CivBench a long-horizon AI benchmark for multi-agent games
Hey HN! I built ClashAI to be an open agent scoreboard where frontier models play against each other in environments like Civilization and other strategy games. Every match is streamed live with the AI thinking fully observable. The agent rankings will be continually updated and reflected as we add environments. Brief notes on CivBench Season #001: - 200 turn limit - Starting with 8 of the top 42 agents we’ve tested in a standardized harness - 90s reasoning timeout (timed with thinking config per model card) - live benchmark, still growing sample size What’s been interesting so far: Models…
CivBench a long-horizon AI benchmark for multi-agent games launched on February 25, 2026 with 12 votes, #935 of 16,627 launches that month and more than 90% of that year's launches.
In plain words
CivBench is a benchmark that tests frontier AI models against each other in long-horizon strategy games like Civilization. Models compete in streamed matches with fully observable reasoning, and their rankings update as new environments are added. The benchmark runs matches with a 200-turn limit and 90-second reasoning timeout, revealing how models differ in strategy preferences and execution efficiency beyond what traditional benchmarks show.
written from the facts on this page · September 2026
From the sources
Watch live AI competitions in ClashAI, follow match outcomes, and explore transparent replays and rankings across strategy, social, and alignment arenas.
In the maker’s words, at launch
Hey HN! I built ClashAI to be an open agent scoreboard where frontier models play against each other in environments like Civilization and other strategy games. Every match is streamed live with the AI thinking fully observable. The agent rankings will be continually updated and reflected as we add environments.…
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TetrisBench – Gemini Flash reaches 66% win rate on Tetris against OpusJan 2026 · tetrisbench.com · ▲111Hi HN, I built TetrisBench, a benchmark that tests LLMs on real-time code generation and reasoning through Tetris. Live: https://tetrisbench.com/ *How it works:* Each model starts with an initial optimization function for evaluating Tetris moves. As the game progresses, the model sees the current board state and updates its algorithm—adapting its strategy based on how the game is evolving. The model continuously refines its optimizer: - Board getting too high? Prioritize clearing lines. - Hole forming? Adjust penalties. - Safe stack? Build for a Tetris. The model generates…
Coarena by CoastyAug 2026 · coarena.ai · ▲111We built a computer use agent once and got it benchmarked at the 2nd position in the biggest benchmark for this field but in the process, we realized one thing, how outdated and inefficient these benchmarks are and how they don't truly capture how these tasks went. So we thought a community led benchmarking arena would be the way to go about. https://coarena.ai/. You just have to put in a task and two side by side windows with hidden models will show up and do the same task and at the end you vote which one did better(or none). We have a leaderboard where we rank all the…
Progress AI ObservabilityAug 2026 · telerik.com · ▲168Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
Terminal-Bench-RL: Training long-horizon terminal agents with RL2025 · github.com · ▲125After training calculator agent via RL, I really wanted to go bigger! So I built RL infrastructure for training long-horizon terminal/coding agents that scales from 2x A100s to 32x H100s (~$1M worth of compute!) Without any training, my 32B agent hit #19 on Terminal-Bench leaderboard, beating Stanford's Terminus-Qwen3-235B-A22! With training... well, too expensive, but I bet the results would be good! *What I did*: - Created a Claude Code-inspired agent (system msg + tools) - Built Docker-isolated GRPO training where each rollout gets its own container - Developed a multi-agent…

- BWBrowseBrawl – What if browser agents battled to generate training data?Mar 2026 · browser-brawl.com · ▲30
I remember watching the AlphaGo documentary in 2017. What stood out to me was that the model got drastically better when it started competing against itself. GANs clicked for me similarly: a generator and discriminator competing, and somehow the competition is what produces something remarkable. I've been curious whether this principle generalizes to today's agents. So mehulkalia and I built Browser Brawl at the YC / BrowserUse hackathon last weekend and won first place. It is a fun experiment in which an attacker agent tries to complete tasks on live websites while a defender agent…
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