LLM Judge for Competitor Validation

• Competitor Validating LLM-as-a-Judge Agent is a specialized LLM filter that uses iterative reasoning, targeted web search, and strict evidence criteria to validate competitor drug candidates.
• It employs a Reasoning–Acting–Observation loop and benchmarks against multi-modal real-world datasets, achieving 90.4% precision and 85.7% recall in filtering false positives.
• The agent accelerates drug asset due diligence by reducing analysis time from days to hours while ensuring transparent, rule-based validation and improved competitive intelligence.

A Competitor Validating LLM-as-a-Judge Agent is a specialized LLM agent used as an autonomous judge to curate and validate lists of competitors within multi-agent AI pipelines, particularly in drug asset due diligence. Its primary task is to filter predicted competitor drug candidates for a given clinical indication, suppressing hallucinations and maximizing precision by leveraging structured reasoning, rule-based evidence criteria, and web-integrated research tools. The agent’s outputs are benchmarked against rigorously derived, multi-modal real-world datasets, and the approach sets new standards in both recall and precision for competitive intelligence workflows in life sciences.

1. Architecture and Operational Workflow

The agent functions as a post-retrieval filter within a multi-agent competitor-discovery pipeline. After an upstream competitor-discovery agent generates a list of candidate drugs for an indication, the LLM-as-a-Judge (referred to as the "Competitor-Validator") enacts a Reasoning–Acting–Observation loop for each candidate:

• Reasoning: For a candidate drug, the agent considers available evidence.
• Acting: The agent formulates web queries and invokes web search via tools (e.g., Gemini-2.5 Flash with live web access) to gather supporting documentation.
• Observation: The model synthesizes responses—extracting information from clinical registries, regulatory filings, published literature, and company/press releases.

Each judgment cycle yields a structured JSON output: a boolean validity (true/false) plus a justification. For complex cases (e.g., aliases), the agent will resolve connections across brand, generic, and development code names using normalization logic and external databases. The process is constrained to a maximum of three iterative research attempts per candidate in production deployment.

The system is calibrated using a set of negative and hard "near-miss" cases labeled by human experts. Prompt templates and evidence-handling logic are refined to strictly enforce that only candidates with clinico-regulatory or authoritative preclinical documentation are marked as true competitors. Mechanistic or speculative associations—lacking direct evidence for a given indication—are systematically excluded.

2. Rigorous Benchmarking and Evaluation Framework

The validation corpus for the Competitor-Validator is constructed by parsing five years’ worth of multi-modal diligence memos from a private biotech venture capital (VC) fund:

• Diverse modalities (free-text, tables, slides, images) are hierarchically parsed by supporting agents.
• Extracted competitor-indication mappings and canonical attributes (e.g., targets, mechanisms, developmental stage) are normalized via LLM-driven alias and attribute resolution.
• The resulting dataset consists of JSON mappings: $(ind_j, C^*_j)$, where $C^*_j$ is the ground-truth competitor set for indication $ind_j$.

Performance is evaluated via per-sample recall and precision:

$$\text{Recall} = \frac{1}{|C^*_j|} \sum_{d \in C^*_j} 1[d \in \hat{C}_j]$$

On the full benchmark, the overall recall achieved for the validated competitor list is 83%, significantly exceeding the results from OpenAI Deep Research (65%) and Perplexity Labs (60%). For the Competitor-Validator (i.e., the LLM-as-a-Judge filter), held-out test evaluation yields 90.4% precision, 85.7% recall, and 88.0% F1-score for filtering false positives among candidate drugs.

For attribute extraction validation, binary accuracy and precision are also computed:

$$\text{Prec} = \frac{|\text{Attributes correctly predicted}|}{|\text{Attributes predicted}|}$$

3. Algorithmic Innovations and Rule-Based Reasoning

The agent is notably built on the ReAct framework, in which it iteratively "thinks," "acts," and "observes" to resolve ambiguities and cross-check claims against external documents. Major technical elements include:

• Iterative web search: Generating targeted queries and rapidly validating facts against up-to-date and authoritative sources.
• Strict evidence criteria: Only drugs with confirmable development or regulatory evidence specifically tied to the indication are accepted; theoretical, umbrella, or mechanistically related drugs are excluded.
• Prompt optimization: Synthetic hard negatives (i.e., near-misses identified by expert review) are used to adjust prompts toward high-precision, conservative decision boundaries.
• Structured output: JSON format, with explicit fields for boolean decision and justification, facilitates downstream integration and auditability.

Challenges, such as indication/ontology mismatches (e.g., "NHL" vs. "Non-Hodgkin lymphoma") and the alias-heavy nature of drug entities, are addressed through LLM-powered normalization agents. These leverage both learned entity resolution and curated external databases.

4. Impact on Enterprise Due Diligence and Analyst Productivity

In a reported deployment to a biotech VC fund for real-world due diligence:

• End-to-end analyst turnaround was reduced from 2.5 days to approximately 3 hours, a $\sim$20$\times$ speed-up.
• The agent not only maintained high precision (preventing the proliferation of hallucinated competitors) but also surfaced additional assets missed in manual reviews, increasing analytic completeness.
• Integration of the system into enterprise pipelines improved both the speed and quality of competitive analysis, bridging the capabilities gap observed with more generic research engines.

This demonstrates that the Competitor-Validating LLM-as-a-Judge can serve as both an accuracy-maximizing filter and a discovery amplifier in production workflows.

5. Technical and Operational Challenges

Several domain-specific complexities are handled by the system:

• Ontology Resolution: Disease and drug names vary across regulatory, scientific, and commercial contexts. Alias resolution requires both rule-based merging and LLM-driven disambiguation.
• Multimodality: Diligence memos contain not just free-text but dense tables, image-based slides, and non-English content. A hierarchical agent parses multimodal content into a consistent schema.
• Data Fragmentation: Relevant data is dispersed across paywalled and rapidly updated registries, company filings, and literature. The agent overcomes this through real-time search agent modules with robust source integration.

Limitations remain: the system’s high-precision configuration may filter out poorly documented or emergent competitors; regular re-calibration may be needed as entity vocabularies evolve and as indication/asset relationships change in external databases.

6. Comparative Analysis and Broader Implications

The agent’s validation metrics substantively outperform those of existing commercial and open-source competitor search tools in both recall and precision. The approach advances best practices for validation and verification of LLM-generated competitive intelligence pipelines by:

• Anchoring judgment in transparent, evidence-driven rule sets;
• Employing multi-iteration verification with external tools;
• Providing auditable, structured output for downstream processes.

The methodology readily generalizes to other high-recall/precision competitive intelligence or due diligence scenarios, particularly those characterized by ontology mismatch, entity aliasing, and fast-evolving knowledge graphs. A plausible implication is that similar multi-agent, LLM-validated pipelines could be constructed for other domains—such as legal compliance, financial monitoring, or scientific literature review—where false positives can have significant downstream ramifications.

7. Conclusions

The Competitor Validating LLM-as-a-Judge Agent exemplifies the convergence of LLM-based autonomous judgment, web-augmented evidence gathering, and structured, rule-based filtering within a high-stakes, production-oriented workflow. Its ability to balance recall, precision, and interpretability while filtering out hallucinated or weakly-evidenced predictions offers a rigorous template for validating competitive intelligence agents more broadly. The system’s demonstrated gains in both analytic throughput and quality mark a substantive advance in the deployment of agent-based LLM judges for decision-critical enterprise applications (Vinogradova et al., 22 Aug 2025).