xid32/SoundMind

• highreadme#1

  Reposition README's opening paragraph to clarify research focus

  原因：

  当前

  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.

  复制粘贴的修复

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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.

  待办完成忽略
• mediumtopics#2

  Add specific topics for bimodal reasoning and multimodal AI

  原因：

  当前

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

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

  待办完成忽略
• mediumreadme#3

  Add a 'Key Components' section to highlight core offerings

  原因：

  复制粘贴的修复

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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.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

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

  你：未被推荐
  AI 推荐顺序：

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

  AI 推荐了 6 个替代方案，却始终没点名 xid32/SoundMind。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

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

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 7 个替代方案，却始终没点名 xid32/SoundMind。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

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

  pass
  ✓AI 明确点名了 xid32/SoundMind

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

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

  pass
  ✓AI 明确点名了 xid32/SoundMind

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

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

  pass
  ✓AI 明确点名了 xid32/SoundMind

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？