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

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:

[
  {
    "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 (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

@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},
}