---
license: cc-by-4.0
language:
  - en
task_categories:
  - text-classification
tags:
  - multi-agent
  - concurrency
  - inter-annotator-agreement
  - human-evaluation
  - long-horizon-planning
  - benchmark
  - llm-agents
size_categories:
  - n<1K
pretty_name: S-Bus Benchmarks and PH-3 Human Annotations
arxiv: 2605.17076
configs:
  - config_name: long_horizon_tasks
    data_files:
      - split: lhp_15
        path: data/long_horizon_tasks/long_horizon_tasks.json
      - split: multidomain_30
        path: data/long_horizon_tasks/tasks_30_multidomain.json
  - config_name: ph3_human_annotations
    data_files:
      - split: annotator_1
        path: data/ph3_human_annotations/annotator_1.csv
      - split: annotator_2
        path: data/ph3_human_annotations/annotator_2.csv
---

# S-Bus: Long-Horizon Planning Benchmarks and PH-3 Human Annotations

Companion datasets for the paper *S-Bus: Automatic Read-Set Reconstruction for
Multi-Agent LLM State Coordination* (Khan, 2026).

- arXiv: https://arxiv.org/abs/2605.17076
- Code (S-Bus server): https://github.com/sajjadanwar0/sbus
- Code (experiments): https://github.com/sajjadanwar0/sbus-experiments

## Configs

### `long_horizon_tasks`

Long-horizon planning benchmarks used to evaluate S-Bus against
LangGraph, CrewAI, and AutoGen.

| Split | Tasks | Step range | Description |
| --- | ---: | --- | --- |
| `lhp_15` | 15 | 20-40 | Original LHP benchmark across 7 domains: software architecture, security and compliance, data and ML pipelines, codebase refactoring, system design, research synthesis, product and API design. |
| `multidomain_30` | 30 | varies | Extended 30-task multi-domain set. |

```python
from datasets import load_dataset
ds = load_dataset("sajjadanwar0/sbus-benchmarks", "long_horizon_tasks", split="lhp_15")
```

### `ph3_human_annotations`

Two independent human annotators labeled 400 (session, candidate-shard)
pairs sampled from S-Bus evaluation runs. For each pair the annotator
judged whether the agent's action genuinely used the candidate shard,
given what the agent itself claimed in its self-report.

The source 400 tasks are available as `source_tasks.json` in the
repository file tree, along with the annotation web tool
(`annotation_tool/annotator.html`) and protocol (`annotation_tool/GUIDE.md`)
so the study can be reproduced or extended by independent annotators.

#### Schema

| Column | Type | Description |
| --- | --- | --- |
| `row_idx` | int | Zero-indexed row identifier. Shared across both rater files. |
| `candidate_shard` | str | The S-Bus shard whose use is being judged (e.g., `migration_plan`, `models_state`, `view_logic`, `permission_check`, `action_registry`). |
| `human_label` | str | One of `yes`, `no`, `unclear`. The rater's judgment of whether the shard was genuinely used. |
| `agent_said_used_it` | str | `yes` or `no` — what the agent itself claimed in its output. The cue the rater scored against. |

#### Inter-rater agreement

Cohen's κ is reported under two regimes because 307 of 400 rows have at
least one `unclear` label, making a 3-class report and a 2-class
(committed) report tell different stories.

| Regime | n | Cohen's κ | Landis & Koch | Raw agreement |
| --- | ---: | ---: | --- | ---: |
| Strict (yes/no only) | 93 | 0.931 | almost perfect | 96.8% |
| Lenient (3-class with `unclear`) | 400 | 0.695 | substantial | — |

**Strict confusion matrix:**

|              | rater 2: yes | rater 2: no |
| ---          | ---:         | ---:        |
| rater 1: yes | 33           | 0           |
| rater 1: no  | 3            | 57          |

**Interpretation.** When raters commit to a definite yes/no, they
near-perfectly agree (κ=0.931). The substantial volume of `unclear`
labels — 77% of rows had at least one — is itself the headline finding:
attributing shard usage from agent traces is genuinely ambiguous even
for trained humans. This motivates not relying on agent self-report and
is the empirical case for the SCR detection mechanism in S-Bus.

#### Reproducing these figures

The scoring script is bundled in the repository under `scripts/`:

```bash
python3 scripts/score_annotations.py \
    data/ph3_human_annotations/annotator_1.csv \
    data/ph3_human_annotations/annotator_2.csv
```

Or using `datasets` directly:

```python
import pandas as pd
from datasets import load_dataset
from sklearn.metrics import cohen_kappa_score

ds = load_dataset("sajjadanwar0/sbus-benchmarks", "ph3_human_annotations")
a1 = ds["annotator_1"].to_pandas()
a2 = ds["annotator_2"].to_pandas()
m = a1.merge(a2, on=["row_idx", "candidate_shard"], suffixes=("_r1", "_r2"))

# Lenient (3-class with unclear)
print("Lenient kappa:", cohen_kappa_score(m["human_label_r1"], m["human_label_r2"]))

# Strict (yes/no committed only)
strict = m[m["human_label_r1"].isin(["yes", "no"]) &
           m["human_label_r2"].isin(["yes", "no"])]
print("Strict kappa: ", cohen_kappa_score(strict["human_label_r1"], strict["human_label_r2"]))
```

#### Agent self-report validity

Comparing each annotator's labels against `agent_said_used_it` shows
the agents systematically over-claim shard usage:

| Annotator | n | Precision | Recall | Accuracy |
| --- | ---: | ---: | ---: | ---: |
| Rater 1 | 96 | 0.681 | 0.941 | 0.823 |
| Rater 2 | 144 | 0.514 | 0.725 | 0.660 |

When an agent claims to have used a shard, 32-49% of those claims are
disputed by the human raters (precision 0.51-0.68). When raters mark a
shard as truly used, agents catch it 73-94% of the time. This precision
gap is the empirical motivation for transactional shard tracking rather
than trusting agent self-report.

## Citation

```bibtex
@article{khan2026sbus,
  title   = {S-Bus: Automatic Read-Set Reconstruction for Multi-Agent LLM State Coordination},
  author  = {Khan, Sajjad},
  journal = {arXiv preprint arXiv:2605.17076},
  year    = {2026}
}
```

## License

CC-BY-4.0 for all data, scripts, and annotation materials in this repository.

## Repository structure

```
sbus-benchmarks/
├── README.md                    # this file
├── LICENSE                      # CC-BY-4.0
├── data/
│   ├── long_horizon_tasks/
│   │   ├── long_horizon_tasks.json
│   │   └── tasks_30_multidomain.json
│   └── ph3_human_annotations/
│       ├── annotator_1.csv      # rater 1 labels (400 rows)
│       ├── annotator_2.csv      # rater 2 labels (400 rows)
│       └── source_tasks.json    # the 400 (session, shard) pairs annotated
├── annotation_tool/
│   ├── annotator.html           # browser-based annotation UI
│   └── GUIDE.md                 # annotation protocol and rubric
└── scripts/
    └── score_annotations.py     # Cohen's kappa + self-report stats
```

## Contact

Sajjad Khan — https://github.com/sajjadanwar0