Consolidation Upstream NciBusiness & Domain Workflows
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Fable-5claude-code · high
Mean reward
0%All trials
Across 8 trials on this task, reward ran from 0% to 0% — 0 of 8 passed.
- $0.64avg cost
- 2mavg time
Summary · Fable-5
Fable-5 passed none of its 8 trials.
Common pattern
No trial passed. Whatever this task demands, the model misses it every time — a reproducible miss, not variance.
Opus-4.8claude-code
Mean reward
0%All trials
Across 8 trials on this task, reward ran from 0% to 0% — 0 of 8 passed.
- $0.59avg cost
- 3mavg time
Summary · Opus-4.8
Opus-4.8 passed none of its 8 trials.
Common pattern
No trial passed. Whatever this task demands, the model misses it every time — a reproducible miss, not variance.
GPT-5.4codex
Mean reward
25%All trials
Across 8 trials on this task, reward ran from 0% to 100% — 2 of 8 passed.
- 23avg turns
- $0.23avg cost
- 2mavg time
Summary · GPT-5.4
GPT-5.4 passed 2 of 8 trials.
Common pattern
2 of 8 trials passed on identical attempts: the capability is there, the reliability is not.
Gemini-3gemini-cli
Mean reward
12.5%All trials
Across 8 trials on this task, reward ran from 0% to 100% — 1 of 8 passed.
- $0.46avg cost
- 4mavg time
Summary · Gemini-3
Gemini-3 passed 1 of 8 trials.
Common pattern
1 of 8 trials passed on identical attempts: the capability is there, the reliability is not.
Published trajectory
The drop publishes a full step record for one of the trials above — every other attempt shipped its score without its route.
“Wrote the corrected self-contained module to [engine.py](/logs/artifacts/engine.py). The fix separates current-period eliminations from equity attribution: current intercompany sales are removed from `revenue`/`cogs`, ending unrealized profit is deferred in `inventory`, upstream ending profit is split correctly between controlling retained earnings and `nci`, and prior-period deferred profit realized this period is released through `cogs` without being double-counted into ending equity. I also verified the artifact with targeted scenarios covering downstream sales, upstream sales with partial ownership/NCI, mixed-direction sales, and beginning unrealized profits realized in the current period, and the module compiles cleanly.”
The full trajectory
- system
<permissions instructions> Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. </permissions instructions><skills_instructions> ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md) - openai-docs: Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.
- user
<environment_context> <cwd>/app</cwd> <shell>bash</shell> <current_date>2026-07-23</current_date> <timezone>Etc/UTC</timezone> <filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type="disabled"><file_system type="unrestricted" /></permission_profile></filesystem> </environment_context>
- user
# Consolidation Upstream NCI You are given a consolidation accounting engine in `/app/consolidation/engine.py`. The engine combines a parent and subsidiary and attempts to eliminate intercompany inventory sales and unrealized profit. The engine passes the example scenarios in the repository, but it does not produce correct consolidated balances for every valid scenario. The verifier will test it against a broader set of cases, including intercompany inventory sales in both directions, non-controlling interests at various ownership percentages, and prior-period intercompany inventory profits that are realized in the current period. ## Your task Inspect the engine and the scenario format, fix the consolidation logic so that it handles all valid scenarios correctly, and write the corrected, self-contained `engine.py` to `/logs/artifacts/engine.py`. The verifier will import it as a Python module and call `engine.consolidate(scenario)`. ## Scenario format ```python { 'parent': { 'revenue': float, 'cogs': float, 'inventory': float, 'retained_earnings': float, }, 'subsidiary': { 'revenue': float, 'cogs': float, 'inventory': float, 'retained_earnings': float, }, 'ownership': float, # 0.0 to 1.0 'intercompany_sales': [ { 'direction': 'downstream' | 'upstream', 'sale_amount': float, 'gross_profit': float, 'remaining_in_inventory': float, # 0.0 to 1.0 }, ... ], 'beginning_unrealized_profits': [ # optional { 'direction': 'downstream' | 'upstream', 'amount': float, # profit deferred in prior periods and realized now }, ... ], } ``` ## Return value `consolidate(scenario)` must return a dict with exactly these keys: ```python { 'retained_earnings': float, 'nci': float, 'inventory': float, 'revenue': float, 'cogs': float, } ``` All
- agent
I’m inspecting the consolidation engine and surrounding scenarios first so I can pin down the current elimination logic and the expected scenario shapes before changing anything.
- agent
Executed exec_command call_A7HGSUbciGx607V4HlT8etCF
- agent
Executed exec_command call_mxQPbVhwZmnTdi64bfeAiES9
- agent
Executed exec_command call_SODTzVhd6xFWLn5kMS1W7a21
- agent
I have the current engine. Next I’m checking the bundled scenarios to see what assumptions the existing code was written against and where it is too narrow.
- agent
Executed exec_command call_od8rf3imINs15puIf6zMlezR
- agent
Executed exec_command call_yo31poF2KCaJC52EfLD8rmZu
- agent
The shipped scenarios only cover the easy cases. I’m searching for any other accounting assumptions in the repo before I rewrite the elimination formulas.
- agent
Executed exec_command call_bqFrhVwXiW4VB95bsvbJnhq0
- agent
Executed exec_command call_vvxXzVEePM7JYlpgJJuf24s1
- agent
I’ve narrowed the core bug: upstream ending profit is being charged entirely to controlling retained earnings instead of being split between controlling interest and NCI, and beginning-profit realizations are almost certainly being pushed into ending equity when they should only affect current-period `cogs`. Plan: 1. Rewrite the consolidation logic around explicit seller-direction allocations. 2. Keep revenue, COGS, and inventory eliminations separate from equity attribution so ending unrealized profit and beginning realized profit are handled cleanly. 3. Verify with targeted scenarios: downstream, upstream with partial NCI, mixed-direction sales, and prior-period realizations.
- agent
I’m writing the corrected module to `/logs/artifacts/engine.py` now, keeping it self-contained so the verifier can import it directly.
- agent
Executed exec_command call_E4C0jLxf0OIQnDrFqEGNfqSb
- agent
Executed apply_patch call_yqPqJOMmNmBmmuXK1FIEC5tE
- agent
The rewritten module is in place. I’m running targeted checks against edge cases the original code mishandled, especially upstream sales with NCI and prior-period profit releases.
- agent
Executed exec_command call_SmXAhbYg5bf8YwAevb0AkXos
- agent
The targeted cases behave as expected now: current upstream profit is split correctly between controlling interest and NCI, and prior-period deferred profit flows through `cogs` without being double-counted into ending equity.
- agent
Executed exec_command call_2D4Q59sP6s8c8CKr66nswY5B
- agent
Executed exec_command call_4dvV1RaGawZELT98090S4gYC
- agent
Wrote the corrected self-contained module to [engine.py](/logs/artifacts/engine.py). The fix separates current-period eliminations from equity attribution: current intercompany sales are removed from `revenue`/`cogs`, ending unrealized profit is deferred in `inventory`, upstream ending profit is split correctly between controlling retained earnings and `nci`, and prior-period deferred profit realized this period is released through `cogs` without being double-counted into ending equity. I also verified the artifact with targeted scenarios covering downstream sales, upstream sales with partial ownership/NCI, mixed-direction sales, and beginning unrealized profits realized in the current period, and the module compiles cleanly.