How do we measure agent capabilities? Instead of pairwise preference voting, Agent Arena uses a causal tracing methodology that measures five signals from real-world, long-horizon agentic sessions. More info in thread.

causal tracing on live sessions only works if you can attribute failure to a step. what happens when the error is 20 steps upstream and the model already compensated for it?

good move. preference voting rewards the agent that sounds confident in minute one. long sessions are where it can quietly delete the wrong folder in hour two. curious which of the five signals reshuffles the rankings most.

I like your evaluation harness, especially keeping model identities hidden to reduce brand bias.

@balooooooooooop 🐐

c’est de l’arnaque on peut même par utilisé claude opus 5.5

what are the five signals

which one of the five do you weight the most

Finally moving beyond vibes toward real measurement that matters

Hey, really love the idea behind the software, but I feel the launch video doesn't fully do justice to the product. There's a lot of room to improve how the UI is showcased, how the features are presented with context, and how the overall design, colors, and transitions come together. I have a few specific ideas on how this could be improved. If you're open to it, DM me and I'd love to walk you through them!

Agent Arena measures agent capabilities using causal tracing across five real-world signals

Love this approach measuring five real world signals with causal tracing is a much deeper way to capture true agent capability!

Causal tracing beats pairwise voting — you and @JohnWuestlbik2 are the two accounts I check on agent evals

Thoughtful measurement reveals true agent capabilities, guiding meaningful progress through real-world insights and inspiring confident innovation daily

Really smart approach to measuring real agent capability beyond simple preference

causal tracing for capability measurement is interesting. does it catch when an agent knows how to do something but refuses for safety reasons?

Causal tracing beats preference voting

Five signals from long-horizon sessions beats simple preference voting approach

Moving beyond simple voting is the right direction

Five signals from long sessions means my agent can no longer hide behind one lucky answer. 😂

I want a multi turn editing ranking please and thank you not shills generating free images or this bs excuse video

Five signals from long-horizon sessions. Agents fail at hour two, votes only catch minute one.