--- license: cc0-1.0 language: - en task_categories: - question-answering - text-retrieval tags: - long-context - reading-comprehension - storytelling - narrative - benchmark - aggregation pretty_name: MM Long Storytelling Bench size_categories: - n<1K configs: - config_name: v3 data_files: - split: retrieval path: data/v3/sources/retrieval.jsonl - split: multihop path: data/v3/sources/multihop.jsonl - split: computation path: data/v3/sources/computation.jsonl - config_name: v2 data_files: - split: retrieval path: data/v2/eval/retrieval.jsonl - split: multihop path: data/v2/eval/multihop.jsonl - split: computation path: data/v2/eval/computation.jsonl --- # MM Long Storytelling Bench — v3 > ⚠️ **The 756 model-drafted questions have been WITHDRAWN from this > dataset's splits** (2026-08-05). They were drafted by a model that is also > an evaluation target, which makes them circular as a measurement > instrument. They are kept in full, with the reasoning, under > [`data/v3/archive/`](data/v3/archive) — nothing was deleted. > > The splits currently hold **6 worked examples** (`status: "example"`), > which document the required format and are not a benchmark. **Do not use > this dataset for evaluation yet.** > > Human-written replacements are in progress: 600 questions across a > 100-source active cohort, each recording its author and a separate > verifier. The corpus below — 125 sources, 13.5M tokens — is complete, > verified, and unaffected. The corpus is the finished part of v3. It roughly doubles v2 and covers every source. | | v2 | v3 | |---|---|---| | sources | 60 | **125** | | distinct authors | 30 | **96** | | tokens | 6,452,749 | **13,502,628** | | questions in splits | 354 | **6 examples** (756 withdrawn → `data/v3/archive/`) | ## What changed from v2 - **65 new sources.** Sampled across 11 genre bands using Project Gutenberg's own subject headings and bookshelves, and across five era bands by author birth year — deliberately reaching past v2's 1890s–1920s concentration. Author cap enforced corpus-wide: no author appears twice. - **Multi-volume works are now whole novels.** v2 contained single volumes of multi-volume works, which are not novels — a middle volume has no beginning and no ending. Nine such fragments were found; eight had complete sets on Project Gutenberg and were merged into complete works, one was dropped. Notably `farjeon_miser_farebrother` and `farjeon_miser_farebrother2` in v2 were **volumes 1 and 3 of the same novel**, with volume 2 missing entirely; v3 ships the complete three-volume novel, and its 12 questions were re-anchored to it with no answer changes. - **Corrected count.** v2's question files hold **354** questions across 59 sources — not the "348 across 58" previously recorded. - **Chapter structure** restored for every source (`sources/structure.json`); 101 of 125 have detectable chapters. ## Verification `verify.py` re-derives every claim from the corpus text rather than trusting stored metadata, and gates on 13 checks — all passing: - story ids unique; stored tokens/words match a recount; every source clears a 50,000-token floor; no source is a volume fragment; `n_chapters` consistent across files - every `gold_story_id` resolves; every question has a non-empty answer and ≥1 supporting quote - **every supporting quote appears verbatim in its gold source, and exactly once** — so each span is deterministically locatable - stored `separation` matches a recount - the answer never appears in its own question; no supporting quote appears in its question Additionally, within a source no question contains another question's answer, since the eval packs a source's questions together. Token counts use `cl100k_base`; word counts use `\w+`. Both reproduce all 60 of v2's stored values exactly. ## Known limitations — please read before using 1. **Nothing is human-verified.** All 756 items are `status: draft`. 2. **The difficulty gate is mostly unrun.** Closed-book answerability has been checked on 30 items only. An item a model can answer *without* the text measures recall, not reading; that check is what makes this a reading benchmark, and it is incomplete. 3. **Four sources were drafted from a partial read.** The largest merged works (`halidom_wonder_club`, `russell_my_shipmate`, `marcet_berthas_visit`, `braddon_fatal_three`, 210k–356k tokens) exceed a single context. Numeric-fact coverage is near-complete via whole-file sweeps, but narrative context for large stretches is unverified. All 24 affected questions carry a `review_flags` entry saying exactly what was and was not read. Their spans are verbatim, unique and correctly separated like any other — but a restatement elsewhere that would make an answer ambiguous cannot be ruled out. 4. **Separation threshold differs from v2.** v3 requires multihop/computation spans ≥10% of the text apart (v2's README claims ≥20%). Note also that `separation` is normalised by total text length, so the merged multi-volume works score lower for the same absolute gap. 5. **No packed eval contexts.** v2 shipped `eval/` with pre-packed k-distractor contexts. v3 ships sources and questions only; packing is pending a decision about the new long tail (six sources now exceed v2's longest novel, up to 355,814 tokens). 6. **Question authoring is model-drafted.** Every question was drafted by a model that read the source, then screened by a deterministic validator. `review_flags` records anything the drafter or the validator flagged. ## Layout ``` data/v3/ sources/stories.jsonl 125 records, full text in `text` sources/manifest.json the same records without text sources/structure.json per-source chapter offsets and counts sources/provenance.md Gutenberg ids, licensing, counts sources/multilingual_pairs.json 3 verified EN↔FI translation pairs sources/{retrieval,multihop,computation}.jsonl 756 questions sources/stories/.md per-source text by_story// story.md + questions.jsonl + rendered README verify_report.md ``` --- # MM Long Storytelling Bench — v2 > ⚠️ **Draft / preview.** Every item ships `status: "draft"`. The dataset is **structurally > verified** (every supporting quote is a verbatim substring of its source; every multi-hop > and computation item aggregates over passages ≥20% of the book apart; no question contains a > word distinctive to its gold novel). It is **not yet difficulty-gated** — the closed-book > G/E/P/S/L evaluation has been run only on a 30-item subset, not the full set. Treat this as a > working research preview, not a finished benchmark. A model may never be the sole author or > verifier of a benchmark item; a human verification pass is pending. A long-context **reading-comprehension** benchmark built on **stories** — the follow-up to **MUDDLE** (Algoverse AI Research, in collaboration with researchers from PocketFM). Where MUDDLE tested finding one fact in a pile of research PDFs, this tests whether a model actually *follows a story* at length. ## The core idea Difficulty comes from **aggregation over a long work**, not from unseen text. A model may know a novel's plot; it does not know how many days separate two dated events, or what three sums scattered across the book add to. The benchmark is deliberately built on novels a model may have read — the guarantee is at the level of the *question*, not the story: each item is designed to be un-answerable without reading, and difficulty is carried by counting, state-tracking, interval arithmetic, and event ordering that no plot summary records. ## Sources — 60 obscure full-length public-domain novels 4,852,314 words / 6,452,749 tokens across 30 authors, each novel 50k–202k tokens. These are **obscure** genre novels (Edwardian mystery, adventure, romance) by prolific but now-forgotten authors — chosen so the model has not absorbed a study-guide apparatus (Wikipedia/SparkNotes chapter summaries, character lists, timelines) that would let it answer an aggregation question from memory. All are US public domain (Project Gutenberg, header / trademark stripped); every author died before 1957. Provenance in `data/v2/sources/provenance.md`. ## The three question types — 354 questions, 118 / 118 / 118 - **retrieval** — the gold novel is identified by a *paraphrased* trait (never its title or a proper noun from it), then a specific incidental detail is asked. The context holds the gold novel beside a same-author, same-era sibling as a hard negative. - **multihop** — ≥2 hops, the bridge entity never named in the question; the supporting passages sit ≥20% of the book apart. - **computation** — aggregation (counting, summing, interval arithmetic, ordering) over ≥2 widely separated regions; no single chapter suffices. Six questions per novel (2 per type), evenly spread. Multi-hop/computation passage separation: median 55%, up to 98%. ## Loading ```python from datasets import load_dataset ds = load_dataset("luoojason/mm-long-storytelling-bench", "v2", split="computation") src = load_dataset("luoojason/mm-long-storytelling-bench", data_files="data/v2/sources/stories.jsonl", split="train") text = {s["id"]: s for s in src} # id -> full novel record row = ds[0] row["question"] # the prompt row["answers"] # gold answer(s) as list, e.g. ["20000"] row["answer_format"] # "string" | "number" | "list" — how to score row["document_ids"] # the packed context, in order (k1: gold + 1 same-author sibling) # Reconstruct the >=100k-token context deterministically: context = "\n\n".join( f'=== {text[d]["title"]} ===\n\n{text[d]["text"]}' for d in row["document_ids"] ) ``` The context is **not inlined** — at k1 each is a whole novel plus a sibling (~220k tokens), and every novel is the gold of six questions, so inlining would duplicate the 60 novels dozens of times (~300 MB). The `document_ids` give the exact packed order; the novels ship once under `sources/`. Larger distractor sets (k3, k7, all 60 novels ≈ 6.5M tokens) come from the same `stories.jsonl` with more `document_ids`; **k1** is the config because it is the smallest packing in which every item clears the 100k-token long-context floor. Score a prediction: for `answer_format == "number"` compare the number; for `"list"` require all items; for `"string"` accept a semantic match (an LLM judge is recommended for free-form answers). Every item is `status: "draft"` pending human verification. ## Raw sources `data/v2/sources/` ships everything for reproduction: the 60 novels (`stories.jsonl` + one file each under `stories/`), the questions split by type with answer + reasoning + verbatim supporting spans + measured passage separation, per-novel `provenance.md` (Gutenberg ids, download counts, word/token/chapter counts), `structure.json` (chapter offsets), and `multilingual_pairs.json` (3 verified obscure English↔Finnish same-work translation pairs for a multilingual split). ## Prior batch `sourced/` holds an earlier, superseded set of 11 obscure Gutenberg short stories and excerpts. v2 keeps the same obscurity property but uses **full-length novels** and **aggregation questions**, which fixes the length and difficulty limitations of that batch. ## Licensing & attribution All source text is **US public domain** (Project Gutenberg, header/trademark stripped); the curation artifacts are released **CC0**. Produced by a researcher at **Algoverse AI Research**, in collaboration with researchers from **PocketFM** — not a PocketFM product or endorsement.