arXiv AI
Aug 12
Optimize Cheap, Deploy Strong: Cost-Aware Cross-Tier Transfer for Evolutionary Optimization
arXiv:2608. 10694v1 Announce Type: cross Abstract: Evolutionary optimization of LLM prompts and agentic programs (e.
Early-Stopping Thresholds for ES-HyperNEAT: A Data-Driven Approach from Fitness Dynamics
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arXiv AI
Jun 10
Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games
arXiv:2606. 10389v1 Announce Type: new Abstract: Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs.
arXiv AI
Jun 30
Knowing in Advance When an Evolutionary Outer Loop Will Not Help: A Pre-Registered Cheap-Baseline Screening Rule
arXiv:2606. 29119v1 Announce Type: cross Abstract: We introduce a pre-registered screening rule that decides, before any implementation, whether an evolutionary / population / lifecycle outer loop over neural-network parameters or structure is worth building.
arXiv Machine Learning
2d ago
Feedback-Calibrated Protein Optimization with Batch-Aligned Tail Arbitration
arXiv:2609.37808v1 Announce Type: new Abstract: Protein optimization aims to discover high-fitness sequences under a limited experimental budget. Existing machine-learning methods use task-specific p...
arXiv AI
Jun 3
q0: Primitives for Hyper-Epoch Pretraining
arXiv:2606. 03938v1 Announce Type: cross Abstract: Multi-epoch training is becoming the standard now that compute is growing faster than the supply of high-quality text.
arXiv AI
Sep 15
Surrogate-Assisted Genetic Programming with Phenotypic Characterisation in Dynamic Multi-Mode Project Scheduling
arXiv:2609.14418v1 Announce Type: cross Abstract: Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple ex...
arXiv AI
4d ago
Rank-Reliable Teacher-Guided Fitness Approximation for Expensive Evolutionary Optimization: A TinyML Architecture Search Study
The paper introduces TGL-NSGA-II, a low‑fidelity framework that uses a pretrained teacher to stratify samples by difficulty and class, then applies a short knowledge‑distillation step (KD‑Lite) before scoring candidates on a stratified evaluation set. The teacher‑guided scores are fused with a Gaussian‑process surrogate to select candidates for full evaluation, and the method is evaluated on keyword spotting and bird‑call classification tasks. Results show high Kendall‑τ values (0.74 and 0.62), a 41% reduction in proxy‑score variance, and improved hypervolume and false‑positive rates compared to full NSGA‑II, while running 2.2× faster under a constrained evaluation budget.
arXiv AI
Sep 2
Bandits in Prod: Hyperparameter Optimization at Inference Time
The paper introduces Online Hyperparameter Optimization (OHPO), framing it as an infinitely many‑armed bandit problem over mixed and conditional search spaces. It proposes the IMABO framework, which couples any bandit policy with any oracle for proposing new configurations, and presents IMOSS—a restart‑free anytime policy with provable regret bounds. Experiments show that IMABO, combined with practical oracles such as TPE, an incumbent‑mutation oracle, and a pretrained tabular foundation model, outperforms random search across a range of settings from classical ML models to LLM‑based agents.
arXiv AI
Jul 17
Reward-Free Evolving Agents via Pairwise Validator
arXiv:2607. 14408v1 Announce Type: new Abstract: A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal.
arXiv AI
Sep 2
Flawed in Nature, Perfect through Evolution
arXiv:2609.00129v1 Announce Type: cross Abstract: The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem they were trained on drifts. This is a nea...
arXiv AI
Sep 15
Compute Allocation for Self-Evolving LLMs: From Depth-Breadth to Multi-Armed Bandits
arXiv:2605.29268v3 Announce Type: replace-cross Abstract: LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existi...
arXiv Machine Learning
Jul 1
Predictable GRPO: A Closed-Form Model of Training Dynamics
arXiv:2606. 30789v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has become a standard tool for improving the reasoning ability of large language models, yet its training dynamics are still described empirically: reward trajectories are fit with low-parameter functional forms whose constants carry no mechanistic meaning, and hyperparameter choices remain a matter of trial and error.