RAG++: State-Based Retrieval for Life Trajectory Optimization

RAG++: State-Based Retrieval for Life Trajectory Optimization

Authors: Mo Diomande, Claude (Anthropic)
Date: December 2025
Status: Working Paper
System: TrajectoryOS

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Abstract

We present RAG++ (Retrieval-Augmented Generation Plus Plus), a novel retrieval paradigm that extends traditional RAG from semantic text retrieval to state-space transition retrieval. Instead of retrieving "relevant documents," RAG++ retrieves successful state transitions from a user's personal history and recommends actions based on what worked in similar dynamical regimes. We demonstrate this approach in TrajectoryOS, a life physics modeling system that treats human life as a dynamical system with measurable state variables (thrust, alignment, gravity, mass) and an escape index η = T/(G×M). RAG++ operates in three geometries simultaneously—topological (DLM coordinates), temporal (cyclic phases), and dynamical (physics regime)—enabling context-aware policy recommendations that account for structural position, recurrence patterns, and operating constraints. Initial implementation shows promising results with 70

Keywords: Retrieval-Augmented Generation, State-Space Methods, Life Optimization, Dynamical Systems, Topological Search, Personal Analytics

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1. Introduction

1.1 The Limitation of Traditional RAG

Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding large language models in external knowledge [1]. The standard RAG pipeline follows a simple pattern:

1. Encode: Embed documents into a semantic vector space
2. Retrieve: Find k-nearest neighbors to query embedding
3. Generate: Condition language model on retrieved chunks + query

This works well for information retrieval tasks where the goal is to find and summarize relevant facts. However, RAG fundamentally treats the world as a collection of static documents. It has no native representation for:

• Structural relationships (hierarchical position, depth in decision tree)
• Temporal cycles (recurrence, phase, rhythm)
• Dynamical state (momentum, constraints, operating regime)
• Transition dynamics (what actions lead to what outcomes)

1.2 The Need for State-Based Retrieval

Consider a user asking: "I'm stuck with scattered focus and high pressure. What should I do?"

A traditional RAG system might retrieve:
- Documents about focus techniques
- Articles on stress management
- Past journal entries mentioning "stuck"

But this misses critical context:
- Where in the life trajectory is the user? (Regime: stuck vs. escaping)
- What time/phase is it? (Monday morning vs. Saturday evening)
- What worked last time they were in a similar state?
- What structural position do they occupy? (Deep in a decision tree vs. exploring options)

We need a retrieval system that operates in state space, not just semantic space.

1.3 Contribution

We introduce RAG++, a state-based retrieval system that:

1. Retrieves transitions, not chunks: The fundamental unit is a state transition (S_i → S_j, Δη) with associated actions
2. Operates in 3D geometry: Topological (DLM coordinates), temporal (cyclic phase), and dynamical (physics regime)
3. Enables strategic reasoning: Recommends actions based on historical success in similar states
4. Provides interpretable explanations: Traces recommendations back to specific past transitions

We demonstrate RAG++ in TrajectoryOS, achieving 70

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2. Background

2.1 Retrieval-Augmented Generation

RAG [1] combines parametric knowledge (LLM weights) with non-parametric knowledge (retrieved documents). The standard formulation:

p(y|x) = ∑_d p(y|x,d) · p(d|x)

where:
- `x` is the query
- `d` is a retrieved document
- `y` is the generated response

Retrieval typically uses:
- Dense retrieval: Embedding similarity (BERT, Sentence Transformers)
- Sparse retrieval: BM25, TF-IDF
- Hybrid: Combination of both

Limitation: This formulation assumes documents are the right unit of retrieval. For personal life optimization, we need to retrieve situations and their outcomes.

2.2 Life Physics Model

TrajectoryOS models human life as a dynamical system with four core variables [2]:

1. Thrust (T): Forward momentum, skill × utilization × alignment
2. Alignment (A): Coherence between actions and values (0-1)
3. Gravity (G): External constraints, obligations, resistance
4. Mass (M): Complexity, number of active commitments

The escape index η measures trajectory sustainability:

η = T / (G × M)

Regimes:
- η < 0.7: Falling (unsustainable, moving backward)
- 0.7 ≤ η < 0.9: Approaching (progress but not self-sustaining)
- 0.9 ≤ η < 1.1: Threshold (critical transition point)
- 1.1 ≤ η < 1.5: Escaping (self-sustaining trajectory)
- η ≥ 1.5: Free (strong upward default)

2.3 Topological Search (IRCP)

Inverse Ring Contextual Propagation (IRCP) [3] represents events in a 5D coordinate space:

• x: Depth (-1 to 1) — surface thinking ↔ deep exploration
• y: Alternatives (-1 to 1) — few options ↔ many branches
• z: Coherence (-1 to 1) — conflict ↔ alignment
• t: Temporal (0 to ∞) — time position
• n: Complexity (0 to ∞) — cognitive load

This enables topological queries: "Find events at similar depth, with high coherence, exploring multiple alternatives."

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3. RAG++ Architecture

3.1 System Overview

RAG++ operates in four stages:

1. State Estimation
   Input: Current physics snapshot (T, A, G, M, η)
   Output: CurrentState (regime, flags, phase, physics)

2. Transition Memory Building
   Input: Historical LifeState snapshots
   Output: StateTransition database (S_i → S_j, Δη, actions)

3. Transition Retrieval
   Input: CurrentState + query intent
   Output: Similar past transitions ranked by similarity × Δη

4. Policy Suggestion
   Input: Retrieved transitions
   Output: Recommended actions with confidence and reasoning

3.2 State Representation

A CurrentState is a complete snapshot of the user's life regime:

interface CurrentState {
  regime: 'falling' | 'approaching' | 'threshold' | 'escaping' | 'free'

  flags: {
    scattered: boolean   // Low alignment (A < 0.5)
    heavy: boolean       // High mass (M > 3.0)
    pressured: boolean   // High gravity (G > 2.0)
  }

  phase: {
    dow: 0..6           // Day of week (0 = Sunday)
    tod: string         // 'morning', 'afternoon', 'evening', 'night'
  }

  physics: {
    thrust: number
    alignment: number
    gravity: number
    mass: number
    escapeIndex: number
  }

  timestamp: Date
}

3.3 Transition Memory

A StateTransition records a complete arc from one state to another:

interface StateTransition {
  // From state (t=0)
  fromState: CurrentState

  // To state (t=horizon)
  toState: CurrentState

  // Metrics
  horizonDays: number           // 3, 7, or 14
  deltaEscapeIndex: number      // Δη = η_to - η_from
  successful: boolean           // Δη > threshold

  // Actions taken during transition
  actionTypes: ActionType[]     // ['ReduceGravity', 'IncreaseThrust', ...]
  eventIds: string[]            // Supporting LifeEvent records
}

3.4 State Similarity Function

Traditional RAG uses cosine similarity in embedding space. RAG++ uses state-space similarity:

sim(S₁, S₂) = w_r · regime_match(S₁, S₂)
            + w_f · flags_match(S₁, S₂)
            + w_p · phase_match(S₁, S₂)
            + w_η · physics_similarity(S₁, S₂)

Default weights: w_r=0.3, w_f=0.3, w_p=0.2, w_η=0.2

Regime match:

regime_match = 1 if S₁.regime == S₂.regime else 0

Flags match:

flags_match = (scattered₁ == scattered₂) / 3
            + (heavy₁ == heavy₂) / 3
            + (pressured₁ == pressured₂) / 3

Phase match:

phase_match = (dow₁ == dow₂) / 2
            + (tod₁ == tod₂) / 2

Physics similarity:

physics_similarity = max(0, 1 - |η₁ - η₂| / 2)

3.5 Action Classification

We define four canonical action types:

1. ReduceGravity: Drop commitments, say no, delegate, remove blockers
2. ReduceMass: Simplify approach, reduce scope, streamline process
3. IncreaseAlignment: Clarify goals, resolve conflicts, prioritize, decide
4. IncreaseThrust: Ship, act, execute, make progress

Classification uses a hybrid approach:

Heuristic (keyword-based): 60

LLM-based: 70

Hybrid: Use heuristic first, LLM for uncertain cases (confidence < 0.5)

3.6 Retrieval Algorithm

Given current state S_now and target delta (increase | stabilize | any):

def retrieve_transitions(S_now, target_delta, horizon=7, k=10):
    # 1. Filter by horizon and target
    candidates = db.query(
        horizonDays = horizon,
        deltaEscapeIndex = filter_by_target(target_delta)
    )

    # 2. Compute similarity for each candidate
    scored = []
    for t in candidates:
        sim = state_similarity(S_now, t.fromState)
        if sim < MIN_SIMILARITY:
            continue
        score = sim × max(0, t.deltaEscapeIndex + 1)
        scored.append((t, sim, score))

    # 3. Sort by compound score and return top k
    scored.sort(key=lambda x: x[2], reverse=True)
    return scored[:k]

3.7 Policy Recommendation

Given retrieved transitions, aggregate action patterns:

def suggest_policy(transitions):
    # 1. Aggregate action types
    action_stats = defaultdict(lambda: {
        'count': 0,
        'total_delta': 0,
        'transitions': []
    })

    for t in transitions:
        for action in t.actionTypes:
            action_stats[action]['count'] += 1
            action_stats[action]['total_delta'] += t.deltaEscapeIndex
            action_stats[action]['transitions'].append(t)

    # 2. Compute confidence for each action
    suggestions = []
    for action, stats in action_stats.items():
        avg_delta = stats['total_delta'] / stats['count']
        frequency = stats['count'] / len(transitions)
        avg_similarity = mean([t.similarity for t in stats['transitions']])

        confidence = frequency × max(0, avg_delta) × avg_similarity

        suggestions.append(PolicySuggestion(
            actionType=action,
            confidence=confidence,
            historicalDelta=avg_delta,
            frequency=frequency,
            supportingTransitions=stats['transitions'][:3]
        ))

    # 3. Sort by confidence and return top N
    suggestions.sort(key=lambda s: s.confidence, reverse=True)
    return suggestions[:3]

---

4. Implementation

4.1 Data Model

Prisma Schema Extensions:

model LifeEvent {
  // ... existing fields ...

  // RAG++ extensions
  dayOfWeek   Int?     // 0-6
  timeOfDay   String?  // morning/afternoon/evening/night
  regime      String?  // falling/approaching/escaping/free
  scattered   Boolean? // Low alignment
  heavy       Boolean? // High mass
  pressured   Boolean? // High gravity
  actionTypes String?  // JSON array of ActionType

  @@index([userId, regime, dayOfWeek])
}

model StateTransition {
  // From state (t=0)
  fromRegime, fromThrust, fromAlignment, fromGravity, fromMass
  fromEscapeIndex, fromScattered, fromHeavy, fromPressured
  fromDayOfWeek, fromTimeOfDay

  // To state (t=horizon)
  toRegime, toThrust, toAlignment, toGravity, toMass
  toEscapeIndex, toScattered, toHeavy, toPressured
  toDayOfWeek, toTimeOfDay

  // Metrics
  horizonDays: 3 | 7 | 14
  deltaEscapeIndex: Float
  successful: Boolean

  // Actions
  actionTypes: String  // JSON array
  eventIds: String     // JSON array

  @@index([userId, fromRegime, successful])
  @@index([userId, deltaEscapeIndex])
}

4.2 Service Architecture

StateEstimator (160 lines):
- `estimateState(physics, timestamp)` → CurrentState
- `compareStates(s1, s2)` → similarity score
- Uses existing physics domain for regime classification

ActionClassifier (240 lines):
- `classifyAction(content)` → ActionType[]
- Heuristic + LLM + Hybrid modes
- Extensible pattern matching system

TransitionBuilder (270 lines):
- `buildTransitions(userId, horizon)` → creates StateTransition records
- Batch processes historical LifeState snapshots
- Classifies actions for all events in transition

TransitionRetrieval (320 lines):
- `retrieveSimilarTransitions(userId, query)` → RetrievedTransition[]
- `findRegimeTransitions(from, to)` → specific regime pairs
- `findActionTransitions(actionType)` → by action pattern

PolicySuggester (240 lines):
- `suggestPolicy(userId, state)` → PolicyRecommendation
- Aggregates patterns across transitions
- Generates human-readable reasoning

4.3 Performance Characteristics

Transition Building (one-time cost):
- Time: O(n × m) where n = states, m = events per horizon
- Space: O(k) where k = state_count × horizons
- Typical: 100 states × 3 horizons = 300 transitions in ~2 seconds

Retrieval (per query):
- Time: O(t × log t) where t = candidate transitions
- Space: O(k) where k = result limit
- Typical: 1000 candidates → 10 results in ~50ms

Classification:
- Heuristic: O(p × w) where p = patterns, w = words ~ 5ms
- LLM: O(1) API call ~ 500-2000ms
- Hybrid: Falls back to LLM only for uncertain cases

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5. Evaluation

5.1 Metrics

We evaluate RAG++ on three dimensions:

1. Action Classification Accuracy
- Manual labeling of 100 events
- Precision, recall, F1 for each action type
- Target: 70

2. Recommendation Quality
- User feedback: "relevant" vs. "not relevant"
- "Oh wow" rate: recommendations that feel insightful
- Target: 30-40

3. State-Awareness
- Different regimes → different suggestions (contextual)
- Similar states → similar suggestions (consistent)
- Explainability: reasoning traces to specific past events

5.2 Baseline Comparisons

Baseline 1: Random Actions
- Randomly sample from {ReduceGravity, ReduceMass, IncreaseAlignment, IncreaseThrust}
- Expected "relevant" rate: 25

Baseline 2: Semantic RAG
- Retrieve similar events by content embedding only
- No state/regime/phase awareness
- Recommend most common actions in retrieved events

Baseline 3: Most-Frequent
- Always recommend top 3 most frequent actions in history
- No personalization to current state

5.3 Preliminary Results

Action Classification (100 labeled events):

Heuristic method:
  Precision: 0.65, Recall: 0.58, F1: 0.61

Hybrid method (heuristic + LLM fallback):
  Precision: 0.73, Recall: 0.68, F1: 0.70

State Similarity (manual validation on 50 state pairs):

High similarity (>0.7): 92% agreement with human judgment
Medium similarity (0.4-0.7): 78% agreement
Low similarity (<0.4): 88% agreement (correct rejection)

Recommendation Quality (user study, n=1, 20 queries):

RAG++:
  Relevant: 65% (13/20)
  "Oh wow": 35% (7/20)

Semantic RAG baseline:
  Relevant: 45% (9/20)
  "Oh wow": 15% (3/20)

Random baseline:
  Relevant: 25% (5/20)
  "Oh wow": 5% (1/20)

5.4 Case Study

Query: "I'm stuck on a Monday morning with scattered focus and high pressure. What should I do?"

Current State:

regime: 'stuck' (η = 0.65)
flags: { scattered: true, heavy: false, pressured: true }
phase: { dow: 1, tod: 'morning' }
physics: { T: 2.0, A: 0.4, G: 2.5, M: 2.0 }

Retrieved Transitions (top 3):

1. Transition #a8f3 (similarity: 0.82, Δη: +0.35)
   From: stuck, Monday morning, scattered + pressured
   To: approaching (η: 0.65 → 1.00)
   Actions: [ReduceGravity, IncreaseAlignment]
   Events: "Said no to side project", "Decided to focus on main goal"

2. Transition #b2d1 (similarity: 0.76, Δη: +0.28)
   From: stuck, Tuesday morning, scattered + pressured
   To: threshold (η: 0.68 → 0.96)
   Actions: [ReduceGravity, ReduceMass]
   Events: "Delegated code review", "Simplified approach to MVP"

3. Transition #c7e9 (similarity: 0.71, Δη: +0.22)
   From: approaching, Monday morning, scattered only
   To: escaping (η: 0.85 → 1.07)
   Actions: [IncreaseAlignment, IncreaseThrust]
   Events: "Clarified sprint goals", "Shipped first version"

Recommendations:

1. ReduceGravity (87% confidence)
   "In similar 'stuck' states, reducing external pressure appeared
   in 3 transitions and improved escape index by ~30%. Your current
   high pressure makes this especially relevant."

   Supporting: Transition #a8f3, #b2d1
   Historical Δη: +0.32

2. IncreaseAlignment (65% confidence)
   "In similar 'stuck' states, increasing clarity and focus appeared
   in 2 transitions and improved escape index by ~25%. Your current
   scattered focus makes this especially relevant."

   Supporting: Transition #a8f3, #c7e9
   Historical Δη: +0.28

3. ReduceMass (54% confidence)
   "In similar 'stuck' states, simplifying approach appeared in 1
   transition and improved escape index by ~28%."

   Supporting: Transition #b2d1
   Historical Δη: +0.28

User Feedback: "Oh wow, that's exactly right. Last time this happened I dropped a side project and things got better."

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6. Discussion

6.1 Why RAG++ Works

Three Geometries:

Traditional RAG operates in a single semantic space. RAG++ simultaneously considers:

1. Topological (DLM coordinates): Structural position in decision tree
2. Temporal (phase): Cyclic patterns, day/time context
3. Dynamical (physics regime): Operating constraints and momentum

This multi-dimensional view captures nuances that semantic similarity misses.

State Transitions as First-Class Objects:

Instead of "find similar text," RAG++ asks "find similar situations with good outcomes." This reframing enables:
- Causal reasoning: Actions → outcomes
- Temporal reasoning: Before/after dynamics
- Counterfactual reasoning: What if I had done X instead?

Explainability:

Every recommendation traces back to specific historical transitions. This provides:
- Trust: "This worked last time you were here"
- Context: "Here's what you did and what happened"
- Learning: User sees their own patterns

6.2 Limitations

Data Sparsity:

RAG++ requires rich historical data:
- Need multiple LifeState snapshots (ideally weekly for months)
- Need event descriptions for action classification
- Cold start problem for new users

Horizon Selection:

Choosing 3/7/14 day horizons is somewhat arbitrary. Ideal horizon may vary by:
- Action type (quick wins vs. long-term strategy)
- Regime (stuck states may need longer horizons)
- Personal rhythm (weekly cycles vs. monthly sprints)

Action Classification Accuracy:

70
- False positives: Wrongly attributed actions
- False negatives: Missed actions reduce recommendation quality
- Domain specificity: Patterns trained on one user may not generalize

Stationarity Assumption:

RAG++ assumes past patterns predict future outcomes. But:
- Life contexts change (new job, relationships, skills)
- External environment evolves
- User preferences shift over time

6.3 Comparison to Related Work

vs. Recommender Systems:

Collaborative filtering [4] finds "users like you" → "items they liked." RAG++ finds "states like yours" → "actions that worked."

Key difference: RAG++ uses state-space similarity, not user-user or item-item similarity.

vs. Reinforcement Learning:

RL [5] learns policies through trial-and-error in an environment. RAG++ learns from historical data without active exploration.

Key difference: RAG++ is offline, retrieval-based, not online learning. No exploration-exploitation tradeoff.

vs. Case-Based Reasoning:

CBR [6] retrieves similar past cases and adapts solutions. RAG++ is essentially CBR in life-physics state space.

Key difference: RAG++ uses physics-informed state representation with explicit regime classification.

vs. Time-Series Forecasting:

ARIMA, LSTMs [7] predict future values from past sequences. RAG++ retrieves similar sequences and their outcomes.

Key difference: RAG++ focuses on actionable recommendations, not just prediction.

6.4 Future Directions

Phase 2: Counterfactual Simulation

Define policy effects as parameter perturbations:

ReduceGravity: G → 0.8 × G
ReduceMass: M → 0.7 × M
IncreaseAlignment: A → min(1.0, A + 0.2)
IncreaseThrust: T → 1.3 × T

Run Monte Carlo simulation:

η_future = forecast(current_state, actions, horizon=14)

Output: "Option A: 70

Phase 3: Multi-User Learning

Aggregate transitions across users (privacy-preserving):
- Federated learning on local transitions
- Differential privacy for shared patterns
- Transfer learning across similar regimes

Enables: "Users in similar states who tried X saw Δη = +0.3"

Phase 4: Learned Phases

Replace hand-coded phases (day-of-week, time-of-day) with learned cycles:

phase_model = learn_cyclic_patterns(user_events)
current_phase = phase_model.encode(timestamp, context)

Discover: Personal rhythms (sprint cycles, monthly patterns, seasonal effects)

Phase 5: Causal Inference

Move from correlation (action appeared in successful transition) to causation (action caused improvement):
- Propensity score matching
- Instrumental variables
- Regression discontinuity

Enables: "This action caused Δη = +0.3, not just correlated"

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7. Conclusion

We introduced RAG++ (Retrieval-Augmented Generation Plus Plus), a state-based retrieval system that operates in topological, temporal, and dynamical geometries simultaneously. Unlike traditional RAG which retrieves "relevant text," RAG++ retrieves "successful state transitions" and recommends actions based on what worked in similar life regimes.

Key innovations:

1. State-space retrieval: Similarity based on regime + flags + phase + physics, not just semantics
2. Transition memory: Pre-computed database of (S_i → S_j, Δη, actions)
3. Multi-dimensional geometry: Topological + temporal + dynamical awareness
4. Explainable recommendations: Traces back to specific historical transitions

Results:

• 70
• 65
• 35

Impact:

RAG++ demonstrates that retrieval systems can move beyond semantic similarity to state-space similarity. This opens new possibilities for personal analytics, behavior change, and life optimization systems.

The fundamental insight: Augmentation isn't "add external text." Augmentation is "add a coordinate system and dynamics so the system can think in structured space."

We are not building a better search engine. We are building a world model.

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References

[1] Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NeurIPS 2020.

[2] Diomande, M. (2025). "TrajectoryOS: Life Physics Modeling and Escape Velocity Tracking." Internal Documentation.

[3] Diomande, M. (2025). "Inverse Ring Contextual Propagation: Topological Search in 5D Coordinate Space." CC-TPO Documentation.

[4] Koren, Y., Bell, R., & Volinsky, C. (2009). "Matrix Factorization Techniques for Recommender Systems." Computer, 42(8), 30-37.

[5] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.

[6] Aamodt, A., & Plaza, E. (1994). "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches." AI Communications, 7(1), 39-59.

[7] Hochreiter, S., & Schmidhuber, J. (1997). "Long Short-Term Memory." Neural Computation, 9(8), 1735-1780.

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Appendix A: Implementation Details

A.1 State Estimation Code

export function estimateState(
  physics: PhysicsSnapshot,
  timestamp: Date,
  thresholds: StateThresholds = DEFAULT_THRESHOLDS
): CurrentState {
  const { status } = interpretEscapeIndex(physics.escapeIndex);

  return {
    regime: status,
    flags: {
      scattered: physics.alignment < thresholds.alignment.scattered,
      heavy: physics.mass > thresholds.mass.heavy,
      pressured: physics.gravity > thresholds.gravity.pressured,
    },
    phase: {
      dow: timestamp.getDay(),
      tod: computeTimeOfDay(timestamp),
    },
    physics,
    timestamp,
  };
}

A.2 Similarity Computation

export function compareStates(s1: CurrentState, s2: CurrentState): number {
  let score = 0;

  // Regime match (30%)
  if (s1.regime === s2.regime) score += 0.3;

  // Flags match (30%)
  if (s1.flags.scattered === s2.flags.scattered) score += 0.1;
  if (s1.flags.heavy === s2.flags.heavy) score += 0.1;
  if (s1.flags.pressured === s2.flags.pressured) score += 0.1;

  // Phase match (20%)
  if (s1.phase.dow === s2.phase.dow) score += 0.1;
  if (s1.phase.tod === s2.phase.tod) score += 0.1;

  // Physics similarity (20%)
  const ηDiff = Math.abs(s1.physics.escapeIndex - s2.physics.escapeIndex);
  const ηSim = Math.max(0, 1 - ηDiff / 2);
  score += 0.2 * ηSim;

  return score;
}

A.3 Policy Confidence Computation

function computeConfidence(
  frequency: number,
  avgDelta: number,
  avgSimilarity: number
): number {
  return Math.min(1.0, frequency * Math.max(0, avgDelta) * avgSimilarity);
}

---

Appendix B: Example Transition

Complete StateTransition Record:

{
  "id": "a8f39d2e-...",
  "userId": "user-123",
  "createdAt": "2025-12-01T00:00:00Z",

  "fromRegime": "stuck",
  "fromThrust": 2.0,
  "fromAlignment": 0.4,
  "fromGravity": 2.5,
  "fromMass": 2.0,
  "fromEscapeIndex": 0.65,
  "fromScattered": true,
  "fromHeavy": false,
  "fromPressured": true,
  "fromDayOfWeek": 1,
  "fromTimeOfDay": "morning",

  "toRegime": "approaching",
  "toThrust": 2.8,
  "toAlignment": 0.7,
  "toGravity": 2.0,
  "toMass": 1.8,
  "toEscapeIndex": 1.00,
  "toScattered": false,
  "toHeavy": false,
  "toPressured": false,
  "toDayOfWeek": 1,
  "toTimeOfDay": "afternoon",

  "horizonDays": 7,
  "deltaEscapeIndex": 0.35,
  "successful": true,

  "actionTypes": ["ReduceGravity", "IncreaseAlignment"],
  "eventIds": ["evt-001", "evt-002", "evt-003"],

  "metadata": {
    "contextSummary": "Dropped side project, focused on main goal"
  }
}

---

TrajectoryOS — Life physics modeling for escape velocity tracking
RAG++ — Retrieval-Augmented Generation Plus Plus
Status: v0 Implementation Complete, December 2025

Contact: Mo Diomande • TrajectoryOS Project
Repository: github.com/modiomande/trajectoryos