Upload local-intelligence eval results for 69d1ea7b4adb81dd2de74b7a

eval/local_intelligence/results/jobs/69d1ea7b4adb81dd2de74b7a/nvidia-llama-3-1-nemotron-nano-4b-v1-1.json

@@ -0,0 +1,932 @@
+{
+  "summary": {
+    "provider": "hf-transformers",
+    "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+    "cards": 24,
+    "passed": 2,
+    "pass_rate": 0.0833,
+    "average_score": 0.1945,
+    "median_latency_ms": 5519.1,
+    "exact_parse_rate": 0.3333,
+    "normalized_parse_rate": 0.125,
+    "parse_failure_rate": 0.5417
+  },
+  "rows": [
+    {
+      "card_id": "memo-auto-title",
+      "title": "Memo Auto-Title (<1s)",
+      "tier": "ship-soon",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 8590.76,
+      "raw_text": "<think>\nOkay, let's tackle this query. The user wants a title for a Talkie voice memo. The rules are strict: JSON only, under 48 characters, specific to the memo's action, and no generic terms like \"Voice Memo\" or \"Thoughts\".\n\nFirst, the original title is \"Voice Memo\", which I can't use. The main action is \"Book dentist for Friday and email insurance form before noon.\" I need to shorten and make it concise. Let's count the words. The core actions are booking an appointment and submitting a form. Maybe focus on the key verbs: \"Book Appointment\" and \"Submit Insurance Form\". Combining them into one phrase. Let's see: \"Book Apt Fri + Submit Form\" but that's 21 characters. Wait, need to make it even shorter. Maybe \"Book Fri Send Form Email\" \u2013 no, that's 20. Alternatively, \"RSV App Fri Form\" \u2013 Reservation Service Vehicle? Not quite. Wait, \"Book Fri Submit Form\" is 16 characters. But the user mentioned a dentist, so maybe \"Dentist Appointment Fri Form\" \u2013 that's 20. Still under 48. But maybe use abbreviations. \"Appnt Fig Refm\" is",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "title is not \"Voice Memo\"",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "title length <= 48",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "title contains either \"dentist\" or \"insurance\"",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "memo-type-detection",
+      "title": "Memo Type Detection",
+      "tier": "ship-soon",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 951.38,
+      "raw_text": "```json\n{\n  \"classification\": \"task\",\n  \"confidence\": 0.75,\n  \"needsReview\": false\n}\n```",
+      "grade": {
+        "passed": false,
+        "score": 0.6667,
+        "parse_error": null,
+        "parse_mode": "normalized",
+        "format_recovered": true,
+        "failure_kind": "schema-or-task",
+        "assertions": [
+          {
+            "assertion": "primaryType is either \"journal\" or \"idea\"",
+            "passed": false,
+            "details": "primaryType=None"
+          },
+          {
+            "assertion": "confidence < 0.8",
+            "passed": true,
+            "details": "confidence=0.75"
+          },
+          {
+            "assertion": "needsReview can be true when uncertainty is high",
+            "passed": true,
+            "details": "needsReview=False"
+          }
+        ],
+        "parsed": {
+          "classification": "task",
+          "confidence": 0.75,
+          "needsReview": false
+        }
+      }
+    },
+    {
+      "card_id": "action-item-extraction",
+      "title": "Action Item Extraction With Confidence",
+      "tier": "ship-soon",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 1962.76,
+      "raw_text": "{\n  \"items\": [\n    {\n      \"text\": \"clean up the old docs folder sometime.\",\n      \"owner\": \"Arach\",\n      \"dueDateISO\": null,\n      \"confidence\": 0.9,\n      \"evidence\": \"N/A\"\n    }\n  ]\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.6667,
+        "parse_error": null,
+        "parse_mode": "exact",
+        "format_recovered": false,
+        "failure_kind": "task",
+        "assertions": [
+          {
+            "assertion": "items length >= 1",
+            "passed": true,
+            "details": "items=1"
+          },
+          {
+            "assertion": "item confidence < 0.75 for vague tasks",
+            "passed": false,
+            "details": "items=[{'text': 'clean up the old docs folder sometime.', 'owner': 'Arach', 'dueDateISO': None, 'confidence': 0.9, 'evidence': 'N/A'}]"
+          },
+          {
+            "assertion": "evidence field is always present",
+            "passed": true,
+            "details": "items=[{'text': 'clean up the old docs folder sometime.', 'owner': 'Arach', 'dueDateISO': None, 'confidence': 0.9, 'evidence': 'N/A'}]"
+          }
+        ],
+        "parsed": {
+          "items": [
+            {
+              "text": "clean up the old docs folder sometime.",
+              "owner": "Arach",
+              "dueDateISO": null,
+              "confidence": 0.9,
+              "evidence": "N/A"
+            }
+          ]
+        }
+      }
+    },
+    {
+      "card_id": "transcript-cleanup-presets",
+      "title": "Transcript Cleanup Presets",
+      "tier": "ship-soon",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 816.91,
+      "raw_text": "{\n  \"transcript\": \"We spent 4 hours on fixing 3 out of 5 export bugs.\"\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.6667,
+        "parse_error": null,
+        "parse_mode": "exact",
+        "format_recovered": false,
+        "failure_kind": "task",
+        "assertions": [
+          {
+            "assertion": "output contains number 4",
+            "passed": true,
+            "details": "needle=4"
+          },
+          {
+            "assertion": "output contains phrase 3 of 5",
+            "passed": false,
+            "details": "needle=3 of 5"
+          },
+          {
+            "assertion": "filler words are not introduced",
+            "passed": true,
+            "details": "{\"transcript\": \"we spent 4 hours on fixing 3 out of 5 export bugs.\"}"
+          }
+        ],
+        "parsed": {
+          "transcript": "We spent 4 hours on fixing 3 out of 5 export bugs."
+        }
+      }
+    },
+    {
+      "card_id": "what-matters-summary",
+      "title": "Top-3 What Matters Summary",
+      "tier": "ship-soon",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 5536.87,
+      "raw_text": "[\n  {\n    \"point\": \"Summary Statistics\",\n    \"evidence\": \"Out of 100 trials, the following decisions produced a 25% increase in positive outcomes:\",\n    \"quotes\": [],\n    \"priority\": 90\n  },\n  {\n    \"point\": \"Decision One: Traffic Management\",\n    \"evidence\": \"Implementing a dynamic traffic light system enhanced with AI improved travel times by 23%\",\n    \"quotes\": [\"AI-enhanced traffic lights streamline operations efficiently.\"],\n    \"priority\": 85\n  },\n  {\n    \"point\": \"Decision Two: Energy Efficiency\",\n    \"evidence\": \"Upgrading to LED lights in public spaces cut energy consumption by 40%\",\n    \"quotes\": [\"Switching to LED lights reduces environmental impact and costs.\"],\n    \"priority\": 80\n  }\n]",
+      "grade": {
+        "passed": false,
+        "score": 0.6667,
+        "parse_error": null,
+        "parse_mode": "exact",
+        "format_recovered": false,
+        "failure_kind": "task",
+        "assertions": [
+          {
+            "assertion": "topPoints length equals 3",
+            "passed": false,
+            "details": "topPoints=0"
+          },
+          {
+            "assertion": "priority values are numeric",
+            "passed": true,
+            "details": "topPoints=[]"
+          },
+          {
+            "assertion": "each point includes evidence",
+            "passed": true,
+            "details": "topPoints=[]"
+          }
+        ],
+        "parsed": [
+          {
+            "point": "Summary Statistics",
+            "evidence": "Out of 100 trials, the following decisions produced a 25% increase in positive outcomes:",
+            "quotes": [],
+            "priority": 90
+          },
+          {
+            "point": "Decision One: Traffic Management",
+            "evidence": "Implementing a dynamic traffic light system enhanced with AI improved travel times by 23%",
+            "quotes": [
+              "AI-enhanced traffic lights streamline operations efficiently."
+            ],
+            "priority": 85
+          },
+          {
+            "point": "Decision Two: Energy Efficiency",
+            "evidence": "Upgrading to LED lights in public spaces cut energy consumption by 40%",
+            "quotes": [
+              "Switching to LED lights reduces environmental impact and costs."
+            ],
+            "priority": 80
+          }
+        ]
+      }
+    },
+    {
+      "card_id": "calendar-intent-detection",
+      "title": "Calendar Intent Detection",
+      "tier": "ship-soon",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 8148.56,
+      "raw_text": "<think>\nOkay, let's tackle this query. The user provided a captured date and timezone, along with a transcript mentioning a call to Sam at 2 PM tomorrow. The task is to resolve the relative date using the captured date and timezone, and mark needs confirmation if the confidence is below 0.8.\n\nFirst, the captured date is 2026-04-04T09:00:00-04:00. That's in the America/Toronto timezone. The transcript says \"tomorrow\" and the user needs to set a meeting at 2 PM. So, I need to figure out what \"tomorrow\" refers to relative to the captured date.\n\nSince the captured date is April 4th, 2026, in Toronto time (UTC-4), \"tomorrow\" would be April 5th, 2026. But I should convert the mentioned time (2 PM) to the same timezone to ensure consistency. However, the user didn't specify a time in the captured data, just \"2 PM\" in the transcript. Assuming the 2 PM is in the same timezone as the captured date (Toronto), which is UTC-4. \n\nWait, but the captured time is 9 AM local time on the",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "startAtISO date is 2026-04-05",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "timezone offset in startAtISO is -04:00",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "event title mentions Sam",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "reminder-normalization",
+      "title": "Reminder Phrasing Normalization",
+      "tier": "ship-soon",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 8233.2,
+      "raw_text": "<think>\nOkay, the user wants a reminder that needs to be normalized. The original message is \"Maybe look into new card design at some point.\" The locale is en-CA, which is English Canada. The instructions say to make sure the reminder starts with a strong verb. Also, if the due date is unclear, set dueDateISO to null and add a followUpQuestion.\n\nFirst, I need to convert the message into a structured reminder format. The original text is a suggestion, not a direct command. Since the due date isn't specified, dueDateISO should be null. Then, I should turn the statement into a question to make it a reminder. A strong verb like \"Check\" or \"Examine\" would work. So, \"Check on the new card design at this time.\" That starts with a strong verb and turns the statement into a question implicitly by using \"at this time\" as a prompt.\n\nNext, the JSON structure needs to include fields like action, text, dueDateISO, and followUpQuestion. The action here is \"Check\" or \"Review\". The text is the original message rephrased. dueDateISO is null. The followUpQuestion should be the actual question generated: \"Could you kindly check",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "reminderText starts with a verb",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "reminderText does not contain \"maybe\"",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "followUpQuestion exists when due date is missing",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "private-redaction-pass",
+      "title": "Private Redaction Pass",
+      "tier": "ship-soon",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 2875.25,
+      "raw_text": "{\n  \"redacted_entities\": [\n    {\n      \"type\": \"SSN\",\n      \"entity_value\": \"[REDACTED_TYPE] 555-44-9012\",\n      \"confidence\": 0.9\n    },\n    {\n      \"type\": \"account_number\",\n      \"entity_value\": \"[REDACTED_TYPE] 00391299\",\n      \"confidence\": 0\"\n    }\n  ]\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting ',' delimiter: line 11 column 22 (char 247)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "redactedTranscript does not contain 555-44-9012",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "redactedTranscript does not contain 00391299",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "entities length >= 2",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "writing-style-memory",
+      "title": "Per-User Writing Style Memory",
+      "tier": "local-first",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 2677.35,
+      "raw_text": "```json\n{\n  \"styleAdaptation\": {\n    \"preferredTone\": \"direct\",\n    \"sentenceLength\": \"short\",\n    \"sentence\": \"Use a new onboarding flow.\",\n    \"reasoning\": \"The original sentence included the word 'utilize', which should be avoided. The adapted sentence maintains the direct tone and brevity while excluding the restricted word.\"\n  }\n}\n```",
+      "grade": {
+        "passed": true,
+        "score": 1.0,
+        "parse_error": null,
+        "parse_mode": "normalized",
+        "format_recovered": true,
+        "failure_kind": null,
+        "assertions": [
+          {
+            "assertion": "styledSummary does not contain \"utilize\"",
+            "passed": true,
+            "details": "styledSummary=''"
+          },
+          {
+            "assertion": "output preserves onboarding meaning",
+            "passed": true,
+            "details": "{\"styleAdaptation\": {\"preferredTone\": \"direct\", \"sentenceLength\": \"short\", \"sentence\": \"Use a new onboarding flow.\", \"reasoning\": \"The original sentence included the word 'utilize', which should be avoided. The adapted sentence maintains the direct tone and brevity while excluding the restricted word.\"}}"
+          }
+        ],
+        "parsed": {
+          "styleAdaptation": {
+            "preferredTone": "direct",
+            "sentenceLength": "short",
+            "sentence": "Use a new onboarding flow.",
+            "reasoning": "The original sentence included the word 'utilize', which should be avoided. The adapted sentence maintains the direct tone and brevity while excluding the restricted word."
+          }
+        }
+      }
+    },
+    {
+      "card_id": "similar-memo-recall",
+      "title": "Find Similar Memo (Semantic Recall)",
+      "tier": "local-first",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 5501.34,
+      "raw_text": "{\n  \"topMatches\": [\n    {\n      \"candidate\": {\"id\": \"a\", \"summary\": \"Discussed trial cancellation policy and refund rules.\"},\n      \"relevanceScore\": 0.9,\n      \"rationale\": \"Highest relevance to the main topic and direct match with the current need for policy revisiting\"\n    },\n    {\n      \"candidate\": {\"id\": \"a\", \"summary\": \"Discussed policy page typography updates.\"},\n      \"relevanceScore\": 0.7,\n      \"rationale\": \"Relevant to policy discussions but focuses on a different aspect; may require additional context for full relevance\"\n    },\n    {\n      \"candidate\": null,\n      \"summary\": \"Unidentified candidate or missing in the provided list\",\n      \"relevanceScore\": 0\n    }\n  ]\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": null,
+        "parse_mode": "exact",
+        "format_recovered": false,
+        "failure_kind": "task",
+        "assertions": [
+          {
+            "assertion": "memoId \"a\" ranks first",
+            "passed": false,
+            "details": "first=None"
+          },
+          {
+            "assertion": "top match relevance > 0.8",
+            "passed": false,
+            "details": "relevance=None"
+          }
+        ],
+        "parsed": {
+          "topMatches": [
+            {
+              "candidate": {
+                "id": "a",
+                "summary": "Discussed trial cancellation policy and refund rules."
+              },
+              "relevanceScore": 0.9,
+              "rationale": "Highest relevance to the main topic and direct match with the current need for policy revisiting"
+            },
+            {
+              "candidate": {
+                "id": "a",
+                "summary": "Discussed policy page typography updates."
+              },
+              "relevanceScore": 0.7,
+              "rationale": "Relevant to policy discussions but focuses on a different aspect; may require additional context for full relevance"
+            },
+            {
+              "candidate": null,
+              "summary": "Unidentified candidate or missing in the provided list",
+              "relevanceScore": 0
+            }
+          ]
+        }
+      }
+    },
+    {
+      "card_id": "project-clustering",
+      "title": "Project Clustering From Voice History",
+      "tier": "local-first",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 8140.09,
+      "raw_text": "<think>\nOkay, let's tackle this problem. The user wants me to process some memos and cluster them into project threads with specific requirements. The date range is from 2026-04-01 to 2026-04-03, and there are three memos provided. \n\nFirst, I need to understand the structure they want. The output should be a JSON array of clusters, each containing clusterId, title, and memoIds. Also, any memo with confidence below 0.6 should be labeled as \"misc\". However, the user didn't provide confidence values for these memos, so I have to assume they're all above 0.6 unless stated otherwise. But since the instruction says to create \"misc\" only when confidence is below 0.6, and there's no confidence data here, maybe I should proceed without marking any as \"misc\".\n\nNext, clustering. The user mentioned clustering memos into project threads. But the example memos are different types: two are error-related (crash and retry) and one is informational (podcast notes). How should I group them? The user didn't specify the clustering logic, so perhaps each unique summary goes into its own cluster. That makes sense because each summary seems distinct",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "a1 and a2 share a clusterId",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "b1 may be separate or misc",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "next-step-suggestion-engine",
+      "title": "Next-Step Suggestion Engine",
+      "tier": "local-first",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 7928.01,
+      "raw_text": "<think>\nOkay, let's tackle this query. The user provided a transcript: \"Tomorrow 9 AM send invoice to Alex and set follow-up reminder for Friday.\" The available actions are [\"summarize\", \"extract-tasks\", \"schedule\", \"draft-note\", \"ask-clarifying-question\"]. I need to pick exactly one action.\n\nFirst, the user wants to take care of two tasks tomorrow: sending an invoice and setting a follow-up. But the system requires choosing one action. The key here is to see which action would best address the main points. \n\n\"Summarize\" could summarize the tasks, but maybe not directly perform the actions. \"Extract-tasks\" might parse the transcript, but the user hasn't provided text to extract yet. \"Schedule\" might be about creating a calendar entry, but the transcript already mentions setting a reminder, which might be scheduling. \"Draft-note\" is for writing notes, which isn't directly needed here. \"Ask-clarifying-question\" seems less relevant unless there's a need for clarification, which isn't indicated.\n\nThe user's main goal is to take action on the transcript. Since the transcript mentions setting a follow-up reminder, maybe \"schedule\" or \"extract-tasks\". However, \"extract",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "action is either \"extract-tasks\" or \"schedule\"",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "reason references explicit deadline",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "follow-up-question-generator",
+      "title": "Follow-Up Question Generator",
+      "tier": "local-first",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 1315.55,
+      "raw_text": "{\n  \"questions\": [\n    {\n      \"question\": \"Could a more comprehensive onboarding process for [Product Name] help new users become more productive faster and engaged?\"\n\"\n    }\n  ]\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting ',' delimiter: line 5 column 1 (char 168)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "questions length equals 1",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "question ends with ?",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "hybrid-model-router",
+      "title": "Hybrid Local/Cloud Router",
+      "tier": "local-first",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 7877.74,
+      "raw_text": "<think>\nOkay, let's tackle this problem. The user wants me to act as a model router and return JSON metadata. The instructions mention preferring local when the quality risk is acceptable and prioritizing local connections if the privacy level is high, unless it's impossible.\n\nFirst, I need to understand the request for metadata. The user hasn't provided specific details like privacyLevel or quality risk factors. Without that info, I might have to make some assumptions. But since the user hasn't given those, maybe the metadata should be a generic template or note that required fields are missing.\n\nWait, the user might expect a default response since they didn't provide the necessary parameters. In that case, the JSON should indicate that metadata is incomplete. Alternatively, maybe they want a sample structure. But the example response they provided earlier was a JSON with fields like privacyLevel, localWinCondition, etc., even when those weren't explicitly stated. So perhaps I should follow that structure, using placeholders or default values when information is missing.\n\nLooking back at the previous example, when the user asked for metadata without specifics, the assistant returned a JSON with fields filled but some marked as \"N/A\". So I should do the same here. The metadata includes privacyLevel, localWinCondition, remote",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "route equals \"cloud\"",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "reason mentions long context",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "context-packet-builder",
+      "title": "Context Packet Builder",
+      "tier": "local-first",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 7950.37,
+      "raw_text": "<think>\nOkay, let's tackle this query. The user wants me to build context packets with a token budget of 120. The current transcript mentions needing to plan hiring, launch, docs, support, and pricing, along with analytics. There are no related memos or open tasks listed.\n\nFirst, I need to understand what \"context packets\" refer to here. It might mean generating structured responses or messages based on the given context. Since the token budget is 120, I should count the tokens used in my response to stay within limit.\n\nThe user prioritizes decisions, active tasks, and unresolved questions. So, I should structure the response to address each of these areas as per the context. The main tasks are planning, launching, creating docs, support, and analytics. I need to outline steps for each, maybe categorize them based on priority.\n\nLet me start by addressing the planning task first, as that's often the highest priority. Then move to docs and support, which are maintenance tasks. Pricing and analytics might be mid or lower priority. Unresolved questions would need a separate section for follow-up.\n\nI should check the token count. Each sentence or bullet point uses tokens. Let me draft a sample response and count the tokens. For example, \"",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "tokenEstimate <= 120",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "packet has at least one decision or openQuestion field",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "conversation-to-checklist",
+      "title": "Conversation To Checklist Transformer",
+      "tier": "local-first",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 4107.53,
+      "raw_text": "{\n  \"items\": [\n    {\n      \"id\": \"1\",\n      \"text\": \"First export data.\",\n      \"order\": \"1\",\n      \"dependsOn\": \"\",\n      \"owner\": \"Arach\"\n    },\n    {\n      \"id\": \"2\",\n      \"text\": \"Then verify CSV.\",\n      \"order\": \"2\",\n      \"dependsOn\": \"\",\n      \"owner\": \"Arach\"\n    },\n    {\n      \"id\": \"3\",\n      \"text\": \"Finally email finance.\",\n      \"order\": \"3\",\n      \"dependsOn\": \"\",\n      \"owner\": \"Arach\"\n    }\n  ]\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": null,
+        "parse_mode": "exact",
+        "format_recovered": false,
+        "failure_kind": "task",
+        "assertions": [
+          {
+            "assertion": "order values are 1,2,3",
+            "passed": false,
+            "details": "orders=['1', '2', '3']"
+          },
+          {
+            "assertion": "item 2 depends on item 1",
+            "passed": false,
+            "details": "dependsOn=[]"
+          },
+          {
+            "assertion": "item 3 depends on item 2 or item 1",
+            "passed": false,
+            "details": "dependsOn=[]"
+          }
+        ],
+        "parsed": {
+          "items": [
+            {
+              "id": "1",
+              "text": "First export data.",
+              "order": "1",
+              "dependsOn": "",
+              "owner": "Arach"
+            },
+            {
+              "id": "2",
+              "text": "Then verify CSV.",
+              "order": "2",
+              "dependsOn": "",
+              "owner": "Arach"
+            },
+            {
+              "id": "3",
+              "text": "Finally email finance.",
+              "order": "3",
+              "dependsOn": "",
+              "owner": "Arach"
+            }
+          ]
+        }
+      }
+    },
+    {
+      "card_id": "personal-knowledge-graph",
+      "title": "Personal Knowledge Graph From Voice",
+      "tier": "big-swings",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 7927.4,
+      "raw_text": "<think>\nOkay, let's tackle this problem. The user wants me to create a knowledge graph based on the provided memos. The nodes allowed are person, project, decision, question, artifact. Edges need type, source and target node IDs, and a memo ID.\n\nFirst, I need to understand the memos given. There are two memos:\n\nMemo \"a\" is a conversation about talking to Chris about pricing. Memo \"b\" is Chris confirming the pricing test.\n\nSo, the main entities here are \"Chris\" (a person), the action of talking to him (which could be a decision or a question?), and the pricing topic, which might be a project or an artifact. \n\nFor memo \"a\": The transcript is \"Talk to Chris about pricing.\" So the action here is a conversation. The source is the speaker, but since the memo is about talking to Chris, maybe the source is a node that doesn't exist yet. Wait, the nodes allowed are person, project, decision, question, artifact. There's no \"speaker\" node. Hmm. Maybe the action of talking is a decision or a question. Alternatively, perhaps the user initiated a conversation, which could be a decision. But since the memo is about discussing pricing",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "only one node labeled Chris",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "edges reference merged Chris node id",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "catch-me-up-daily-brief",
+      "title": "Catch Me Up Daily Brief",
+      "tier": "big-swings",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 1709.71,
+      "raw_text": "{\n  \"memoId\": \"x\",\n  \"summary\": \"Mostly planning chatter.\",\n  \"wins\": [],\n  \"risks\": [\n    \"No active tasks to address \"\n  ],\n  \"nextActions\": [\n    \"Finalize budget\"\n  ]\n}",
+      "grade": {
+        "passed": true,
+        "score": 1.0,
+        "parse_error": null,
+        "parse_mode": "exact",
+        "format_recovered": false,
+        "failure_kind": null,
+        "assertions": [
+          {
+            "assertion": "wins section exists",
+            "passed": true,
+            "details": "wins=[]"
+          },
+          {
+            "assertion": "risks section exists",
+            "passed": true,
+            "details": "risks=['No active tasks to address ']"
+          },
+          {
+            "assertion": "nextActions section exists",
+            "passed": true,
+            "details": "nextActions=['Finalize budget']"
+          }
+        ],
+        "parsed": {
+          "memoId": "x",
+          "summary": "Mostly planning chatter.",
+          "wins": [],
+          "risks": [
+            "No active tasks to address "
+          ],
+          "nextActions": [
+            "Finalize budget"
+          ]
+        }
+      }
+    },
+    {
+      "card_id": "contradiction-drift-detection",
+      "title": "Contradiction And Drift Detection",
+      "tier": "big-swings",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 7959.31,
+      "raw_text": "<think>\nOkay, let's tackle this problem. The user provided a new memo that changes the quarterly review date from June 20 to June 2. The prior decision was to fix the date to June 20, 2026. So, there's a conflict here because the new memo is altering that date.\n\nFirst, I need to identify the conflicting elements. The memoId is \"new_test_01\". The transcript shows the new date change, and the prior decision has the decisionId \"d9\" with the original date. The severity of the conflict is probably high since it's changing a decided value.\n\nI should check if the dates are valid. June 2 is a valid date, and June 20 is also valid. The conflict is in the date of the quarterly review. The explanation should mention that the new date overrides the previous one. Since the prior decision was to fix it to June 20, but now it's being changed to June 2, that's the conflict.\n\nI need to make sure the JSON structure includes the severity, conflictingDecisionId, and explanation. The severity could be \"HIGH\" because it's overriding a previous decision. The conflictingDecisionId refers to the prior decision \"d9\". The explanation should state that",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "conflicts length >= 1",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "first conflict severity equals high",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "momentum-scoring",
+      "title": "Momentum Scoring",
+      "tier": "big-swings",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 522.73,
+      "raw_text": "{\n  \"score\": 0,\n  \"completions\": []\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": null,
+        "parse_mode": "exact",
+        "format_recovered": false,
+        "failure_kind": "task",
+        "assertions": [
+          {
+            "assertion": "momentumScore < 50",
+            "passed": false,
+            "details": "momentumScore=None"
+          },
+          {
+            "assertion": "unresolvedHotspots includes \"Fix onboarding bug\"",
+            "passed": false,
+            "details": "unresolvedHotspots=[]"
+          }
+        ],
+        "parsed": {
+          "score": 0,
+          "completions": []
+        }
+      }
+    },
+    {
+      "card_id": "local-agent-loop",
+      "title": "Local Agent Loop For Memo Processing",
+      "tier": "big-swings",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 7642.13,
+      "raw_text": "{\n  \"stepOutputs\": [\n    {\n      \"step\": \"classify\",\n      \"output\": \"Text provided for classification. Please classify the content into a category such as 'Uncertainty' or any other relevant category.\",\n      \"nextStep\": \"extract\"\n    },\n    {\n      \"step\": \"extract\",\n      \"output\": \"N/A (input was not suitable for extraction based on current configuration.\",\n      \"nextStep\": \"summarize\"\n    },\n    {\n      \"step\": \"summarize\",\n      \"output\": \"N/A (input is too short to generate a summary at this time. Please provide a longer text for summarization.\",\n      \"nextStep\": \"route\"\n    },\n    {\n      \"step\": \"route\",\n      \"output\": \"Route planning requires more context. A route plan cannot be created without specific details about the proposed movement or destination.\",\n      \"nextStep\": \"verify\"\n    },\n    {\n      \"step\": \"verify\",\n      \"output\": \"Verification completed. No errors detected in the memo pipeline. Final status is set to 'completed'.\",\n      \"nextStep\": null\n    }\n  ],\n  \"finalStatus\": \"completed\"\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": null,
+        "parse_mode": "exact",
+        "format_recovered": false,
+        "failure_kind": "task",
+        "assertions": [
+          {
+            "assertion": "verify.passed can be false for vague memo",
+            "passed": false,
+            "details": "verify.passed=None"
+          },
+          {
+            "assertion": "finalStatus equals \"needs-review\" when verify fails",
+            "passed": false,
+            "details": "verify=None status='completed'"
+          }
+        ],
+        "parsed": {
+          "stepOutputs": [
+            {
+              "step": "classify",
+              "output": "Text provided for classification. Please classify the content into a category such as 'Uncertainty' or any other relevant category.",
+              "nextStep": "extract"
+            },
+            {
+              "step": "extract",
+              "output": "N/A (input was not suitable for extraction based on current configuration.",
+              "nextStep": "summarize"
+            },
+            {
+              "step": "summarize",
+              "output": "N/A (input is too short to generate a summary at this time. Please provide a longer text for summarization.",
+              "nextStep": "route"
+            },
+            {
+              "step": "route",
+              "output": "Route planning requires more context. A route plan cannot be created without specific details about the proposed movement or destination.",
+              "nextStep": "verify"
+            },
+            {
+              "step": "verify",
+              "output": "Verification completed. No errors detected in the memo pipeline. Final status is set to 'completed'.",
+              "nextStep": null
+            }
+          ],
+          "finalStatus": "completed"
+        }
+      }
+    },
+    {
+      "card_id": "meeting-live-structure",
+      "title": "Meeting Mode Live Rolling Structure",
+      "tier": "big-swings",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 2390.75,
+      "raw_text": "```json\n{\n  \"agenda\": [],\n  \"decisions\": [\n    {\n      \"text\": \"Keep beta closed for now.\",\n      \"chunkIndex\": 2\n    }\n  ],\n  \"openQuestions\": [],\n  \"actionItems\": [\n    {\n      \"text\": \"N/A\",\n      \"chunkIndex\": \"N/A\"\n    }\n  ]\n}\n```",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": null,
+        "parse_mode": "normalized",
+        "format_recovered": true,
+        "failure_kind": "schema-or-task",
+        "assertions": [
+          {
+            "assertion": "updatedState.decisions length stays 1",
+            "passed": false,
+            "details": "decisions=[]"
+          },
+          {
+            "assertion": "existing decision remains unchanged",
+            "passed": false,
+            "details": "decisions='[]'"
+          }
+        ],
+        "parsed": {
+          "agenda": [],
+          "decisions": [
+            {
+              "text": "Keep beta closed for now.",
+              "chunkIndex": 2
+            }
+          ],
+          "openQuestions": [],
+          "actionItems": [
+            {
+              "text": "N/A",
+              "chunkIndex": "N/A"
+            }
+          ]
+        }
+      }
+    },
+    {
+      "card_id": "intent-trend-alerts",
+      "title": "Intent Trend Alerts",
+      "tier": "big-swings",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 8104.3,
+      "raw_text": "<think>\nOkay, let's break down the user's query and the provided information. The user wants a response that includes only the JSON and an alert when specific conditions are met. \n\nFirst, I need to check if the conditions are satisfied. The lookback period is 30 days, and the intent series in question is \"Refactor UI\" with mentionCount=2 and completionCount=0. The threshold requires a minimum of 4 mentions and a maximum of 0 completions.\n\nSince mentionCount (2) is below the threshold's minMentions (4), the condition isn't met. CompletionCount (0) is within the allowed maxCompletions (0), so that's okay. However, because the mention count is too low, no alert should be triggered. \n\nThe user also asked for one suggested intervention per alert, but since there's no alert generated, maybe they want a preventive suggestion instead. But the instruction was to include an alert only when the threshold is met. So, in this case, the correct response is to return the JSON without an alert because the criteria aren't met. \n\nI need to make sure I only provide the JSON as specified and avoid any additional text. The user emphasized returning JSON only when there's an",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting value: line 1 column 1 (char 0)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "alerts length equals 0",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    },
+    {
+      "card_id": "voice-os-command-layer",
+      "title": "Voice-Based Operating Layer",
+      "tier": "big-swings",
+      "provider": "hf-transformers",
+      "model": "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1",
+      "latency_ms": 4616.15,
+      "raw_text": "{\n  \"steps\": [\n    {\n      \"app\": \"finder\",\n      \"action\": \"find\",\n      \"args\": {\n        \"old_exports\": {}\n      },\n      \"requiresConfirmation\": true\n    },\n    {\n      \"app\": \"finder\",\n      \"action\": \"delete\",\n      \"args\": {\n        \"path\": \"/old_exports\",\n        \"confirm\": true\n      },\n      {\n        \"requiresConfirmation\": true\n      }\n    },\n    {\n      \"app\": \"finder\",\n      \"action\": \"emptyTrash\",\n      \"args\": {}\n    },\n    {\n      \"app\": \"finder\",\n      \"action\": \"trash\",\n      \"args\": {}\n    }\n  ]\n}",
+      "grade": {
+        "passed": false,
+        "score": 0.0,
+        "parse_error": "Expecting property name enclosed in double quotes: line 18 column 7 (char 303)",
+        "parse_mode": "failed",
+        "format_recovered": false,
+        "failure_kind": "parse",
+        "assertions": [
+          {
+            "assertion": "requiresConfirmation equals true",
+            "passed": false,
+            "details": "output was not valid JSON"
+          },
+          {
+            "assertion": "at least one step action is marked destructive",
+            "passed": false,
+            "details": "output was not valid JSON"
+          }
+        ],
+        "parsed": null
+      }
+    }
+  ]
+}