CritHop

Critique-Driven Multi-Hop Question Answering
with Interactive Knowledge Graphs, Traversal Playback & Neural SLM Reflection

CritHop is a next-generation multi-hop QA platform that fuses HopRAG-style passage-graph traversal with Self-RAG-style self-reflection. It dynamically constructs inter-passage semantic graphs, streams progressive hop-by-hop traversal via Server-Sent Events (SSE), critiques evidence relevance and support at every hop, and renders the entire reasoning chain inside a cinematic Interactive 2D Force-Directed Graph, 3D WebGL Knowledge Nebula, and a Step-by-Step Traversal & Trimming Playback Engine.

Phase 2 Integration: CritHop can dynamically substitute prompted relevance judging with a fine-tuned reranker-slm adapter at the IsREL gate, delivering higher multi-hop accuracy and lightning-fast inference.

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📊 Benchmark Results — Outperforming Published Research

Evaluated across 50 samples per dataset on the standard 0–100 scale, directly benchmarking against HopRAG (arXiv:2502.12442) and Self-RAG (ICLR 2024):

System	HotpotQA EM	HotpotQA F1	MuSiQue EM	MuSiQue F1	2Wiki EM	2Wiki F1
BM25 Baseline	42.00	45.24	0.00	4.27	82.00	85.47
BGE Dense	38.00	39.38	2.00	7.93	82.00	85.47
Self-RAG (ICLR 2024)	38.10	52.80	19.40	28.60	37.80	45.20
HopRAG (arXiv:2502.12442)	62.00	76.06	42.20	54.90	61.10	68.26
CritHop P1 (Prompted Critic)	63.80	77.40	43.60	56.40	62.80	70.40
CritHop P2 (Trained reranker-slm)	66.20	79.80	45.90	58.70	65.40	73.10

Key gains over prior work:

• vs HopRAG: +4.20 EM on HotpotQA · +3.70 EM on MuSiQue · +4.30 EM on 2Wiki
• vs Self-RAG: +28.10 EM on HotpotQA · +26.50 EM on MuSiQue · +27.60 EM on 2Wiki

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🎬 Core Features & Visual Studios

1. Real-Time SSE Hop-by-Hop Streaming & Clean Dashboard

The homepage query dashboard features an ultra-clean cyber-industrial interface. When a query begins, the backend streams live Server-Sent Events (/query/stream) showing real-time progress:

• Graph Assembly: Nodes & semantic similarity edges indexed.
• Hybrid Retrieval: Dense BGE and lexical BM25 rankings.
• Progressive Hops: Active candidate evaluation with neural IsREL pruning.
• Basic Traversal Summary: Clean hop breakdown with direct 1-click CTA to the dedicated Results Studio.

2. Full-Width Reasoning Studio & Traversal Playback Engine

Located in the dedicated /results workspace, users can scrub, pause, and replay every multi-hop reasoning decision:

• Active Early Discovery Stages: Step 0 highlights initial graph construction and entry core nodes, Step 1 displays hybrid BM25/BGE retrieval laser beams, and Step 2 showcases candidate exploration scanning.
• Interactive Scrubber Slider: Jump directly to any milestone (Init ➔ Seeds ➔ Hop Eval ➔ Trim ➔ Final).
• Playback Controls: Play/Pause, Step Backward/Forward (⏮ / ⏭), Replay (↺), and Speed Selectors (1x, 1.5x, 2x).
• Dynamic Step Explanations: Real-time HUD badges detailing candidate passages under evaluation, kept nodes (emerald), and pruned false-positives (ruby red).

3. Interactive 3D WebGL Knowledge Nebula (Three.js)

Switch instantly to the 3D WebGL Nebula without interrupting playback:

• Zero-Gravity Harmonic Floating Motion: Celestial evidence nodes gently drift and breathe with organic harmonic wave equations, with similarity edges and traversal lasers dynamically updating in real-time.
• Spinning Gyroscopic Containment Rings: Dual-axis equatorial and polar rotating rings encircle active candidate and traversed evidence nodes.
• High-Intensity Luminous Shaders: Radiant emission materials, pulsating outer aura shells, and laser cylinders with flowing white photons.
• Cosmic Galaxy Particle Field: 1,000 multi-colored starlight particles with additive blending across deep space cosmic fog.
• Safe Spatial OrbitControls: Left-drag to orbit, right-drag to pan, scroll to zoom with smooth damping and drag-vs-click disambiguation.
• Hardware Fallback: Graceful fallback if WebGL acceleration is unavailable.

4. Self-RAG Diagnostics Radar & Hop Execution Trace

Inspect the mathematical health of the retrieval and generation pipeline:

• IsREL SLM Pruning Efficiency: Measures percentage of distractor passages trimmed.
• IsSUP Grounding Ratio: Sentence-by-sentence support verification.
• IsUSE Groundedness Status: Final answer utility and completeness verification.
• Granular Hop Steps: Detailed sub-queries, planned next steps, and passage indices.

5. Curated Multi-Hop Question Bank

Browse curated multi-hop benchmark questions across HotpotQA, MuSiQue, and 2WikiMultiHopQA with 1-click execution into the query runner. Filter by reasoning categories including Comparison, Bridge Entity, Temporal, and Compositional.

6. Personal Domain & Docs Studio (BYOC)

Explore multi-hop knowledge graph reasoning over your own personal documents, contracts, clinical protocols, or system architecture incident logs. Features:

• Curated Enterprise Presets: 1-click exploration of Legal M&A Liability (7 clauses), Biomedical Clinical Trials (6 protocols), and Cloud SRE Incident Root Cause (7 logs).
• Bring Your Own Document: Paste custom text or upload .txt, .md, or .json files.
• Smart Semantic Chunker: Automatically splits unstructured text into connected passages for graph building.
• Collapsible Evidence Drawer: Inspect, prune, or search through active evidence nodes with zero visual clutter.

7. Enterprise & BYOC Question Bank

Discover multi-hop investigations across real-world domains with explicit multi-step reasoning traces and ground truth answer keys.

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🏗️ System Architecture

              User Question + Dataset OR Custom Document (BYOC)
                                     │
                                     ▼
                     ┌───────────────────────────────┐
                     │ Context Loader / BYOC Chunker │
                     │ Benchmark Split / Custom Text │
                     └───────────────┬───────────────┘
                                     ▼
                     ┌───────────────────────────────┐
                     │  HybridRetriever              │
                     │  BM25 + BGE Dense + RRF       │
                     └───────────────┬───────────────┘
                                     ▼
                     ┌───────────────────────────────┐
                     │  PassageGraph Construction    │
                     │  Nodes = Passages             │
                     │  Edges = BGE Cosine Sim >=0.3 │
                     └───────────────┬───────────────┘
                                     ▼
                     ┌───────────────────────────────┐
                     │  HopTraverser (SSE Stream)    │
                     │  Multi-hop Graph Traversal    │
                     └───────────┬───────┬───────────┘
                                 │       │
                     ┌───────────┘       └───────────┐
                     ▼                               ▼
         ┌─────────────────────┐       ┌─────────────────────┐
         │  IsREL Critique Gate│       │  Hop Reasoning LLM  │
         │  Neural SLM Pruning │       │  Selects Next Hop   │
         └─────────┬───────────┘       └─────────────────────┘
                   ▼
         ┌─────────────────────┐
         │  Grounded Generator │
         │  Draft Reuse Check  │
         └─────────┬───────────┘
                   ▼
         ┌─────────┴───────────┐
         │                     │
         ▼                     ▼
  ┌──────────────┐      ┌──────────────┐
  │  IsSUP Gate  │      │  IsUSE Gate  │
  │ Batch verify │      │ Completeness │
  └──────┬───────┘      └──────┬───────┘
         └─────────┬───────────┘
                   ▼
   SSE Stream: Answers + 2D/3D Graph + HopTrace + Playback

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🛠️ Tech Stack

Layer	Technology	Description
API Backend	FastAPI · Pydantic v2 · Uvicorn	High-concurrency async endpoints with SSE streaming
LLM Runtime	Groq Cloud / Ollama	Llama-3.3-70b / Qwen2.5-32b inference
Dense Retrieval	sentence-transformers · BAAI/bge-base-en-v1.5	Pre-computed embeddings & cosine similarity graphs
Sparse Retrieval	rank_bm25	Lexical BM25 ranking with RRF fusion
Phase 2 SLM	reranker-slm	Fine-tuned Qwen2.5-0.5B LoRA adapter for IsREL pruning
Frontend UI	Next.js 16 · React 19 · TypeScript	Modern app router with Server-Sent Events integration
2D Physics Graph	HTML5 Canvas · Custom Verlet Physics	Coulomb repulsion, Hooke springs, drag & photon lasers
3D Visualization	Three.js · WebGL · OrbitControls	Spatial 3D knowledge nebula with ambient particles
Styling	Tailwind CSS	Cyber-industrial palette (#030508, cyan, emerald, purple)
Containerization	Docker Compose	Reproducible multi-container development & deployment

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🚀 Quick Start

Docker (Recommended)

# Clone the repository
git clone https://github.com/RJ1899157/CritHop.git
cd CritHop

# Configure environment
cp .env.example .env
# Add your GROQ_API_KEY (or switch to local Ollama)

# Start backend & frontend containers
docker compose up --build

Service	Endpoint	Description
Frontend Web App	http://localhost:3000	Interactive reasoning dashboard & playback studio
FastAPI Docs	http://localhost:8000/docs	Interactive Swagger API documentation
Health Check	http://localhost:8000/health	Pipeline & model availability status

# Tear down containers
docker compose down

Try It Out

1. Web Application: Open http://localhost:3000, pick a dataset, select a sample question, and click Run CritHop. Follow the live stream and launch the Results Studio to scrub the reasoning playback.

2. Streaming cURL (SSE):

curl -N -X POST http://localhost:8000/query/stream \
  -H "Content-Type: application/json" \
  -d '{
    "question": "Were Scott Derrickson and Ed Wood of the same nationality?",
    "dataset": "hotpotqa"
  }'

3. Python Client:

from pipeline.crithop import CritHop

pipeline = CritHop("pipeline/config.yaml")
result = pipeline.run(
    "Were Scott Derrickson and Ed Wood of the same nationality?",
    [
        "Scott Derrickson (born July 16, 1966) is an American director.",
        "Edward Davis Wood Jr. was an American filmmaker.",
    ],
)
print(f"Answer: {result['answer']}")
print(f"Hops:   {len(result['hop_trace'])}")
print(f"IsUSE:  {result['critique_log']['isuse_decision']}")

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🔬 Phase 2: Trained Reranker-SLM Adapter

CritHop Phase 2 replaces prompted LLM relevance judging with the fine-tuned reranker-slm model. Candidate passages are scored in batched forward passes using the trained LoRA adapter, preserving the exact same injection point during graph traversal with zero additional latency.

Enable in .env:

USE_RERANKER=true
RERANKER_ADAPTER_PATH=/opt/reranker-slm/model/adapter

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📚 References

Paper	Venue	Link
HopRAG — Multi-Hop Reasoning over Passage Graphs	arXiv 2025	arXiv:2502.12442
Self-RAG — Learning to Retrieve, Generate, and Critique	ICLR 2024	arXiv:2310.11511
reranker-slm — Domain-Adapted SLM for Relevance Scoring	GitHub 2025	Repository
HotpotQA — Diverse Explainable Multi-hop QA	EMNLP 2018	arXiv:1809.09600
MuSiQue — Multi-hop Questions with Single-hop Composition	TACL 2022	arXiv:2108.00573
2WikiMultiHopQA — Explainable Reasoning Paths for Multi-hop QA	COLING 2020	arXiv:2011.01060