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TOPIC · ENTITY #2068

chain-of-thought prompting

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RESEARCH · 1 SOURCE · arXiv cs.AI

Smart Coder Framework: structuring human–LLM collaborative qualitative coding (arXiv:2610.33205v1)

The paper proposes the Smart Coder Framework, a three‑phase approach (Instruction, Prompt Strategy, Reliability Check) to structure human–LLM collaborative qualitative coding. The authors evaluated the framework on 72 middle‑school math teacher interview transcripts (12 for prompt development, 60 for formal evaluation) against a human‑consensus benchmark using a factorial design comparing instructed vs uninstructed prompts, few‑shot vs few‑shot+chain‑of‑thought, and single‑pass vs self‑consistency; instruction consistently improved agreement, chain‑of‑thought helped further under adequate instruction, and self‑consistency gave smaller theme‑dependent gains while flagging unstable decisions for human review.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

arXiv paper finds large reasoning models struggle on everyday logical reasoning

A new arXiv preprint (arXiv:2610.22538v1) introduces a benchmark of linguistic and pragmatic reasoning phenomena to evaluate whether large reasoning models (LRMs) align with everyday logical reasoning. The authors report that LRMs often degrade when everyday reasoning departs from formal logic, some non-reasoning models prompted with chain-of-thought outperform comparable LRMs, and additional instruction prompting gives only limited control over reasoning behavior.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research presents 'Talk like a Graph' and GraphQA benchmark for encoding graphs to LLMs

Researchers at Google (Bahare Fatemi and Bryan Perozzi) propose methods to translate graph-structured data into text that large language models can reason over, and introduce GraphQA, a benchmark of graph reasoning tasks and graph generators. They report that LLM performance varies with encoding method, task type, and graph structure, and that choosing the right encoding can improve graph-task performance by up to about 60%.

7.0