{
  "id": 7924121,
  "title": "Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking",
  "url": "https://urgent.news/2026/09/16/beyond-outcomes-dual-view-relational-learning-for-efficient-agent",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-16T16:49:50.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.18909v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Agent benchmarks are substantially more costly to evaluate than conventional LLM benchmarks. Benchmark compression is therefore a natural solution, yet existing methods primarily model redundancy in task--model final-score distributions, which is important in agentic evaluation. To address this limitation, we analyze large-scale trajectories and identify six complementary process signals that are…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}