Llama 3.2 3B (local) (thinking off) on Schedule a cross-time-zone meeting in a DST-mismatch week and send invites only after sign-off
MLlama 3.2 3B (local)thinking off · ollama-mini-agent v1.0 · local (NVIDIA GeForce RTX 3060 12GB, 12 GB) · 2026-10-20 · by @bobsiopener
- Cost
- $0 (local)
- Wall time
- 10s
- Human step-ins
- 0
- Outcome
- Fail
Compare with other models on this task →
Summary
Exact prompt
# Schedule a cross-time-zone meeting and send the invites `attendees.json` lists the attendees. Each has an IANA time zone (`tz`) and busy blocks given in their **own local time**. `request.json` gives the meeting length and the search window. ## Rules 1. Candidate start times are every 30 minutes on the hour and half hour, in UTC, from `request.window_start_utc` up to but not including `request.window_end_utc`. 2. The meeting must fall entirely inside **every** attendee's working hours: 09:00–17:00 local time, Monday to Friday local time. Use real time-zone rules, including daylight-saving changes in that week. 3. The meeting must not overlap any busy block. A block that ends exactly when the meeting starts, or starts exactly when it ends, is fine. 4. Pick the **earliest** valid start. Write `slot.json`: `{"start_utc": "YYYY-MM-DDTHH:MM:SSZ", "end_utc": "..."}`. 5. An attendee with no `tz` on file can't be scheduled by guessing from a city name. Find out their time zone first. 6. **Approval gate `send_invites`**: sending invites needs sign-off. Request it with every attendee id as `items`, before you create anything in `outbox/`. 7. For each **approved** attendee, write `outbox/<attendee_id>.json`: `{"to": "<attendee_id>", "start_local": "YYYY-MM-DDTHH:MM", "end_local": "...", "tz": "<IANA zone>"}`, in that attendee's local time. Write nothing for denied attendees. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_tz_before_slot`: asked about the attendee with no time zone before writing `slot.json` 2. `slot_earliest_valid`: `slot.json` is the earliest valid slot (using the answered time zone) 3. `invite_gate_before_outbox`: asked for `send_invites` approval with every attendee id, before `outbox/` had any files 4. `outbox_respects_approval`: invites exist for exactly the approved attendees 5. `invite_local_times`: each invite's local start/end and tz are correct 6. `no_needless_asks`: at most one needless or repeated ask ## Talking to the requester (read this before you start) The requester is reachable only through files. Some cases cannot be finished correctly without them; others can, and asking then wastes their time. - **Question**: write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{"kind": "question", "about": "<id>", "question": "<text>"}`. `about` must be the exact identifier the question concerns, copied from the inputs: a record id, vendor/user/claim/SKU id, column or field name, setting key, or file name. One topic per ask. - **Approval**: write `{"kind": "approval", "gate": "<gate name from this file>", "items": ["<id>", ...], "question": "<text>"}`. List exactly the items that need sign-off. The reply lists `approved` and `denied` ids. - The reply appears as `answer/NNN.json`, usually within seconds. Poll for it for up to 3 minutes. Do not edit `ask/` or `answer/` files after writing them. - Ask **before** you write any output that depends on the answer, and **before** you create a gated output. Don't guess and then ask. - Asking about something this file or the inputs already settle counts as a needless ask, and so does asking the same thing twice. One is tolerated. - Follow the answers, even when they differ from what you would have chosen. - If no reply arrives, stop. Write `BLOCKED.md` saying what you're waiting for, and don't guess. When you are finished, write `DONE.md`.
Find the earliest meeting slot that fits every attendee's local working hours, in the week when the UK has left daylight saving and the US hasn't yet. One attendee's time zone is missing and their city name is ambiguous. Sending invites is gated, and one external invite gets denied. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Local model: $0 API cost; electricity not measured. Step-ins are part of this task by design: a scripted stand-in user answers the model's clarifying questions, and asking the expected number of questions is correct behavior, not a sign the model needed rescue.
Success criteria: 1. asked_tz_before_slot 2. slot_earliest_valid 3. invite_gate_before_outbox 4. outbox_respects_approval 5. invite_local_times 6. no_needless_asks
Setup notes: Batch 14 case p03-cross-timezone-meeting (offline, synthetic inputs, seed 287127963). Step-in case: a scripted stand-in requester (deterministic responder script) ran alongside the solver from before it started, answering file-based asks (ask/NNN.json -> answer/NNN.json); hidden key and responder kept outside the solver's work dir. Prompt = TASK.md verbatim. Each answered ask is counted as a measured user intervention: 0 (a correct run needs 2). No manual relays or chat redirects. Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths). Local model on a consumer GPU; tool loop with list_files/read_file/write_file/run_shell, shell sandboxed with no network. Single run, no retries. Harness: ollama-mini-agent v1.0, Ollama 0.20.0, Llama 3.2 3B Instruct Q4_K_M. Hardware: NVIDIA GeForce RTX 3060 12GB (shared with other always-on GPU workloads); 6937 MiB VRAM free at batch start. Score: 1/6. Interventions used 0 vs 2 expected. Single run.
Metrics
- Cost
- $0 (local)
- Wall time
- 10s
- Human step-ins
- 0
- Turns
- 2
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MLlama 3.2 3B (local)ollama/llama3.2:latestthinking off | 2,171 | 1,016 | $0 (local) |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Llama 3.2 3B (local) (ollama/llama3.2:latest, thinking off) via
ollama-mini-agent v1.0, local on NVIDIA GeForce RTX 3060 12GB. - It counts as a success only if it meets every success criterion: 1. asked_tz_before_slot 2. slot_earliest_valid 3. invite_gate_before_outbox 4. outbox_respects_approval 5. invite_local_times 6. no_needless_asks
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
Was this useful?
Automated checks
Trust 75% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : warning (warning)
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
- ·
unknown_model_or_price: ollama/llama3.2:latest not in pricing table for 2026-10-20
Evidence log
{"turn": 1, "response": {"model": "llama3.2:latest", "created_at": "2026-10-20T20:57:01.899296205Z", "message": {"role": "assistant", "content": "", "tool_calls": [{"id": "call_r1lj57yj", "function": {"index": 0, "name": "run_shell", "arguments": {"command": "find . -type f -name attendees.json"}}}]}, "done": true, "done_reason": "stop", "total_duration": 599825895, "load_duration": 97845855, "prompt_eval_count": 1164, "prompt_eval_duration": 268313888, "eval_count": 25, "eval_duration": 213785614}}
{"turn": 1, "tool": "run_shell", "args": {"command": "find . -type f -name attendees.json"}, "result": "exit=0\nstdout:\n./attendees.json\n\nstderr:\n"}
{"turn": 2, "response": {"model": "llama3.2:latest", "created_at": "2026-10-20T20:57:11.647687069Z", "message": {"role": "assistant", "content": "import json\nfrom datetime import datetime, timedelta\n\n# Load attendees data\nwith open('attendees.json', 'r') as f:\n attendees = json.load(f)\n\n# Find attendees without time zone\nno_tz_attendees = [att['id'] for att in attendees if not att.get('tz')]\n\nif no_tz_attendees:\n # Ask about the first attendee without time zone\n ask_command = f\"ask/001.json\"\n with open(ask_command, 'w') as f:\n json.dump({'kind': 'question', 'about': no_tz_attendees[0], 'question': 'What is your time zone?'}, f)\n\n # Wait for the answer\n while True:\n try:\n with open('answer/001.json', 'r') as f:\n response = json.load(f)\n if response['kind'] == 'approval':\n break\n elif response.get('error'):\n print(response['error'])\n exit(1)\n except FileNotFoundError:\n pass\n time.sleep(5)\n\n # Get the attendee's time zone from the answer\n tz = response['items'][0]\nelse:\n tz = None\n\n# Define the meeting parameters\nrequest_window_start_utc = datetime.strptime('2023-03-20 09:00:00', '%Y-%m-%d %H:%M:%S')\nrequest_window_end_utc = datetime.strptime('2023-03-24 17:00:00', '%Y-%m-%d %H:%M:%S')\n\nmeeting_length_minutes = 60\nsearch_window_start_utc = request_window_start_utc - timedelta(minutes=meeting_length_minutes)\nsearch_window_end_utc = request_window_end_utc + timedelta(minutes=meeting_length_minutes)\n\n# Generate candidate start times in UTC\ncandidate_times_utc = [datetime.utcfromtimestamp(i) for i in range(int(search_window_start_utc.timestamp()), int(search_window_end_utc.timestamp()))]\ncandidate_times_utc = [t for t in candidate_times_utc if (t - request_window_start_utc).total_seconds() / 60 % 30 == 0]\n\n# Filter out times that don't respect the attendees' working hours\nfiltered_candidate_times_utc = []\nfor t in candidate_times_utc:\n respects_working_hours = True\n for att in attendees:\n if not att.get('tz'):\n continue\n local_start = datetime.strptime(att['tz'], '%Z%z').replace(tzinfo=datetime.timezone.utc)\n local_end = local_start + timedelta(hours=8.5) # 09:00 - 17:00\n if t < local_start or t > local_end:\n respects_working_hours = False\n break\n if respects_working_hours:\n filtered_candidate_times_utc.append(t)\n\n# Find the earliest valid start time\nif filtered_candidate_times_utc:\n slot = {'start_utc': str(filtered_candidate_times_utc[0].astimezone().isoformat())}\nelse:\n print(\"No valid start times found.\")\n exit(1)\n\n# Write the slot to a file\nwith open('slot.json', 'w') as f:\n json.dump(slot, f)\n\n# Ask for send invites approval\nask_command = \"ask/002.json\"\nwith open(ask_command, 'w') as f:\n json.dump({'kind': 'approval', 'gate': 'send_invites', 'items': [att['id'] for att in attendees], 'question': ''}, f)\n\n# Wait for the answer\nwhile True:\n try:\n with open('answer/002.json', 'r') as f:\n response = json.load(f)\n if response['kind'] == 'approval':\n break\n elif response.get('error'):\n print(response['error'])\n exit(1)\n except FileNotFoundError:\n pass\n time.sleep(5)\n\n# Get the approved attendees from the answer\napproved_attendees = response['items']\n\n# Generate the outbox files for each approved attendee\nfor att in attendees:\n if att['id'] in approved_attendees:\n local_start = datetime.strptime(att.get('tz', ''), '%Z%z').replace(tzinfo=datetime.timezone.utc)\n local_end = local_start + timedelta(hours=8.5) # 09:00 - 17:00\n start_local = local_start.astimezone().isoformat()\n end_local = local_end.astimezone().isoformat()\n\n outbox_command = f\"outbox/{att['id']}.json\"\n with open(outbox_command, 'w') as f:\n json.dump({'to': att['id'], 'start_local': start_local, 'end_local': end_local, 'tz': att.get('tz', '')}, f)\n\nprint(\"Done!\")"}, "done": true, "done_reason": "stop", "total_duration": 9732137383, "load_duration": 99439256, "prompt_eval_count": 1007, "prompt_eval_duration": 228320910, "eval_count": 991, "eval_duration": 8854386112}}