ลนrรณdลo: โGenerative thermodynamic computingโ (arXiv:2506.15121, Whitelam 2025)
Kluczowa idea: Zamiast uลผywaฤ sieci neuronowej do โodszumianiaโ (jak w diffusion models), wykorzystujemy naturalnฤ ewolucjฤ fizycznego ukลadu stochastycznego (dynamika Langevina). Dane โwyลaniajฤ siฤ z szumuโ wprost z dynamiki termodynamicznej.
Formuลa uczenia:
Maksymalizuj prawdopodobieลstwo generowania odwrรณconych trajektorii procesu "zaszumiania"
โ generacja z minimalnฤ
emisjฤ
ciepลa (minimalna dysypacja)
Tak, masz racjฤ! To fundamentalna zmiana paradygmatu:
[Stary model]
LLM โ dลuga odpowiedลบ tekstowa (droga inferencja)
[Nowy model - Whitelam/Bielik]
LLM (Bielik) โ formalizacja + warunek c โ Langevin/EBM sampler โ rozwiฤ
zanie
โ
"setki wyspecjalizowanych agentรณw"
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ NLP2CMD Thermodynamic โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โ โ
โ โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ โ
โ โ Router โโโโโโถโ Formalizer โโโโโโถโ Orchestratorโ โ
โ โ (Intent) โ โ (Bielik) โ โ (Parallel) โ โ
โ โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ โโโโโโโโฌโโโโโโโ โ
โ โ โ
โ โโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโฌโโโโโโโดโโโโโโ โ
โ โผ โผ โผ โผ โ
โ โโโโโโโโโโโโโโ โโโโโโโโโโโโโโ โโโโโโโโโโโโโโ โโโโโโโโโโโ โ
โ โ SQL Agent โ โShell Agent โ โ K8s Agent โ โLangevin โ โ
โ โ (Classic) โ โ (Classic) โ โ (Classic) โ โ Sampler โ โ
โ โโโโโโโโโโโโโโ โโโโโโโโโโโโโโ โโโโโโโโโโโโโโ โโโโโโโโโโโ โ
โ โ
โ โโโโโโโโโโโโโโ โโโโโโโโโโโโโโ โโโโโโโโโโโโโโ โโโโโโโโโโโ โ
โ โConstraint โ โ Schedule โ โ Resource โ โ EBM โ โ
โ โ Solver โ โ Planner โ โ Allocator โ โ Sampler โ โ
โ โโโโโโโโโโโโโโ โโโโโโโโโโโโโโ โโโโโโโโโโโโโโ โโโโโโโโโโโ โ
โ โ
โ โโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโ โ
โ โผ โผ โ
โ โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ โ
โ โ Aggregator โ โ Cache โ โ
โ โ (Results) โ โ (Semantic) โ โ
โ โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ โ
โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# src/nlp2cmd/thermodynamic/langevin.py
@dataclass
class LangevinConfig:
"""Configuration for Langevin dynamics sampler."""
mu: float = 1.0 # Mobility coefficient
kT: float = 1.0 # Thermal energy (temperature)
dt: float = 0.01 # Time step
n_steps: int = 1000 # Number of steps
dim: int = 64 # Latent dimension
class LangevinSampler:
"""
Thermodynamic sampler using overdamped Langevin dynamics.
Implements: ลผ = -ฮผโV(z;c) + โ(2ฮผkT) ฮพ(t)
Where:
- z: latent state
- c: condition from LLM
- V: energy function (learnable)
- ฮพ: white noise
"""
def __init__(self, energy_model: EnergyModel, config: LangevinConfig):
self.energy = energy_model
self.config = config
def sample(self, condition: torch.Tensor, n_samples: int = 1) -> torch.Tensor:
"""Generate samples via Langevin dynamics."""
z = torch.randn(n_samples, self.config.dim) # Start from noise
for step in range(self.config.n_steps):
# Compute energy gradient
grad_V = self.energy.gradient(z, condition)
# Langevin update
noise = torch.randn_like(z)
z = z - self.config.mu * grad_V * self.config.dt \
+ math.sqrt(2 * self.config.mu * self.config.kT * self.config.dt) * noise
return z
def estimate_entropy_production(self, trajectory: torch.Tensor) -> float:
"""
Estimate entropy production along trajectory.
Lower = more reversible = better generative quality.
"""
# Compute heat dissipation Q along trajectory
Q = 0.0
for t in range(len(trajectory) - 1):
dz = trajectory[t+1] - trajectory[t]
grad_V = self.energy.gradient(trajectory[t])
Q += torch.dot(grad_V, dz)
return Q.item()
# src/nlp2cmd/thermodynamic/energy.py
class ConstraintEnergy(nn.Module):
"""
Energy function for constraint satisfaction problems.
V(z; c) = ฮฃ_a ฮป_a ฯ_a(z; c)
Where:
- ฯ_a: penalty functions for constraint violations
- ฮป_a: weights (learnable or fixed)
"""
def __init__(self, constraint_types: List[str]):
super().__init__()
self.penalties = nn.ModuleDict({
ct: ConstraintPenalty(ct) for ct in constraint_types
})
self.lambdas = nn.ParameterDict({
ct: nn.Parameter(torch.ones(1)) for ct in constraint_types
})
def forward(self, z: torch.Tensor, condition: dict) -> torch.Tensor:
"""Compute total energy."""
total_energy = 0.0
for name, penalty in self.penalties.items():
if name in condition.get('constraints', {}):
constraint_spec = condition['constraints'][name]
violation = penalty(z, constraint_spec)
total_energy += self.lambdas[name] * violation
return total_energy
def gradient(self, z: torch.Tensor, condition: dict) -> torch.Tensor:
"""Compute energy gradient โV(z;c)."""
z.requires_grad_(True)
V = self.forward(z, condition)
grad = torch.autograd.grad(V, z, create_graph=True)[0]
return grad
class SchedulingEnergy(ConstraintEnergy):
"""Energy model for scheduling problems."""
CONSTRAINT_TYPES = [
'no_overlap', # Tasks can't overlap
'resource_limit', # Resource capacity constraints
'precedence', # Task ordering constraints
'deadline', # Deadline constraints
'preference', # Soft preferences
]
def __init__(self):
super().__init__(self.CONSTRAINT_TYPES)
class AllocationEnergy(ConstraintEnergy):
"""Energy model for resource allocation."""
CONSTRAINT_TYPES = [
'capacity', # Don't exceed capacity
'demand', # Meet demand
'balance', # Load balancing
'cost', # Minimize cost
]
# src/nlp2cmd/thermodynamic/router.py
class ThermodynamicRouter:
"""
Routes problems to appropriate solver:
- Classic DSL agents for simple queries
- Langevin/EBM for constraint satisfaction
"""
THERMODYNAMIC_INTENTS = {
'schedule', # Scheduling problems
'allocate', # Resource allocation
'optimize', # General optimization
'sample', # Bayesian sampling
'plan', # Planning with constraints
'route', # Routing/TSP problems
}
CLASSIC_INTENTS = {
'query', # SQL queries
'execute', # Shell commands
'deploy', # Docker/K8s
'transform', # Data transformation
}
def route(self, intent: str, complexity: float) -> str:
"""
Decide solver type based on intent and complexity.
Returns: 'classic' | 'langevin' | 'hybrid'
"""
if intent in self.THERMODYNAMIC_INTENTS:
if complexity > 0.7:
return 'langevin'
else:
return 'hybrid' # Langevin + classic verification
else:
return 'classic'
# src/nlp2cmd/thermodynamic/orchestrator.py
class ThermodynamicOrchestrator:
"""
Orchestrates parallel execution of multiple samplers.
Key features:
- Parallel sampling (setki agentรณw)
- Majority voting across samples
- Energy-based ranking
- Entropy production monitoring
"""
def __init__(self, agents: Dict[str, Agent]):
self.agents = agents
self.executor = ThreadPoolExecutor(max_workers=32)
async def solve_parallel(
self,
problem: Problem,
n_parallel: int = 8,
voting: str = 'energy' # 'energy' | 'majority' | 'best'
) -> Solution:
"""
Solve problem with parallel samplers.
1. Dispatch to n_parallel agents
2. Collect solutions
3. Vote/select best
"""
# Parallel execution
futures = []
for i in range(n_parallel):
agent = self.select_agent(problem)
future = self.executor.submit(agent.solve, problem, seed=i)
futures.append(future)
# Collect results
solutions = [f.result() for f in as_completed(futures)]
# Vote
if voting == 'energy':
# Select lowest energy solution
return min(solutions, key=lambda s: s.energy)
elif voting == 'majority':
# Select most common solution
return self.majority_vote(solutions)
else:
# Select best by custom metric
return max(solutions, key=lambda s: s.score)
def estimate_energy_savings(
self,
problem: Problem,
classic_tokens: int,
langevin_steps: int
) -> dict:
"""
Estimate energy savings vs pure LLM approach.
Classic LLM: ~1-5J per 1000 tokens (GPU inference)
Langevin (digital): ~0.1-0.5J per 1000 steps
Langevin (analog): ~0.001-0.01J per 1000 steps (theoretical)
"""
llm_energy = classic_tokens * 0.003 # ~3mJ per token
langevin_digital = langevin_steps * 0.0003 # ~0.3mJ per step
langevin_analog = langevin_steps * 0.00001 # ~0.01mJ per step (future)
return {
'llm_only': llm_energy,
'hybrid_digital': llm_energy * 0.1 + langevin_digital,
'hybrid_analog': llm_energy * 0.1 + langevin_analog,
'savings_digital': (llm_energy - (llm_energy * 0.1 + langevin_digital)) / llm_energy,
'savings_analog': (llm_energy - (llm_energy * 0.1 + langevin_analog)) / llm_energy,
}
# src/nlp2cmd/thermodynamic/regularizer.py
class EntropyProductionRegularizer:
"""
Regularizer based on Whitelam's principle:
L = -E[log P(ฯฬ)] + ฮป E[Q(ฯฬ)]
Where Q is heat (entropy production) along trajectory.
Lower entropy production = more reversible = better generative quality.
"""
def __init__(self, lambda_entropy: float = 0.1):
self.lambda_entropy = lambda_entropy
def compute_loss(
self,
log_prob: torch.Tensor,
trajectory: torch.Tensor,
energy_model: EnergyModel
) -> torch.Tensor:
"""
Compute regularized loss.
Args:
log_prob: Log probability of generated samples
trajectory: Full sampling trajectory
energy_model: Energy function
Returns:
Regularized loss = -log_prob + ฮป * entropy_production
"""
# Standard generative loss
generative_loss = -log_prob.mean()
# Entropy production along trajectory
entropy_prod = self._estimate_entropy_production(trajectory, energy_model)
return generative_loss + self.lambda_entropy * entropy_prod
def _estimate_entropy_production(
self,
trajectory: torch.Tensor,
energy_model: EnergyModel
) -> torch.Tensor:
"""
Estimate entropy production (heat dissipation).
For overdamped Langevin:
ฯ = (1/kT) โซ Fยทv dt โ ฮฃ (โV ยท ฮz) / kT
"""
sigma = 0.0
for t in range(len(trajectory) - 1):
dz = trajectory[t+1] - trajectory[t]
grad_V = energy_model.gradient(trajectory[t])
sigma += torch.sum(grad_V * dz)
return sigma / self.kT
| # | Feature | Opis | Priorytet |
|---|---|---|---|
| 1 | LangevinSampler | Core sampler z dynamikฤ Langevina | P0 |
| 2 | EnergyModels | Modele energii dla rรณลผnych domen (scheduling, allocation, planning) | P0 |
| 3 | ThermodynamicRouter | Router decydujฤ cy: classic vs Langevin | P0 |
| 4 | ParallelOrchestrator | Zrรณwnoleglenie samplerรณw | P0 |
| # | Feature | Opis | Priorytet |
|---|---|---|---|
| 5 | EntropyRegularizer | Regularizacja przez produkcjฤ entropii | P1 |
| 6 | EnergyEstimator | Szacowanie zuลผycia energii (LLM vs Langevin) | P1 |
| 7 | HybridPlanner | LLM formalizuje, Langevin rozwiฤ zuje | P1 |
| 8 | BatchSampling | Batch processing dla wielu problemรณw | P1 |
| # | Feature | Opis | Priorytet |
|---|---|---|---|
| 9 | SchedulingAgent | Agent do harmonogramowania (Langevin-based) | P2 |
| 10 | AllocationAgent | Agent do alokacji zasobรณw | P2 |
| 11 | RoutingAgent | Agent TSP/VRP z EBM | P2 |
| 12 | BayesianSampler | Posterior sampling dla inference | P2 |
| 13 | LatentGenerator | Generacja w przestrzeni latent (multimodal) | P2 |
| # | Feature | Opis | Priorytet |
|---|---|---|---|
| 14 | AnalogInterface | Interface do hardware analogowego | P3 |
| 15 | EdgeDeployment | Deployment na edge devices | P3 |
| 16 | FPGABackend | FPGA accelerator dla Langevin | P3 |
Klasyczne podejลcie (pure LLM):
- Input: 500 tokenรณw (opis problemu)
- Output: 2000 tokenรณw (reasoning + solution)
- Total: 2500 tokenรณw
- Energia: 2500 ร 3mJ = 7.5J
Podejลcie Whitelam/Bielik:
- LLM (formalizacja): 500 + 200 = 700 tokenรณw ร 3mJ = 2.1J
- Langevin sampling: 5000 krokรณw ร 0.3mJ = 1.5J
- Total: 3.6J
- Oszczฤdnoลฤ: 52%
Z hardware analogowym (przyszลoลฤ):
- LLM (formalizacja): 2.1J
- Langevin (analog): 5000 krokรณw ร 0.01mJ = 0.05J
- Total: 2.15J
- Oszczฤdnoลฤ: 71%
User Input (NL)
โ
โผ
โโโโโโโโโโโโโโโ
โ Router โ โ Klasyfikacja: classic vs thermodynamic
โโโโโโโโฌโโโโโโโ
โ
โโโโโโโโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโโโ
โ โ โ
โผ โผ โผ
โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ
โ Classic โ โ Formalizer โ โ Hybrid โ
โ Agent โ โ (LLM) โ โ Mode โ
โโโโโโโโฌโโโโโโโ โโโโโโโโฌโโโโโโโ โโโโโโโโฌโโโโโโโ
โ โ โ
โ โผ โ
โ โโโโโโโโโโโโโโโ โ
โ โ Condition โ โ
โ โ c โ โ
โ โโโโโโโโฌโโโโโโโ โ
โ โ โ
โ โโโโโโโโโโโโโโผโโโโโโโโโโโโโ โ
โ โผ โผ โผ โ
โ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โ
โ โLangevin โ โLangevin โ โLangevin โ โ
โ โSampler 1โ โSampler 2โ โSampler Nโ โ
โ โโโโโโฌโโโโโ โโโโโโฌโโโโโ โโโโโโฌโโโโโ โ
โ โ โ โ โ
โ โโโโโโโโโโโโโโผโโโโโโโโโโโโโ โ
โ โผ โ
โ โโโโโโโโโโโโโโโ โ
โ โ Voting โ โ
โ โ (Energy/MV) โ โ
โ โโโโโโโโฌโโโโโโโ โ
โ โ โ
โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโ
โผ
โโโโโโโโโโโโโโโ
โ Aggregator โ
โโโโโโโโฌโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโ
โ Output โ
โโโโโโโโโโโโโโโ
from nlp2cmd import ThermodynamicNLP2CMD
from nlp2cmd.thermodynamic import LangevinSampler, SchedulingEnergy
# Initialize thermodynamic system
nlp = ThermodynamicNLP2CMD(
llm="bielik-7b", # For formalization
samplers={
'scheduling': LangevinSampler(
energy_model=SchedulingEnergy(),
config=LangevinConfig(n_steps=5000, kT=0.1)
),
},
parallel_workers=8
)
# Solve scheduling problem
result = nlp.solve("""
Zaplanuj harmonogram 10 zadaล na 3 maszyny.
Kaลผde zadanie trwa 1-4 godziny.
Maszyna A moลผe pracowaฤ 0-8h, B: 8-16h, C: caลฤ
dobฤ.
Minimalizuj czas zakoลczenia wszystkich zadaล.
""")
# Result contains:
# - solution: dict with task assignments
# - energy: final energy (quality metric)
# - entropy_production: reversibility metric
# - llm_tokens: tokens used by Bielik
# - langevin_steps: steps in sampler
# - energy_savings: estimated vs pure LLM
Masz racjฤ co do zmiany paradygmatu:
Kluczowe korzyลci:
Ograniczenia: