---
pretty_name: "SignalDepth E15 Context Budget"
license: cc-by-4.0
language:
- en
tags:
- llm-evaluation
- benchmark
- prompt-engineering
- local-llm
- code
- tabular
- datasets
task_categories:
- text-generation
configs:
- config_name: conditions
  default: true
  data_files:
  - split: train
    path: data/e15_conditions.csv
- config_name: models
  data_files:
  - split: train
    path: data/e15_models.csv
- config_name: tasks
  data_files:
  - split: train
    path: data/e15_tasks.csv
- config_name: failure_modes
  data_files:
  - split: train
    path: data/e15_failure_modes.csv
---

# SignalDepth E15 Context Budget

This is a small prompt-sensitivity benchmark slice for separating two explanations that often get conflated:

1. the prompt is too short
2. the task contract is underspecified

The narrow result: on this deterministic Python code-task suite, making sparse prompts longer did not help. Making the task contract explicit did.

## Key Result

| Condition | Average pass rate | Read |
|---|---:|---|
| short_sparse | 0.25 | short and underspecified |
| long_sparse | 0.25 | longer, same missing contract |
| short_dense | 1.00 | short but contract-explicit |
| long_dense | 1.00 | longer and contract-explicit |

Length marginal: `short = 0.625`, `long = 0.625`.

Density marginal: `sparse = 0.25`, `dense = 1.00`.

## Scope

- deterministic Python code tasks
- 4 local-model run archives
- 4 tasks
- 4 prompt conditions
- 192 graded calls
- `k=3`
- temperature `0.0`

This is not a general long-context benchmark, not a model leaderboard, and not evidence about non-code task families.

## Conditions

- `short_sparse`: short prompt, task contract underspecified
- `long_sparse`: longer prompt, same missing task contract
- `short_dense`: short prompt with explicit I/O and edge-condition constraints
- `long_dense`: longer prompt with explicit task contract

Dense here means contract-explicit, not merely wordy.

## Files

- `data/e15_summary.json`: extracted E15 aggregate from SignalDepth public findings
- `data/e15_conditions.csv`: condition-level pass rates
- `data/e15_models.csv`: model-by-condition pass rates
- `data/e15_tasks.csv`: task-by-condition pass rates
- `data/e15_failure_modes.csv`: sparse-prompt failure counts
- `schemas/experiment.schema.json`: public experiment schema
- `docs/context-budget.md`: method/result note
- `docs/methodology.md`: benchmark methodology
- `assets/context-budget.svg`: chart

Dataset Viewer configs are `conditions` (default), `models`, `tasks`, and `failure_modes`. The JSON summary is included as the canonical aggregate object and is not mapped to a viewer table.

## Reproduce Comparable Runs

The public harness is available here:

https://github.com/signaldepth/prompt-sensitivity-bench

Example:

```bash
cd harness
uv run python validate.py e15 --model-name qwen3.5:4b --k 3
```

Fresh runs should be read as replications on the same prompt family, not byte-for-byte replays of the archived local runs.

## Raw Data Boundary

This dataset publishes the derived public E15 summary and reproducibility scaffold. Full raw per-trial archives remain local/private unless curated for a later release.

That boundary is intentional: the artifact is meant to make the condition design, aggregate result, and public harness easy to inspect without implying a full raw-output dump.

## Citation

If you use this result, cite the Hugging Face dataset and the GitHub repository:

- Dataset: `signaldepth/e15-context-budget`
- Code: https://github.com/signaldepth/prompt-sensitivity-bench