Hierarchical Multi-Agent Reasoning System with Integrated Fact-Verification and Self-Correction Loops

Feature Requests
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:rocket: Feature Request

Implement a multi-agent reasoning architecture where specialized sub-models collaborate on complex tasks, with built-in fact-checking and self-correction mechanisms to eliminate confabulation.

Core Components:

  1. Specialist Agents: Deploy domain-specific reasoning modules (e.g., “Quantitative Analyst”, “Citation Validator”, “Logical Coherence Checker”, “Cross-Domain Synthesizer”) that can be dynamically invoked based on query type

  2. Fact-Verification Layer: Integrate real-time citation validation against DOI resolution services, PubMed API, arXiv API, and other authoritative databases. Any claimed citation must be algorithmically verified before inclusion in responses

  3. Self-Correction Loops: Implement recursive verification where:

    • Agent A generates claim
    • Agent B fact-checks claim against retrieved sources
    • Agent C verifies internal logical consistency
    • If conflicts detected, loop back to Agent A with specific corrections
    • Continue until consensus or explicit uncertainty acknowledgment

  4. Hierarchical Reasoning: Support meta-reasoning where the system can:

    • Break complex problems into sub-problems
    • Assign sub-problems to appropriate specialist agents
    • Synthesize verified sub-solutions into coherent final answer
    • Maintain state across 50+ reasoning steps without coherency drift

  5. Confidence Calibration: Each claim tagged with:

    • Source confidence (verified citation vs. reasoning inference)
    • Agent consensus level (unanimous vs. contested)
    • Uncertainty quantification (“high confidence” vs. “speculative”)

:magnifying_glass_tilted_left: Problem Statement

Current LLMs (including Perplexity) face three critical limitations that prevent reliable use in high-stakes research and decision-making:

  1. Confabulation: Models generate plausible but incorrect information (fake citations, fabricated statistics) due to RLHF training optimizing for confident responses over accuracy

  2. Coherency Drift: Extended reasoning tasks (>15-20 steps) lead to logical contradictions and inconsistency with earlier claims

  3. No Self-Awareness: Current systems cannot distinguish between verified facts, logical inferences, and speculative extrapolations

These limitations require extensive manual verification, limiting the practical utility of AI for complex analytical tasks. For example, developing the computational biology frameworks I recently published required ~1 week of prompt engineering just to implement verification loops catching confabulations.

:light_bulb: Proposed Solution

API Implementation:

response = perplexity.chat.completions.create(
    model="sonar-reasoning-pro",  # New reasoning-optimized model
    messages=[{"role": "user", "content": "complex query"}],
    reasoning_config={
        "enable_multi_agent": True,
        "specialist_agents": ["citation_validator", "logic_checker", "quantitative_analyst"],
        "verification_depth": "high",  # low/medium/high
        "max_iterations": 5,  # self-correction loops
        "confidence_threshold": 0.8  # minimum consensus
    }
)

Benefits:

  1. Eliminates Confabulation: Real-time fact-checking prevents fake citations from appearing in responses

  2. Maintains Coherency: Multi-agent consensus checking catches logical contradictions across long reasoning chains

  3. Transparency: Users see confidence levels and know which claims are verified vs. inferred

  4. Efficiency: Automated verification replaces manual fact-checking, reducing research time from weeks to hours

  5. Competitive Differentiation: This would position Perplexity as the only LLM provider guaranteeing factual accuracy for research applications

Use Case: A researcher could ask “Develop a multi-target cancer therapy framework with validated citations” and receive:

  • Only real, verified citations (no DOI: 10.1038/fake.2023 hallucinations)
  • Quantitative claims backed by actual data from retrieved papers
  • Clear tagging: [Verified Fact] vs. [Logical Inference] vs. [Speculative]
  • Maintained coherency across 50+ reasoning steps

This would transform Perplexity from “helpful but requires verification” to “research-grade reliable.”