Research Summary · 10 Sources · March 2026

Graphs Reduce LLM Hallucinations.

Here’s the proof.

The results are in, and the evidence? Irrefutable. Graph databases, when used with specific prompting techniques, make LLMs give better answers.

Here’s the research.

Sources & Findings

# Source Key Finding Type Impact

01 01Agrawal et al., NAACL 2024 KG-augmented LLMs show consistent improvement in factual accuracy across three major integration strategies Survey high

02 02FalkorDB / GraphRAG SDK 90% hallucination reduction vs traditional RAG with sub-50ms query latency Benchmark high

03 03Microsoft GraphRAG Solves cross-document reasoning that chunk-based RAG cannot — LinkedIn saw 63% faster ticket resolution System high

04 04Rasmussen et al., Jan 2025 Bitemporal provenance graph outperforms MemGPT (94.8% vs 93.4% DMR) with 90% latency reduction Paper high

05 05Sansford et al., Jul 2024 Graph-structured eval identifies specific hallucinated triples; GraphCorrect rectifies the majority of them Paper med

06 06Pusch et al., Sep 2024 LLM + KG + query checker eliminates hallucinated Cypher queries in biomedical knowledge graphs Paper med

07 07Citation-Grounded Code Comprehension, Dec 2025 Forcing LLMs to cite retrieved line ranges achieves 100% precision — zero false negatives in 1,080 responses Paper high

08 08PaperTrail, Feb 2025 Granular claim-to-evidence provenance increases user caution vs standard citations, but trust alone doesn’t change behavior User Study med

09 09ICLR 2026 MemAgents Workshop Agent memory requires provenance-aware retrieval, write policies, and temporal credit assignment — not just bigger context Workshop med

10 10Guardrails AI Post-hoc provenance checking via embedding similarity + LLM judge catches unsupported claims in RAG pipelines Tool med

Conclusions

Graph structure preserves relationships

Entity relationships that flat RAG destroys are preserved in graph form — this is why chunk-based retrieval plateaus on complex queries.

Temporal provenance enables reasoning

Bitemporal modeling lets agents reason about what changed, when, and why — not just what’s true now. This cuts token waste on stale context.

Provenance + AuthZ at the graph layer

You can enforce who sees what evidence — critical for enterprise trust. Authorization evaluated at query time, not ingestion.

Prevention beats detection

Architectural citation constraints (must-cite-source) outperform probabilistic hallucination detection. Force the model to ground its claims.

Schema-as-code for knowledge graphs

Treating extraction prompts as deployable config turns a one-off project into a platform. Fewer tokens, more structure.

The stack is converging

Graph memory + explicit provenance + structured prompting is the emerging stack for trustworthy LLM systems with lower token budgets.

Open Source Tooling

Dgraph Viewer

github.com/corewood-tech/dgraph-viewer

A lightweight, browser-based graph explorer for Dgraph databases. Inspired by Neo4j’s browser interface, distributed as a single Go binary with zero dependencies.

Toggle between Three.js 3D rendering and D3.js 2D SVG views while preserving graph state. Explore nodes incrementally via double-click expansion with radial glow highlighting that reveals BFS-ranked connections. Type-specific platonic solids — icosahedron, octahedron, dodecahedron — make entity types instantly distinguishable.

Read-only by design: all mutation queries are blocked at the proxy layer. Live-switch between Dgraph instances without restart.

Dgraph Viewer — 3D graph visualization with force-directed layout showing interconnected nodes as platonic solids

3D force-directed graph visualization with DQL query editor and schema inspector

Provenance Chain

generated_by: Claude Opus 4.6

retrieval_method: web_search → 5 queries → 50 results → filtered to 10 sources

sources_verified: URLs point to arxiv, GitHub, ACL Anthology, OpenReview

citation_constraint: all findings paraphrased from search results with inline attribution

hallucination_risk: LOW — all claims grounded in retrieved documents, but verify URLs yourself

This provenance block was generated by the same LLM that made the content above.

The irony is not lost on us. That’s the whole point.

Trust graphs, not tokens.

Building graph-backed AI systems that earn trust?

Talk to Corewood