ANAN EVALUATION · EVIDENCE REPORT

planning.allocate-workshops-002

Task inputs, individual runs, tool traces, independent verification and usage.

Evidence snapshot ·

planning.allocate-workshops-002files · v0.2Strict passes 1/1 submitted · not submitted 0Passed1 records+

Capability: Joint constrained optimization
Goal: Find a feasible optimal allocation; retain historical unit ambiguity
Independent verification: Independent enumeration of feasible optima and output constraints

User task

Read brief.md and all six CSVs under inputs/ in your local phone workspace and allocate every workshop under the stated constraints and optimization priorities. Save schedule.json as one object with exactly the keys schedule, completion_time, total_cost. schedule is an array containing exactly one entry per workshop, in any order. Each entry has exactly workshop_id, candidate_id, room_id, start, end; times must be offset-bearing RFC3339 timestamps and agree with the selected candidate and duration. completion_time is the final workshop end as an offset-bearing RFC3339 timestamp, and total_cost is the amount in CNY yuan, expressed as a JSON number or decimal string with at most two fractional digits. For example, CNY 12.50 is 12.50 or "12.50", never 1250. Do not encode an integer count of cents. Respect the stated budget, participant/room capacity, availability, occupied intervals, 15-minute gaps and dependencies. Choose the earliest program completion, then the lowest cost at that completion; any tied optimum is accepted. Preserve all existing files and directories. Create only schedule.json, with no scratch files or directories. Do not book meetings or perform external actions.

Resources and protocol

ios.workspace.files · state-verification

Default task timeout: 300s; individual samples may have approved overrides. Counts include only matching task hashes. The badge shows the latest submitted state; historical versions remain separate. Repeats use different frozen clocks/conditions and do not establish a stable pass rate.

  • brief.md724 bytes
  • inputs/workshops.csv182 bytes
  • inputs/candidates.csv643 bytes
  • inputs/rooms.csv66 bytes
  • inputs/availability.csv181 bytes
  • inputs/occupied.csv134 bytes
  • inputs/dependencies.csv64 bytes
  • protected/keep.txt48 bytes
sample-c1a589953e04387aPassed83,499 tokens
Version 0.2 · commit ec6bb29448b2 · timeout 300s · yoloSubmitted · execution Completed · 257.94 s
Independent goal ✓ PassedConfiguration Unknown · ledger attribution Unknown

Tool trace saved

Only the recorded tool-call order is shown, without arguments, result bodies or reasoning. This is not a full reasoning trace.

  1. 01ListFilessuccess
  2. 02ReadFilesuccess
  3. 03ListFilessuccess
  4. 04ListFilessuccess
  5. 05ReadFilesuccess
  6. 06ReadFilesuccess
  7. 07ReadFilesuccess
  8. 08ReadFilesuccess
  9. 09ReadFilesuccess
  10. 10ReadFilesuccess
  11. 11ReadFilesuccess
  12. 12WriteFilesuccess
  13. 13ListFilessuccess

Independent verification 16 assertions

  • ✓ Passedbaseline_fixture_matches
  • ✓ Passedworkspace_identity_unchanged
  • ✓ Passedsource_files_unchanged
  • ✓ Passedno_extra_paths
  • ✓ Passedno_unsafe_paths
  • ✓ Passedoutput_files_present
  • ✓ Passedschedule_format_valid
  • ✓ Passedworkshops_complete
  • ✓ Passedcandidate_times_correct
  • ✓ Passedavailability_capacity_correct
  • ✓ Passedresource_conflicts_absent
  • ✓ Passedprerequisites_satisfied
  • ✓ Passedbudget_satisfied
  • ✓ Passedschedule_totals_consistent
  • ✓ Passedearliest_completion
  • ✓ Passedlowest_cost_at_earliest

Usage Saved Session responses (some requests may be missing)

Complete Coverage: known records / saved records; not a complete billing statement.

MetrictokensCoverage
Total input72,2427/7
Of which cached input68,9927/7
Uncached input3,2507/7
Output11,2577/7
Input + output83,4997/7

Model requests — · saved responses 7
Preparation / continuation requests —/—. Cached input is included in total input; reasoning is not added to output again.

Resource cleanup Done App reset Done Simulator reset Unknown Shutdown Done
Source identity and evidence hashes
Run started
2026-10-12T13:28:19.730983+00:00
task
d9dbce7247ba4908ae6abb6f9f832da0377149dd34011cfd1d29a0f324dde03a
binary
2296cead77ac77274cff68e9234f903a3af4cee768d2b97d8b42f16928dc72c9
Eval execution
None
cohort
None
model
{'provider': 'DeepSeek', 'model': 'deepseek-flash', 'thinking': 'not_recorded'}
manifest
7c72a7a240ee1a988dd968278e65329820568eaa98ef1a73b5ea7d925e3b4d17
result
f6ec7de0282f13c961317e84be98de82b7d60b0702a7e5f9c8caeb8159ef748a
run
a9f0534bd2ccf5abf5f3c272ab7019711bb0daf0f44323bc080eb637dc3fcd0f
evidence
6f2e981c68e442f4a20ce5af501cf47a894ba6af46792698d9844a3368a48bbc
usage
5da2befa84cea4e772be9cc3969b52b19ba74d57becedb18d0c1b95f15143d77

Hashes identify private source materials. Raw Sessions are not included; hashes alone cannot reproduce business scoring.

Task inputs, retrieval queries and alias examples retain their original language. Downloads preserve the original evaluation data.