Self-Reflective Chain-of-Thought (SRCoT): Evidence-Based Text Generation via a Closed-Loop Retrieve--Reflect--Revise Pipeline on Edge LLMs
SRCoT is a local retrieve-reflect-revise pipeline for source-labeled LLM outputs, shown only in synthetic case studies.
SRCoT organizes LLM generation as a closed Retrieve-Reflect-Revise loop with provenance-labeled stores for environment state, knowledge, experience, and short-term memory. BM25 retrieval and intermediate artifacts inform revision, clarification, or abstention. Qualitative synthetic case studies in emergency medical services and Munich-Sim ran locally on an Apple M4 laptop with 128 GB of memory. The authors present it as an inspectable proof of concept, not evidence of hallucination reduction, clinical validity, or operational safety.
- Closed-loop retrieve, reflect, and revise generation pipeline.
- Provenance-labeled stores separate knowledge, experience, and memory.
- Synthetic EMS and Munich-Sim traces ran on an Apple M4 laptop.
- Authors do not claim clinical validity or fewer hallucinations.
From the original article
1 min read at arxiv.orgLarge language models (LLMs) can generate fluent outputs that lack source support or conflict with local operational constraints. We introduce Self-Reflective Chain-of-Thought (SRCoT), a modular architecture that organizes generation as a closed Retrieve--Reflect--Revise loop. SRCoT separates environment state, persistent knowledge, accumulated experience, and short-term memory into provenance-labeled stores.
By Fabian Broszio · arxiv.org
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