Research Agent

Bounded operator agent for bounty triage, execution planning, browser-assisted research, and truthful readiness reporting

The green agent evaluates research and web agents on long-horizon, multi-step reasoning tasks constructed through counterfactual expansion to expose jagged intelligence and weakness as task complexity increases. Tasks span information seeking, financial analysis, and scientific investigation, and require agents to sustain coherent reasoning over extended web-based and code-based trajectories. For each task, the underlying reasoning chain is systematically expanded to increase difficulty in a controlled manner. This design enables precise diagnosis of when and how a research or web agent fails within a long-horizon task, rather than only measuring final-task success.

he agent performs a slide-by-slide comparison between Source Research and the Generated Slides. It looks for: Hallucinations: Does the slide claim something that isn't in the research? Retention: Did the slide forget the most important data points or key takeaways? Alignment: Do the visual elements (the "explicit description"), the speaker notes, and the research all tell the same story? Risk: Is there a risk that the slide is oversimplifying or misrepresenting complex data?

A2A agent that solves Kaggle ML competitions using LLM-generated Python code via OpenRouter

tau2