ProXPL Core Specification: Pillars 6-10

6. Distributed-Native Primitives

Concept

The language runtime is cluster-aware. Variables are not just memory addresses; they can be distributed CRDTs (Conflict-free Replicated Data Types). The "network" is abstracted away.

Syntax

dist_decl ::= "let" identifier ":" "distributed" "<" type ">"
node_block ::= "node" "(" role_expr ")" "{" statement_list "}"
sync_op ::= "sync" "(" identifier ")"

Example:

// A counter synchronized across all nodes
let global_hits: distributed<counter> = 0;

node(role == "worker") {
    global_hits++; // Local increment, broadcast via Gossip
}

sync(global_hits); // Block until consistent (Output Consistency)

Runtime Behavior (Gossip & CRDT)

The ProXPL initializes a background DHT (Distributed Hash Table) and Gossip Protocol (e.g., SWIM).
distributed<int> is lowered to a G-Counter (Grow-only Counter) or PN-Counter CRDT.
Increments are non-blocking messages. sync creates a barrier.

Implementation Task (Rust - Runtime)

struct Distributed<T: Crdt> {
    local_state: T,
    peers: Vec<SocketAddr>,
}

impl<T: Crdt> Distributed<T> {
    fn update(&mut self, op: Op) {
        self.local_state.apply(op);
        self.gossip_queue.push(op);
    }

    // Background thread
    fn gossip_loop(&self) {
        while let Some(msg) = self.gossip_queue.next() {
            for peer in self.peers.iter().random(3) {
                send(peer, msg);
            }
        }
    }
}

---

7. Neuro-Symbolic Compiler

Concept

The compiler includes an embedded ONNX runtime. It gathers metrics from previous runs (profile-guided optimization on steroids) and uses a trained model to suggest optimizations or detect probability of bugs.

Syntax

ai_directive ::= "optimize" "with" "ai" [ "(" model_path ")" ]
predict_anno ::= "@predict" "(" goal_expr ")"

Example:

@predict(bug_probability > 0.8)
fn complex_calculation() {
    // ...
}

optimize with ai; // Compiler re-orders instructions based on inference

Compiler Logic (Inference Engine)

1. Feature Extraction: The compiler extracts AST features (branch depth, cyclomatic complexity, variable usage).
2. Inference: Pass features to model.onnx.
3. Action: Model predicts "Inline this", "Unroll loop 4x", or "Warn: Heap Fragmentation Risk".

Implementation Task (C++ - LLVM Pass)

void NeuroOptimizePass::run(Module &M) {
    auto features = ExtractGraphFeatures(M);
    auto tensor = OnnxSession.Run(features);

    float unroll_factor = tensor.get_output(0);
    if (unroll_factor > 0.8) {
        // Apply aggressive unrolling
        SetLoopMetadata(M, "llvm.loop.unroll.full");
    }
}

---

8. Constraint-Driven Execution

Concept

Instead of imperative steps ("do A, then B"), define the goal and constraints. The runtime uses a SAT/SMT solver to find a valid execution path.

Syntax

constraint_def ::= "constraint" identifier "{" logic_expr "}"
solve_block ::= "solve" "for" identifier [ "minimizing" expr ]

Example:

let x: int;
let y: int;

constraint Layout {
    x > 0;
    y > 0;
    x + y == 100;
}

solve for (x, y); // Runtime finds x=1, y=99 or any valid pair

Runtime Behavior (SMT Solver)

The runtime embeds a solver (like Z3 or localized MiniSat).
Execution pauses at solve. The variables are converted to symbolics, constraints asserted, and check-sat is called. The model values are written back to variables.

Implementation Task (Rust - Z3 Bindings)

fn execute_solve_block(constraints: Vec<Expr>) -> Env {
    let ctx = z3::Context::new(&cfg);
    let solver = z3::Solver::new(&ctx);

    for c in constraints {
        solver.assert(&c.to_z3(&ctx));
    }

    if solver.check() == z3::SatResult::Sat {
        let model = solver.get_model();
        return model.extract_values();
    } else {
        panic!("Unsatisfiable Constraints");
    }
}

---

9. Knowledge-Persistent Memory

Concept

Programs often restart (stateless microservices). ProXPL programs are stateful by default if requested. persistent scopes map variables to an on-disk embedded KV store (RocksDB/LMDB) automatically.

Syntax

persist_decl ::= "persistent" block

Example:

persistent {
    // Value survives process restart
    let run_count: int = 0; 
}
run_count++;

Runtime Behavior (Memory-Mapped Store)

Variables in persistent blocks are not allocated on the stack/heap. They are pointers into a memory-mapped file (mmap).
The runtime manages the layout and serialization.

Implementation Task (C++ - mmap)

void* persistent_alloc(size_t size, const char* var_name) {
    // Open pro.db
    int fd = open("pro.db", O_RDWR);

    // Find offset for var_name in header
    size_t offset = db_lookup(fd, var_name);

    // Map it
    void* ptr = mmap(0, size, PROT_READ|PROT_WRITE, MAP_SHARED, fd, offset);
    return ptr;
}

---

10. Jurisdictional Policy Enforcement

Concept

Compliance as Code. The compiler enforces data usage policies (GDPR/CCPA) by injecting compile-time checks on variable access based on tagged regions.

Syntax

policy_def ::= "policy" identifier "{" rules "}"
region_tag ::= "region" "(" identifier ")"

Example:

policy GDPR {
    forbid "US-Server" read user.email;
}

region("US-Server") {
    print(user.email); // COMPILER ERROR: Policy Violation
}

Compiler Logic (Policy Injection)

The compiler builds a "Policy Graph" mapping regions to allowed data access.
During the semantic analysis phase, every variable access is checked against the active Policy Graph.

Implementation Task (Rust - Semantic Check)

fn check_policy_access(current_region: &str, variable: &Var, operation: Op) {
    let policy = global_policy_table.get("GDPR");

    if let Some(rule) = policy.forbids(current_region, variable) {
        emit_error(
            "PolicyViolation", 
            format!("Access to {} forbidden in region {} by {}", variable.name, current_region, rule.name)
        );
    }
}