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TD Commons Disclosure Summary

Citation

Title: "Automated Synthesis and Verification of Distributed Systems Using Universal Plan Intermediate Representation (UPIR)"

Author: Subhadip Mitra

Published: November 10, 2025

URL: https://www.tdcommons.org/dpubs_series/8852/

License: Creative Commons Attribution 4.0 (CC BY 4.0)

Abstract

The disclosure describes UPIR, an intermediate representation that combines formal verification, program synthesis, and machine learning to automatically generate verified code from specifications for distributed systems. It addresses the implementation gap where high-level architectural designs fail to meet requirements during deployment.

Key Technical Components

1. Compositional Verification Engine

Description: Uses SMT solving with dependency tracking and proof caching to achieve incremental verification.

Key Features: - O(1) incremental verification complexity - Proof caching with cryptographic certificates - 274x speedup for 64-component systems vs monolithic approaches

Implementation Guidance: - Use Z3 theorem prover for SMT solving - Cache proofs indexed by property and architecture hash - Track dependencies between properties for selective invalidation

2. CEGIS-based Synthesis Engine

Description: Applies Counterexample-Guided Inductive Synthesis to distributed systems, generating program sketches and filling holes through SMT solving.

Key Features: - Average synthesis time: 1.97ms - Success rates: 43-75% for different system types - Generates code for streaming, batch, and API systems

Implementation Guidance: - Create templates for common architectures (streaming, batch, API) - Identify parameters as holes (window size, parallelism, timeouts) - Use counterexamples to refine synthesis

3. Constrained RL Optimizer

Description: Uses Proximal Policy Optimization (PPO) to improve system parameters while maintaining formal invariants.

Key Features: - Converges within 45 episodes - 60.1% latency reduction - 194.5% throughput improvement - Maintains formal properties during optimization

Implementation Guidance: - Encode architecture as state (component count, connections, etc.) - Define actions as parameter modifications - Reward based on constraint satisfaction and performance - Verify properties before accepting changes

4. Pattern Extraction via Clustering

Description: Discovers reusable architectural patterns through clustering similar system designs.

Key Features: - 89.9% pattern reuse potential identified - Similarity-based pattern matching - Success rate tracking for patterns

Implementation Guidance: - Extract features from architectures (components, connections, patterns) - Use K-means or DBSCAN clustering - Abstract clusters into parameterized patterns

Performance Benchmarks

Per the disclosure:

Metric Value
Verification speedup (64 components) 274x
Synthesis time (average) 1.97ms
Synthesis success rate 43-75%
RL convergence 45 episodes
Latency reduction 60.1%
Throughput improvement 194.5%
Pattern reuse potential 89.9%
Test iterations 100

Core Data Model

From the disclosure:

FormalSpecification

  • Temporal properties (ALWAYS, EVENTUALLY, WITHIN)
  • Resource constraints (latency, cost, throughput)
  • Environmental assumptions

Architecture

  • Components with properties
  • Connections between components
  • Deployment configuration
  • Applied patterns

Evidence

  • Source and type (benchmark, test, production)
  • Confidence tracking (Bayesian updates)
  • Timestamp and metadata

ReasoningNode

  • Decision and rationale
  • Supporting evidence references
  • Alternative options considered
  • Confidence aggregation

Implementation

  • Generated code
  • Synthesis proof
  • Performance profile

Key Algorithms

Incremental Verification

1. Compute hash of property and architecture
2. Check proof cache
3. If cached: return result (O(1))
4. Else:
   a. Encode architecture as SMT constraints
   b. Encode property as temporal formula
   c. Use Z3 to prove or find counterexample
   d. Generate proof certificate
   e. Cache result

CEGIS Synthesis

1. Generate program sketch with holes from spec
2. Loop (max 100 iterations):
   a. Use SMT to find values for holes
   b. Instantiate program
   c. Verify against specification
   d. If verified: SUCCESS, return with proof
   e. Else: Add counterexample, continue
3. If max iterations: return PARTIAL

RL Optimization

1. Encode architecture as state vector
2. Select action using PPO policy
3. Decode action to modify architecture
4. Compute reward from metrics vs constraints
5. Verify formal properties still hold
6. If valid: Update policy, accept change
7. Else: Reject change, penalize action

Novel Contributions

According to the disclosure, the key innovations are:

  1. Combination: First system to combine formal verification + synthesis + RL for distributed systems
  2. Incremental verification: O(1) complexity through proof caching
  3. CEGIS for distributed systems: Applying synthesis to cloud architectures
  4. Constrained RL: Optimization that maintains formal invariants
  5. Pattern extraction: Automated discovery of reusable patterns

Implementation Priorities

Based on the disclosure, implement in this order:

  1. Week 1: Core data model (FormalSpecification, Evidence, UPIR)
  2. Week 2: Verification engine (Z3 integration, proof caching)
  3. Week 3: Synthesis engine (CEGIS, sketch generation)
  4. Week 4: Learning system (PPO, architecture optimization)
  5. Week 5: Pattern extraction and examples

Usage Scenarios

The disclosure describes these use cases:

Streaming ETL Pipeline

  • Specify: latency <100ms, exactly-once, data consistency
  • Synthesize: Apache Beam pipeline with optimal window size
  • Verify: Temporal properties hold
  • Optimize: Learn parallelism and buffer sizes from production

Batch Processing

  • Specify: completion time, cost constraints
  • Synthesize: MapReduce job with optimal batch size
  • Verify: Resource bounds satisfied
  • Optimize: Learn chunk size from execution metrics

API Service

  • Specify: response time, availability, throughput
  • Synthesize: Flask/FastAPI service with timeouts
  • Verify: Performance requirements met
  • Optimize: Learn connection pool size, cache TTLs

References in Disclosure

The TD Commons disclosure cites these foundational works:

  • CEGIS: Solar-Lezama et al. "Program Synthesis by Sketching" (2008)
  • PPO: Schulman et al. "Proximal Policy Optimization Algorithms" (2017)
  • Temporal Logic: Pnueli "The Temporal Logic of Programs" (1977)
  • SMT: Z3 Theorem Prover, De Moura & Bjørner

Implementation Notes

What the Disclosure Specifies:

  • High-level architecture and algorithms
  • Performance benchmarks to achieve
  • Core data structures
  • Workflow and integration

What You Must Design:

  • Exact class hierarchies
  • Method signatures and APIs
  • Error handling strategies
  • Testing approaches
  • Code organization

This gives you freedom to make independent implementation choices while following the disclosed concepts.

  • Public disclosure under CC BY 4.0
  • Not patented (defensive publication)
  • Anyone can implement with attribution
  • No patent claims can be filed on these concepts

This is your primary reference for the clean room implementation.