Structured Reasoning in Language Models

Latest papers

36 papers8 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem
Feb 25, 2026Heejin JoStructured Reasoning in Language Models Prompt Engineering
Independent Researcher
Unlocking Reasoning Capability on Machine Translation in Large Language Models
Feb 16, 2026Sara Rajaee, Sebastian Vincent, Alexandre Berard +3Structured Reasoning in Language Models Large Reasoning Models
University of Amsterdam · Cohere · Cohere Labs
LLMs Think with DAGs: Probing the Latent Graph Structure of Reasoning
Jan 24, 2026Tianjun Zhong, Linyang He, Ziyang Li +1Structured Reasoning in Language Models LLM Reasoning with Graphs
Columbia University · Johns Hopkins University
Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring
Dec 16, 2025Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +1LLM Inference Efficiency Efficient Language Model Reasoning
1IBM Research Europe, Dublin, Ireland · 2Trinity College Dublin, Ireland · 3ADAPT Research Centre, Dublin, Ireland
Base Models Know How to Reason, Thinking Models Learn When
Oct 8, 2025Constantin Venhoff, Iván Arcuschin, Philip Torr +2LLM Interpretability RL for Language Model Reasoning
University of Oxford, UK · MATS · Poseidon Research.
Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm
Sep 28, 2025Kaisen Yang, Tinghe Zhang, Rushi Shah +4LLM Reasoning Structured Reasoning
1Qizhi Institute · 2Dept. of Computer Science, Tsinghua University · College of AI, Tsinghua University +4

Feb 25, 2026J/K move · Enter open · S save

Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem

Heejin Jo

Independent Researcher

Structured Reasoning in Language Models Prompt Engineering Language Model Reasoning Evaluation Implicit Reasoning in Language Models Constraint-Guided Reasoning in Language Models LLM Grounding

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Large language models consistently fail the "car wash problem," a viral reasoning benchmark requiring implicit physical constraint inference. We present a variable isolation study (n=20 per condition, 6 conditions, 120 total trials) examining which prompt architecture layers in a production system enable correct reasoning. Using Claude 3.5 Sonnet with controlled hyperparameters (temperature 0.7, top_p 1.0), we find that the STAR (Situation-Task-Action-Result) reasoning framework alone raises accuracy from 0% to 85% (p=0.001, Fisher's exact test, odds ratio 13.22). Adding user profile context via vector database retrieval provides a further 10 percentage point gain, while RAG context contributes an additional 5 percentage points, achieving 100% accuracy in the full-stack condition. These results suggest that structured reasoning scaffolds -- specifically, forced goal articulation before inference -- matter substantially more than context injection for implicit constraint reasoning tasks.

Latest papers

Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem

Unlocking Reasoning Capability on Machine Translation in Large Language Models

LLMs Think with DAGs: Probing the Latent Graph Structure of Reasoning

Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring

Base Models Know How to Reason, Thinking Models Learn When

Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm