Even superforecasters are guessing that they'll soon be obsolete. To live in time is to wonder what will happen next. In every human society, there are people who obsess over the world's patterns to predict the future. In antiquity, they told kings which stars would appear at nightfall. Today they build the quantitative models that nudge governments into opening spigots of capital.

Automated negotiation has emerged as a critical area of research in multiagent systems, with applications spanning e-commerce, resource allocation, and autonomous decision-making. This paper presents ChargingBoul, a negotiating agent that competed in the 2022 Automated Negotiating Agents Competition (ANAC) and placed second in individual utility by an exceptionally narrow margin. ChargingBoul employs a lightweight yet effective strategy that balances concession and opponent modeling to achieve high negotiation outcomes. The agent classifies opponents based on bid patterns, dynamically adjusts its bidding strategy, and applies a concession policy in later negotiation stages to maximize utility while fostering agreements. We evaluate ChargingBoul's performance using competition results and subsequent studies that have utilized the agent in negotiation research. Our analysis highlights ChargingBoul's effectiveness across diverse opponent strategies and its contributions to advancing automated negotiation techniques. We also discuss potential enhancements, including more sophisticated opponent modeling and adaptive bidding heuristics, to improve its performance further.

Mexico's consumer protection agency and FIFA are working on a "ticket relocation system" that will allow those with extra World Cup tickets to sell them safely and at appropriate prices. The Mexican government has presented its strategy to turn this summer's World Cup soccer tournament into an engine to strengthen trade, sports, tourism, and culture in the country where most of the games will be hosted. The Mexico 2026 Social World Cup project includes cultural events like soccer matches between robots, a public transit plan, and a new app where fans can sell securely sell any tickets they can't use. During a conference last week, Mexican President Claudia Sheinbaum stated that the intention is "to leave a sporting legacy in our country that goes beyond the competition itself." "[In this World Cup ] the eyes of the world will be here," Sheinbaum said, "and what they will see is a great country with an enormous cultural heritage. They will see that we are building a nation that is fairer, freer, and more democratic."

The rapid proliferation of Large Language Models (LLMs) has revolutionized AI-assisted code generation. This rapid development of LLMs has outpaced our ability to properly benchmark them. Prevailing benchmarks emphasize unit-test pass rates and syntactic correctness. Such metrics understate the difficulty of many real-world problems that require planning, optimization, and strategic interaction. We introduce a multi-agent reasoning-driven benchmark based on a real-world logistics optimization problem (Auction, Pickup, and Delivery Problem) that couples competitive auctions with capacity-constrained routing. The benchmark requires building agents that can (i) bid strategically under uncertainty and (ii) optimize planners that deliver tasks while maximizing profit. We evaluate 40 LLM-coded agents (by a wide range of state-of-the-art LLMs under multiple prompting methodologies, including vibe coding) against 17 human-coded agents developed before the advent of LLMs. Our results over 12 double all-play-all tournaments and $\sim 40$k matches demonstrate (i) a clear superiority of human(graduate students)-coded agents: the top 5 spots are consistently won by human-coded agents, (ii) the majority of LLM-coded agents (33 out of 40) are beaten by very simple baselines, and (iii) given the best human solution as an input and prompted to improve upon, the best performing LLM makes the solution significantly worse instead of improving it. Our results highlight a gap in LLMs' ability to produce code that works competitively in the real-world, and motivate new evaluations that emphasize reasoning-driven code synthesis in real-world scenarios.

These proceedings include the Late-Breaking Abstracts accepted for the Evo* 2025 Conference, hosted in Trieste (Italy), from April 23th to 25th. These extended abstracts were presented through short talks at the conference, providing an overview of ongoing research and initial results on the application of diverse Evolutionary Computation strategies and other Nature-Inspired methodologies to practical problem domains. Collectively, these contributions point to encouraging directions for future work, underscoring the potential of nature-inspired approaches-- especially Evolutionary Algorithms -- for advancing research and enabling new applications.

Work mainly done while the authors were with the Department of Statistics, University of Oxford 36th Conference on Neural Information Processing Systems (NeurIPS 2022).

England have cruised through World Cup qualifying, winning all seven of their games and scoring 20 unanswered goals, setting several records and leaving them on the cusp of another. If Thomas Tuchel's team beat Albania in Sunday's final qualifier (17:00 GMT) and keep a clean sheet, they will become the first European side to play at least six qualifiers and win them all without conceding. A clean sweep of victories - regardless of goals conceded - is also a rare achievement. Excluding the early years of the World Cup, when teams often played just a handful of preliminary matches, only four European countries have finished with a 100% winning record. Germany were the last side to do so on the way to the 2018 tournament, though they went on to suffer a shock early exit in Russia.

We consider the classical problem of finding the minimum feedback arc set on tournaments (MFAST).