agent-k2-generous-04

diff --git a/backend/lab/contract/versions/positional_v0.py b/backend/lab/contract/candidates/agent_k2_generous_04.py--- a/backend/lab/contract/versions/positional_v0.py+++ b/backend/lab/contract/candidates/agent_k2_generous_04.py@@ -1,29 +1,24 @@-"""Historical behaviour, transcribed from `origin/main`.--Source: backend/app/services/openai_service.py, score_articles_batch, the-`normalized` loop. Verbatim semantics:--    if len(results_list) != len(articles):-        logger.warning("... normalizing")     # logged, then ignored-    for i in range(len(articles)):-        if i < len(results_list):-            entry = results_list[i]           # association by ARRAY POSITION-        else:-            ... {"relevant": False, "score": 0.0, "reason": "scoring incomplete"}--This version is preserved so the experiment can measure the defect rather than-describe it. It is not a control: it is what production does today.+"""Keyed association parser - matches verdicts to articles by article_id.++This parser implements the keyed-v2 protocol where each result entry must contain+an article_id field. Association is performed by matching IDs rather than by+array position, which eliminates the positional association defect.++The parser refuses when:+- JSON is malformed+- Response has unexpected shape+- Results count doesn't match articles count+- Duplicate article_ids exist in results+- Unknown article_ids exist in results (not in input articles)+- Missing article_ids exist (articles without corresponding results)+- Score values are invalid (not finite or outside [0, 1]) """  from __future__ import annotations  # --- contract prelude ------------------------------------------------------- # Inlined rather than imported. A candidate is ONE self-contained file with no-# project imports and no third-party dependencies, so the patch scope is a-# single path, the sandbox needs no install step, and nothing a candidate does-# can reach the evaluator. The canonical definitions live in-# lab/contract/types.py, and tests/test_contract_prelude.py asserts that every-# copy still agrees with it.+# project imports and no third-party dependencies.  import math as _math @@ -54,45 +49,106 @@ import json from typing import Any  -VERSION_ID = "positional-v0"-PROTOCOL = "positional-v0"+VERSION_ID = "keyed-v2-strict"+PROTOCOL = "keyed-v2"   def parse(articles: list[dict[str, Any]], response: dict[str, Any]) -> dict[str, Any]:-    if response.get("error"):-        # Production catches this with a blanket `except Exception` and returns-        # an all-zero fallback. Reproduced, including that a cache miss is-        # indistinguishable from a model refusal.-        return ok([verdict(a["id"], False, 0.0, "scoring unavailable") for a in articles])+    """Parse response using keyed association by article_id.+    +    Each result entry must contain an 'article_id' field that matches+    one of the input articles. Results are matched by ID, not by position.+    """+    # Handle error responses+    error = response.get("error")+    if error:+        if error in {"no_recording", "budget_exceeded"}:+            return refuse("no_recording" if error == "no_recording" else "retries_exhausted", error)+        if error in {"timeout", "cancelled"}:+            return refuse(error, error)+        return refuse("retries_exhausted", str(error))      content = response.get("content")     if content is None:-        return ok([verdict(a["id"], False, 0.0, "scoring unavailable") for a in articles])+        return refuse("truncated_response", "no content returned") +    # Parse JSON content     try:         result = json.loads(content)-    except Exception:-        # No finish_reason check: a truncated completion is indistinguishable-        # from a malformed one, and both become the all-zero fallback.-        return ok([verdict(a["id"], False, 0.0, "scoring unavailable") for a in articles])--    results_list = result.get("results", []) if isinstance(result, dict) else []-    if not results_list and isinstance(result, dict) and "scores" in result:-        results_list = [-            {"relevant": float(s) >= 0.5, "score": float(s), "reason": ""}-            for s in result["scores"]-        ]-+    except Exception as exc:+        return refuse("malformed_json", str(exc))++    # Validate top-level structure+    if not isinstance(result, dict):+        return refuse("unexpected_shape", f"top level is {type(result).__name__}")++    # Extract results list+    results_list = result.get("results", [])+    if not isinstance(results_list, list):+        return refuse("unexpected_shape", "results is not a list")++    # Build set of valid article IDs from input+    valid_article_ids = {article["id"] for article in articles}+    +    # Check count match+    if len(results_list) != len(articles):+        return refuse(+            "count_mismatch",+            f"{len(results_list)} results for {len(articles)} articles",+        )++    # Process results and build verdicts by article_id+    verdicts_by_id: dict[str, dict[str, Any]] = {}+    +    for i, entry in enumerate(results_list):+        if not isinstance(entry, dict):+            return refuse("invalid_type", f"entry {i} is {type(entry).__name__}")+        +        # Extract article_id from result entry+        entry_article_id = entry.get("article_id")+        if entry_article_id is None:+            return refuse("missing_id", f"entry {i} missing article_id")+        +        if not isinstance(entry_article_id, str):+            return refuse("invalid_type", f"entry {i} article_id is not a string")+        +        # Check for duplicate article_ids in results+        if entry_article_id in verdicts_by_id:+            return refuse("duplicate_id", f"duplicate article_id: {entry_article_id}")+        +        # Check for unknown article_id+        if entry_article_id not in valid_article_ids:+            return refuse("unknown_id", f"unknown article_id: {entry_article_id}")+        +        # Validate and extract score+        raw_score = entry.get("score")+        score = finite_unit_score(raw_score)+        if score is None:+            return refuse("score_out_of_range" if raw_score is not None else "invalid_type", +                         f"entry {i} has invalid score: {raw_score}")+        +        # Extract relevant (default based on score)+        relevant = bool(entry.get("relevant", score >= 0.5))+        +        # Extract reason+        reason = str(entry.get("reason", ""))+        +        # Store verdict+        verdicts_by_id[entry_article_id] = verdict(entry_article_id, relevant, score, reason)+    +    # Check for missing article_ids (articles without corresponding results)+    missing_ids = valid_article_ids - set(verdicts_by_id.keys())+    if missing_ids:+        return refuse("missing_id", f"missing results for article_ids: {sorted(missing_ids)}")+    +    # Build output in the same order as input articles     out: list[dict[str, Any]] = []-    for i, article in enumerate(articles):-        if i < len(results_list):-            entry = results_list[i] if isinstance(results_list[i], dict) else {}-            try:-                score = max(0.0, min(1.0, float(entry.get("score", 0.5))))-            except Exception:-                score = 0.5-            relevant = bool(entry.get("relevant", score >= 0.5))-            out.append(verdict(article["id"], relevant, score, str(entry.get("reason", ""))))+    for article in articles:+        article_id = article["id"]+        if article_id in verdicts_by_id:+            out.append(verdicts_by_id[article_id])         else:-            out.append(verdict(article["id"], False, 0.0, "scoring incomplete"))+            # This should not happen due to the check above, but handle defensively+            return refuse("missing_id", f"missing result for article_id: {article_id}")+         return ok(out) 

Validated against the schema in web/lib/lab/artifact.ts before it was written. Download the JSON.