An independent research & engineering studio

FormulaCode is a live, repository-scale benchmark that evaluates agentic performance engineering against human-expert patches on 957 real bottlenecks mined from 245,477 PRs across 70 ASV-instrumented scientific-Python repositories, using correctness rollback, statistical significance testing, and an expert-relative Advantage metric with a built-in contamination probe.

A self-evolving multi-agent framework that repairs three failure modes of MLE agents - inter-branch information isolation, memoryless search, and one-shot generation - via Progressive Monte Carlo Graph Search, Retrospective Memory, and Hierarchical Planning with Adaptive Code Generation, reaching a 65.3% medal rate on MLE-Bench at half the standard budget (12 h vs. 24 h) and the best result on 11 of 15 AlphaEvolve math tasks.

MARS treats automated AI research as cost-constrained search over a space of whole software repositories, combining budget-aware MCTS with an efficiency-shaped reward, a Design-Decompose-Implement modular construction pipeline, and a comparative reflective memory whose lessons are distilled from diffs against the previous best solution.

Three independent open-source derivatives show that AlphaEvolve-style program evolution can be made reproducible and radically more sample-efficient - GigaEvo by engineering the unspecified infrastructure, ShinkaEvolve by principled parent/LLM selection (new circle-packing SOTA in 150 samples), and ThetaEvolve by batch generation plus test-time RL (new best-known bounds from a single 8B open model).

FM Agent combines LLM-driven mutation and crossover with a multi-island evolutionary architecture, cold-start population generation, domain-specific evaluators, and a Ray-based asynchronous infrastructure, achieving state-of-the-art results across MLE-Bench, ALE-Bench, KernelBench, and classical mathematics problems.

The survey argues that "Agentic Science" - large language model (LLM) agents autonomously running the full hypothesis–experiment–analysis–refinement cycle - constitutes a distinct stage of AI for Science, and unifies the field through a three-layer framework of five foundational capabilities, four core processes, and four application domains.

The survey defines "deep research" as a paradigm beyond retrieval-augmented generation (RAG), in which agents iteratively plan, retrieve, and synthesize web-grounded analytical reports, and organizes the field by a capability-centric four-stage pipeline - planning, question developing, web exploration, report generation - rather than by whole-system enumeration.