# Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications

arXiv: https://arxiv.org/abs/2502.04384 · HTML: https://mlsanity.com/paper/2502.04384/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models](https://mlsanity.com/paper/2305.13711/) (2023)
- [Cumulative Reasoning with Large Language Models](https://mlsanity.com/paper/2308.04371/) (2023)
- [Towards Mitigating Hallucination in Large Language Models via Self-Reflection](https://mlsanity.com/paper/2310.06271/) (2023)
- [Demystifying Chains, Trees, and Graphs of Thoughts](https://mlsanity.com/paper/2401.14295/) (2024)
- [Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation](https://mlsanity.com/paper/2208.05309/) (2022)

## Built on by (most cited first, 8 of 8)

- [Expert Preference-based Evaluation of Automated Related Work Generation](https://mlsanity.com/paper/2508.07955/) (2025)
- [FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning](https://mlsanity.com/paper/2506.16123/) (2025)
- [Probabilities Are All You Need: A Probability-Only Approach to Uncertainty Estimation in Large Language Models](https://mlsanity.com/paper/2511.07694/) (2025)
- [Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery](https://mlsanity.com/paper/2507.16229/) (2025)
- [How Modality Shapes Perception and Reasoning: A Study of Error Propagation in ARC-AGI](https://mlsanity.com/paper/2511.15717/) (2025)
- [Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches](https://mlsanity.com/paper/2606.01145/) (2026)
- [Inspector: Conversational and Lightweight Analyzer of Analog Circuit Layouts Using LLM and CNNs](https://mlsanity.com/paper/2609.34976/) (2026)
- [THEIA: A Multimodal Dataset and Benchmark for Vision-Language Analysis of Layout](https://mlsanity.com/paper/2609.35035/) (2026)

## Abstract (arXiv)

This paper presents SOLOMON, a novel Neuro-inspired Large Language Model (LLM) Reasoning Network architecture that enhances the adaptability of foundation models for domain-specific applications. Through a case study in semiconductor layout design, we demonstrate how SOLOMON enables swift adaptation of general-purpose LLMs to specialized tasks by leveraging Prompt Engineering and In-Context Learning techniques. Our experiments reveal the challenges LLMs face in spatial reasoning and applying domain knowledge to practical problems. Results show that SOLOMON instances significantly outperform their baseline LLM counterparts and achieve performance comparable to state-of-the-art reasoning model, o1-preview. We discuss future research directions for developing more adaptive AI systems that can continually learn, adapt, and evolve in response to new information and changing requirements.