✽ How Do the AI-bot reviews compare to Agency Reviews?
Headframes AI-RA Review Comparison
Although the goal of the AI-RAs is to provide constructive feedback and not have a calibrated score, it is important that the AI-RAs are in the "ball park" of what a real reviewer might see and address. Thus, a series of "real" proposals were evaluated with each bot, and compared to agency reviews. A high-level summary is below. The summary is developed by AI of 20+ reviews for proposals that were both awards and declines from various federal agencies. AI was "blind" to the intended results.
Trained with a technical data set
Technical Content
Frankenbot is uncompromising on rigor. It demands clear benchmarks, quantitative metrics, and validation, and it consistently downgrades proposals when these are absent. Novelty must be demonstrated, not just claimed, and risks must be accompanied by real contingency strategies. Translational pathways are expected, otherwise feasibility is marked down.
Impact & Broader Relevance
Mission alignment is recognized as a strength, but other impact elements are treated skeptically. Educational and workforce development plans are often rated as weak, particularly when they feel disconnected from the technical work. Broader impacts outside of education are viewed as underdeveloped unless they present clear, scalable outcomes.
Writing & Presentation Style
Frankenbot provides critical but constructive reviews, leaning toward a coaching style. The narrative is balanced but direct, pointing out gaps in logic or evidence. It highlights where proposals fail to justify claims, while offering clear cues on how to strengthen the argument.
Trained as a STEM generalist
Technical Content
Grump is more forgiving in evaluating technical claims. It often accepts novelty when asserted, even without rigorous benchmarking. Objectives are praised when mission-aligned, though less attention is paid to the absence of quantitative metrics. Risks are noted, but rarely pressed with the same intensity as Frankenbot.
Impact & Broader Relevance
Mission alignment is also recognized as a strength. However, educational and workforce elements are generally weak, and broader impacts are often treated as secondary. Still, Grump is less strict about integration than Frankenbot, often letting these sections pass with minor criticism.
Writing & Presentation Style
Grump’s tone is blunt but constructive. The reviews emphasize clarity and alignment but can be terse in critique. Unlike Frankenbot’s coaching style, Grump tends to call out weaknesses directly without elaborating as much on remedies.
Expert reviews of the proposal
Technical Content
Agency reviews vary more widely. Some are supportive, noting strong mission relevance or infusion potential, while others flag major concerns about feasibility, overlap, or unclear novelty. Agencies consistently criticize weak metrics and insufficient validation. Risk discussions are detailed, often noting specific technical or operational vulnerabilities.
Impact & Broader Relevance
Agencies emphasize mission tie-ins as a major strength. Outreach is usually praised, but integration with research is sometimes called vague or incremental. Broader impacts are treated as muted, with reviewers flagging when no new contributions beyond education are offered.
Writing & Presentation Style
Agency feedback is more polarized than Frankenbot or Grump. Some reviews are highly supportive, while others are dismissive and critical. This creates a heterogeneous narrative tone, reflecting the diverse perspectives of different review panels.
"Interesting! Human experts were more picky on the feasibility. AI cannot capture the years of experience that human experts built regarding failed experiments, delayed deadlines, human errors, or logistic roadblocks."
