Lightweight Benchmark for Self‑Reconfiguring Systems (LBSR)

MIT License – GitHub repository: lbsr-benchmark

This benchmark measures adaptability, reconfiguration efficiency, and architectural evolution – parameters ignored by all current static benchmarks (MMLU, ARC, BIG‑bench, etc.). It is designed for systems that can change their own structure (hardware, topology, algorithm) during evaluation.

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1. Core Philosophy

Existing benchmarks assume a fixed model. This benchmark assumes the system can:

• Reconfigure its internal architecture between tasks (or even during a task).
• Learn how to reconfigure better over time (meta‑learning).
• Exploit physical/substrate advantages (e.g., analog parallelism, optical Fourier transforms).
• Trade off reconfiguration time vs. computation time.

The benchmark outputs not just accuracy but a multi‑dimensional profile including:

• Reconfiguration overhead (time, energy, resource cost)
• Adaptation rate (how quickly performance improves on a stream of related tasks)
• Architectural search efficiency (how many reconfigurations needed to find a good topology)
• Substrate utilization (how well the system exploits its physical capabilities)

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2. Benchmark Structure

The benchmark consists of three test suites, each focusing on different missing parameters.

Suite A: Streaming Problem Classes (Adaptability)

• Input: A stream of 1000 problems, each belonging to one of 5 hidden classes (e.g., linear algebra, graph search, number theory, logic, pattern completion). The system does not know the class labels.
• Metric: After each problem, the system may reconfigure. We measure:
  • Cumulative solved rate over time.
  • Reconfiguration frequency and average reconfiguration time.
  • Transfer efficiency: Performance improvement on class k after having seen class j.

Suite B: Architectural Search (Meta‑Learning)

• Input: A single complex problem (e.g., find a Hamiltonian path in a 50‑node graph). The system can try multiple architectures (topologies, algorithms) on sub‑problems.
• Metric:
  • Time to first solution (including reconfigurations).
  • Number of architectural trials before success.
  • Exploration vs. exploitation ratio (how many random vs. optimized reconfigurations).

Suite C: Substrate‑Aware Operations

• Input: A set of operations that can be implemented either digitally (software) or analog‑optically (if available). Examples: matrix multiply, FFT, prime sieve, PDE solve.
• Metric:
  • Speedup over a reference digital implementation (on a standard CPU).
  • Energy per operation (if measurable).
  • Precision (for analog ops, the error relative to digital).

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3. Implementation for a Simulated Hive Mind

The benchmark is substrate‑agnostic – it can be run on any system that exposes a reconfiguration API. For the laser‑based fluidic hive mind, we provide a Python simulator that emulates the key properties: reconfigurable topology, analog parallelism, and reconfiguration cost.

3.1 Python Simulator Architecture

class HiveMindSimulator:
    def __init__(self, num_nodes=256):
        self.num_nodes = num_nodes
        self.topology = "mesh"          # initial topology
        self.reconfig_time = 0.0        # cumulative reconfiguration time
        self.compute_time = 0.0
        self.holographic_memory = {}     # stored patterns
    
    def reconfigure(self, new_topology):
        """Change the internal network topology. Cost = 1 ms per changed link."""
        # simulate fluidic reconfiguration delay
        delay = self._estimate_reconfig_delay(self.topology, new_topology)
        self.reconfig_time += delay
        self.topology = new_topology
    
    def solve(self, problem):
        """Solve a problem using current topology. Returns answer and compute time."""
        start = time.time()
        # ... optical computation simulation ...
        self.compute_time += time.time() - start
        return answer

3.2 Benchmark Runner

class LBSR:
    def __init__(self, hive):
        self.hive = hive
        self.results = {}
    
    def run_suite_A(self, problem_stream):
        solved = 0
        reconf_count = 0
        for i, problem in enumerate(problem_stream):
            # Optionally reconfigure before each problem
            if i % 10 == 0:
                self.hive.reconfigure("random")   # simulate adaptation
                reconf_count += 1
            answer = self.hive.solve(problem)
            if answer == problem.ground_truth:
                solved += 1
        self.results['suite_A'] = {
            'solved_rate': solved / len(problem_stream),
            'avg_reconfig_time': self.hive.reconfig_time / max(reconf_count,1),
            'total_compute_time': self.hive.compute_time
        }
        return self.results

3.3 Metrics Output (JSON)

{
  "suite_A": {
    "solved_rate": 0.87,
    "reconfiguration_overhead_seconds": 0.023,
    "adaptation_gain": 0.42,
    "compute_time_seconds": 12.4
  },
  "suite_B": {
    "trials_to_success": 17,
    "exploration_ratio": 0.35,
    "best_topology": "hypercube",
    "wallclock_time_seconds": 5.2
  },
  "suite_C": {
    "speedup_over_cpu": {"matmul": 124, "fft": 89, "prime_sieve": 450},
    "analog_precision_bits": 6.2
  }
}

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4. Missing Parameters Tracked (Unique to LBSR)

Parameter	How measured	Why ignored by others
Reconfiguration overhead	Time & energy to change topology	All benchmarks assume fixed model
Adaptation rate	Performance improvement per task seen	Static benchmarks measure only final accuracy
Architectural search efficiency	Number of trials to find good structure	No benchmark rewards finding new algorithms
Substrate speedup	Ratio of analog vs. digital performance	Benchmarks are digital‑centric
Precision vs. speed trade‑off	User‑controllable analog precision	Not measured
Memory of past configurations	Reuse of successful topologies	No concept of architectural memory

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5. How to Use the Benchmark

1. Install:

   git clone https://github.com/yourname/lbsr-benchmark
   cd lbsr-benchmark
   pip install -r requirements.txt

2. Implement your system’s adapter – a class that inherits from HiveMindInterface and implements reconfigure() and solve().

3. Run:

   from lbsr import LBSR
   from my_hive import MyHive
   
   hive = MyHive()
   benchmark = LBSR(hive)
   results = benchmark.run_all_suites()
   print(results)

4. Submit results (optional) to a public leaderboard that tracks these new metrics.

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6. Example: Running on a Simulated Fluidic Hive

We provide a reference implementation of a simulated fluidic optical hive that reconfigures by changing a virtual topology (mesh, torus, star, tree). The simulator models:

• Reconfiguration delay proportional to number of changed optical paths.
• Computation time for matrix multiply using optical analog model (O(1) for fixed size).
• Analog noise that degrades precision.

This allows anyone to test the benchmark even without real hardware.

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7. Future Extensions (Roadmap)

• Hardware‑in‑the‑loop: Real microfluidic chip control via Python API.
• Energy measurement: Integration with power monitors.
• Distributed benchmark: Multiple hives collaborating.
• Dynamic problem generation: Using GANs to create never‑seen‑before tasks.

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8. License and Contribution

MIT License – Free for academic and commercial use. Contributions welcome via pull requests. The benchmark is designed to evolve as new reconfigurable systems emerge.

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9. Conclusion

The Lightweight Benchmark for Self‑Reconfiguring Systems (LBSR) fills a critical gap: measuring how well a system can change its own architecture to adapt to diverse problems. It outputs a rich set of metrics that no current AGI benchmark provides. By publishing it under MIT license, we invite the community to adopt, extend, and challenge the next generation of adaptive intelligence – including the laser‑based fluidic hive mind.