Thinking with Sound: Audio Chain-of-Thought Enables Multimodal Reasoning in Large Audio-Language Models (2025 · 6 citations)
Large Audio-Language Models (LALMs) turn a recording into a fixed set of embeddings once. After that step they cannot go back to the signal. Because of this, they perform poorly when audio is noisy or degraded, or when a task depends on fine acoustic detail. The authors propose Thinking-with-Sound (TwS), a framework…
Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLMs (2025 · 5 citations)
Large Reasoning Models (LRMs) such as DeepSeek-R1 produce long Chain-of-Thought traces, yet they often behave erratically.
Vulnerability of LLMs to Vertically Aligned Text Manipulations (2025 · 5 citations)
The paper asks whether decoder-based LLMs are as vulnerable as encoder models like BERT when some words in a text are written vertically, one character per line.

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Updated 31 October 2026