{
  "url": "https://appliedfrontier.nl/en/research/signal-712c181240cc2c5c/",
  "title": "Don't Lose the Thread: Empowering Long-Horizon LLM Agents with Cognitive Resource Self-Allocation",
  "published": "2026-10-29",
  "excerpt": "Agents powered by large language models (LLMs) have demonstrated remarkable progress in solving complex reasoning tasks. However, LLM agents often falter on long-horizon tasks due",
  "content_sha256": "612c981669683a859a2f12154378c62d8e62626c82832cef10382c628e615fc6",
  "extracted_chars": 1467,
  "artifact_links": [
    "https://arxiv.org/abs/2610.40350"
  ],
  "arxiv_id": "2610.40350",
  "extraction_scope": "preprint_abstract_metadata",
  "full_text_available": false,
  "id": "signal-712c181240cc2c5c",
  "org": "Research community",
  "topic": "Agent systems",
  "source_kind": "preprint",
  "status": "unreviewed-source-report",
  "analysis_method": "deterministic_research_triage_v2",
  "novelty_verified": false,
  "proof_checked": false,
  "peer_review_verified": false,
  "matched_terms": [
    "reasoning"
  ],
  "checked_at": "2026-11-01T14:17:04+00:00",
  "first_seen": "2026-10-31T19:32:55+00:00",
  "source_hash": "a27dfb11db40ead6c1664d43adf21e58534280f24452d94572f649132b8eb867",
  "source_url": "https://arxiv.org/abs/2610.40350",
  "language": "en",
  "indexing": "noindex_until_editorially_enriched"
}