MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning

Evaluating models on multistep soft reasoning tasks in the form of free text narratives.

Overview

MuSR is a dataset designed for evaluating models on multistep soft reasoning tasks in the form of free text narratives. It is composed of 3 domains: murder_mysteries, object_placements, and team_allocation, which have 250, 256, and 250 instances respectively in the dataset.

Usage

Installation

There are two ways of using Inspect Evals, from pypi as a dependency of your own project and as a standalone checked out GitHub repository.

If you are using it from pypi, install the package and its dependencies via:

pip install inspect-evals

If you are using Inspect Evals in its repository, start by installing the necessary dependencies with:

uv sync

Running evaluations

Now you can start evaluating models. For simplicity’s sake, this section assumes you are using Inspect Evals from the standalone repo. If that’s not the case and you are not using uv to manage dependencies in your own project, you can use the same commands with uv run dropped.

uv run inspect eval inspect_evals/musr --model openai/gpt-5-nano

You can also import tasks as normal Python objects and run them from python:

from inspect_ai import eval
from inspect_evals.musr import musr
eval(musr)

After running evaluations, you can view their logs using the inspect view command:

uv run inspect view

For VS Code, you can also download Inspect AI extension for viewing logs.

If you don’t want to specify the --model each time you run an evaluation, create a .env configuration file in your working directory that defines the INSPECT_EVAL_MODEL environment variable along with your API key. For example:

INSPECT_EVAL_MODEL=anthropic/claude-opus-4-1-20250805
ANTHROPIC_API_KEY=<anthropic-api-key>

Options

You can control a variety of options from the command line. For example:

uv run inspect eval inspect_evals/musr --limit 10
uv run inspect eval inspect_evals/musr --max-connections 10
uv run inspect eval inspect_evals/musr --temperature 0.5

See uv run inspect eval --help for all available options.

Parameters

musr

• domain (Literal[‘murder_mysteries’, ‘object_placements’, ‘team_allocation’]): Which domain in the dataset to evaluate. (default: 'murder_mysteries')
• prompt_technique (Literal[‘regular’, ‘cot’, ‘cot+’]): The prompt technique to use. “regular” includes only the narrative and the question. “cot” uses chain-of-thought prompting. “cot+” includes a hint. (default: 'regular')
• example_count (int): Number of solved examples to include at the beginning of each prompt. Defaults to 0. Currently only supports 1 example. (default: 0)
• shuffle (bool): Whether we shuffle the dataset for evaluation (default: True)

Dataset

Here is a truncated example from the dataset:

The tension between them was palpable. Alice had been awarded a major journalist award that Gabrielle had desired. This only deepened their rivalry, with Gabrielle feeling overlooked for this recognition in the Jazz scene.

Winston cast his gaze over the club once more—a hub of pulsating rhythms now eerily silent.

A significant part of the evening was Gabrielle’s recorded interview with Alice. It played on the local radio, their professional rivalry subtly echoing under their professional demeanor.

With a deep breath, Winston knew he had a tall task ahead. The jazz club, where Alice was last seen alive was now shrouded in an eerie silence, the vibrant rhythms of what used to be a lively night echoing in the abandoned stage. It was up to him to piece together the missing notes and bring the symphony of this unsolved case to a satisfying finale.

Who is the most likely murderer?

Pick one of the following choices: A - Eugene B - Gabrielle

The model is tasked to answer the question and choose the appropriate option.

Evaluation

The prompts are based on the official MuSR repository. The in-built multiple_choice scorer is used for evaluation.