energy green reverse retrieval augmented generation
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energy green reverse retrieval augmented generation

A significant evolution.

Arborist from unturf is the next layer built directly on the client-side Reverse RAG foundation from UncloseAI. It takes the simple live page context idea and scales it into a provable auditable large-scale system while staying fully open source under AGPL-3.0.

Core Idea: Merkle-Providence Reverse RAG

It keeps the spirit of Reverse RAG generate first then rigorously verify but adds heavy cryptographic and mechanical guarantees.

Merkle trees plus hash chains create tamper-evident audit trails for every answer. Every answer gets a cryptographic proof linking back to the exact source bytes. The verifier is mechanical and deterministic not another LLM using exact matching spans named entities and so on. The model never writes the actual quotes. It only references labels like E1 E2 and Arborist renders the real source text. This kills fabricated citations by design.

Here is how it builds on UncloseAI Reverse RAG:

UncloseAI Reverse RAG uses client-side live DOM injection. Arborist adds Reverse RAG plus Merkle proofs plus audit chain. This gives much stronger verifiability.

UncloseAI works on a single webpage. Arborist scales to 10 million plus documents like Wikipedia-class corpora. This is massive scaling.

UncloseAI uses fresh context to reduce errors. Arborist adds mechanical verifier with grounded partly grounded and ungrounded labels for near-zero hallucination claims.

UncloseAI offers live page context. Arborist provides cryptographic proofs plus falsification states for auditable and shareable results.

UncloseAI is pure client-side with no database. Arborist uses SQLite shards with no vector database needed and remains lightweight.

Both are open but Arborist uses strong copyleft AGPL-3.0.

Standout Capabilities of Arborist:

Answers you can prove. Every cached answer is content-addressed and replayable. Falsification-friendly. You can explicitly challenge and audit wrong answers while preserving the chain. Federation. It works across peers in a mesh. No vector database required like the original Reverse RAG. It uses FTS5 plus clever chunking plus optional rerankers but rerankers do not touch the proof path. Strong measured results on misattribution with 100 percent catch rate at 0 percent false positives in their tests.

This feels like the unstoppable version because it combines the simplicity and freshness of client-side Reverse RAG with enterprise-grade verifiability through Merkle proofs audit chains and deterministic checking.

This is impressive work and genuinely pushes verifiable AI forward.