{
 "body": "\n## What it measures\n\nMuSR (Multistep Soft Reasoning) tests whether a model can answer a question that requires combining\nfacts scattered across a long narrative, rather than reasoning that is stated in one place. Each item is\nan algorithmically generated story of roughly 1,000 words in one of three domains: a murder mystery\n(identify the culprit), an object placement puzzle (determine where an object ended up after being\nmoved), or a team allocation problem (assign people to tasks under stated constraints). The stories are\nproduced by a \"neurosymbolic synthetic-to-natural generation algorithm,\" which builds a structured\nreasoning tree first and then renders it into free-form prose, so the correct answer depends on\nconnecting details spread through the narrative rather than pattern-matching a single sentence. It is a\nsingle-turn, English-language, text-only task.\n\n## How it is scored\n\nModels are graded on accuracy against a set of answer options that differs by domain: murder mysteries\noffer 2 choices, object placements 5, and team allocation 3, so the chance baseline is 50%, 20% and\nroughly 33% respectively rather than one fixed number across the whole benchmark. Hugging Face's Open\nLLM Leaderboard v2, the suite that made MuSR widely reported, evaluates all three domains zero-shot and\nreports normalized accuracy (acc_norm), where a score of 0 corresponds to that domain's own chance\nbaseline and 100 to a perfect score, then averages the three domains weighted by size. The original\npaper itself reported GPT-4 at 80.4% (murder mysteries), 60.9% (object placements) and 68.4% (team\nallocation), against human majority-vote performance of 94.1%, 95.0% and 100.0% on the same three\ndomains.\n\n## Dataset and licence\n\nMuSR contains 756 items: 250 murder mysteries, 256 object placements and 250 team allocation problems,\nwith narratives ranging from roughly 3,800 to 7,300 characters. There is no train/dev/test split; it is\nreleased as one evaluation set. The authors' GitHub repository carries an MIT licence; a Hugging Face\nmirror (TAUR-Lab/MuSR) instead labels the dataset CC BY 4.0, and this page could not resolve which\nlicence statement the authors consider authoritative.\n\n## Who publishes it\n\nMuSR was introduced by Zayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri and Greg Durrett of the\nDepartment of Computer Science at the University of Texas at Austin, first posted to arXiv in October\n2023 with a revised version in March 2024, and presented as a spotlight paper at ICLR 2024. The authors\nmaintain the reference dataset and generation code on GitHub. Since mid-2024 it has been most visibly run\nand reported through Hugging Face's Open LLM Leaderboard v2, which evaluates it as one of six standard\ntasks.\n\n## Lineage\n\nMuSR has no formal predecessor or successor of its own. It is one of six tasks Hugging Face selected for\nOpen LLM Leaderboard v2 in June 2024, alongside MMLU-Pro, GPQA, MATH Level 5, IFEval and BBH; GPQA's\nharder \"Diamond\" subset has its own page in this repository (gpqa_diamond.md), though the leaderboard\nitself runs plain GPQA rather than Diamond specifically. MuSR is not a variant of any of those\nbenchmarks; it was selected for the suite as its own, independently developed reasoning format.\n\n## Saturation and contamination\n\nHugging Face's own release material for Open LLM Leaderboard v2 states plainly that, as of June 2024,\n\"few models score better than random performance\" on MuSR, one of the reasons it was chosen for the\nsuite: a benchmark that still separates models is more useful than one where every model clusters near\nthe ceiling. This page could not confirm a specific current top score from a live, dated source during\nthis research; the leaderboard's own Space returned only an unrendered loading screen rather than\nrenderable results. Contamination risk sits at medium: Hugging Face selected MuSR partly for its \"youth,\" reasoning\nthat a dataset released in October 2023 had less opportunity to leak into training corpora than older\nbenchmarks, but by the time of this research that dataset is roughly three years old, hosted without\ngating or a canary string, which will have eroded that original advantage somewhat.\n\n## How to run it\n\nHugging Face's fork of lm-evaluation-harness implements the three domains as\n`leaderboard_musr_murder_mysteries`, `leaderboard_musr_object_placements` and\n`leaderboard_musr_team_allocation`, grouped under `leaderboard_musr` and combined as a size-weighted mean\nof normalized accuracy (acc_norm), all evaluated zero-shot. The authors' GitHub repository also ships a\ndataset-generation pipeline (requiring OpenAI API access), since MuSR's items were themselves generated\nwith GPT-4; most model reports use the fixed, pre-generated 756-item set rather than regenerating new\ninstances.\n\n## Reading the numbers\n\nA high MuSR score shows a model can hold a roughly 1,000-word narrative in mind and connect scattered\ndetails to answer correctly, a reasonable proxy for long-range narrative and commonsense reasoning, but\nnot a general measure of reasoning ability. Because the three domains carry different chance baselines\n(50%, 20%, 33%), check whether a reported number is raw accuracy or Open LLM Leaderboard v2's normalized\naccuracy before comparing scores across sources, since the two can diverge substantially for the\n2-choice murder mystery domain, where a raw score near 50% is barely better than guessing. Given how far\neven strong 2023-era models sat below the paper's human baselines, treat MuSR as still meaningfully\nseparating models rather than as a saturated benchmark.\n",
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 ],
 "page": {
  "aliases": [
   "Multistep Soft Reasoning"
  ],
  "category": "reasoning",
  "contamination": {
   "note": "Hugging Face selected MuSR for Open LLM Leaderboard v2 partly for its \"youth,\" reasoning that a dataset released in October 2023 had less opportunity to leak into training corpora than older benchmarks. By the time of this research the dataset is roughly three years old, hosted without gating or a canary string, which will have eroded that original advantage somewhat.\n",
   "risk": "medium"
  },
  "dataset": {
   "languages": [
    "en"
   ],
   "license": "MIT (authors' GitHub repository); a Hugging Face mirror separately labels it CC BY 4.0",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 756,
   "size_note": "756 items: 250 murder mysteries, 256 object placements and 250 team allocation problems, with narratives ranging from roughly 3,800 to 7,300 characters. There is no train/dev/test split; it is released as a single evaluation set.\n",
   "splits": "single evaluation set, no train/test split",
   "url": "https://huggingface.co/datasets/TAUR-Lab/MuSR"
  },
  "freshness": {
   "researched": "2026-09-08",
   "researched_by": "sonnet-5 agent, batch 1b, slice O",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "leaderboard_musr",
   "opencompass": "",
   "other": "Confirmed in EleutherAI's lm-evaluation-harness: the three per-domain tasks (leaderboard_musr_murder_mysteries, leaderboard_musr_object_placements, leaderboard_musr_team_allocation) are grouped as leaderboard_musr and combined as a size-weighted mean of normalized accuracy (acc_norm) across the three, all evaluated zero-shot. This is the configuration Hugging Face's Open LLM Leaderboard v2 documentation describes using.\n"
  },
  "id": "musr",
  "last_updated": "",
  "leaderboard_url": "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard",
  "lineage": {
   "family": "",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "MuSR (Multistep Soft Reasoning) tests whether a model can answer a question that requires combining facts scattered across a long narrative, rather than reasoning that is stated in one place. Each item is an algorithmically generated story of roughly 1,000 words in one of three domains: a murder mystery (identify the culprit), an object placement puzzle (determine where an object ended up after being moved), or a team allocation problem (assign people to tasks under stated constraints). The stories are produced by a \"neurosymbolic synthetic-to-natural generation algorithm,\" which builds a structured reasoning tree first and then renders it into free-form prose, so the correct answer depends on connecting details spread through the narrative rather than pattern-matching a single sentence. It is a single-turn, English-language, text-only task.\n",
  "metric": {
   "baseline_note": "The chance baseline differs by domain: murder mysteries offer 2 answer choices (50% chance), object placements 5 (20% chance), and team allocation 3 (roughly 33% chance), so no single random baseline applies across the whole benchmark. The original paper reports human majority-vote performance of 94.1% (murder mysteries), 95.0% (object placements) and 100.0% (team allocation); no single overall human baseline figure was found in the sources reviewed, so none is set here.\n",
   "direction": "higher_is_better",
   "max_score": 100,
   "name": "accuracy (normalized)",
   "unit": "%"
  },
  "name": "MuSR",
  "page_kind": "benchmark",
  "paper": {
   "arxiv": "2310.16049",
   "title": "MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning",
   "url": "https://arxiv.org/abs/2310.16049",
   "year": 2023
  },
  "publisher": {
   "authors": [
    "Zayne Sprague",
    "Xi Ye",
    "Kaj Bostrom",
    "Swarat Chaudhuri",
    "Greg Durrett"
   ],
   "org": "University of Texas at Austin",
   "url": "https://github.com/Zayne-sprague/MuSR"
  },
  "released": "2023-10",
  "repo_url": "https://github.com/Zayne-sprague/MuSR",
  "saturation": {
   "note": "Hugging Face's own release material for Open LLM Leaderboard v2 states plainly that, as of June 2024, \"few models score better than random performance\" on MuSR, one of the reasons it was chosen for the suite. This page could not confirm a specific current top score from a live, dated source during this research; the leaderboard's own Space returned only an unrendered loading screen rather than renderable results, so top_score and as_of are left unset rather than estimated.\n",
   "status": "open"
  },
  "sources": [
   {
    "accessed": "2026-09-08",
    "title": "MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning",
    "url": "https://arxiv.org/abs/2310.16049"
   },
   {
    "accessed": "2026-09-08",
    "title": "Zayne-sprague/MuSR GitHub repository",
    "url": "https://github.com/Zayne-sprague/MuSR"
   },
   {
    "accessed": "2026-09-08",
    "title": "TAUR-Lab/MuSR dataset card, Hugging Face",
    "url": "https://huggingface.co/datasets/TAUR-Lab/MuSR"
   },
   {
    "accessed": "2026-09-08",
    "title": "Performances are plateauing, let's make the leaderboard steep again (Open LLM Leaderboard v2 announcement), Hugging Face",
    "url": "https://huggingface.co/spaces/open-llm-leaderboard/blog"
   },
   {
    "accessed": "2026-09-08",
    "title": "Open LLM Leaderboard: About, Hugging Face docs",
    "url": "https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about"
   },
   {
    "accessed": "2026-09-08",
    "title": "leaderboard_musr group config, EleutherAI lm-evaluation-harness",
    "url": "https://raw.githubusercontent.com/EleutherAI/lm-evaluation-harness/main/lm_eval/tasks/leaderboard/musr/_musr.yaml"
   }
  ],
  "status": "active",
  "subcategory": "multistep narrative and long-context reasoning",
  "summary": "Algorithmically generated murder mysteries, object placement puzzles and team allocation problems needing long-range narrative reasoning.",
  "tags": [
   "reasoning",
   "long-context",
   "chain-of-thought",
   "open-llm-leaderboard-v2",
   "narrative"
  ],
  "task_format": "A narrative of roughly 1,000 words followed by a multiple-choice question; the model must integrate facts scattered through the text to select the correct option.\n"
 }
}