By virtually every key metric, efforts to fight climate change are going too slowly, according to findings by a coalition of climate groups. In some cases, things are moving in the wrong direction. An eroded iceberg is seen is seen floating near Horseshoe Island, Antarctica. In the 10 years since the signing of the Paris Agreement, the backbone of international climate action, humanity has made impressive progress. Renewable energy is increasingly cheap and reliable, while electric vehicles are becoming better every year.

Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Y et, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge-- situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles.

Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Y et, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge-- situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles.

Abstract--LLM-based agents have shown promise in various cooperative and strategic reasoning tasks, but their effectiveness in competitive multi-agent environments remains underexplored. T o address this gap, we introduce PillagerBench, a novel framework for evaluating multi-agent systems in real-time competitive team-vs-team scenarios in Minecraft. It provides an extensible API, multi-round testing, and rule-based built-in opponents for fair, reproducible comparisons. We also propose T actiCrafter, an LLM-based multi-agent system that facilitates teamwork through human-readable tactics, learns causal dependencies, and adapts to opponent strategies. Our evaluation demonstrates that T actiCrafter outperforms baseline approaches and showcases adaptive learning through self-play. Additionally, we analyze its learning process and strategic evolution over multiple game episodes. T o encourage further research, we have open-sourced PillagerBench, fostering advancements in multi-agent AI for competitive environments. Witnessing rapid advancements, Large Language Models (LLMs) have emerged as powerful tools for complex reasoning, decision-making, and facilitating multi-agent collaboration [27, 20, 22]. This has driven increasing interest in developing cooperative multi-agent systems [3, 7], leading to the creation of benchmarks based on diverse cooperative games such as Minecraft [4] and Overcooked [1]. Minecraft, in particular, has become an important platform due to its open-ended environment and rich state and action spaces [19, 25]. However, current Minecraft-based benchmarks mainly address cooperative tasks characterized by stationary dynamics and fixed objectives, making them insufficient for evaluating adaptability and strategic decision-making in competitive, dynamic environments. Traditional reinforcement learning benchmarks like StarCraft Multi-Agent Challenge (SMAC) [16] and Lux AI Challenge [18] introduce instability and nonstationarity through competitive adversaries but lack the rich, open-ended interactions found in Minecraft. Bridging this gap by integrating both cooperative and competitive elements within a single dynamic environment is essential to rigorously assess the adaptability and generalizability of advanced multi-agent systems.

Active automata learning infers automaton models of systems from behavioral observations, a technique successfully applied to a wide range of domains. Compositional approaches for concurrent systems have recently emerged. We take a significant step beyond available results, including those by the authors, and develop a general technique for compositional learning of a synchronizing parallel system with an unknown decomposition. Our approach automatically refines the global alphabet into component alphabets while learning the component models. We develop a theoretical treatment of distributions of alphabets, i.e., sets of possibly overlapping component alphabets. We characterize counter-examples that reveal inconsistencies with global observations, and show how to systematically update the distribution to restore consistency. We present a compositional learning algorithm implementing these ideas, where learning counterexamples precisely correspond to distribution counterexamples under well-defined conditions. We provide an implementation, called CoalA, using the state-of-the-art active learning library LearnLib. Our experiments show that in more than 630 subject systems, CoalA delivers orders of magnitude improvements (up to five orders) in membership queries and in systems with significant concurrency, it also achieves better scalability in the number of equivalence queries.

This story was originally published by WIRED and is reproduced here as part of the Climate Desk collaboration. Last Tuesday, President Donald Trump held a press conference to announce the signing of executive orders intended to shape American energy policy in favor of one particular source: coal, the most carbon-intense fossil fuel. "I call it beautiful, clean coal," President Trump said while flanked by a crowd of miners at the White House. "I tell my people never use the word coal unless you put'beautiful, clean' before it." Trump has talked about saving coal, and coal jobs, for as long as he's been in politics.

On Tuesday, President Donald Trump held a press conference to announce the signing of executive orders intended to shape American energy policy in favor of one particular source: coal, the most carbon-intense fossil fuel. "I call it beautiful, clean coal," President Trump said while flanked by a crowd of miners at the White House. "I tell my people never use the word coal, unless you put'beautiful, clean' before it." Trump has talked about saving coal, and coal jobs, for as long as he's been in politics. This time, he's got a convenient vehicle for his policies: the growth of AI and data centers, which could potentially supercharge American energy demand over the coming years.

Donald Trump signed four executive orders on Tuesday aimed at reviving coal, the dirtiest fossil fuel that has long been in decline, and which substantially contributes to planet-heating greenhouse gas emissions and pollution. Environmentalists expressed dismay at the news, saying that Trump was stuck in the past and wanted to make utility customers "pay more for yesterday's energy". The US president is using emergency authority to allow some older coal-fired power plants scheduled for retirement to keep producing electricity. The move, announced at a White House event on Tuesday afternoon, was described by White House officials as being in response to increased US power demand from growth in datacenters, artificial intelligence and electric cars. Trump, standing in front of a group of miners in hard hats, said he would sign an executive order "that slashes unnecessary regulations that targeted the beautiful, clean coal".

FIRST ON FOX: The Department of Energy, the Department of the Interior and the Environmental Protection Agency are set to announce a bevy of new actions Tuesday afternoon that will "unleash" coal energy following President Donald Trump's expected signature on an executive order reinvigorating "America's beautiful clean coal industry," Fox News Digital learned. "The American people need more energy, and the Department of Energy is helping to meet this demand by unleashing supply of affordable, reliable, secure energy sources -- including coal," Department of Energy Secretary Chris Wright said in a Tuesday statement provided to Fox News Digital. "Coal is essential for generating 24/7 electricity generation that powers American homes and businesses, but misguided policies from previous administrations have stifled this critical American industry," he said. "With President Trump's leadership, we are cutting the red tape and bringing back common sense." Trump is expected to sign an executive order Tuesday afternoon that will cut through red tape surrounding the coal industry, including directing the National Energy Dominance Council to designate coal as a "mineral," end a current pause to coal leasing on federal lands, promote coal and coal technology exports, and encourage the use of coal to power artificial intelligence initiatives, Fox News Digital learned of the upcoming executive order.

President Trump this week continued to make his environmental priorities clear by vowing to open up hundreds of coal power plants in the United States in an effort to advance competition against China. "After years of being held captive by Environmental Extremists, Lunatics, Radicals, and Thugs, allowing other Countries, in particular China, to gain tremendous Economic advantage over us by opening up hundreds of all Coal Fire Power Plants, I am authorizing my Administration to immediately begin producing Energy with BEAUTIFUL, CLEAN COAL," Trump wrote in a post on social media Monday. Though the post was not linked to any particular policy plans or documents, it arrives as the White House takes aim at various environmental agencies and clean-energy initiatives. In the last week alone, the administration has announced plans to significantly roll back regulations that govern coal production and to potentially lay off up to 65% of scientists and researchers at the Environmental Protection Agency, among other actions. Coal accounts for about 16% of the country's electricity generation, according to the U.S. Energy Information Administration -- down from about 50% in 2000 as natural gas and nuclear and renewable energy have grown.