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paceevolve-evolution-search Improves LLM-based evolutionary search by addressing context pollution, mode collapse, and weak collaboration through hierarchical context management, momentum-based backtracking, and adaptive sampling policies.
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name paceevolve-evolution-search title PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution version 0.0.2 engine skillxiv-v0.0.2-claude-opus-4.6 license MIT url https://arxiv.org/abs/2601.10657 keywords ["evolutionary-search","LLM-optimization","long-horizon","progress-aware","context-management"] description Improves LLM-based evolutionary search by addressing context pollution, mode collapse, and weak collaboration through hierarchical context management, momentum-based backtracking, and adaptive sampling policies.
Overview
Enhance LLM-driven evolutionary search for long-horizon optimization tasks. Address three key failure modes: context pollution from accumulated experiment data, mode collapse from imbalanced exploration-exploitation, and weak collaboration between parallel search trajectories.
When to Use
For autonomous optimization and search tasks requiring many candidate evaluations
When LLMs guide evolutionary algorithms over extended search spaces
For hyperparameter tuning, architecture search, or program synthesis
When you need sustained self-improvement over multiple generations
When NOT to Use
For simple single-pass optimization
When evolutionary search already works well without LLM guidance
For tasks with very limited evaluation budget
In low-latency applications
Key Technical Components
Hierarchical Context Management (HCM)
Prevent context pollution by pruning irrelevant historical data.
class ContextManager :
def __init__ (self, max_context_tokens=2000 ):
self .max_tokens = max_context_tokens
self .experiment_history = []
self .context_cache = {}
def maintain_context_hierarchy (self ):
"""Organize context by relevance tiers"""
tiers = {
"recent" : [],
"best" : [],
"diverse" : []
}
tiers["recent" ] = self .experiment_history[-10 :]
best_experiments = sorted
self
lambda
"fitness"
True
10
"best"
"diverse"
self
self
10
return
def
select_diverse_representatives
self, experiments, k=10
"""Select diverse experiments by clustering"""
if
len
return
self
for
in
max
lambda
"fitness"
return
def
prune_context
self, current_problem
"""Remove irrelevant historical data"""
for
in
self
self
self
self
0.3
0.3
0.4
if
self
return
def
compute_recency
self, experiment
"""Score based on how recent"""
len
self
self
return
1.0
1.0
def
compute_performance
self, experiment
"""Normalize fitness score"""
"fitness"
for
in
self
min
max
if
return
0.5
return
"fitness"
def
compute_problem_relevance
self, experiment, problem
"""Semantic relevance to current problem"""
if
"problem_id"
"id"
return
1.0
return
0.1
Momentum-Based Backtracking (MBB) Escape local optima by reverting to diverse solutions.
class MomentumBacktracker :
def __init__ (self, backtrack_window=5 ):
self .backtrack_window = backtrack_window
self .trajectory_history = []
self .fitness_trend = []
def detect_local_optimum (self ):
"""Check if stuck in local optimum"""
if len (self .fitness_trend) < self .backtrack_window:
return False
recent_trend = self .fitness_trend[-self .backtrack_window:]
improvement = max (recent_trend) - min (recent_trend)
stagnation = improvement < STAGNATION_THRESHOLD
solution_similarity = self .compute_avg_similarity()
low_diversity = solution_similarity > SIMILARITY_THRESHOLD
is_stuck = stagnation and low_diversity
return {
"is_stuck" : is_stuck,
"stagnation_score" : improvement,
"diversity_score" : 1.0 - solution_similarity
}
def backtrack_with_momentum (self, current_fitness ):
"""Revert to promising past solution with momentum"""
checkpoint = self .select_backtrack_checkpoint()
if checkpoint is None :
return None
current_solution = self .trajectory_history[-1 ]["solution" ]
checkpoint_solution = checkpoint["solution" ]
momentum = self .compute_momentum_vector(current_solution, checkpoint_solution)
backtrack_solution = self .blend_solutions(
checkpoint_solution,
momentum,
alpha=0.7
)
return {
"solution" : backtrack_solution,
"checkpoint_fitness" : checkpoint["fitness" ],
"momentum_direction" : momentum
}
def select_backtrack_checkpoint (self ):
"""Select good past solution with different structure"""
candidates = []
for i in range (max (0 , len (self .trajectory_history) - 20 ), len (self .trajectory_history)):
solution = self .trajectory_history[i]
if solution["fitness" ] > BACKTRACK_FITNESS_THRESHOLD:
diversity = self .compute_diversity_from_recent(solution)
if diversity > DIVERSITY_THRESHOLD:
candidates.append(solution)
if not candidates:
return None
return max (candidates, key=lambda x: x["fitness" ])
def blend_solutions (self, base_solution, momentum, alpha=0.7 ):
"""Blend checkpoint with momentum direction"""
blended = alpha * base_solution + (1 - alpha) * momentum
return blended
def compute_momentum_vector (self, current, checkpoint ):
"""Direction of progress from checkpoint to current"""
return current - checkpoint
def compute_diversity_from_recent (self, solution ):
"""How different from recent solutions"""
recent_solutions = [s["solution" ] for s in self .trajectory_history[-5 :]]
similarities = [
self .compute_solution_similarity(solution["solution" ], recent)
for recent in recent_solutions
]
avg_similarity = np.mean(similarities)
return 1.0 - avg_similarity
Self-Adaptive Sampling Policy Dynamically balance exploration and exploitation.
class AdaptiveSamplingPolicy :
def __init__ (self ):
self .exploration_rate = 0.5
self .sampling_history = []
def compute_adaptive_rate (self, recent_progress ):
"""Adjust exploration based on progress"""
improvement = np.mean(recent_progress)
if improvement > HIGH_PROGRESS_THRESHOLD:
self .exploration_rate = max (0.1 , self .exploration_rate - 0.1 )
elif improvement < LOW_PROGRESS_THRESHOLD:
self .exploration_rate = min (0.9 , self .exploration_rate + 0.1 )
return self .exploration_rate
def sample_next_candidate (self, best_candidates, random_candidates, exploration_rate ):
"""Choose between exploiting best or exploring random"""
if np.random.random() < exploration_rate:
return np.random.choice(random_candidates)
else :
return np.random.choice(best_candidates)
def integrate_backtracking (self, backtrack_solution, exploration_rate ):
"""Incorporate backtracking into sampling"""
backtrack_exploration = min (
exploration_rate + 0.3 ,
0.9
)
return backtrack_exploration
def compute_sampling_efficiency (self ):
"""Track whether sampling strategy is effective"""
total_samples = len (self .sampling_history)
improvements = sum (
1 for i in range (1 , len (self .sampling_history))
if self .sampling_history[i]["fitness" ] > self .sampling_history[i-1 ]["fitness" ]
)
efficiency = improvements / total_samples
return efficiency
Integration: Complete Loop Combine all components into complete search loop.
class PACEevolveSearch :
def __init__ (self, llm_generator ):
self .generator = llm_generator
self .context_manager = ContextManager()
self .backtracker = MomentumBacktracker()
self .sampler = AdaptiveSamplingPolicy()
def search_iteration (self, problem, budget=100 ):
"""Single generation of evolutionary search"""
for step in range (budget):
context = self .context_manager.maintain_context_hierarchy()
self .context_manager.prune_context(problem)
candidates = self .generator.generate_candidates(
problem,
context,
num_candidates=10
)
candidates = self .evaluate_candidates(candidates)
stuck = self .backtracker.detect_local_optimum()
if stuck["is_stuck" ]:
backtrack = self .backtracker.backtrack_with_momentum(
max (e["fitness" ] for e in candidates)
)
if backtrack:
candidates.append(backtrack["solution" ])
best_candidates = sorted (candidates, key=lambda x: x["fitness" ], reverse=True )[:5 ]
self .sampler.compute_adaptive_rate(
[e["fitness" ] for e in candidates]
)
if best_candidates:
self .context_manager.experiment_history.append(best_candidates[0 ])
return self .context_manager.experiment_history
Performance Characteristics
State-of-the-art on LLM-SR and KernelBench benchmarks
Discovers solutions exceeding previous records
Sustained improvement over 100+ iterations
Effective on Modded NanoGPT and similar tasks
Recommendations
Initialize exploration rate at 0.5; let it adapt
Backtrack when stagnation detected; don't backtrack too frequently
Prune context every 10-20 iterations to prevent pollution
Monitor sampling efficiency; adjust thresholds if needed
References
Context pollution prevents long-horizon optimization
Mode collapse causes premature convergence
Momentum-based backtracking enables escape from local optima
Adaptive sampling maintains balance without manual tuning
