---
title: "Mova: world → answer — papers it applies — Elamur Research"
description: "11 papers Mova applies at the stage «world → answer»: Answering from the world, with a reason: school tests, reading questions, word problems."
canonical: "https://research.elamur.ai/free/en/projects/mova/answer/"
---
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Mova · papers by stage

# world → answer

Answering from the world, with a reason: school tests, reading questions, word problems. The 11 works of [Mova](https://mova.elamur.ai/free/en/)'s own list that this stage uses; measured marks a work whose effect Mova measured in its own experiments (6 here). Each title opens our card: the summary, how Mova uses the work, its references and who cites it. What the stage does is on [Mova's pipeline page](https://mova.elamur.ai/free/en/); every stage is on [the main list](https://md.research.elamur.ai/free/en/projects/mova/main/).

- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://md.research.elamur.ai/free/en/papers/weston-2015-towards-ai-complete-question-answering/)measured Jason Weston et al. · 2015 **What Mova took:** `world/src/babi.rs` — the `world babi` command solves all 20 tasks with a reader built on the `en` parse tree, verb classes, and a first-level `relation` block, without training on the questions.
- [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)measured Matt Gardner et al. · 2020 **What Mova took:** `world/src/babi.rs` (`CONTRAST` table and `contrast()`; command `world babi-contrast`): the bAbI story reader is tested on contrast variants of the bAbI tasks. These keep the same stories but swap in new names, places and…
- [SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning](https://md.research.elamur.ai/free/en/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/)measured Roshanak Mirzaee et al. · 2021 **What Mova took:** `global/seeds/shortcuts/scene.md` — a first-level knowledge seed for scene descriptions: categories of colours, sizes and shapes, generic nouns ("thing", "object", "one"), synonyms of above/below, and the relations near, far and…
- [StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts](https://md.research.elamur.ai/free/en/papers/shi-2022-stepgame-new-benchmark-robust-multi/)measured Zhengxiang Shi, Qiang Zhang, Aldo Lipani · 2022 **What Mova took:** `world/src/stepgame.rs` — the `world stepgame` command evaluates Mova's world model on this benchmark.
- [STaR: Bootstrapping Reasoning With Reasoning](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)measured Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman · 2022 **What Mova took:** `math/src/bin/mathsolve.rs`, flag `--star N`: GSM8K training problems without an exact gold tree go into a pool; in each round the current (averaged perceptron) solver proposes its top-5 expression trees, and a tree whose value…
- [How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark](https://md.research.elamur.ai/free/en/papers/yang-2025-llm-reasoning-distracted-irrelevant-context/)measured Minglai Yang et al. · 2025 **What Mova took:** `math/src/bin/mathsolve.rs`, flag `--augment`: following the idea of training on noisy problems (together with Anantheswaran et al. 2024), each training problem gets a copy with a sentence containing a number from another problem…
- [A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/) Alane Suhr et al. · 2019 **What Mova took:** `global/seeds/shortcuts/scene.md` — the first-level "scene" seed…
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://md.research.elamur.ai/free/en/papers/warstadt-2020-blimp-benchmark-linguistic-minimal-pairs/) Alex Warstadt et al. · 2020 **What Mova took:** `en/seeds/errors/attraction-of-phrase.md` — the rules `en.errors.attraction-of` and `en.errors.attraction-of-auxcop` flag agreement attraction through an of-phrase (A pattern of arrests indicate…), excluding quantity nouns…
- [Are NLP Models really able to Solve Simple Math Word Problems?](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/) Arkil Patel, Satwik Bhattamishra, Navin Goyal · 2021 **What Mova took:** SVAMP is the main benchmark of the math solver in `math` (the "steps" model and its judge); nearly every idea tested in the solver is reported as an SVAMP delta.
- [Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension](https://md.research.elamur.ai/free/en/papers/xu-2022-fantastic-questions-where-find-them/) Ying Xu et al. · 2022 **What Mova took:** `world/src/ftqa.rs`: loader for the FairytaleQA CSV (copy from Hugging Face, Apache-2.0 per its card); it reconstructs full stories from per-question sections and splits sentences. Used only for evaluation, never for training.
- [Evaluating large language models for the tasks of PoS tagging within the Universal Dependency framework](https://md.research.elamur.ai/free/en/papers/machado-2024-evaluating-large-language-models-tasks/) Mateus Machado, Evandro Ruiz · 2024 **What Mova took:** `en/seeds/errors/llm-annotator-failure-modes.md` — failure mode 3 ("labels outside the tagset"): Maritaca produced 93 distinct labels instead of 17 UPOS tags. This supports Mova's practice of passing every LLM-drafted annotation…