AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation
arXiv:2606.09556v1 Announce Type: new Abstract: AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds. We test a different hypothesis in drug-asset valuation: for knowledge-intensive scientific decisions, the limiting factor is often the evidence substrate the agent can access. We run a controlled three-arm ablation on a production valuation agent: A is a plain web-only LLM analyst, B adds public structured tools plus a 14-dimension valuation playbook, verifier, objectivity policy and red-team, and C adds the proprie
The proliferation of AI agents necessitates deeper understanding of their practical limitations beyond model architecture, especially in expert domains.
It highlights that specialized data access and structured reasoning, not just raw model size, are critical differentiators for AI agent performance in high-stakes applications.
The focus for AI agent development shifts towards bespoke data acquisition, knowledge representation, and rigorous procedural safeguards, rather than solely foundational model improvements.
- · Companies with proprietary data assets
- · AI agent developers specializing in domain-specific data integration
- · Sectors requiring high-fidelity knowledge extraction (e.g., pharma, legal, finan
- · Generic web-only LLM agent providers
- · Companies without robust data infrastructure
- · Investors overlooking proprietary data's value in AI deployments
AI agents' utility in complex domains is heavily gated by access to high-quality, domain-specific data and structured reasoning frameworks.
This drives increased investment in curating and securing proprietary datasets, transforming them into competitive assets for AI-driven insights.
The market for AI agents will stratify, with specialized agents commanding higher value due to their exclusive data and refined reasoning, leading to competitive advantage for their proprietors.
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Read at arXiv cs.AI