20 recent papers · updated 2026-04-26 · sources deduplicated from arXiv, OpenAlex and Semantic Scholar
2026-04-22 | Future Living Lab of Alibaba Method: Textual Parameter Graph Optimization (TPGO) with Group Relative Agent Optimization (GRAO) meta-learning | LLM role: decomposition_guide, diagnostic_feedback_generator, optimization_proposal_generator, meta_learner
He et al. introduce Textual Parameter Graph Optimization (TPGO), a framework that optimizes multi-agent systems by representing their configurations as modular graphs and evolving them using textual gradients. The results are backed by solid empirical evidence, demonstrating a 7-10% absolute success rate improvement on complex benchmarks like MCP-Universe and GAIA over strong baselines (ReAct, MiroFlow). The key insight is the Group Relative Agent Optimization (GRAO) mechanism, which maintains an 'Optimization Experience Memory' of past successful and failed graph edits, clustering and retrieving them to teach the LLM optimizer how to generate better updates over time.
2026-04-06 | Independent Researchers Method: Elo tournament selection for evolutionary optimization with comparative error reports and Deep Focus refinement | LLM role: evolutionary_search
RoboPhD optimizes LLM agent evolution under tight evaluation budgets by replacing traditional validation sets with an Elo-based tournament on training data and allowing agents to evolve their own diagnostic instrumentation. The results are backed by solid empirical comparisons, outperforming GEPA and Autoresearch on 3 out of 4 benchmarks (ARC-AGI, Text2SQL, DocFinQA) under a strict 1,500 evaluation budget.
2026-04-01 | IBM Research Method: LLM-agent-instantiated evolutionary computation over structured scientific artifacts (theory+code or code_only) | LLM role: evolutionary_search
CliffSearch is an LLM-based evolutionary framework that co-evolves algorithm theory and code, using specialized agents for crossover, two-path mutation (exploration vs. repair) and explicit reviewer gating. The results are backed by concrete empirical runs on nanoGPT optimizer discovery and transformer hyper-connection search, demonstrating the discovery of genuinely novel geometric routing and optimizer variants rather than trivial hyperparameter tweaks. The single most useful takeaway is the 'reviewer-gated selection' where an LLM explicitly scores candidates on originality and correctness as a hard survival gate before benchmark scores are considered.
2026-03-30 | Örebro University Method: Co-evolutionary framework leveraging LLMs to generate environments and policies as executable Python code, modeled as a two-player zero-sum game and solved using Policy Space Response Oracles (PSRO) to compute a mixed-strategy Nash equilibrium (MSNE) over policy populations. | LLM role: llm_evolutionary_agent
COvolve uses LLMs to adversarially co-evolve Python code for both environments (tasks) and policies (agents), using Policy Space Response Oracles (PSRO) to compute a mixed-strategy Nash equilibrium (MSNE) that prevents catastrophic forgetting. The results are backed by solid empirical data across MiniGrid, PyGame and CARLA, demonstrating that the MSNE approach maintains robust performance across a growing historical archive of environments much better than greedy retention.
2026-03-30 | Machine Perception and Interaction Lab, Örebro University, Sweden Method: Evolutionary search over executable learning update rules using LLM-guided macro mutation and diversity-aware crossover | LLM role: evolutionary_search
This paper evolves executable reinforcement learning update rules using LLMs as macro-mutation and crossover operators, explicitly forbidding standard RL mechanisms to force the discovery of novel algorithms. The results are backed by solid empirical evaluations on Gymnasium benchmarks, showing the evolved algorithms match or beat standard baselines like PPO and SAC on several tasks, though they struggle on a few complex continuous control environments.
2026-03-15 | Carnegie Mellon University, MBZUAI, Hong Kong Baptist University, The University of Sydney Method: CausalEvolve with causal scratchpad leveraging LLMs to identify and reason about guiding factors for evolution, incorporating outcome-level and procedure-level factors, multi-arm bandit for intervention and abductive reasoning. | LLM role: heuristic_generator
CausalEvolve enhances LLM evolutionary search frameworks (like AlphaEvolve and ShinkaEvolve) by introducing a causal scratchpad that extracts outcome- and procedure-level factors to explicitly guide program mutations via a Multi-Armed Bandit. The results are backed by solid empirical numbers, showing it outperforms the state-of-the-art ShinkaEvolve across four algorithmic and mathematical tasks (Hadamard, Autocorrelation, Circle Packing, AIME) by up to 9.1% in best-found scores.
2026-03-18 | University of Oxford, University College London, University of California, Santa Barbara, University of Wisconsin–Madison, Delft University of Technology Method: Procedural generation of algorithm discovery tasks using configurable parameters for domains, modules and datasets | LLM role: research_agent, prompt_optimizer
This paper introduces DiscoGen, a procedural generator that combinatorially creates millions of algorithm discovery tasks (varying domains, editable modules and datasets) with strict meta-train/meta-test splits to evaluate and train Algorithm Discovery Agents (ADAs). The results are backed by extensive empirical evaluation of open-source LLMs on a fixed subset (DiscoBench), demonstrating that current ADAs struggle with multi-module discovery and that prompt-tuning over a diverse set of procedurally generated tasks significantly improves generalization. The single most useful takeaway is the combinatorial task generation approach (toggling which modules are editable vs.
2026-03-12 | Tsinghua University, Peking University, Southern University of Science and Technology, University of Bristol, The Hong Kong University of Science and Technology (Guangzhou), Xi’an Jiaotong University Method: Automated benchmark construction via reverse engineering and self-play; EvoRePE: Inference-time representation engineering for latent space steering. | LLM role: code_writer, constraint_generator, evaluator, prompt_optimizer, decomposition_guide, evolutionary_search
Wang et al. introduce CreativeBench to evaluate LLM code generation creativity and propose EvoRePE, a representation engineering technique that extracts a 'creativity vector' from AlphaEvolve search trajectories to steer model activations at inference time. The results are backed by solid empirical evaluations, showing that injecting this vector improves novelty and correctness even without running the full evolutionary search.
2026-03-12 | Intel Corporation Method: MAP-Elites quality-diversity search with kernel-specific behavioral dimensions, meta-prompt evolution and template-based parameter optimization | LLM role: code_writer, prompt_optimizer
KernelFoundry is an LLM-based evolutionary framework for GPU kernel optimization that combines MAP-Elites quality-diversity search with meta-prompt co-evolution and gradient-informed mutation hints. The results are rigorously backed by numbers, showing a 2.1x speedup over the AI CUDA Engineer baseline on KernelBench L2 and successful optimization of Llama 3 operations.
2026-03-11 | Google DeepMind, Google, University of California, Berkeley Method: AlphaEvolve, an LLM-based code mutation agent | LLM role: evolutionary_search
Nagda et al. (DeepMind) apply the AlphaEvolve framework to discover novel stochastic search algorithms that improve lower bounds for five classical Ramsey numbers and match SoTA on 23 others. The results are mathematically verified and represent genuine SoTA advances in extremal combinatorics, proving the framework's capability to generate highly specialized, non-trivial heuristics.
2026-03-09 | Nanjing University, Huawei Noah’s Ark Lab Method: Evolutionary Stagewise Algorithm Design (EvoStage) with multi-agent system and global-local perspective mechanism | LLM role: decomposition_guide, code_writer, reflection_agent, evolutionary_search
EvoStage enhances LLM-based automated algorithm design by decomposing the generation process into sequential stages, using a multi-agent system (coordinator and coders) to iteratively refine code based on real-time intermediate execution feedback. The results are highly credible and backed by strong empirical numbers; it achieves state-of-the-art HPWL on 16 chip placement benchmarks and beats AlphaEvolve/EoH on Bayesian Optimization tasks using an incredibly small budget of just 9 to 25 evaluations. The single most useful takeaway is the shift from black-box end-to-end evaluation to stagewise intermediate feedback, where a coordinator agent reflects on mid-execution metrics to guide the next stage of heuristic design.
2026-03-10 | Google DeepMind Method: Policy-Space Response Oracles (PSRO) with Large Language Model (LLM) as a code-generating oracle, enhanced by iterative refinement or evolutionary search (AlphaEvolve) | LLM role: code_writer
This paper replaces the deep RL oracle in Policy-Space Response Oracles (PSRO) with an LLM that generates interpretable Python code policies, using AlphaEvolve to iteratively refine the code against opponent meta-strategies. The results are backed by solid empirical metrics, showing that the AlphaEvolve variant achieves competitive exploitability and higher population returns than RL baselines (IMPALA) and CFR+ on Repeated Rock-Paper-Scissors and Leduc Poker.
2026-03-05 | Argonne National Laboratory, Rice University Method: LLM-guided evolutionary search for regularization algorithms combining LLM-driven code generation with semantically-guided crossover and mutation | LLM role: evolutionary_search
This paper applies LLM-guided evolutionary search (similar to FunSearch/AlphaEvolve) to discover novel regularization algorithms for ptychographic image reconstruction. The results are backed by solid empirical metrics, showing up to +0.26 SSIM improvements over unregularized baselines across multiple datasets. Second, the LLM autonomously discovered the benefit of embedding stateful optimizers (like Adam) and iterative sub-loops directly inside a single heuristic step.
2026-03-03 | City University of Hong Kong Method: BehaveSim, a method for measuring algorithmic similarity based on problem-solving trajectories (PSTrajs) quantified using Dynamic Time Warping (DTW) | LLM role: heuristic_generator
Zhang et al. propose BehaveSim, a metric that measures algorithmic similarity by applying Dynamic Time Warping (DTW) to the sequence of intermediate solutions (trajectories) generated during execution, rather than relying on static code analysis. By integrating this into FunSearch and EoH to enforce behavioral diversity, they achieve significant performance gains, notably reducing the optimality gap on TSP by ~7.8% compared to standard FunSearch.
2026-03-03 | The Hong Kong Polytechnic University, The University of Hong Kong Method: Bilevel meta-optimization with stability-inducing neural unrolling, using a structured Mamba-based neural operator and a gradient-derived composite solver. | LLM role: none
Gao et al. propose L2E, a meta-learned neural optimizer that uses Mamba blocks to parameterize evolutionary operators within a stability-enforcing unrolled loop (Krasnosel'skii-Mann iteration). Results on BBOB and LSGO-1000D are strong, showing it outperforms Transformer-based L2O methods (GLHF) and classical heuristics (DE) in sample efficiency and zero-shot generalization.
2026-02-23 | University of California, Berkeley, Bespoke Labs Method: LLM-driven evolutionary algorithm with hierarchical adaptive optimization using an accumulated improvement signal to dynamically modulate local exploration intensity, global resource allocation via multi-armed bandit and meta-level solution tactics generation | LLM role: semantic_mutation_operator
AdaEvolve replaces static evolutionary schedules with a three-tier adaptive controller: local exploration intensity based on an 'accumulated improvement signal' (pseudo-gradient), global compute allocation via a normalized bandit and meta-level 'tactic' generation when stagnation occurs. Results are highly convincing, showing SOTA on Circle Packing (beating AlphaEvolve) and 185 other tasks while using the same LLM backbone as baselines, proving the gains are algorithmic. The most stealable insight is the $G_t$ signal metric—an exponential moving average of squared normalized improvements—which allows auto-tuning exploration rates without manual intervention.
2026-02-18 | Google DeepMind Method: AlphaEvolve, an LLM-powered evolutionary coding agent | LLM role: code_writer
DeepMind applies AlphaEvolve to discover new variants of CFR and PSRO by evolving Python code for regret accumulation and meta-strategy solving. They identify VAD-CFR and SHOR-PSRO, which outperform human-designed SOTA (DCFR, PCFR+) on benchmarks like Leduc Poker and Liar's Dice; results are rigorous, using exact exploitability.
2026-02-17 | Massachusetts Institute of Technology Method: Language-Guided Optimization (LaGO) framework decomposing heuristic discovery into forward, backward and update stages, utilizing LLMs for reasoned evolution, code-writing analysis, co-evolution of constructive and refinement heuristics and diversity-aware population management. | LLM role: evolutionary_search
LaGO decomposes automated heuristic design into three explicit modules: evaluation, a code-writing 'Analyst' (backward pass) and a diversity-aware 'Generator' (update), while co-evolving constructive and refinement heuristics. The authors demonstrate significant gains (+0.17 QYI) on PDPTW and Crew Pairing against ReEvo and EoH, showing that joint optimization of initialization and improvement prevents local optima.
2026-02-17 | University of Wisconsin-Madison Method: LLM-driven evolutionary optimization with nested parameter tuning | LLM role: code_writer, mutation_operator, report_generator
MadEvolve extends AlphaEvolve by embedding a gradient-based optimization loop (via JAX) inside the fitness evaluation, allowing the LLM to focus purely on code structure while an optimizer (Adam) handles continuous parameters. They demonstrate 20-30% performance gains on complex cosmological reconstruction tasks, validated on held-out simulations. The critical takeaway is the architectural pattern: prompt the LLM to write differentiable code rather than tuning constants and use a UCB1 bandit to dynamically select between cheap and expensive models.
2026-02-18 | Walmart Global Tech, Santa Clara University, Independent Researcher Method: LLM-guided program evolution based on AlphaEvolve | LLM role: evolutionary_search
RankEvolve applies AlphaEvolve with MAP-Elites to evolve Python retrieval functions, achieving significant gains over BM25 on BEIR/BRIGHT by rediscovering concepts like soft stop-words and PMI-based scoring. The results are empirically rigorous, showing that 'Freeform' seeds (defining only I/O contracts) significantly outperform 'Composable' or 'Constrained' seeds, albeit at a 10x latency cost.
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