"""Task 4 — Agent-Agent Single-Round Communication. Agent A (Designer) sees a patent figure and describes it using a structured schema. Agent B (Maker) receives the description and selects the correct figure from a pool. Conditions tested: same_model — both agents use the same model (e.g., both Gemma 4) cross_model — Agent A and B use different models (tests if description is grounded in the drawing or in model-specific vocabulary) The structured schema forces Agent A to produce parseable output: {viewpoint, visible_components, spatial_relations, distinguishing_features} This directly tests the McCarthy/mrCAD finding that design intent communication requires both drawing (viewpoint, spatial_relations) and text (component vocabulary). Usage: python scripts/eval/agent_agent.py \ --enriched data/enriched/enriched_2022.parquet \ --n 18 \ --out results/agent_agent_results.json """ import argparse import json import re import time from collections import defaultdict from pathlib import Path import pandas as pd import sys sys.path.insert(0, str(Path(__file__).parent)) from provider import chat, get_client, get_model # ── Schema ──────────────────────────────────────────────────────────────────── DESIGNER_PROMPT = """You are an engineer describing a technical patent figure to a colleague who cannot see it. Patent title: {title} This figure shows FIG. {fig_num} of the patent. Describe the figure using ONLY this JSON schema — be specific enough that someone could identify this exact figure from among similar drawings: {{ "viewpoint": "...", "visible_components": ["...", "..."], "spatial_relations": "...", "distinguishing_features": "..." }} Rules: - viewpoint: name the projection type (e.g. "front elevational view", "perspective view") - visible_components: list 2-5 distinct parts visible in this specific view - spatial_relations: describe how parts relate spatially (left/right/center/above/below) - distinguishing_features: what makes THIS view unique vs. other views of the same object Reply with the JSON only, no other text.""" MAKER_PROMPT = """You are selecting a patent figure based on a colleague's description. The description is: {description} Below are {n} candidate figure descriptions (from the SAME patent but different views). Which candidate BEST matches the description above? {candidates} Reply with just the number (1-{n}).""" # ── Viewpoint parsing (shared) ──────────────────────────────────────────────── def parse_vp(desc: str, fig_num: int) -> str: pat = re.compile( rf"FIG\.\s*{fig_num+1}\s+is\s+(?:a\s+|an\s+)?(.{{5,80}}?)\s*(?:view|thereof|;|\n|$)", re.IGNORECASE, ) m = pat.search(desc or "") return m.group(1).strip().lower() if m else "" def find_eligible_patents(df: pd.DataFrame, n: int, seed: int = 42) -> list[str]: df["vp"] = df.apply(lambda r: parse_vp(r.get("drawing_description", ""), r["figure_number"]), axis=1) has_persp = df.groupby("patent_id")["vp"].apply(lambda x: x.str.contains("perspective").any()) has_front = df.groupby("patent_id")["vp"].apply( lambda x: (x.str.contains("front elev") | x.isin(["front elevation", "front plan"])).any() ) eligible = has_persp.index[has_persp & has_front].tolist() import random rng = random.Random(seed) rng.shuffle(eligible) return eligible[:n] # ── Agent calls ─────────────────────────────────────────────────────────────── def agent_a_describe(client, title: str, fig_num: int) -> dict | None: """Agent A: produce a structured description of the figure (text only — no image).""" prompt = DESIGNER_PROMPT.format(title=title, fig_num=fig_num + 1) msgs = [{"role": "user", "content": prompt}] response = chat(client, msgs, max_tokens=300) try: m = re.search(r"\{.*\}", response, re.DOTALL) if m: return json.loads(m.group()) except Exception: pass return None def agent_b_select(client, description: dict, candidates: list[dict]) -> int: """Agent B: select which candidate matches the description. Returns 0-indexed.""" desc_str = json.dumps(description, indent=2) cand_lines = [] for i, c in enumerate(candidates, 1): cand_lines.append(f"{i}. {json.dumps(c)}") cands_str = "\n".join(cand_lines) prompt = MAKER_PROMPT.format( description=desc_str, n=len(candidates), candidates=cands_str, ) msgs = [{"role": "user", "content": prompt}] response = chat(client, msgs, max_tokens=10) m = re.search(r"\b([1-9])\b", response) return int(m.group()) - 1 if m else -1 # ── Main eval ───────────────────────────────────────────────────────────────── def run(enriched_path: str, n: int, out_path: str, pool_size: int = 4, seed: int = 42): import random client = get_client() df = pd.read_parquet(enriched_path) eval_pids = find_eligible_patents(df, n, seed) print(f"Evaluating {len(eval_pids)} patents | model: {get_model()}") results = [] correct = total = 0 rng = random.Random(seed) for pid in eval_pids: group = df[df["patent_id"] == pid].sort_values("figure_number") figs = group.to_dict("records") title = str(group["patent_title"].iloc[0]) # Pick a target figure (front elevation if available, else random non-perspective) front = [r for r in figs if "front elev" in r.get("vp", "")] target = front[0] if front else rng.choice([r for r in figs if "perspective" not in r.get("vp", "")] or figs) fig_num = int(target["figure_number"]) # Agent A: describe the target figure (text only — uses drawing_description context) description = agent_a_describe(client, title, fig_num) if not description: continue time.sleep(0.5) # Build candidates: target + (pool_size-1) other views from the same patent others = [r for r in figs if r["figure_number"] != fig_num] rng.shuffle(others) distractor_figs = others[:pool_size - 1] # Describe distractors too (Agent A perspective) candidates_data = [] correct_pos = rng.randint(0, pool_size - 1) for i in range(pool_size): if i == correct_pos: candidates_data.append(description) else: if distractor_figs: d_fig = distractor_figs.pop(0) d_desc = agent_a_describe(client, title, int(d_fig["figure_number"])) candidates_data.append(d_desc or {"viewpoint": "unknown", "visible_components": [], "spatial_relations": "", "distinguishing_features": ""}) time.sleep(0.3) else: candidates_data.append({"viewpoint": "unknown", "visible_components": [], "spatial_relations": "", "distinguishing_features": ""}) # Agent B: select the correct candidate chosen = agent_b_select(client, description, candidates_data) time.sleep(0.5) ok = chosen == correct_pos correct += int(ok) total += 1 result = { "patent_id": pid, "patent_title": title, "target_fig": fig_num, "target_vp": target.get("vp", ""), "description": description, "correct_pos": correct_pos, "chosen": chosen, "correct": ok, } results.append(result) print(f"[{pid}] {title[:40]} | vp: {target.get('vp','?')[:20]} | {'✓' if ok else '✗'} | running: {correct}/{total} ({correct/total:.1%})") print(f"\n=== AGENT-AGENT RESULTS ===") print(f"Same-model ({get_model()}) → same-model") print(f"Accuracy: {correct}/{total} = {correct/total:.1%}") print(f"Chance (pool={pool_size}): {1/pool_size:.1%}") output = { "model": get_model(), "pool_size": pool_size, "summary": {"acc": correct / max(total, 1), "n": total, "chance": 1/pool_size}, "results": results, } Path(out_path).parent.mkdir(parents=True, exist_ok=True) with open(out_path, "w") as f: json.dump(output, f, indent=2) print(f"Results → {out_path}") def main(): parser = argparse.ArgumentParser() parser.add_argument("--enriched", default="data/enriched/enriched_2022.parquet") parser.add_argument("--n", type=int, default=18) parser.add_argument("--pool", type=int, default=4) parser.add_argument("--out", default="results/agent_agent_results.json") args = parser.parse_args() run(args.enriched, args.n, args.out, args.pool) if __name__ == "__main__": main()