wifi-densepose/vendor/midstream/plans/05-temporal-neural-solver-integration.md

Temporal-Neural-Solver Integration Strategy

Executive Summary

This document outlines the integration of the temporal-neural-solver crate into the Lean Agentic Learning System. Temporal-neural-solver combines neural network architectures with temporal logic solving to enable AI systems to reason about time-dependent constraints, temporal planning, and sequential decision-making with formal guarantees.

Research Background

Temporal Logic and Neural Networks

Temporal Logic [1]: Formal system for reasoning about propositions qualified in terms of time.

Types of Temporal Logic:

1. Linear Temporal Logic (LTL) [2]:

   • G φ (Globally): φ holds at all future states
   • F φ (Finally): φ holds at some future state
   • X φ (Next): φ holds at next state
   • φ U ψ: φ holds until ψ becomes true

2. Computation Tree Logic (CTL) [3]:

   • Branching-time logic
   • Multiple possible futures

3. Metric Temporal Logic (MTL) [4]:

   • Time-bounded operators
   • Real-time constraints

Neural-Symbolic Integration

Key Approaches:

1. Neural Theorem Proving [5]: Use neural networks to guide logical proof search
2. Differentiable Logic [6]: Make logical operations differentiable for gradient-based learning
3. Logic Tensor Networks [7]: Embed logical knowledge in tensor space
4. Neural Module Networks [8]: Compose neural modules for structured reasoning

Applications in AI

Temporal Planning:

• Robot navigation with safety constraints
• Multi-step decision-making
• Reinforcement learning with temporal specifications

Formal Verification:

• Prove neural network properties
• Verify safety constraints
• Generate certificates of correctness

References

[1] Pnueli, A. (1977). "The temporal logic of programs." Proceedings of FOCS '77, 46-57.

[2] Baier, C., & Katoen, J. P. (2008). "Principles of Model Checking." MIT Press.

[3] Clarke, E. M., et al. (1999). "Model checking." MIT Press.

[4] Koymans, R. (1990). "Specifying real-time properties with metric temporal logic." Real-Time Systems, 2(4), 255-299.

[5] Rocktäschel, T., & Riedel, S. (2017). "End-to-end differentiable proving." NIPS 2017.

[6] Sourek, G., et al. (2018). "Lifted relational neural networks." Journal of Artificial Intelligence Research, 62, 69-100.

[7] Serafini, L., & Garcez, A. d'Avila. (2016). "Logic tensor networks." arXiv:1606.04422.

[8] Andreas, J., et al. (2016). "Neural module networks." CVPR 2016.

Integration Architecture

┌─────────────────────────────────────────────────────────────┐
│         Temporal-Neural-Solver Architecture                 │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌──────────────────┐      ┌─────────────────┐            │
│  │  Temporal Logic  │      │  Neural         │            │
│  │  Specification   │─────►│  Encoder        │            │
│  │  (LTL/MTL)       │      │                 │            │
│  └──────────────────┘      └────────┬────────┘            │
│                                     │                      │
│                            ┌────────▼────────┐            │
│                            │  Differentiable │            │
│                            │  Reasoning      │            │
│                            │  Engine         │            │
│                            └────────┬────────┘            │
│                                     │                      │
│  ┌──────────────────┐      ┌────────▼────────┐            │
│  │  Constraint      │◄─────│  Solution       │            │
│  │  Satisfaction    │      │  Generator      │            │
│  └──────────────────┘      └────────┬────────┘            │
│                                     │                      │
│                            ┌────────▼────────┐            │
│                            │  Formal         │            │
│                            │  Verification   │            │
│                            └─────────────────┘            │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Use Cases

1. Safe Agent Planning

Problem: Agent must satisfy temporal safety constraints (e.g., "never enter unsafe state").

Solution: Use temporal logic to specify constraints, neural solver to find satisfying plans.

Implementation:

// Specify temporal constraint
let safety_constraint = ltl!(
    G(not(unsafe_state)) & F(goal_state)
);

// Create solver
let solver = TemporalNeuralSolver::new();

// Find plan that satisfies constraint
let plan = solver.solve_with_constraint(
    initial_state,
    safety_constraint,
    max_steps,
)?;

// Verify plan
assert!(solver.verify_plan(&plan, &safety_constraint));

2. Temporal Reward Shaping

Problem: Shape rewards to encourage temporally extended behavior.

Solution: Encode temporal patterns as rewards using neural-symbolic integration.

Implementation:

// Define temporal pattern: "eventually reach A, then reach B"
let pattern = mtl!(
    F[0..100](at_location_A) & F[100..200](at_location_B)
);

// Create reward shaper
let shaper = TemporalRewardShaper::from_specification(pattern);

// Use in RL training
let shaped_reward = base_reward + shaper.compute_bonus(&trajectory);

3. Multi-Agent Coordination

Problem: Coordinate multiple agents with temporal constraints on interactions.

Solution: Solve multi-agent temporal logic specifications.

Implementation:

// Specify coordination constraint
let coordination = ctl!(
    AG(agent1.action == pickup => AF(agent2.action == deliver))
);

// Solve for coordinated policy
let policies = solver.solve_multi_agent(
    &agents,
    &coordination,
)?;

Technical Specifications

API Design

pub struct TemporalNeuralSolver {
    encoder: TemporalEncoder,
    reasoning_engine: DifferentiableReasoner,
    verifier: FormalVerifier,
    config: SolverConfig,
}

pub enum TemporalFormula {
    LTL(LTLFormula),
    CTL(CTLFormula),
    MTL(MTLFormula),
}

pub struct LTLFormula {
    // G φ, F φ, X φ, φ U ψ
    operator: LTLOperator,
    operands: Vec<Box<LTLFormula>>,
}

pub struct Solution {
    pub trajectory: Vec<State>,
    pub actions: Vec<Action>,
    pub satisfaction_proof: Proof,
    pub confidence: f64,
}

impl TemporalNeuralSolver {
    pub fn new(config: SolverConfig) -> Self;

    pub fn solve_with_constraint(
        &self,
        initial_state: State,
        constraint: TemporalFormula,
        horizon: usize,
    ) -> Result<Solution, SolverError>;

    pub fn verify_plan(
        &self,
        plan: &Plan,
        constraint: &TemporalFormula,
    ) -> bool;

    pub fn synthesize_controller(
        &self,
        specification: TemporalFormula,
    ) -> Controller;

    pub fn compute_robustness(
        &self,
        trajectory: &Trajectory,
        formula: &MTLFormula,
    ) -> f64;
}

// Macros for convenient specification
macro_rules! ltl {
    (G($e:expr)) => { LTLFormula::globally($e) };
    (F($e:expr)) => { LTLFormula::eventually($e) };
    (X($e:expr)) => { LTLFormula::next($e) };
    ($e1:expr & $e2:expr) => { LTLFormula::and($e1, $e2) };
}

Performance Requirements

Operation	Target	Rationale
Formula encoding	<10ms	Real-time planning
Solution search	<500ms	Interactive response
Verification	<100ms	Quick validation
Robustness calc	<50ms	Online monitoring

Integration Points

1. Constrained Agent Planning

Location: src/lean_agentic/agent.rs

Enhancement:

impl AgenticLoop {
    pub async fn plan_with_temporal_constraints(
        &self,
        context: &Context,
        constraints: Vec<TemporalFormula>,
    ) -> Result<Plan, Error> {
        let solver = TemporalNeuralSolver::new(config);

        // Combine constraints
        let combined = constraints.into_iter()
            .fold(TemporalFormula::true_(), |acc, c| {
                TemporalFormula::and(acc, c)
            });

        // Solve
        let solution = solver.solve_with_constraint(
            context.current_state(),
            combined,
            self.config.max_planning_depth,
        )?;

        // Convert to plan
        Ok(Plan::from_solution(solution))
    }
}

2. Safe Learning with Temporal Specifications

Location: src/lean_agentic/learning.rs

Enhancement:

pub struct SafeStreamLearner {
    learner: StreamLearner,
    solver: TemporalNeuralSolver,
    safety_specs: Vec<TemporalFormula>,
}

impl SafeStreamLearner {
    pub async fn learn_safely(
        &mut self,
        experience: Experience,
    ) -> Result<(), Error> {
        // Propose update
        let proposed_update = self.learner.compute_update(&experience);

        // Verify safety
        if !self.verify_safety(&proposed_update) {
            return Err(Error::SafetyViolation);
        }

        // Apply update
        self.learner.apply_update(proposed_update)?;

        Ok(())
    }

    fn verify_safety(&self, update: &Update) -> bool {
        let predicted_trajectory = self.simulate_with_update(update);

        self.safety_specs.iter().all(|spec| {
            self.solver.verify_plan(&predicted_trajectory, spec)
        })
    }
}

3. Temporal Knowledge Reasoning

Location: src/lean_agentic/knowledge.rs

Enhancement:

impl KnowledgeGraph {
    pub fn query_temporal(
        &self,
        query: TemporalQuery,
    ) -> Vec<TemporalFact> {
        let solver = TemporalNeuralSolver::new(config);

        // Encode query as temporal formula
        let formula = query.to_temporal_formula();

        // Find satisfying facts
        self.temporal_facts.iter()
            .filter(|fact| solver.check_satisfaction(fact, &formula))
            .cloned()
            .collect()
    }

    pub fn infer_temporal_relations(
        &mut self,
        solver: &TemporalNeuralSolver,
    ) {
        // Infer new temporal facts from existing knowledge
        for fact1 in &self.temporal_facts {
            for fact2 in &self.temporal_facts {
                if let Some(inferred) = solver.infer_relation(fact1, fact2) {
                    self.add_temporal_fact(inferred);
                }
            }
        }
    }
}

Implementation Phases

Phase 1: Core Solver (Week 1-2)

• Implement LTL parser and encoder
• Create neural encoding network
• Build differentiable reasoning engine
• Add basic SAT solver
• Write unit tests

Phase 2: Temporal Operators (Week 3)

• Implement all LTL operators (G, F, X, U)
• Add MTL time-bounded operators
• Create CTL branching operators
• Implement robustness semantics
• Write integration tests

Phase 3: Neural Integration (Week 4)

• Create logic tensor networks
• Implement neural module networks
• Add gradient-based optimization
• Integrate with agent planning
• Benchmark performance

Phase 4: Verification (Week 5)

• Implement model checking
• Add counterexample generation
• Create certificate generation
• Add safety verification
• Write documentation

Benchmarking Strategy

Benchmark Suite

#[bench]
fn bench_ltl_encoding(b: &mut Bencher) {
    let formula = ltl!(G(safe) & F(goal));
    let solver = TemporalNeuralSolver::new(config);

    b.iter(|| {
        solver.encode_formula(&formula)
    });
}

#[bench]
fn bench_planning_with_constraints(b: &mut Bencher) {
    let solver = TemporalNeuralSolver::new(config);
    let constraint = ltl!(G(not(unsafe)) & F(goal));

    b.iter(|| {
        solver.solve_with_constraint(
            initial_state.clone(),
            constraint.clone(),
            100,
        )
    });
}

#[bench]
fn bench_robustness_calculation(b: &mut Bencher) {
    let solver = TemporalNeuralSolver::new(config);
    let trajectory = generate_trajectory(100);
    let formula = mtl!(F[0..50](goal));

    b.iter(|| {
        solver.compute_robustness(&trajectory, &formula)
    });
}

Validation Tests

#[test]
fn test_safety_verification() {
    let solver = TemporalNeuralSolver::new(config);

    // Constraint: never enter unsafe state
    let safety = ltl!(G(not(unsafe_state)));

    // Safe plan
    let safe_plan = generate_safe_plan();
    assert!(solver.verify_plan(&safe_plan, &safety));

    // Unsafe plan
    let unsafe_plan = generate_unsafe_plan();
    assert!(!solver.verify_plan(&unsafe_plan, &safety));
}

#[test]
fn test_liveness_verification() {
    let solver = TemporalNeuralSolver::new(config);

    // Constraint: eventually reach goal
    let liveness = ltl!(F(goal_state));

    let plan_with_goal = generate_plan_reaching_goal();
    assert!(solver.verify_plan(&plan_with_goal, &liveness));

    let plan_without_goal = generate_plan_not_reaching_goal();
    assert!(!solver.verify_plan(&plan_without_goal, &liveness));
}

Neural Network Architecture

Temporal Encoder

pub struct TemporalEncoder {
    embedding: nn::Embedding,
    lstm: nn::LSTM,
    attention: nn::MultiheadAttention,
    output: nn::Linear,
}

impl TemporalEncoder {
    pub fn encode(&self, formula: &TemporalFormula) -> Tensor {
        // Convert formula to sequence
        let sequence = formula.to_sequence();

        // Embed
        let embedded = self.embedding.forward(&sequence);

        // LSTM encoding
        let (encoded, _) = self.lstm.forward(&embedded);

        // Attention over temporal operators
        let attended = self.attention.forward(&encoded, &encoded, &encoded);

        // Final encoding
        self.output.forward(&attended)
    }
}

Differentiable Reasoner

pub struct DifferentiableReasoner {
    rule_network: nn::Sequential,
    memory: nn::Parameter,
    iterations: usize,
}

impl DifferentiableReasoner {
    pub fn reason(&self, encoded_formula: &Tensor, state: &State) -> Tensor {
        let mut reasoning_state = self.memory.clone();

        for _ in 0..self.iterations {
            // Apply reasoning rules
            reasoning_state = self.rule_network.forward(&vec![
                reasoning_state.clone(),
                encoded_formula.clone(),
                state.to_tensor(),
            ]);
        }

        reasoning_state
    }
}

Success Criteria

• Formula encoding < 10ms
• Planning with constraints < 500ms
• Verification < 100ms
• 95%+ accuracy on benchmark temporal logic problems
• Successfully verify safety properties
• Generate correct counterexamples
• Full test coverage (>90%)

Safety Guarantees

Soundness

/// Guarantee: If solver.verify_plan returns true,
/// the plan ACTUALLY satisfies the specification
#[test]
fn test_soundness() {
    let solver = TemporalNeuralSolver::new(config);

    for _ in 0..1000 {
        let spec = generate_random_specification();
        let plan = generate_random_plan();

        if solver.verify_plan(&plan, &spec) {
            // Formally check with external model checker
            assert!(model_check(&plan, &spec));
        }
    }
}

Completeness

/// Best-effort: If a solution exists, try to find it
/// (May not always succeed due to search complexity)
#[test]
fn test_completeness_on_simple_problems() {
    let solver = TemporalNeuralSolver::new(config);

    // For simple, known-solvable problems
    let simple_specs = generate_simple_specifications();

    for spec in simple_specs {
        let solution = solver.solve_with_constraint(
            initial_state,
            spec.clone(),
            100,
        );

        // Should find solution for simple problems
        assert!(solution.is_ok());
    }
}

Future Enhancements

1. Probabilistic Temporal Logic: Handle uncertainty
2. Multi-Objective Optimization: Balance multiple temporal constraints
3. Continuous-Time Logic: Support hybrid systems
4. Quantitative Verification: Compute satisfaction probabilities
5. Adaptive Complexity: Adjust solver based on problem difficulty

References

[1] Pnueli (1977). The temporal logic of programs. [2] Baier & Katoen (2008). Principles of Model Checking. [3] Clarke et al. (1999). Model checking. [4] Koymans (1990). Metric temporal logic. [5] Rocktäschel & Riedel (2017). End-to-end differentiable proving. [6] Sourek et al. (2018). Lifted relational neural networks. [7] Serafini & Garcez (2016). Logic tensor networks. [8] Andreas et al. (2016). Neural module networks.

Appendix A: LTL Semantics

Syntax

φ ::= p | ¬φ | φ₁ ∧ φ₂ | X φ | φ₁ U φ₂

Derived Operators

F φ ≡ true U φ           (Eventually)
G φ ≡ ¬F¬φ               (Globally)
φ₁ R φ₂ ≡ ¬(¬φ₁ U ¬φ₂)   (Release)

Semantics

σ, i ⊨ p      iff p ∈ σ(i)
σ, i ⊨ ¬φ     iff σ, i ⊭ φ
σ, i ⊨ φ₁∧φ₂  iff σ, i ⊨ φ₁ and σ, i ⊨ φ₂
σ, i ⊨ X φ    iff σ, i+1 ⊨ φ
σ, i ⊨ φ₁Uφ₂  iff ∃j≥i: σ,j ⊨ φ₂ and ∀i≤k<j: σ,k ⊨ φ₁

Appendix B: Example Usage

use midstream::temporal_neural_solver::*;

// Create solver
let solver = TemporalNeuralSolver::new(config);

// Define safety specification
let safety = ltl!(
    G(not(collision)) &     // Never collide
    F(goal_reached) &       // Eventually reach goal
    G(battery > 20)         // Always maintain battery
);

// Solve for plan
let solution = solver.solve_with_constraint(
    robot.current_state(),
    safety,
    horizon = 100,
)?;

// Verify solution
assert!(solver.verify_plan(&solution, &safety));

// Execute plan
for action in solution.actions {
    robot.execute(action);
}

// Monitor robustness online
let robustness = solver.compute_robustness(
    &robot.trajectory(),
    &safety,
);

if robustness < 0.0 {
    eprintln!("Warning: Safety specification violated!");
}