{
  "schema_version": "1.0",
  "id": "archive:https://arxiv.org/abs/2607.09600v1",
  "slug": "2607-09600v1-0uoqpsx",
  "url": "https://feed7.dev/p/2607-09600v1-0uoqpsx",
  "title": "Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation",
  "why_included": "Agora routes reasoning steps through an auction among expert models and tools, adding a single control for cost versus quality and outperforming matched baselines on five benchmarks.",
  "summary": "**The gist** **Agora** treats reasoning steps as auctioned tasks, letting expert models and tools bid using adjusted competence estimates. Tests across **five benchmarks** beat matched single-model, routing, and cascade baselines.",
  "practical_implication": "**Why it matters** Agent builders can rethink routing as a per-step allocation problem: account for both solver competence and cost, and use the framework’s **single auction parameter** to tune the trade-off.",
  "agent_context": "**The gist** **Agora** treats reasoning steps as auctioned tasks, letting expert models and tools bid using adjusted competence estimates. Tests across **five benchmarks** beat matched single-model, routing, and cascade baselines.\n\n**Why it matters** Agent builders can rethink routing as a per-step allocation problem: account for both solver competence and cost, and use the framework’s **single auction parameter** to tune the trade-off.\n\n**Watch out** The supplied abstract gives **no effect sizes, costs, or benchmark names**. Results are limited to comparable candidate pools, so gains may depend on the available experts and competence calibration.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2607.09600v1",
    "published_at": null
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "agent",
  "domains": [
    "research"
  ],
  "topics": [
    "multi-agent",
    "model-selection",
    "reasoning"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The supplied abstract gives **no effect sizes, costs, or benchmark names**. Results are limited to comparable candidate pools, so gains may depend on the available experts and competence calibration."
  ],
  "lifecycle": "Current",
  "published_at": null,
  "modified_at": null,
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/2607-09600v1-0uoqpsx",
    "json": "https://feed7.dev/p/2607-09600v1-0uoqpsx.json",
    "markdown": "https://feed7.dev/p/2607-09600v1-0uoqpsx.md"
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}