xid32/SoundMind

• highreadme#1

  Reposition README's opening paragraph to clarify research focus

  Why:

  CURRENT

  This repository is the official implementation of *SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models* (EMNLP 2025). We introduce **SoundMind-RL**, a novel rule-based reinforcement learning framework that empowers large-scale audio-language models with advanced logical reasoning capabilities across both audio and textual modalities. To enable such training, we build the **SoundMind dataset**, an Audio Logical Reasoning (ALR) benchmark comprising 6,446 high-quality samples annotated with chain-of-thought reasoning in both audio and text forms.

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  This repository presents **SoundMind**, a research project focused on advancing **Audio Logical Reasoning (ALR)**. We introduce the **ALR dataset**, consisting of 6,446 text-audio annotated samples specifically designed for complex reasoning tasks. Building on this resource, we propose **SoundMind-RL**, a novel rule-based reinforcement learning (RL) algorithm tailored to endow audio language models (ALMs) with deep bimodal reasoning abilities. This is the official implementation for our EMNLP 2025 paper.

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• mediumtopics#2

  Add specific topics for bimodal reasoning and multimodal AI

  Why:

  CURRENT

  audio-language-model, audio-reasoning, dataset, reinforcement-learning

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  audio-language-model, audio-reasoning, dataset, reinforcement-learning, bimodal-reasoning, multimodal-ai

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• mediumreadme#3

  Add a 'Key Components' section to highlight core offerings

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  ## Key Components
  
  This repository provides:
  
  *   **SoundMind-RL:** A novel rule-based reinforcement learning framework designed to empower audio-language models (ALMs) with advanced logical and bimodal reasoning capabilities.
  *   **Audio Logical Reasoning (ALR) Dataset:** A benchmark comprising 6,446 high-quality text-audio annotated samples, specifically curated for complex reasoning tasks and chain-of-thought training.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  Where can I find a large dataset for audio logical reasoning tasks?

  you: not recommended
  AI recommended (in order):

  1. AudioCommons
  2. DCASE Challenge Datasets
  3. AudioSet
  4. Freesound Annotations Dataset
  5. TAU Urban Acoustic Scenes 2020 Mobile
  6. ESC-50

  AI recommended 6 alternatives but never named xid32/SoundMind. This is the gap to close.

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• CATEGORY QUERY

  How to improve bimodal reasoning in audio language models using reinforcement learning?

  you: not recommended
  AI recommended (in order):

  1. Hugging Face TRL Library (huggingface/trl)
  2. DeepMind's Acme (deepmind/acme)
  3. Stable Baselines3 (DLR-RM/stable-baselines3)
  4. Farama Foundation Gymnasium (Farama-Foundation/Gymnasium)
  5. Ray RLlib (ray-project/ray)
  6. PyTorch Lightning (Lightning-AI/lightning)
  7. TensorFlow Keras (keras-team/keras)

  AI recommended 7 alternatives but never named xid32/SoundMind. This is the gap to close.

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• Metadata completeness

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• README presence

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• Compared to common alternatives in this category, what is the core differentiator of xid32/SoundMind?

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• If a team adopts xid32/SoundMind in production, what risks or prerequisites should they evaluate first?

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• In one sentence, what problem does the repo xid32/SoundMind solve, and who is the primary audience?

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  ✓AI named xid32/SoundMind explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?