{
  "id": 9689999,
  "title": "SAGE: Mitigating Long-Horizon Reasoning Biases via Topological Guidance",
  "url": "https://urgent.news/2026/09/24/sage-mitigating-long-horizon-reasoning-biases-via-topological-guidance",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-24T17:33:29.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.30192v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Long-horizon reasoning remains a central challenge for large language models (LLMs) under sparse-reward regimes. We argue that this brittleness arises from two biases induced by complex reasoning spaces: an exploration bias, where models are drawn toward locally plausible but structurally unstable branches, and a compounding bias, where small local deviations accumulate across depth and suppress…",
  "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."
}