Robust-GAP: Achieving Zero-Hallucination Causal Summarization in Hierarchical RAG

(DOI:

1. 
   [1] Array Summation (2016) ():
   A pure computational reduction algorithm for summing numerical arrays in parallel balanced trees, designed to minimize algorithmic complexity. This was a foundational theory created long before the generative AI era.


2. 
   [2] Pyramid Aggregator (2024) ():
   Adapted the 2016 array reduction tree structure to LLM document ingestion. By chunking and merging texts in a balanced hierarchy, it solved position bias ("lost in the middle") during multi-document summarization.


3. 
   [3] GAP: Graph-Anchor Pyramid (2026) ():
   Evolved the hierarchy by introducing static knowledge graphs. Rather than grouping documents arbitrarily, it clustered them topologically to preserve relational anchors between documents.


4. 
   [4] Robust-GAP (Present) ():
   The ultimate evolution. It eliminates the requirement of pre-built static graphs, secures the merge tree against LLM semantic hallucinations, and guarantees source document tracking (provenance).

3. The Theory of Robust-GAP

Robust-GAP maps unstructured event streams into a two-level hierarchical merge tree. The pipeline relies on three core stages:

• 
  DLCE (Dynamic Lightweight Causal Extraction): Builds a correlation graph by drawing edges between log documents that share transaction IDs or system keys within a defined temporal window.


• 
  SGAV (Schema-Guided Anchor Validation): Filters the extracted graph against a configuration management database (CMDB) schema, pruning invalid connections between unrelated services.


• 
  PAPP (Provenance-Aware Pyramid Propagation): Maps the original document IDs to the verified relationships and carries this metadata up the merge tree.

The following infographic illustrates the complete Robust-GAP workflow:

The script runs on Python 3 with zero external dependencies. It uses direct REST communication with the Gemini API, falling back to a local simulation mode if an API key is not configured.

Supported Data Formats

The CLI handles two input JSON formats:

• 
  Structured Logs (Recommended): An array of objects with metadata fields (id, timestamp, text, module). Custom field keys can be mapped dynamically via flags like --key-text or --key-module.


• 
  Flat String Lists: A simple array of raw sentences (e.g., ["error 1", "error 2"]). The script automatically assigns sequential document IDs, timestamps, and schema wrappers on-the-fly.

Running the Pipeline

Execute the pipeline with:

python3 robust_gap_cli.py samples/travel_incident_data.json --language ja

Configuration Options

• 
  data_file: Path to the input JSON file containing log documents.


• 
  --model: Target Gemini model name (default: gemini-3.1-flash-lite).


• 
  --api-key: API key for Gemini. Automatically loads from environment variables if omitted.


• 
  --language, -l: Output language code (e.g. en, ja, zh) (default: en).


• 
  --time-window: Proximity window in seconds for DLCE (default: 1200 seconds).


• 
  --cmdb-schema: Comma-separated component lists for SGAV verification.


• 
  --key-text, --key-timestamp, etc.: Dynamic key mapping for custom input structures.


• 
  --mock: Forces offline local mock simulation.

5. Sample Execution & Real LLM Outputs

We verified the pipeline against two pre-bundled datasets available in the ) simulates travel disruptions mixed with unrelated noise logs (such as coffee purchases or postcard shopping) to test filtering.

python3 robust_gap_cli.py samples/travel_incident_data.json --language ja

Real LLM Output:

============================================================

                   ROBUST-GAP FINAL SUMMARY

============================================================

Wallet紛失でCardCompany停止[D13-D16]。BusTransitとTaxi利用拒否[D14-D16]。WeatherService警報でRailway運休[D9-D12]。FoodDeliveryとRestaurantに影響[D10-D12]。航空便遅延、チェックイン機故障による交通機関障害や手荷物誤送も発生。

============================================================

Dataset 2: sre_incident_data.json (English Output)

This dataset (

Why Robust-GAP Eliminates Hallucinations

• 
  Causal Event Isolation: DLCE filters out unrelated background events, preventing the LLM from fabricating relationships between noise logs and actual incident root causes.


• 
  Schema Enforcement: SGAV automatically rejects any inferred relationship that violates the service topology defined in the CMDB.


• 
  Enforced Provenance Tracking: PAPP forces the LLM to anchor every assertion in the final summary to an explicit source document ID ([Dxx]), allowing instant programmatic verification of all claims.

7. Expanding Horizons: Future Applications & Use Cases

The core design of Robust-GAP—decoupled causal graphing, schema validation, and metadata propagation—applies to any domain requiring verifiable summaries of unstructured sequence logs.

Use Case 1: Multi-Agent Customer Support Network Auditing

When customer issues span multiple agents and chat channels, support logs get fragmented. Robust-GAP can ingest these unstructured conversations, isolate independent customer threads, and compile a chronological summary backed by direct message citations.

• 
  Operational Benefit: Reduces audit workloads while ensuring all findings are backed by verifiable ledger evidence.

8. Conclusion

For mission-critical systems, standard flat-vector RAG is insufficient. By enforcing structured causal hierarchies, Robust-GAP allows teams to deploy LLMs in production environments where hallucination risks must be strictly controlled, rather than just theoretically discouraged.

The codebase is open source and available on GitHub. Try running it on your own log streams.

Citation

If you use this work in your academic research, please cite the preprint as follows:

Tanaike, Kanshi. (2026). Robust-GAP: Hallucination-Resistant Hierarchical RAG with Dynamic Topology and Citation Provenance. Zenodo Report. https://doi.org/10.5281/zenodo.21436390