README.md

---
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - code-generation
  - tool-use
  - agent
  - knapsack
  - synthetic-data
  - runtime-semantics
pretty_name: "Agents Learn Their Runtime - Evaluation Task Instances"
size_categories:
  - n<1K
configs:
  - config_name: tasks
    default: true
    data_files:
      - split: easy
        path: easy/knapsack/*.json
      - split: hard
        path: hard/knapsack/*.json
---

# Agents Learn Their Runtime -- Task Definitions

Paper: [Agents Learn Their Runtime: Interpreter Persistence as Training-Time Semantics](https://arxiv.org/abs/2603.01209)

200 procedurally generated **Opaque Knapsack** tasks, split into 100 Easy and 100 Hard instances. These are the shared evaluation problems solved by all models in the paper's experiments.

## The Opaque Knapsack Task

A partially observable constrained optimization problem. An agent is given a set of items identified only by opaque IDs and must:

1. Call `inspect(item_id)` to reveal an item's weight, value, and class (costs one unit of a limited inspection budget)
2. Call `take_item(item_id)` to select items, maximizing total value without exceeding a weight capacity
3. Respect class-validity constraints (only certain item classes are allowed)

Item properties are hidden behind random IDs, so the task is unsolvable by memorization. The agent must track running weight totals, budget usage, and candidate rankings across multiple steps.

## Related Datasets

| Dataset | What it contains |
|---|---|
| **This dataset** | Task definitions (the problems) |
| [Training traces](https://huggingface.co/datasets/AutomatedScientist/agents-learn-runtime-train) | 2,000 teacher solutions by **Gemini 3 Flash** (1K persistent + 1K stateless), used to fine-tune LoRA adapters |
| [Benchmark traces](https://huggingface.co/datasets/AutomatedScientist/agents-learn-runtime-benchmarks) | 1,200 inference traces from **Qwen3-8B** (base + 2 LoRA adapters) solving these exact tasks across 12 conditions |

## Structure

```
tasks/
├── easy/knapsack/
│   └── knapsack-0000000000.json ... knapsack-0000000099.json
└── hard/knapsack/
    └── knapsack-0000000000.json ... knapsack-0000000099.json
```

## File Schema

Each JSON file fully specifies a single task instance:

```json
{
  "task_id":   "unique identifier",
  "family":    "knapsack",
  "seed":      12345,
  "difficulty": {
    "n_items":             36,
    "capacity":            34,
    "budget_coverage":     0.58,
    "p_valid":             0.2,
    "optimal_set_size":    3,
    "max_item_dominance":  0.38
  },
  "public":    { "capacity": 34, "budget": 21, "valid_classes": ["A", "C"] },
  "private":   { "items": { "item_abc123": {"weight": 5, "value": 12, "class": "A"} } },
  "reference": { "optimal_value": 47, "optimal_items": ["item_abc123", "..."] },
  "nl":        { "title": "...", "instructions": "...", "output_format": "..." }
}
```

| Field | Description |
|---|---|
| `public` | Parameters revealed to the agent (capacity, budget, valid classes) |
| `private` | Ground-truth item properties, hidden behind `inspect()` at runtime |
| `reference` | Optimal solution for scoring |
| `nl` | Natural-language prompt given to the agent |
| `difficulty` | Generation parameters controlling problem hardness |

## Reproduction

Tasks are generated via `make tasks` in the [source repo](https://github.com/mrcabbage972/agents-learn-runtime), which calls `pythonformer.cli` with config files from `pythonformer/task_configs/`.

## License

Apache License 2.0

## Citation

```bibtex
@article{may2026agents,
  title={Agents Learn Their Runtime: Interpreter Persistence as Training-Time Semantics},
  author={May, Victor and Salgarkar, Aaditya and Wang, Yishan and Misra, Diganta and Nguyen, Huu},
  journal={arXiv preprint arXiv:2603.01209},
  year={2026}
}
```