Automated Algorithm Discovery

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Automated algorithm discovery studies how to construct or improve executable procedures—such as heuristics, numerical solvers, learning rules, and hardware policies—without specifying every design choice by hand. Common approaches use evolutionary or tree search with language models to modify code, program synthesis over reusable components, reinforcement learning, or learned search controllers; candidates are assessed with benchmarks, simulators, formal checks, and staged evaluation. Persistent memory, modular representations, diversity archives, and adaptive budget allocation help reuse findings and reduce search stagnation. A central challenge is establishing that gains reflect sound, useful algorithms rather than benchmark overfitting, noisy evaluations, recombinations of human designs, or exploitable evaluators—and that they transfer under real-world constraints.

Latest papers

170 papers33 papers in the last four weeks, up 120% on the four weeks before. 0.2% of all new papers.

Mar 18, 2026

Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms

Although multigrid is asymptotically optimal for solving many important partial differential equations, its efficiency relies heavily on the careful selection of the individual algorithmic components. In contrast to recent approaches that can optimize certain multigrid components using deep learning techniques, we adopt a complementary strategy, employing evolutionary algorithms to construct efficient multigrid cycles from proven algorithmic building blocks. Here, we will present its application to generate efficient algebraic multigrid methods with so-called flexible cycling, that is, level-specific smoothing sequences and non-recursive cycling patterns. The search space with such non-standard cycles is intractable to navigate manually, and is generated using genetic programming (GP) guided by context-free grammars. Numerical experiments with the linear algebra library, hypre, demonstrate the potential of these non-standard GP cycles to improve multigrid performance both as a solver and a preconditioner.

Dinesh Parthasarathy, Wayne Mitchell, Arjun Gambhir +2

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany · Lawrence Livermore National Laboratory, USA · CERFACS, Toulouse, France and Department of Applied Mathematics, VSB-Technical University of Ostrava, Ostrava, Czech Republic

Automated Algorithm Discovery

Mar 10, 2026

Reinforced Generation of Combinatorial Structures: Ramsey Numbers

We present improved lower bounds for nine classical Ramsey numbers:

\mathbf{R}(3, 13)

is increased from

60

to

61

,

\mathbf{R}(3, 18)

from

99

to

100

,

\mathbf{R}(4, 13)

from

138

to

139

,

\mathbf{R}(4, 14)

from

147

to

148

,

\mathbf{R}(4, 15)

from

158

to

159

,

\mathbf{R}(4, 16)

from

170

to

174

,

\mathbf{R}(4, 18)

from

205

to

209

,

\mathbf{R}(4, 19)

from

213

to

219

, and

\mathbf{R}(4, 20)

from

234

to

237

. These results were achieved using AlphaEvolve, an LLM-based code mutation agent. Beyond these new results, we successfully recovered lower bounds for all Ramsey numbers known to be exact, and matched the best known lower bounds across many other cases. These include bounds for which previous work does not detail the algorithms used. Virtually all known Ramsey lower bounds are derived computationally, with bespoke search algorithms each delivering a handful of results. AlphaEvolve is a single meta-algorithm yielding search algorithms for all of our results.

Ansh Nagda, Prabhakar Raghavan, Abhradeep Thakurta

University of California, Berkeley, and Google DeepMind. · Google. · Google DeepMind.

Automated Algorithm Discovery

Mar 9, 2026

Hubble: An LLM-Driven Agentic Framework for Safe, Diverse, and Reproducible Alpha Factor Discovery

Automated alpha discovery is difficult because the search space of formulaic factors is combinatorial, the signal-to-noise ratio in daily equity data is low, and unconstrained program generation is operationally unsafe. We present Hubble, an agentic factor mining framework that combines large language models (LLMs) with a domain-specific operator language, an abstract syntax tree (AST) execution sandbox, a dual-channel retrieval-augmented generation (RAG) module, and a family-aware selection mechanism. Instead of treating the LLM as an unconstrained code generator, Hubble restricts generation to interpretable operator trees, evaluates every candidate through a deterministic cross-sectional pipeline, and feeds back both top formulas and structured family-level diagnostics to subsequent rounds. The current system additionally introduces positive/negative RAG, formula-similarity penalties, standardized multi-metric scoring, dual reporting of RankIC and Pearson IC, and persistent diagnostics artifacts for post-hoc research analysis. On a U.S. equity universe of roughly 500 stocks, our main run evaluates 104 valid candidates across three rounds with zero runtime crashes and discovers a top set dominated by range, volatility, and trend families rather than crowded volume-only motifs. We then fix the resulting top-5 factors and validate them on a held-out period from 2025-06-01 to 2026-03-13. In this out-of-sample window, the two range factors and two volatility factors remain positive and several achieve HAC-significant Pearson IC and long-short evidence, whereas the weakest in-sample trend factor decays materially. These results suggest that safe LLM-guided search can be upgraded from a syntax-compliant generator into a reproducible alpha-research workflow that jointly optimizes validity, diversity, interpretability, and family-level generalization.

Runze Shi, Shengyu Yan, Yuecheng Cai +1

Alpha Factor Discovery Quantitative Finance Automated Algorithm Discovery Large Language Model-Guided Scientific Discovery

Mar 9, 2026

Advancing Automated Algorithm Design via Evolutionary Stagewise Design with LLMs

With the rapid advancement of human science and technology, problems in industrial scenarios are becoming increasingly challenging, bringing significant challenges to traditional algorithm design. Automated algorithm design with LLMs emerges as a promising solution, but the currently adopted black-box modeling deprives LLMs of any awareness of the intrinsic mechanism of the target problem, leading to hallucinated designs. In this paper, we introduce Evolutionary Stagewise Algorithm Design (EvoStage), a novel evolutionary paradigm that bridges the gap between the rigorous demands of industrial-scale algorithm design and the LLM-based algorithm design methods. Drawing inspiration from CoT, EvoStage decomposes the algorithm design process into sequential, manageable stages and integrates real-time intermediate feedback to iteratively refine algorithm design directions. To further reduce the algorithm design space and avoid falling into local optima, we introduce a multi-agent system and a "global-local perspective" mechanism. We apply EvoStage to the design of two types of common optimizers: designing parameter configuration schedules of the Adam optimizer for chip placement, and designing acquisition functions of Bayesian optimization for black-box optimization. Experimental results across open-source benchmarks demonstrate that EvoStage outperforms human-expert designs and existing LLM-based methods within only a couple of evolution steps, even achieving the historically state-of-the-art half-perimeter wire-length results on every tested chip case. Furthermore, when deployed on a commercial-grade 3D chip placement tool, EvoStage significantly surpasses the original performance metrics, achieving record-breaking efficiency. We hope EvoStage can significantly advance automated algorithm design in the real world, helping elevate human productivity.

Chen Lu, Ke Xue, Chengrui Gao +5

National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · Huawei Noah’s Ark Lab, China

Automated Algorithm Discovery Automated Heuristic Design Evolutionary Optimization Electronic Design Automation Bayesian Optimization

Mar 8, 2026

CDEoH: Category-Driven Automatic Algorithm Design With Large Language Models

With the rapid advancement of large language models (LLMs), LLM-based heuristic search methods have demonstrated strong capabilities in automated algorithm generation. However, their evolutionary processes often suffer from instability and premature convergence. Existing approaches mainly address this issue through prompt engineering or by jointly evolving thought and code, while largely overlooking the critical role of algorithmic category diversity in maintaining evolutionary stability. To this end, we propose Category Driven Automatic Algorithm Design with Large Language Models (CDEoH), which explicitly models algorithm categories and jointly balances performance and category diversity in population management, enabling parallel exploration across multiple algorithmic paradigms. Extensive experiments on representative combinatorial optimization problems across multiple scales demonstrate that CDEoH effectively mitigates convergence toward a single evolutionary direction, significantly enhancing evolutionary stability and achieving consistently superior average performance across tasks and scales.

Yu-Nian Wang, Shen-Huan Lyu, Ning Chen +3

Key Laboratory of Water Big Data Technology of Ministry of Water Resources, College of Computer Science and Software Engineering, Hohai University, Nanjing, China · Department of Computer Science, City University of Hong Kong, Hong Kong, China · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

Automated Heuristic Design Automated Algorithm Discovery Large Language Model-Guided Optimization Evolutionary Optimization Combinatorial Optimization

Mar 5, 2026

Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning

Ptychography is a computational imaging technique widely used for high-resolution materials characterization, but high-quality reconstructions often require the use of regularization functions that largely remain manually designed. We introduce Ptychi-Evolve, an autonomous framework that uses large language models (LLMs) to discover and evolve novel regularization algorithms. The framework combines LLM-driven code generation with evolutionary mechanisms, including semantically-guided crossover and mutation. Experiments on three challenging datasets (X-ray integrated circuits, low-dose electron microscopy of apoferritin, and multislice imaging with crosstalk artifacts) demonstrate that discovered regularizers outperform conventional reconstructions, achieving up to +0.26 SSIM and +8.3~dB PSNR improvements. Besides, Ptychi-Evolve records algorithm lineage and evolution metadata, enabling interpretable and reproducible analysis of discovered regularizers.

Xiangyu Yin, Ming Du, Junjing Deng +3

Advanced Photon Source, Argonne National Laboratory, Lemont, IL, USA · Department of Materials Science and NanoEngineering, Rice University, Houston, TX, USA

Image Reconstruction Automated Algorithm Discovery Computational Imaging

Mar 5, 2026

Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery

This paper demonstrates that artificial intelligence can accelerate mathematical discovery by autonomously solving an open problem in theoretical physics. We present a neuro-symbolic system, combining the Gemini Deep Think large language model with a systematic Tree Search (TS) framework and automated numerical feedback, that successfully derived novel, exact analytical solutions for the power spectrum of gravitational radiation emitted by cosmic strings. Specifically, the agent evaluated the core integral

I(N,α)

for arbitrary loop geometries, directly improving upon recent AI-assisted attempts \cite{BCE+25} that only yielded partial asymptotic solutions. To substantiate our methodological claims regarding AI-accelerated discovery and to ensure transparency, we detail system prompts, search constraints, and intermittent feedback loops that guided the model. The agent identified a suite of 6 different analytical methods, the most elegant of which expands the kernel in Gegenbauer polynomials

C_l^{(3/2)}

to naturally absorb the integrand's singularities. The methods lead to an asymptotic result for

I(N,α)

at large

N

that both agrees with numerical results and also connects to the continuous Feynman parameterization of Quantum Field Theory. We detail both the algorithmic methodology that enabled this discovery and the resulting mathematical derivations.

Michael P. Brenner, Vincent Cohen-Addad, David Woodruff

Google Research · School of Engineering and Applied Sciences, Harvard University · School of Computer Science, Carnegie Mellon University and Google Research

Large Language Model-Guided Scientific Discovery Automated Algorithm Discovery

Mar 3, 2026

From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors

Large Language Models (LLMs) have already been widely adopted for automated algorithm design, demonstrating strong abilities in generating and evolving algorithms across various fields. Existing work has largely focused on examining their effectiveness in solving specific problems, with search strategies primarily guided by adaptive prompt designs. In this paper, through investigating the token-wise attribution of the prompts to LLM-generated algorithmic codes, we show that providing high-quality algorithmic code examples can substantially improve the performance of the LLM-driven optimization. Building upon this insight, we propose leveraging prior benchmark algorithms to guide LLM-driven optimization and demonstrate superior performance on two black-box optimization benchmarks: the pseudo-Boolean optimization suite (pbo) and the black-box optimization suite (bbob). Our findings highlight the value of integrating benchmarking studies to enhance both efficiency and robustness of the LLM-driven black-box optimization methods.

Qi Huang, Furong Ye, Ananta Shahane +2

Leiden Institute of Advanced Computer Science (LIACS), Leiden University, 2333 CC Leiden, The Netherlands

Large Language Model-Guided Optimization Automated Algorithm Discovery Automated Heuristic Design Black-Box Optimization

Mar 3, 2026

Rethinking Code Similarity for Automated Algorithm Design with LLMs

The rise of Large Language Model-based Automated Algorithm Design (LLM-AAD) has transformed algorithm development by autonomously generating code implementations of expert-level algorithms. Unlike traditional expert-driven algorithm development, in the LLM-AAD paradigm, the main design principle behind an algorithm is often implicitly embedded in the generated code. Therefore, assessing algorithmic similarity directly from code, distinguishing genuine algorithmic innovation from mere syntactic variation, becomes essential. While various code similarity metrics exist, they fail to capture algorithmic similarity, as they focus on surface-level syntax or output equivalence rather than the underlying algorithmic logic. We propose BehaveSim, a novel method to measure algorithmic similarity through the lens of problem-solving behavior as a sequence of intermediate solutions produced during execution, dubbed as problem-solving trajectories (PSTrajs). By quantifying the alignment between PSTrajs using dynamic time warping (DTW), BehaveSim distinguishes algorithms with divergent logic despite syntactic or output-level similarities. We demonstrate its utility in two key applications: (i) Enhancing LLM-AAD: Integrating BehaveSim into existing LLM-AAD frameworks (e.g., FunSearch, EoH) promotes behavioral diversity, significantly improving performance on three AAD tasks. (ii) Algorithm analysis: BehaveSim clusters generated algorithms by behavior, enabling systematic analysis of problem-solving strategies--a crucial tool for the growing ecosystem of AI-generated algorithms. Data and code of this work are open-sourced at https://github.com/RayZhhh/behavesim.

Rui Zhang, Zhichao Lu

Department of Computer Science, City University of Hong Kong

Automated Algorithm Discovery

Feb 26, 2026

EvoX: Meta-Evolution for Automated Discovery

Recent work such as AlphaEvolve has shown that combining LLM-driven optimization with evolutionary search can effectively improve programs, prompts, and algorithms across domains. In this paradigm, previously evaluated solutions are reused to guide the model toward new candidate solutions. Crucially, the effectiveness of this evolution process depends on the search strategy: how prior solutions are selected and varied to generate new candidates. However, most existing methods rely on fixed search strategies with predefined knobs (e.g., explore-exploit ratios) that remain static throughout execution. While effective in some settings, these approaches often fail to adapt across tasks, or even within the same task as the search space changes over time. We introduce EvoX, an adaptive evolution method that optimizes its own evolution process. EvoX jointly evolves candidate solutions and the search strategies used to generate them, continuously updating how prior solutions are selected and varied based on progress. This enables the system to dynamically shift between different search strategies during the optimization process. Across nearly 200 real-world optimization tasks, EvoX outperforms existing AI-driven evolutionary methods including AlphaEvolve, OpenEvolve, GEPA, and ShinkaEvolve on the majority of tasks.

Shu Liu, Shubham Agarwal, Monishwaran Maheswaran +14

UC Berkeley · Stanford University · Bespoke Labs

Evolutionary Optimization LLMs for Evolutionary Optimization Large Language Model-Guided Optimization Automated Algorithm Discovery

Feb 25, 2026

ArchAgent: Agentic AI-driven Computer Architecture Discovery

Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems have demonstrated significant advances in algorithm design, improving code efficiency, and enabling discovery across scientific domains. Bridging these worlds, we present ArchAgent, an automated computer architecture discovery system built on AlphaEvolve. We show ArchAgent's ability to automatically design/implement state-of-the-art (SoTA) cache replacement policies (architecting new mechanisms/logic, not only changing parameters), broadly within the confines of an established cache replacement policy design competition. In two days without human intervention, ArchAgent generated a policy achieving a 5.3% IPC speedup improvement over the prior SoTA on public multi-core Google Workload Traces. On the heavily-explored single-core SPEC06 workloads, it generated a policy in just 18 days showing a 0.9% IPC speedup improvement over the existing SoTA (a similar "winning margin" as reported by the existing SoTA). ArchAgent achieved these gains 3-5x faster than prior human-developed SoTA policies. Agentic flows also enable "post-silicon hyperspecialization" where agents tune runtime-configurable parameters exposed in hardware policies to further align the policies with a specific workload (mix). Exploiting this, we demonstrate a 2.4% IPC speedup improvement over prior SoTA on SPEC06 workloads. Finally, we outline broader implications for computer architecture research in the era of agentic AI. For example, we demonstrate the phenomenon of "simulator escapes", where the agentic AI flow discovered and exploited a loophole in a popular microarchitectural simulator - a consequence of the fact that these research tools were designed for a (now past) world where they were exclusively operated by humans acting in good-faith.

Raghav Gupta, Akanksha Jain, Abraham Gonzalez +10

University of California, Berkeley · Google · Google DeepMind

Automated Algorithm Discovery AI Agents for Scientific Discovery

Feb 25, 2026

Neural Learning of Fast Matrix Multiplication Algorithms: A StrassenNet Approach

Fast matrix multiplication can be described as searching for low-rank decompositions of the matrix--multiplication tensor. We design a neural architecture, StrassenNet, which reproduces the Strassen algorithm for

2\times 2

multiplication. Across many independent runs the network always converges to a rank-

7

tensor, thus numerically recovering Strassen's optimal algorithm. We then train the same architecture on

3\times 3

multiplication with rank

r\in\{19,\dots,23\}

. Our experiments reveal a clear numerical threshold: models with

r=23

attain significantly lower validation error than those with

r\le 22

, suggesting that

r=23

could actually be the smallest effective rank of the matrix multiplication tensor

3\times 3

. We also sketch an extension of the method to border-rank decompositions via an

\varepsilon

--parametrisation and report preliminary results consistent with the known bounds for the border rank of the

3\times 3

matrix--multiplication tensor.

Paolo Andreini, Alessandra Bernardi, Monica Bianchini +4

Tensor Decomposition Automated Algorithm Discovery Low-Rank Matrix Decomposition

Feb 20, 2026

Automating the Analysis and Improvement of Dynamic Programming Algorithms with Applications to Natural Language Processing

This thesis develops a system for automatically analyzing and improving dynamic programs, such as those that have driven progress in natural language processing and computer science, more generally, for decades. Finding a correct program with the optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. This thesis aims to automate this laborious process. To this end, we develop an approach based on 1. a high-level, domain-specific language called Dyna for concisely specifying dynamic programs 2. a general-purpose solver to efficiently execute these programs 3. a static analysis system that provides type analysis and worst-case time/space complexity analyses 4. a rich collection of meaning-preserving transformations to programs, which systematizes the repeated insights of numerous authors when speeding up algorithms in the literature 5. a search algorithm for identifying a good sequence of transformations that reduce the runtime complexity, given an initial, correct program We show that, in practice, automated search -- like the mental search performed by human programmers -- can find substantial improvements to the initial program. Empirically, we show that many speed-ups described in the NLP literature could have been discovered automatically by our system. We provide a freely available prototype system at https://github.com/timvieira/dyna-pi.

Tim Vieira

The Johns Hopkins University

Dynamic Programming Automated Algorithm Discovery Static Code Analysis

Feb 19, 2026

AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing

PDEs are central to scientific and engineering modeling, yet designing accurate numerical solvers typically requires substantial mathematical expertise and manual tuning. Recent neural network-based approaches improve flexibility but often demand high computational cost and suffer from limited interpretability. We introduce AutoNumerics, a multi-agent framework that autonomously designs, implements, debugs, and verifies numerical solvers for general PDEs directly from natural language descriptions. Unlike black-box neural solvers, our framework generates transparent solvers grounded in classical numerical analysis. We introduce a coarse-to-fine execution strategy and a residual-based self-verification mechanism. Experiments on 24 canonical and real-world PDE problems demonstrate that AutoNumerics achieves competitive or superior accuracy compared to existing neural and LLM-based baselines, and correctly selects numerical schemes based on PDE structural properties, suggesting its viability as an accessible paradigm for automated PDE solving.

Jianda Du, Youran Sun, Haizhao Yang

Department of Mathematics, University of Maryland, College Park, MD, USA · Department of Computer Science, University of Maryland, College Park, MD, USA

PDE Solving Automated Algorithm Discovery AI Agents for Scientific Discovery LLM-Based Program Synthesis

Feb 18, 2026

RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution

Retrieval algorithms like BM25 and query likelihood with Dirichlet smoothing remain strong and efficient first-stage rankers, yet improvements have mostly relied on parameter tuning and human intuition. We investigate whether a large language model, guided by an evaluator and evolutionary search, can automatically discover improved lexical retrieval algorithms. We introduce RankEvolve, a program evolution setup based on AlphaEvolve, in which candidate ranking algorithms are represented as executable code and iteratively mutated, recombined, and selected based on retrieval performance across 12 IR datasets from BEIR and BRIGHT. RankEvolve starts from two seed programs: BM25 and query likelihood with Dirichlet smoothing. The evolved algorithms are novel, effective, and show promising transfer to the full BEIR and BRIGHT benchmarks as well as TREC DL 19 and 20. Our results suggest that evaluator-guided LLM program evolution is a practical path towards automatic discovery of novel ranking algorithms.

Jinming Nian, Fangchen Li, Dae Hoon Park +1

Santa Clara University, Santa Clara, CA, USA · Independent Researcher, Bothell, WA, USA · Walmart Global Tech, Sunnyvale, CA, USA

Information Retrieval Automated Algorithm Discovery Lexical Retrieval Large Language Model-Guided Optimization Evolutionary Optimization

Feb 18, 2026

Simple Baselines are Competitive with Code Evolution

Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving existing code. While code evolution pipelines have shown impressive performance across domains, many are highly complex and are typically not compared to simpler alternatives. To prevent bad comparisons from stagnating the field, we propose two simple baselines and compare them to popular pipelines across several domains: finding better mathematical bounds, designing agentic scaffolds, developing heuristic combinatorial optimization algorithms, and machine learning competitions. Surprisingly, the baselines match or outperform existing methods across all domains, prompting a closer look at which factors drive performance in different settings. For mathematical bounds, we find that the search space matters far more than the exact search algorithm, with both simple and complex methods performing similarly given the same search space. Thus, expanding the search space is what matters for improving performance, and does so across all pipelines. Moreover, different domain knowledge embedded in prompts can significantly affect the search's efficiency, potentially leading to overestimating a pipeline's sample efficiency. For automated agentic scaffold design, we show that typical high-variance evaluations lead to all automated search methods picking subpar scaffolds relative to a simple majority vote. To mitigate this, we propose improved evaluation procedures that reduce stochasticity while keeping the search economically feasible. Overall, these results indicate that further improving code evolution's performance depends more on the setup surrounding the search pipeline than on the pipeline itself. We hope these insights will enable developing new code evolution methods and help spur meaningful progress in the field.

Yonatan Gideoni, Sebastian Risi, Yarin Gal

University of Oxford · Sakana AI and ITU Copenhagen

LLMs for Evolutionary Optimization Code Generation Evaluation Agentic Code Generation Automated Algorithm Discovery

Feb 17, 2026

MadEvolve: Evolutionary Optimization of Cosmological Algorithms with Large Language Models

We develop a general framework to discover scientific algorithms and apply it to three problems in computational cosmology. Our code, MadEvolve, is similar to Google's AlphaEvolve, but places a stronger emphasis on free parameters and their optimization. Our code starts with a baseline human algorithm implementation, and then optimizes its performance metrics by making iterative changes to its code. As a further convenient feature, MadEvolve automatically generates a report that compares the input algorithm with the evolved algorithm, describes the algorithmic innovations and lists the free parameters and their function. Our code supports both auto-differentiable, gradient-based parameter optimization and gradient-free optimization methods. We apply MadEvolve to the reconstruction of cosmological initial conditions, 21cm foreground contamination reconstruction and effective baryonic physics in N-body simulations. In all cases, we find substantial improvements over the base algorithm. We make MadEvolve and our three tasks publicly available at madevolve.org.

Tianyi Li, Shihui Zang, Moritz Münchmeyer

Department of Physics, University of Wisconsin-Madison

Automated Algorithm Discovery Scientific Code Generation Evolutionary Optimization Large Language Model-Guided Scientific Discovery

Feb 16, 2026

FactorMiner: A Self-Evolving Agent with Skills and Experience Memory for Financial Alpha Discovery

Formulaic alpha factor mining is a critical yet challenging task in quantitative investment, characterized by a vast search space and the need for domain-informed, interpretable signals. However, finding novel signals becomes increasingly difficult as the library grows due to high redundancy. We propose FactorMiner, a lightweight and flexible self-evolving agent framework designed to navigate this complex landscape through continuous knowledge accumulation. FactorMiner combines a Modular Skill Architecture that encapsulates systematic financial evaluation into executable tools with a structured Experience Memory that distills historical mining trials into actionable insights (successful patterns and failure constraints). By instantiating the Ralph Loop paradigm -- retrieve, generate, evaluate, and distill -- FactorMiner iteratively uses memory priors to guide exploration, reducing redundant search while focusing on promising directions. Experiments on multiple datasets across different assets and Markets show that FactorMiner constructs a diverse library of high-quality factors with competitive performance, while maintaining low redundancy among factors as the library scales. Overall, FactorMiner provides a practical approach to scalable discovery of interpretable formulaic alpha factors under the "Correlation Red Sea" constraint.

Yanlong Wang, Jian Xu, Hongkang Zhang +3

Tsinghua Shenzhen International Graduate School, Tsinghua University · Pengcheng Laboratory · Shenzhen Finance Institute, Chinese University of Hong Kong

Alpha Factor Discovery Algorithmic Trading Automated Algorithm Discovery Memory-Augmented Agents

Feb 10, 2026

ImprovEvolve: Basin-Hopping Meets LLM-Guided Evolutionary Search

LLM-guided evolutionary computation, most notably AlphaEvolve, has been remarkably successful in discovering novel mathematical constructions by solving challenging optimization problems. The standard approach is to evolve a monolithic program that directly outputs a candidate solution. We present ImprovEvolve, an algorithmic alternative that drastically reduces cognitive load on the LLM. Instead of prompting the model for an end-to-end optimizer, we evolve a program with three specialized operators of initialization, local improvement, and perturbation. We then approach the optimum by iteratively applying local improvements and intensity-scheduled perturbations, effectively driving a basin-hopping search with LLM-evolved subroutines. For hexagon in hexagon packing, ImprovEvolve discovers new state-of-the-art packings of 11, 12, 15, and 16 hexagons, and additionally for 14, 17, and 23 hexagons after minimal expert tuning of the generated code. For the second autocorrelation inequality, the evolved and human-scaled program pushes the lower bound from 0.96102 to 0.96258. For spherical codes, the ImprovEvolve program lowers the best-known maximum cosine for the majority of 90 randomly chosen diverse state-of-the-art spherical codes, achieving relative improvements of up to 2.4%.

Alexey Kravatskiy, Valentin Khrulkov, Ivan Oseledets

1MIRIAI, Russia · 2FusionBrain Lab, Russia · 3AXXX, Russia +1

Large Language Model-Guided Scientific Discovery Evolutionary Optimization Metaheuristic Optimization Large Language Model-Guided Optimization Combinatorial Optimization Global Optimization+2

Feb 10, 2026

Learning to Discover Iterative Spectral Algorithms

We introduce AutoSpec, a neural network framework for discovering iterative spectral algorithms for large-scale numerical linear algebra and numerical optimization. Our self-supervised models adapt to input operators using coarse spectral information (e.g., eigenvalue estimates and residual norms), and predict recurrence coefficients for computing or applying a matrix polynomial tailored to a downstream task. The effectiveness of AutoSpec relies on three ingredients: an architecture whose inference pass implements short, executable numerical linear algebra recurrences; efficient training on small synthetic problems with transfer to large-scale real-world operators; and task-defined objectives that enforce the desired approximation or preconditioning behavior across the range of spectral profiles represented in the training set. We apply AutoSpec to discovering algorithms for representative tasks on spd matrices: accelerating matrix function approximation; accelerating sparse linear solvers; and spectral filtering/preconditioning for eigenvalue computations. On real-world matrices, the learned procedures deliver up to order-of-magnitude improvements in accuracy and/or reductions in iteration count, relative to spectrum-agnostic baselines. We find clear connections to classical theory: the induced polynomials may exhibit equioscillation behavior characteristic of Chebyshev polynomial approximation. The code is available at: https://github.com/zihanghliu/AutoSpec .

Zihang Liu, Oleg Balabanov, Yaoqing Yang +1

1International Computer Science Institute · University of California, Berkeley · 4Lawrence Berkeley National Laboratory +1

Automated Algorithm Discovery Learning to Optimize Spectral Methods