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RetroThinker: Enabling Retrospective Thinking in Speech LLMs

Paper recorded by Signals 4 on 2026-09-10 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-10 on arXiv · recorded by Signals 4 on 2026-09-11

Category: cs.AI · 人工智能 · first seen 2026-09-11

Abstract

Speech large language models (SpeechLLMs) offer reduced latency and retain paralinguistic nuances that are typically lost in cascaded automatic speech recognition (ASR) and text-based LM architectures. However, they continue to lag behind text-only LLMs on complex reasoning tasks, while real-time spoken interaction imposes strict latency constraints. Although prior works employ Chain-of-Thought (C

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#111 most recent of 300 cs.AI papers we have recorded · ↑ newer: The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement · ↓ older: Explainability Assistant: A Conversational XAI Interface for Interpret
Cite this page: RetroThinker: Enabling Retrospective Thinking in Speech LLMs: the #111 most recent of 300 cs.AI papers we have recorded (as of 2026-09-10). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/retrothinker-enabling-retrospective-thinking-in-speech-llms.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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