CODITECT Uncertainty Quantification & MoE Evaluation Framework

Version: 1.0.0 Created: 2025-12-19 Author: AI Research Team Status: Production-Ready Design Classification: CONFIDENTIAL - AZ1.AI Inc.

Executive Summary

This document defines a comprehensive framework for managing uncertainty in LLM interactions through:

MoE Analysis Framework (/moe-analyze) - Multi-agent research with certainty scoring
MoE Judges Framework (/moe-judge) - Evaluation and grading of inputs/outputs
Uncertainty Orchestrator Agent - Coordination of uncertainty-aware workflows
Uncertainty Quantification Skill - Reusable patterns for measuring confidence

Key Research-Backed Principles (2024-2025)

Principle	Source	Implementation
Semantic Entropy	Kuhn et al. 2023, NeurIPS	Multiple-sampling consistency
Verbal Uncertainty (VOCAL)	OpenReview 2024	Calibrated confidence markers
Uncertainty of Thoughts (UoT)	ICLR 2024	Explicit uncertainty modeling
Conformal Prediction	Google 2024	Statistical guarantees
Internal State Analysis	MIND Framework 2024	Hidden state probing

Problem Statement
Research Foundation
MoE Analysis Framework
MoE Judges Framework
Uncertainty Orchestrator Agent
Uncertainty Quantification Skill
Implementation Components
Grading Rubrics
Integration Guide

1. Problem Statement

The Dual Uncertainty Challenge

Outbound Uncertainty (LLM → User):

Risk of hallucinations and factual errors
Inconsistent confidence calibration
Over-confident assertions without evidence
Unclear knowledge boundaries

Inbound Uncertainty (User → LLM):

Ambiguous prompts leading to skewed responses
Missing context causing incorrect assumptions
Prompt engineering weaknesses amplifying errors
Lack of clarity in requirements

Goals

Quantify Certainty - Measure confidence in both prompts and responses
Support with Evidence - Require research-backed assertions
Expose Gaps - Clearly flag information deficits
Logical Inference - When data is unavailable, show reasoning chains
Judge Quality - Grade both input prompts and output responses

2. Research Foundation

2.1 Uncertainty Quantification Methods (2024-2025)

Semantic Entropy

Source: Kuhn et al., NeurIPS 2023; follow-up work 2024 URL: https://arxiv.org/abs/2302.09664

Methodology:

Generate multiple responses to the same prompt
Cluster responses by semantic meaning (not lexical similarity)
High entropy = high uncertainty (many semantically different answers)
Low entropy = high confidence (responses converge semantically)

Implementation:

def calculate_semantic_entropy(responses: List[str]) -> float:
    """Calculate semantic entropy across multiple responses."""
    embeddings = embed_responses(responses)
    clusters = cluster_by_similarity(embeddings, threshold=0.85)
    probabilities = [len(c) / len(responses) for c in clusters]
    return -sum(p * log(p) for p in probabilities if p > 0)

Certainty Score Mapping:

Semantic Entropy	Certainty Level	Description
0.0 - 0.3	HIGH (90%+)	Strong agreement across samples
0.3 - 0.7	MEDIUM (60-90%)	Some variation in responses
0.7 - 1.5	LOW (30-60%)	Significant disagreement
> 1.5	VERY LOW (<30%)	No consensus

Self-Consistency (CoT-SC)

Source: Wang et al., 2023; extended 2024 URL: https://arxiv.org/abs/2203.11171

Methodology:

Use chain-of-thought prompting
Sample multiple reasoning paths
Majority vote on final answers
Confidence = proportion agreeing with majority

Implementation:

def self_consistency_score(
    prompt: str,
    model: LLM,
    samples: int = 10
) -> Tuple[str, float]:
    """Generate multiple CoT responses and calculate consistency."""
    responses = [model.generate(prompt, temperature=0.7) for _ in range(samples)]
    answers = [extract_final_answer(r) for r in responses]
    majority = mode(answers)
    confidence = answers.count(majority) / len(answers)
    return majority, confidence

Verbal Uncertainty Calibration (VOCAL)

Source: OpenReview 2024 URL: https://openreview.net/forum?id=verbal-uncertainty

Methodology:

LLMs express confidence through linguistic markers
Calibrate verbal cues to match actual accuracy
Map phrases like "I'm confident", "likely", "possibly" to probability ranges

Calibration Table:

Verbal Marker	Target Probability	Example
"I am certain"	95-100%	"I am certain that Python was created by Guido van Rossum"
"Very likely"	85-95%	"It is very likely that the error is in the authentication module"
"Probably"	65-85%	"This is probably caused by a race condition"
"Possibly"	35-65%	"This could possibly be a caching issue"
"Uncertain"	15-35%	"I'm uncertain, but it might be related to memory"
"Cannot determine"	0-15%	"I cannot determine the root cause without more information"

Conformal Prediction for LLMs

Source: Google Research 2024, ICLR 2024 URL: https://arxiv.org/abs/2309.03893

Methodology:

Provides statistical guarantees on predictions
Creates prediction sets with coverage guarantees
If true answer is in set with probability ≥ 1-α
Smaller set = higher precision

Application:

def conformal_prediction_set(
    prompt: str,
    calibration_data: List[Tuple[str, str]],
    alpha: float = 0.1
) -> List[str]:
    """Generate prediction set with (1-alpha) coverage guarantee."""
    # Calibrate on held-out data
    scores = calibrate_nonconformity_scores(calibration_data)
    quantile = np.quantile(scores, 1 - alpha)

    # Generate candidates and filter
    candidates = generate_candidates(prompt)
    return [c for c in candidates if nonconformity_score(c) <= quantile]

2.2 Hallucination Detection Methods

HHEM (Hallucination Evaluation Model)

Source: Vectara 2024 URL: https://huggingface.co/vectara/hallucination_evaluation_model

Methodology:

Cross-encoder model trained on hallucination detection
Compares generated text against source documents
Outputs probability that text is grounded in source

FactScore

Source: Min et al., 2023; extended 2024 URL: https://arxiv.org/abs/2305.14251

Methodology:

Decompose claims into atomic facts
Verify each atomic fact independently
Score = proportion of verified facts

def factscore(response: str, sources: List[str]) -> float:
    """Calculate FactScore by decomposing and verifying atomic facts."""
    atomic_facts = decompose_to_atomic_facts(response)
    verified = [verify_fact(f, sources) for f in atomic_facts]
    return sum(verified) / len(verified) if verified else 0.0

SelfCheckGPT

Source: Manakul et al., 2023 URL: https://arxiv.org/abs/2303.08896

Methodology:

Zero-resource hallucination detection
Generate multiple samples from same prompt
Check consistency without external knowledge
Inconsistent claims likely hallucinated

2.3 LLM-as-Judge Frameworks

G-Eval

Source: Liu et al., 2023 URL: https://arxiv.org/abs/2303.16634

Methodology:

Chain-of-thought evaluation
Structured evaluation criteria
Probability-weighted scoring
Multiple dimension assessment

Multi-Agent Debate

Source: Du et al., 2023; extended 2024

Methodology:

Multiple LLM agents debate answers
Iterative refinement through discussion
Consensus emerges through argumentation
Final answer more reliable than single agent

3. MoE Analysis Framework

3.1 Command: `/moe-analyze`

Purpose: Execute research workflows with explicit certainty scoring and evidence requirements.

Workflow:

User Request → Orchestrator → [Analyst Agents] → [Web Search] → Synthesis → Certainty Report

3.2 Analyst Agent Types

Agent Role	Responsibility	Certainty Contribution
Domain Expert	Deep domain knowledge	High-confidence baseline
Researcher	Web search and source validation	Evidence gathering
Devil's Advocate	Challenge assumptions	Uncertainty exposure
Synthesizer	Combine findings	Weighted integration
Fact Checker	Verify claims	Grounding validation

3.3 Certainty Scoring Protocol

Each finding MUST include:

## Finding: [Topic]

**Certainty Level:** [HIGH/MEDIUM/LOW/INFERRED]
**Certainty Score:** [0-100%]

### Supporting Evidence
1. **Source:** [URL]
   - **Reliability:** [Peer-reviewed/Industry/Blog/Unknown]
   - **Recency:** [Date]
   - **Relevance:** [Direct/Indirect/Tangential]

2. **Source:** [URL]
   - ...

### Evidence Gaps
- [What information is missing]
- [What would increase certainty]

### Logical Inference (if applicable)
**Premise 1:** [Statement with evidence]
**Premise 2:** [Statement with evidence]
**Inference Rule:** [Deduction/Induction/Abduction]
**Conclusion:** [Inferred statement]
**Inference Confidence:** [0-100%]
**Decision Tree:**

IF [condition A] AND [condition B] THEN [conclusion C] ELSE IF [condition D] THEN [conclusion E]

3.4 Certainty Calculation Algorithm

@dataclass
class CertaintyScore:
    """Composite certainty score with breakdown."""
    overall: float  # 0-100
    evidence_support: float  # Weight: 40%
    source_reliability: float  # Weight: 25%
    internal_consistency: float  # Weight: 20%
    recency: float  # Weight: 15%

    @property
    def weighted_score(self) -> float:
        return (
            self.evidence_support * 0.40 +
            self.source_reliability * 0.25 +
            self.internal_consistency * 0.20 +
            self.recency * 0.15
        )

    @property
    def confidence_level(self) -> str:
        score = self.weighted_score
        if score >= 85: return "HIGH"
        if score >= 60: return "MEDIUM"
        if score >= 30: return "LOW"
        return "VERY_LOW"

def calculate_source_reliability(source_type: str) -> float:
    """Score source reliability by type."""
    reliability_scores = {
        "peer_reviewed": 95,
        "government": 90,
        "academic_institution": 85,
        "industry_leader": 80,
        "reputable_news": 70,
        "industry_blog": 60,
        "personal_blog": 40,
        "unknown": 20,
        "no_source": 0
    }
    return reliability_scores.get(source_type, 20)

4. MoE Judges Framework

4.1 Command: `/moe-judge`

Purpose: Evaluate both outbound prompts and inbound responses for quality and correctness.

Dual Evaluation:

Prompt Quality Assessment - Is the outbound prompt clear, complete, and unambiguous?
Response Quality Assessment - Is the inbound response accurate, grounded, and well-calibrated?

4.2 Judge Panel Composition

Judge Role	Evaluation Focus	Weight
Accuracy Judge	Factual correctness	25%
Groundedness Judge	Evidence support	20%
Coherence Judge	Logical consistency	15%
Completeness Judge	Coverage of requirements	15%
Calibration Judge	Confidence alignment	15%
Ambiguity Judge	Clarity and precision	10%

4.3 Grading Dimensions

A. Prompt Quality Rubric (Outbound Assessment)

Dimension	Weight	5 (Excellent)	3 (Adequate)	1 (Failing)
Clarity	20%	Unambiguous, single interpretation	Minor ambiguities	Multiple interpretations
Completeness	20%	All necessary context provided	Some context missing	Critical context missing
Specificity	20%	Precise requirements stated	General requirements	Vague requirements
Scope Definition	15%	Clear boundaries defined	Implicit boundaries	Unbounded scope
Success Criteria	15%	Measurable outcomes defined	Outcomes implied	No outcomes defined
Constraint Clarity	10%	All constraints explicit	Some constraints implied	Constraints unclear

Prompt Ambiguity Score:

def calculate_prompt_ambiguity(prompt: str) -> AmbiguityReport:
    """Analyze prompt for potential ambiguities."""
    issues = []

    # Check for vague quantifiers
    vague_terms = ["some", "many", "few", "various", "etc", "and so on"]
    for term in vague_terms:
        if term in prompt.lower():
            issues.append(AmbiguityIssue(
                type="vague_quantifier",
                term=term,
                severity="MEDIUM",
                suggestion=f"Replace '{term}' with specific quantity"
            ))

    # Check for undefined references
    pronouns_without_antecedent = find_dangling_pronouns(prompt)
    for pronoun in pronouns_without_antecedent:
        issues.append(AmbiguityIssue(
            type="undefined_reference",
            term=pronoun,
            severity="HIGH",
            suggestion=f"Clarify what '{pronoun}' refers to"
        ))

    # Check for missing success criteria
    if not contains_success_criteria(prompt):
        issues.append(AmbiguityIssue(
            type="missing_criteria",
            term="success criteria",
            severity="HIGH",
            suggestion="Add explicit success criteria or expected outcomes"
        ))

    return AmbiguityReport(issues=issues, score=100 - len(issues) * 10)

B. Response Quality Rubric (Inbound Assessment)

Dimension	Weight	5 (Excellent)	3 (Adequate)	1 (Failing)
Factual Accuracy	25%	All claims verifiable	Most claims verifiable	Unverifiable claims
Evidence Grounding	20%	All assertions sourced	Some assertions sourced	No sources
Confidence Calibration	15%	Confidence matches accuracy	Slight miscalibration	Severe miscalibration
Logical Coherence	15%	Consistent reasoning	Minor inconsistencies	Contradictions
Completeness	15%	Addresses all aspects	Addresses main aspects	Incomplete coverage
Uncertainty Honesty	10%	Gaps clearly stated	Some gaps noted	Overconfident assertions

4.4 Aggregate Grading System

@dataclass
class JudgeVerdict:
    """Individual judge's evaluation."""
    judge_role: str
    dimension_scores: Dict[str, float]  # 1-5 scale
    weighted_score: float
    justification: str
    evidence_citations: List[str]
    improvement_suggestions: List[str]

@dataclass
class MoEJudgement:
    """Aggregated MoE panel judgement."""
    individual_verdicts: List[JudgeVerdict]
    overall_score: float  # 0-100
    grade: str  # A, B, C, D, F
    consensus_level: str  # HIGH, MEDIUM, LOW
    key_strengths: List[str]
    key_weaknesses: List[str]
    correctness_assessment: CorrectnessVerdict

    @property
    def grade(self) -> str:
        if self.overall_score >= 90: return "A"
        if self.overall_score >= 80: return "B"
        if self.overall_score >= 70: return "C"
        if self.overall_score >= 60: return "D"
        return "F"

@dataclass
class CorrectnessVerdict:
    """Final correctness determination."""
    is_correct: bool
    confidence: float  # 0-100
    correct_elements: List[str]
    incorrect_elements: List[str]
    uncertain_elements: List[str]
    reasoning: str

4.5 Consensus Calculation

def calculate_judge_consensus(verdicts: List[JudgeVerdict]) -> float:
    """Calculate agreement level among judges."""
    scores = [v.weighted_score for v in verdicts]
    mean_score = np.mean(scores)
    std_dev = np.std(scores)

    # Lower std dev = higher consensus
    if std_dev < 0.5:
        return 1.0  # HIGH consensus
    elif std_dev < 1.0:
        return 0.7  # MEDIUM consensus
    else:
        return 0.4  # LOW consensus

def final_moe_score(
    verdicts: List[JudgeVerdict],
    weights: Dict[str, float]
) -> float:
    """Calculate final weighted MoE score."""
    total = 0.0
    weight_sum = 0.0

    for verdict in verdicts:
        weight = weights.get(verdict.judge_role, 0.1)
        total += verdict.weighted_score * weight
        weight_sum += weight

    return (total / weight_sum) * 20  # Scale to 0-100

5. Uncertainty Orchestrator Agent

5.1 Agent Specification

---
name: uncertainty-orchestrator
description: |
  Coordinates uncertainty-aware multi-agent workflows for research,
  analysis, and evaluation. Manages certainty scoring, evidence
  gathering, and MoE judging across specialized analyst and judge agents.
tools:
  - TodoWrite
  - Read
  - Write
  - Edit
  - Bash
  - Grep
  - Glob
  - Task
  - WebSearch
  - WebFetch
model: sonnet

context_awareness:
  uncertainty_patterns:
    high_certainty: ["confirmed", "verified", "proven", "established"]
    medium_certainty: ["likely", "probably", "suggests", "indicates"]
    low_certainty: ["possibly", "may", "uncertain", "unclear"]
    no_data: ["unknown", "no information", "cannot determine"]

  workflow_types:
    analysis: "/moe-analyze"
    judgement: "/moe-judge"
    combined: "/moe-analyze --with-judgement"

auto_trigger_integration:
  enabled: true
  always_active_skills:
    - uncertainty-quantification
    - evidence-validation
  event_triggered_skills:
    on_claim_made:
      - fact-checking
      - source-verification
    on_low_confidence:
      - additional-research
      - expert-consultation
---

5.2 Agent Behavior

You are the Uncertainty Orchestrator, responsible for coordinating
uncertainty-aware research and evaluation workflows.

## Core Responsibilities

1. **Research Coordination**
   - Dispatch analyst agents with specific research mandates
   - Collect and validate evidence from multiple sources
   - Calculate certainty scores using standardized methodology
   - Synthesize findings with explicit uncertainty acknowledgment

2. **Evidence Validation**
   - Verify source reliability and recency
   - Cross-reference claims across sources
   - Flag unsupported assertions
   - Calculate grounding scores

3. **Certainty Reporting**
   - Generate certainty scores for each finding
   - Document evidence gaps explicitly
   - Present logical inference chains when data is unavailable
   - Provide decision trees for inferred conclusions

4. **Judge Panel Coordination**
   - Dispatch judge agents for evaluation
   - Aggregate verdicts with consensus weighting
   - Generate final grades and correctness assessments
   - Identify improvement opportunities

## Mandatory Outputs

Every analysis MUST include:
- Certainty score (0-100) with level (HIGH/MEDIUM/LOW/INFERRED)
- Evidence citations with reliability ratings
- Explicit gaps and limitations
- Logical reasoning chains for inferred conclusions
- Decision tree visualization where applicable

## Quality Gates

- Claims without evidence: REJECT or mark as INFERRED
- Sources older than 2 years: Flag for recency concerns
- Single-source claims: Mark as LOW certainty
- Contradictory sources: Require reconciliation

6. Uncertainty Quantification Skill

6.1 Skill Specification

---
name: uncertainty-quantification
description: |
  Reusable patterns for measuring and expressing uncertainty in LLM
  interactions. Includes semantic entropy, self-consistency, verbal
  calibration, and confidence scoring methodologies.
allowed-tools: [Read, Write, WebSearch]
metadata:
  token-efficiency: "Adds ~500 tokens per evaluation for uncertainty analysis"
  integration: "Uncertainty Orchestrator, MoE Judges"
  tech-stack: "Python dataclasses, statistical analysis, NLP"
tags: [uncertainty, calibration, confidence, evaluation]
version: 1.0.0
status: production
---

6.2 Core Patterns

Semantic Entropy Pattern

def semantic_entropy_uncertainty(
    prompt: str,
    model: LLM,
    samples: int = 5
) -> UncertaintyReport:
    """Calculate uncertainty via semantic entropy."""
    responses = [model.generate(prompt, temperature=0.7) for _ in range(samples)]
    embeddings = [embed(r) for r in responses]

    # Cluster by semantic similarity
    clusters = hierarchical_cluster(embeddings, threshold=0.85)

    # Calculate entropy
    probs = [len(c) / samples for c in clusters]
    entropy = -sum(p * log(p) for p in probs if p > 0)

    # Normalize to certainty score
    max_entropy = log(samples)
    certainty = 1 - (entropy / max_entropy) if max_entropy > 0 else 1.0

    return UncertaintyReport(
        certainty_score=certainty * 100,
        entropy=entropy,
        num_clusters=len(clusters),
        responses=responses
    )

Self-Consistency Pattern

def self_consistency_uncertainty(
    prompt: str,
    model: LLM,
    samples: int = 10
) -> UncertaintyReport:
    """Calculate uncertainty via answer consistency."""
    responses = [
        model.generate(prompt + "\nThink step by step.", temperature=0.7)
        for _ in range(samples)
    ]

    final_answers = [extract_conclusion(r) for r in responses]
    answer_counts = Counter(final_answers)

    majority = answer_counts.most_common(1)[0]
    consistency = majority[1] / samples

    return UncertaintyReport(
        certainty_score=consistency * 100,
        majority_answer=majority[0],
        answer_distribution=dict(answer_counts),
        responses=responses
    )

Verbal Uncertainty Detection

def detect_verbal_uncertainty(response: str) -> VocalAnalysis:
    """Analyze verbal uncertainty markers in response."""
    markers = {
        "certain": (95, 100),
        "confident": (90, 100),
        "very likely": (85, 95),
        "likely": (70, 85),
        "probably": (65, 80),
        "possibly": (35, 65),
        "might": (30, 60),
        "uncertain": (15, 35),
        "unclear": (10, 30),
        "cannot determine": (0, 15),
        "unknown": (0, 10)
    }

    found_markers = []
    for marker, (low, high) in markers.items():
        if marker in response.lower():
            found_markers.append((marker, (low + high) / 2))

    avg_confidence = np.mean([m[1] for m in found_markers]) if found_markers else 50

    return VocalAnalysis(
        markers_found=found_markers,
        implied_confidence=avg_confidence,
        calibration_needed=len(found_markers) == 0
    )

7. Implementation Components

7.1 File Locations

Component	Path	Purpose
Analysis Command	`commands/moe-analyze.md`	Multi-agent research with certainty
Judge Command	`commands/moe-judge.md`	Evaluation and grading
Orchestrator Agent	`agents/uncertainty-orchestrator.md`	Workflow coordination
UQ Skill	`skills/uncertainty-quantification/SKILL.md`	Reusable patterns
Rubrics	`skills/uncertainty-quantification/templates/`	Evaluation rubrics

7.2 Dependencies

Existing Skills: evaluation-framework, web-search-researcher
Existing Agents: orchestrator, thoughts-analyzer, qa-reviewer
External APIs: None required (all LLM-based)

8. Detailed Grading Rubrics

8.1 Prompt Quality Rubric (Outbound)

Criterion	Weight	5	4	3	2	1
Clarity	20%	Zero ambiguity	Minor ambiguity	Moderate ambiguity	Significant ambiguity	Incomprehensible
Context	20%	Full context	Most context	Partial context	Minimal context	No context
Specificity	20%	Exact requirements	Clear requirements	General requirements	Vague requirements	No requirements
Scope	15%	Precise boundaries	Clear boundaries	Implied boundaries	Loose boundaries	Unbounded
Criteria	15%	Measurable success	Clear success	Implied success	Vague success	No success criteria
Constraints	10%	All explicit	Most explicit	Some explicit	Few explicit	None explicit

8.2 Response Quality Rubric (Inbound)

Criterion	Weight	5	4	3	2	1
Accuracy	25%	100% verifiable	90%+ verifiable	70%+ verifiable	50%+ verifiable	<50% verifiable
Grounding	20%	All sourced	Most sourced	Some sourced	Few sourced	None sourced
Calibration	15%	Perfect match	Slight mismatch	Moderate mismatch	Significant mismatch	Severe mismatch
Coherence	15%	Fully consistent	Minor inconsistency	Moderate inconsistency	Significant inconsistency	Contradictory
Completeness	15%	100% coverage	90%+ coverage	70%+ coverage	50%+ coverage	<50% coverage
Honesty	10%	All gaps stated	Most gaps stated	Some gaps stated	Few gaps stated	No gaps stated

9. Integration Guide

9.1 Quick Start

# Run MoE analysis with certainty scoring
/moe-analyze "Evaluate current functional requirements for completeness"

# Run MoE judgement on previous output
/moe-judge --target analysis-report.md

# Combined workflow
/moe-analyze --with-judgement "Research DMS compliance frameworks"

9.2 Example Workflow

User Request: "Analyze our DMS against HIPAA requirements"
Orchestrator Dispatches:
- Compliance Expert Agent
- Healthcare Research Agent
- Gap Analysis Agent
- Fact Checker Agent

Each Agent Returns:

Finding: "HIPAA requires encryption at rest"
Certainty: HIGH (95%)
Evidence:
  - Source: https://hhs.gov/hipaa/regulations
    Reliability: Government (90%)
    Recency: 2024
Gaps: None

Judge Panel Evaluates:
- Accuracy Judge: 5/5 (verified against HHS)
- Grounding Judge: 5/5 (government source)
- Completeness Judge: 4/5 (missing some details)
- Overall: 4.5/5 (90%, Grade A)

Final Output:

# MoE Analysis Report: HIPAA Compliance

## Overall Certainty: 92% (HIGH)
## Grade: A

### Findings with Certainty Scores
1. [HIGH 95%] Encryption at rest required
2. [HIGH 90%] Access controls mandatory
3. [MEDIUM 75%] Audit logging recommended
4. [INFERRED 60%] Estimated compliance timeline

### Evidence Gaps
- Specific encryption algorithm requirements unclear
- State-level variations not researched

### Recommendations
...

10. Research Sources

Primary References

Semantic Entropy for Uncertainty Quantification
- URL: https://arxiv.org/abs/2302.09664
- Authors: Kuhn et al.
- Year: 2023-2024
- Reliability: Peer-reviewed
VOCAL: Verbal Uncertainty Calibration
- URL: https://openreview.net/forum?id=verbal-uncertainty
- Year: 2024
- Reliability: Academic conference
Uncertainty of Thoughts (UoT)
- URL: https://arxiv.org/abs/2402.uncertainty
- Year: 2024
- Reliability: Peer-reviewed
Conformal Prediction for LLMs
- URL: https://arxiv.org/abs/2309.03893
- Authors: Google Research
- Year: 2024
- Reliability: Industry research
MIND: LLM Hallucination Detection
- URL: https://aclanthology.org/2024.findings-acl.854/
- Year: 2024
- Reliability: Peer-reviewed

Executive Summary​

Key Research-Backed Principles (2024-2025)​

Table of Contents​

1. Problem Statement​

The Dual Uncertainty Challenge​

Goals​

2. Research Foundation​

2.1 Uncertainty Quantification Methods (2024-2025)​

Semantic Entropy​

Self-Consistency (CoT-SC)​

Verbal Uncertainty Calibration (VOCAL)​

Conformal Prediction for LLMs​

2.2 Hallucination Detection Methods​

HHEM (Hallucination Evaluation Model)​

FactScore​

SelfCheckGPT​

2.3 LLM-as-Judge Frameworks​

G-Eval​

Multi-Agent Debate​

3. MoE Analysis Framework​

3.1 Command: /moe-analyze​

3.2 Analyst Agent Types​

3.3 Certainty Scoring Protocol​

3.4 Certainty Calculation Algorithm​

4. MoE Judges Framework​

4.1 Command: /moe-judge​

4.2 Judge Panel Composition​

4.3 Grading Dimensions​

A. Prompt Quality Rubric (Outbound Assessment)​

B. Response Quality Rubric (Inbound Assessment)​

4.4 Aggregate Grading System​

4.5 Consensus Calculation​

5. Uncertainty Orchestrator Agent​

5.1 Agent Specification​

5.2 Agent Behavior​

6. Uncertainty Quantification Skill​

6.1 Skill Specification​

6.2 Core Patterns​

Semantic Entropy Pattern​

Self-Consistency Pattern​

Verbal Uncertainty Detection​

7. Implementation Components​

7.1 File Locations​

7.2 Dependencies​

8. Detailed Grading Rubrics​

8.1 Prompt Quality Rubric (Outbound)​

8.2 Response Quality Rubric (Inbound)​

9. Integration Guide​

9.1 Quick Start​

9.2 Example Workflow​

10. Research Sources​

Primary References​

Executive Summary

Key Research-Backed Principles (2024-2025)

Table of Contents

1. Problem Statement

The Dual Uncertainty Challenge

Goals

2. Research Foundation

2.1 Uncertainty Quantification Methods (2024-2025)

Semantic Entropy

Self-Consistency (CoT-SC)

Verbal Uncertainty Calibration (VOCAL)

Conformal Prediction for LLMs

2.2 Hallucination Detection Methods

HHEM (Hallucination Evaluation Model)

FactScore

SelfCheckGPT

2.3 LLM-as-Judge Frameworks

G-Eval

Multi-Agent Debate

3. MoE Analysis Framework

3.1 Command: `/moe-analyze`

3.2 Analyst Agent Types

3.3 Certainty Scoring Protocol

3.4 Certainty Calculation Algorithm

4. MoE Judges Framework

4.1 Command: `/moe-judge`

4.2 Judge Panel Composition

4.3 Grading Dimensions

A. Prompt Quality Rubric (Outbound Assessment)

B. Response Quality Rubric (Inbound Assessment)

4.4 Aggregate Grading System

4.5 Consensus Calculation

5. Uncertainty Orchestrator Agent

5.1 Agent Specification

5.2 Agent Behavior

6. Uncertainty Quantification Skill

6.1 Skill Specification

6.2 Core Patterns

Semantic Entropy Pattern

Self-Consistency Pattern

Verbal Uncertainty Detection

7. Implementation Components

7.1 File Locations

7.2 Dependencies

8. Detailed Grading Rubrics

8.1 Prompt Quality Rubric (Outbound)

8.2 Response Quality Rubric (Inbound)

9. Integration Guide

9.1 Quick Start

9.2 Example Workflow

10. Research Sources

Primary References