import asyncio import json import io import contextlib import re from dataclasses import dataclass from typing import Callable, Awaitable import nest_asyncio nest_asyncio.apply() TOOL_DEFINITIONS = [ { “name”: “execute_code”, “description”: “Execute Python code in the Colab kernel. Returns stdout, results, or errors. State persists between calls.” “parameters”: { “type”: “object”, “properties”: { “code”: {“type”: “string”,…

From enhancing international business logistics to freeing up more hospital beds to helping farmers, MIT Professor Dimitris Bertsimas SM ’87, PhD ’88 summarized how his work in operations research has helped drive real-world improvements, while delivering the 54th annual James R. Killian Faculty Achievement Award Lecture at MIT on Thursday, March 19.Bertsimas also described…

When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Lucene, has been the dominant answer to that question for decades.  It scores documents by looking at three…

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the CartPole environment. Instead of using a fully packaged RL…

The current state of AI agent development is characterized by significant architectural fragmentation. Software devs building autonomous systems must generally commit to one of several competing ecosystems: LangChain, AutoGen, CrewAI, OpenAI Assistants, or the more recent Claude Code. Each of these ‘Five Frameworks’ utilizes a proprietary method for defining agent logic, memory persistence, and…

header(“11. DISORDERED STRUCTURE -> ORDERED APPROXIMATION”) disordered = Structure( Lattice.cubic(3.6), [{“Cu”: 0.5, “Au”: 0.5}], [[0, 0, 0]], ) disordered.make_supercell([2, 2, 2]) print(“Disordered composition:”, disordered.composition) try: disordered_oxi = disordered.copy() disordered_oxi.add_oxidation_state_by_element({“Cu”: 1, “Au”: 1}) ordered_transform = OrderDisorderedStructureTransformation() ordered_candidates = ordered_transform.apply_transformation( disordered_oxi, return_ranked_list=3, ) for idx, cand in enumerate(ordered_candidates): s = cand[“structure”].copy() s.remove_oxidation_states() print(f”Ordered candidate {idx+1}: formula={s.composition.formula},…