
|
| def execute_flow(seq, topic, related, model=None, seed=None, divergent_n=3): |
| """seq: list of refinement operator names (Generate is always prepended). |
| Returns (final_idea, trace) where trace is the list of (op, idea) steps.""" |
| trace = [] |
| s = (seed or 0) |
| current = ops.op_generate(topic, related, model=model, seed=s) |
| pool = [current] |
| trace.append(("Generate", current)) |
| for i, name in enumerate(seq): |
| s += 1 |
| if name == "Divergent": |
| alts = ops.op_divergent_expand(topic, related, current, n=divergent_n, model=model, seed=s) |
| pool = pool + alts |
| current = alts[0] |
| elif name == "Convergent": |
| current = ops.op_convergent_select(topic, related, pool, model=model, seed=s) |
| pool = [current] |
| else: |
| fn = ops.OPERATORS.get(name) |
| if fn is None: |
| continue |
| current = fn(topic, related, current, model=model, seed=s) |
| pool[-1] = current |
| trace.append((name, current)) |
| return current, trace |
|
|
|
|
| def idea_to_text(idea): |
| if not idea: |
| return "" |
| return (f"Title: {idea.get('title','')}\n" |
| f"Motivation/Problem: {idea.get('problem','')}\n" |
| f"Method: {idea.get('method','')}\n" |
| f"Evaluation: {idea.get('evaluation','')}") |
|
|
|
|
| def motivation_text(idea): |
| return f"Title: {idea.get('title','')}\nMotivation: {idea.get('problem','')}" if idea else "" |
|
|
