---
license: mit
language:
- en
tags:
- mechanistic-interpretability
- circuit-analysis
- transformers
- benchmark
pretty_name: AgenticInterpBench
size_categories:
- n<1K
---

# AgenticInterpBench
 
A benchmark for evaluating language-model agents on **transformer circuit explanation**. Given an already-localized circuit, the agent must recover what each component does: a functional role tag from a 5-class taxonomy, a task-specific natural-language note, and a derived description of the overall task.

The benchmark has **84 semi-synthetic circuits with 163 annotated components**, plus a manually annotated real-model circuit (three-operand addition in Llama-3-8B). It was introduced in *Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?* and is used to evaluate the HyVE (Hypothesize, Validate, Explain) agent framework.
 
## Contents
 
```text
annotations/case_{id}.json # 84 (task specifications, I/O examples, gold component roles)
real_circuits/llama3_abc_annotations.json # The AF1 Circuit / Llama-3-8B reference annotation (10 components)
```
 
Each `annotations/case_{id}.json` carries the task summary, up to five input–output examples, the originating RASP program, the target model, and the localized components. Every component has an `id`, a TransformerLens `hook`, and a gold `role` (`tag` + task-specific `note`), following the running `frac_prevs` example:
 
```json
[
  {
    "id": "L0_MLP",
    "hook": "blocks.0.mlp.hook_post",
    "role": {
      "tag": "INDICATOR",
      "note": "Computes per-position feature indicating whether the token at that position is 'x' or not."
    },
    "labels": ["is_x_3"]
  },
  {
    "id": "L1H2_ATTN",
    "hook": "blocks.1.attn.hook_result[2]",
    "role": {
      "tag": "AGGREGATOR",
      "note": "Aggregates prefix fraction by attending over previous positions."
    },
    "labels": ["frac_prevs_1"]
  }
]
```
 
`real_circuits/llama3_abc_annotations.json` holds reference roles for the 10-component AF1 circuit (Mamidanna et al., 2025), with per-component notes, and supporting findings.
 
## Role taxonomy
 
| Tag | Description |
|---|---|
| INDICATOR | Detects a property of the current token and emits a binary/predicate signal. |
| AGGREGATOR | Summarizes selected positions into a count, fraction, or accumulated quantity. |
| ROUTER | Moves content between positions via positional or index-based selection. |
| MAPPER | Transforms each position independently into a non-binary output. |
| COMBINER | Fuses two or more upstream signals into one output. |
 
## Statistics
| | |
|---|---|
| Circuits | 84 |
| Annotated components | 163 |
| MLP / attention components | 120 / 43 |
| Components per circuit (avg / min / max) | 1.94 / 1 / 10 |
| Tags (MAPPER / COMBINER / AGGREGATOR / INDICATOR / ROUTER) | 72 / 33 / 32 / 15 / 11 |
 
## Provenance & license
 
The annotation layer here is the authors' own work, released under the **MIT License**. The synthetic tasks and circuits derive from [InterpBench](https://huggingface.co/cybershiptrooper/InterpBench) (Gupta et al., 2025), which retrains Tracr-compiled RASP programs; the two IOI tasks are excluded. Model weights live in the InterpBench repo and are loaded separately at runtime by HyVE.
 
## Citation
 
```bibtex
@misc{khan2026languagemodelagentshelpful,
      title={Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?},
      author={Ayan Antik Khan and Harsh Kohli and Yuekun Yao and Huan Sun and Ziyu Yao},
      year={2026},
      eprint={2606.24026},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2606.24026},
}
```