# Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition

arXiv: https://arxiv.org/abs/2510.06774 · HTML: https://mlsanity.com/paper/2510.06774/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Placed in

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

## Builds on (most cited first, 15 of 15)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation](https://mlsanity.com/paper/2305.15541/) (2023)
- [Autoformalization in the Wild: Assessing LLMs on Real-World Mathematical Definitions](https://mlsanity.com/paper/2502.12065/) (2025)
- [VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic Reasoning](https://mlsanity.com/paper/2501.14540/) (2025)
- [REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once](https://mlsanity.com/paper/2507.10541/) (2025)
- [Do Large Language Models Excel in Complex Logical Reasoning with Formal Language?](https://mlsanity.com/paper/2505.16998/) (2025)
- [forallutoexistsval: Autonomous Assessment of LLMs in Formal Synthesis and Interpretation Tasks](https://mlsanity.com/paper/2403.18327/) (2024)
- [Integrating Expert Knowledge into Logical Programs via LLMs](https://mlsanity.com/paper/2502.12275/) (2025)

## Built on by (most cited first, 7 of 7)

- [LogicTrack: Auditing Reasoning Trajectories of Large Language Models with Formal Logic Solvers](https://mlsanity.com/paper/2609.21492/) (2026)
- [Logic-Parametric Neuro-Symbolic NLI: Controlling Logical Formalisms for Verifiable LLM Reasoning](https://mlsanity.com/paper/2601.05705/) (2026)
- [Decompose-and-Formalise: Recursively Verifiable Natural Language Inference](https://mlsanity.com/paper/2601.19605/) (2026)
- [From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks](https://mlsanity.com/paper/2604.27453/) (2026)
- [SciR: A Controllable Benchmark for Scientific Reasoning in LLMs](https://mlsanity.com/paper/2606.13020/) (2026)
- [Neurosymbolic Clinical Trial Matching via LLM-Driven Abduction and Logical Verification](https://mlsanity.com/paper/2606.20895/) (2026)
- [Accountable AI with Grounded, Faithful, Consistent, Actionable Rationales: A Case Study in Clinical Trial Matching with VERDICT](https://mlsanity.com/paper/2609.03366/) (2026)

## Abstract (arXiv)

Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature, i.e., the integration of a target solver is predetermined at design time, hindering the ability to employ diverse formal inference strategies. To address this, we introduce an adaptive, multi-paradigm, neuro-symbolic inference framework that: (1) automatically identifies formal reasoning strategies from problems expressed in natural language; and (2) dynamically selects and applies specialized formal logical solvers via autoformalization interfaces. Extensive experiments on individual and multi-paradigm reasoning tasks support the following conclusions: LLMs are effective at predicting the necessary formal reasoning strategies with an accuracy above 90 percent. This enables flexible integration with formal logical solvers, resulting in our framework outperforming competing baselines by 27 percent and 6 percent compared to GPT-4o and DeepSeek-V3.1, respectively. Moreover, adaptive reasoning can even positively impact pure LLM methods, yielding gains of 10, 5, and 6 percent on zero-shot, CoT, and symbolic CoT settings with GPT-4o. Finally, although smaller models struggle with adaptive neuro-symbolic reasoning, post-training offers a viable path to improvement. Overall, this work establishes the foundations for adaptive LLM-symbolic reasoning, offering a path forward for unifying material and formal inferences on heterogeneous reasoning challenges.