The paper introduces LineageEvo, a method that incorporates lineage-level control into large‑language‑model (LLM) based evolutionary factor mining. It uses lineage‑conditioned LLM variation to guide semantic generation and lineage‑stratified search allocation to balance quality and diversity across evolutionary branches. Experiments on CSI 500 and S&P 500 show that LineageEvo outperforms existing approaches in predictive performance and produces a more balanced, structurally diverse factor pool.
arXiv:2607. 26642v1 Announce Type: new Abstract: Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery.
Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery. However, existing LLM-based systems often delegate both factor construction and search decisions to the agent itself, without an explicit exploration space or a principled mechanism for navigating that space.
arXiv:2610.14959v2 Announce Type: new Abstract: Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space. Recent LLM-based...
arXiv:2607. 04108v1 Announce Type: new Abstract: Large language models are increasingly used as evolutionary engines for scientific discovery: generate candidates, select winners, feed them back as parents, and repeat.
EvoTreeNAD is a genealogy‑guided evolutionary algorithm that autonomously discovers neural architectures without a predefined seed or search space. Starting from an empty root, it builds a persistent genealogy where each node represents a complete architecture; top‑percentile values from nodes and descendants steer lineage selection. The method combines an Idea Agent that proposes variants and a Code Agent that implements them, with theoretical analysis showing stationary variation regimes and empirical results demonstrating superior performance on CIFAR‑10/100 and MedMNIST‑v2 tasks.
arXiv:2607. 08758v1 Announce Type: new Abstract: Scientific ideas rarely start from a blank page.
arXiv:2610.28568v1 Announce Type: new Abstract: Generative retrieval (GR) casts search and recommendation as autoregressive generation of discrete semantic identifiers (SIDs). SID design largely dete...
arXiv:2608. 13297v1 Announce Type: new Abstract: In evolutionary search, a weak child can be a valuable ancestor that makes high-fitness regions reachable.
DIGEST is a new method for symbolic regression that combines a lightweight LLM with Genetic Programming. It uses a pretrained decoder block as a symbolic policy to seed GP populations, providing flexible generation of candidate expressions. The approach also feeds high‑quality GP discoveries back into the LLM to refine future generations, leading to improved recovery rates and faster search times on benchmark datasets.
arXiv:2607. 22314v1 Announce Type: new Abstract: Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoidable under limited token budgets.
arXiv:2606. 10587v1 Announce Type: cross Abstract: Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses.
AlphaMemo is a self‑evolving alpha‑mining agent that uses Structured Search‑Process Memory to improve factor discovery. Instead of storing only final factors or full search trajectories, it records reusable evidence about which edit motifs succeed or fail in specific parent‑factor contexts, extracted from Abstract Syntax Tree differences. The system incorporates confidence‑gated residual memory, a search‑ledger prior, and asymmetric veto control to suppress high‑confidence failure patterns, leading to better out‑of‑sample performance and fixed‑budget discovery efficiency on CSI 500 and S&P 500 datasets.