When Web Agents Finish but Still Fail: Reproducible Triggers and Trace Diagnostics for Parallel Web Exploration
arXiv:2606.20724v2 Announce Type: replace-cross Abstract: Long-horizon web agents often fail in ways hidden by final-answer evaluation: they may visit useful pages, produce a well-formed answer, and terminate confidently while still missing fields, over-including unsupported items, or relying on stale evidence. We study these failures with Parallel WebBench, a parallel web-exploration benchmark containing 1,679 verified records: 350 manually curated parallel tasks and 1,329 reconstructed records with verified URL-based trajectories. We train WebExplorer-style agents with GRPO under human-only,
The rapid advancement and deployment of long-horizon web agents are foregrounding critical issues related to their reliability and evaluation methodologies, necessitating robust diagnostic tools.
A strategic reader should care because the effectiveness and trustworthiness of AI agents are paramount for their integration into critical workflows and infrastructure, influencing productivity and decision-making.
The development of reproducible triggers and trace diagnostics introduces a more rigorous framework for identifying and mitigating agent failures, moving beyond simple final-answer evaluations.
- · AI agent developers
- · Enterprises deploying AI agents
- · AI assurance and testing platforms
- · Researchers in AI reliability
- · Companies relying on superficial AI agent evaluations
- · Inefficient AI agent development processes
Improved reliability and trust in autonomous web agents, leading to broader adoption.
Accelerated development of more complex and critical AI agent applications, transforming white-collar work.
Enhanced AI agent capabilities could lead to new forms of automated economic activity and decision-making, increasing institutional dependency on autonomous systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG