Fetching from the wire…
Public story · 2026-07-25 · high
Plain LLMs hallucinated as the compliance knowledge base grew, while Euclid-MCP returned exact, faster answers.
Why now: This finding is part of the July 25, 2026 briefing, timing that matters if you're patching RAG hallucinations with embeddings instead of adding a solver.
Bartolomeo Bogliolo released Euclid-MCP, an open-source tool that routes multi-step logical reasoning to SWI-Prolog instead of a language model, per a new arXiv paper. For anyone doing policy or compliance checks with retrieval-augmented generation, that's a direct challenge to the architecture: embeddings return similar-looking text, not verified logic. Bogliolo's results say the gap between the two widens as the rule set grows.
The server pairs with Euclid-IR, a Horn-clause intermediate representation built so an LLM can emit it directly, then hands proving off to Prolog. The tool interface follows a translate-run-inspect-repair loop, so the calling model can see the actual proof trace instead of trusting an opaque final answer.
On an IT security and compliance test case, plain LLMs held up fine on small knowledge bases. Yes, but as the base grew, they started hallucinating systematically. Euclid-MCP kept returning exact answers, with lower latency and a more compact output than the LLM-only runs.
Teams patching RAG hallucinations with better embeddings are fixing the wrong layer. Rule-heavy retrieval needs a solver behind the model, not a bigger vector index. Bogliolo's numbers come from one IT security and compliance case. The paper doesn't claim the result generalizes beyond it. Worth watching whether other MCP tools built on Euclid-IR turn up outside SWI-Prolog, since the format is engine-agnostic by design.
This is part of the July 25, 2026 briefing, timing that matters if you're patching RAG hallucinations with embeddings instead of adding a solver.
Each link below shares sources, entities, or timing with this story.
Anthropic released MCP / Shared entities / Same source domain / Earlier coverage / Tension
Linked by a graph relationship (Anthropic released MCP); both cover LLMs, MCP, RAG; reported by the same outlet (arxiv.org).
OpenAI supports MCP / Shared entity: MCP / Same source domain / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (OpenAI supports MCP); both cover MCP; reported by the same outlet (arxiv.org).
Claude Code uses MCP / Shared entities / Same source domain / Earlier coverage
Linked by a graph relationship (Claude Code uses MCP); both cover LLMs, MCP; reported by the same outlet (arxiv.org).
OpenAI supports MCP / Shared entity: LLMs / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (OpenAI supports MCP); both cover LLMs; overlapping topics (access, against, llms).
OpenAI supports MCP / Shared entity: LLMs / Same source domain / Shared topic / Earlier coverage
Linked by a graph relationship (OpenAI supports MCP); both cover LLMs; reported by the same outlet (arxiv.org).
Claude Code uses MCP / Shared entity: MCP / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (Claude Code uses MCP); both cover MCP; overlapping topics (access, architecture).
ChatGPT supports MCP / Shared entities / Earlier coverage
Linked by a graph relationship (ChatGPT supports MCP); both cover MCP, RAG; earlier MCP coverage from 2026-07-07.
Cloudflare supports MCP / Shared entities / Earlier coverage
Linked by a graph relationship (Cloudflare supports MCP); both cover MCP, RAG; earlier MCP coverage from 2026-06-07.