Summary (Overview)

• Identifies a novel failure mode in code-level autonomous research loops (ARLs): The paper introduces "## Summary (Overview)

• Identifies a novel failure mode in code-level autonomous research loops (ARLs): The paper introduces

Summary (Overview)

• **Identifies a novel failure mode in code-level# Summary

Overview

• **Identifies a novel failure mode in codeimport numpy as np import matplotlib.pyplot as plt from sklearn.cluster import HDBSCAN from sentence_transformers import SentenceTransformer from sklearn.metrics.pairwise import cosine_similarity from collections import deque import math import random

Define the mechanism taxonomy categories with representative keywords

Define the mechanism taxonomy categories with representative keywords

categories = { "Optimizer": ["optimizer", "adam", "sgd", "lion", "learning rate", "momentum"], "Scheduling": ["warmup", "decay", "schedule", "cosine", "linear"], "Architecture": ["layer", "norm", "attention", "transformer", "embedding", "rmsnorm"], "Data": ["filter", "rebalance", "mix", "augment", "sample"],

Define the mechanism taxonomy categories with representative# Define the mechanism taxonomy categories with representative keywords

categories = { "Optimizer": ["optimizer", "adam", "sgd", "lion", "learning rate", " "Scheduling": ["w# Summary

Overview

• **Identifies a novel failure mode in code-level autonomous research loops

loops (ARLs)**: The paper introduces