Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog

• URL: http://arxiv.org/abs/2607.21412v1
• Date: Thu, 23 Jul 2026 15:15:37 GMT
• Title: Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
• Abstract summary: Euclid-MCP is an open-source server that provides deterministic logical reasoning via SWI-Prolog.<n>The server exposes a compact tool interface that supports a translate-run-inspect loop, enabling LLM clients to delegate inference.<n>We argue that semantic RAG is fundamentally unsuited for rule enforcement, and that Euclid-MCP can serve as a stable, shared reasoning substrate.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially in safety-critical or compliance-sensitive domains. Recent neuro-symbolic approaches address this gap by coupling neural models with external symbolic engines, yet most integrations are bespoke and lack a standardized interface for tool-augmented agents. This paper presents Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. Euclid-MCP introduces Euclid-IR, an engine-agnostic intermediate representation for Horn-clause logic that is human-readable, easy for LLMs to generate, and straightforward to compile into Prolog or alternative backends. The server exposes a compact tool interface that supports a translate-run-inspect-repair loop, enabling LLM clients to delegate inference while retaining full access to proof traces and derivation logs. We evaluate Euclid-MCP on a realistic IT security and compliance use case. Results show that while LLMs alone are sufficient on small knowledge bases, they hallucinate systematically on larger problems, whereas Euclid-MCP delivers exact answers with lower latency and more compact outputs. We argue that semantic RAG is fundamentally unsuited for rule enforcement, and that Euclid-MCP can serve as a stable, shared reasoning substrate for both RAG-based assistants and agentic systems.

Related papers

• The Imitation Game: When LLMs Learn to Reason Like Programs via Code-Centric Reasoning Data Synthesis [50.473217152006875]
  MIMIC is a framework that leverages executable code as a rigorous medium for reasoning data synthesis.<n>MIMIC transforms algorithms into verifiable reasoning trajectories through narrative fusion, code-guided test synthesis, and dynamic code instrumentation.<n>Our method significantly elevates accuracy across general reasoning, complex mathematical benchmarks, and fine-grained deterministic tasks.
  arXiv  Detail & Related papers  (2026-09-13T17:04:16Z)
• When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning [85.36421257648294]
  Chain-of-Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks.<n>We propose Communicative Language Symbolism Routing (R)<n>R treats each Language Symbolism Framework as a reusable symbolic protocol with compact symbols, usage rules, and a message-passing contract.<n>R reduces latency-oriented generated token completion by $3sim 6times$ compared to standard CoT.
  arXiv  Detail & Related papers  (2026-06-28T12:02:42Z)
• PrologMCP: A Standardized Prolog Tool Interface for LLM Agents [4.416593559466468]
  We introduce PrologMCP, a task-agnostic, open-source server that exposes Prolog as a stateful tool through the Model Context Protocol (MCP)<n>We evaluate a formalizer agent enhanced with PrologMCP against standard and reasoning LLMs.
  arXiv  Detail & Related papers  (2026-06-12T20:14:51Z)
• PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback [0.0]
  PRIMA operates as coordinated multi-agent research systems over multi-hour runs.<n>Main contributions are three operational patterns for surviving failure modes.<n>A Graph Isomorphism case study grounds the architectural claims in a generated artifact.
  arXiv  Detail & Related papers  (2026-05-23T23:27:46Z)
• PLUME: Latent Reasoning Based Universal Multimodal Embedding [52.35354073629127]
  Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model.<n>Recent approaches improve UME by generating explicit chain-of-thought (CoT) rationales before extracting embeddings.<n>We propose PLUME, a latent reasoning framework that advances UME by replacing verbalized CoT with a short autoregressive rollout of continuous latent states.
  arXiv  Detail & Related papers  (2026-04-02T14:04:53Z)
• LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval [74.72139580745511]
  LaSER is a novel self-distillation framework that internalizes explicit reasoning into the latent space of retrievers.<n>Our method successfully combines the reasoning depth of explicit CoT pipelines with the inference efficiency of standard dense retrievers.
  arXiv  Detail & Related papers  (2026-03-02T04:11:18Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• Do LLMs Dream of Discrete Algorithms? [0.7646713951724011]
  Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence.<n>Their reliance on probabilistic inference limits their effectiveness in domains requiring strict logical reasoning.<n>This paper proposes a neurosymbolic approach that augments LLMs with logic-based reasoning modules.
  arXiv  Detail & Related papers  (2025-06-29T22:03:01Z)
• Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition [95.54406667705999]
  Pangu Embedded is an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs)<n>It addresses the significant computational costs and inference latency challenges prevalent in existing reasoning-optimized LLMs.<n>It delivers rapid responses and state-of-the-art reasoning quality within a single, unified model architecture.
  arXiv  Detail & Related papers  (2025-05-28T14:03:02Z)
• Computational Reasoning of Large Language Models [51.629694188014064]
  We introduce textbfTuring Machine Bench, a benchmark to assess the ability of Large Language Models (LLMs) to execute reasoning processes.<n> TMBench incorporates four key features: self-contained and knowledge-agnostic reasoning, a minimalistic multi-step structure, controllable difficulty, and a theoretical foundation based on Turing machine.
  arXiv  Detail & Related papers  (2025-04-29T13:52:47Z)
• SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning [30.938876549335067]
  This paper presents SymRTLO, a novel neuron-symbolic RTL optimization framework.<n>A symbolic module is proposed for analyzing and optimizing finite state machine (FSM) logic.<n> Experiments on the RTL-Rewriter benchmark with Synopsys Design Compiler and Yosys show that SymRTLO improves power, performance, and area (PPA) by up to 43.9%, 62.5%, and 51.1%, respectively.
  arXiv  Detail & Related papers  (2025-04-14T16:15:55Z)
• Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment [21.12989936864145]
  Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs)<n>We propose Reasoning-as-Logic-Units (RaLU), which constructs a more reliable reasoning path by aligning logical units between the generated program and their corresponding NL descriptions.
  arXiv  Detail & Related papers  (2025-02-05T08:23:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.