Smarter Science: Automating the Search for Better Experiments

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Author: Denis Avetisyan

A new framework leverages community knowledge to help researchers quickly identify the most relevant datasets and baseline models for their work.

Collective perception isn’t about singular understanding, but an augmented retrieval process where shared observation subtly reshapes the landscape of what is known, inevitably prioritizing some perceptions over others.
Collective perception isn’t about singular understanding, but an augmented retrieval process where shared observation subtly reshapes the landscape of what is known, inevitably prioritizing some perceptions over others.

This paper presents AgentExpt, a system using AI agents and interaction chains to improve the recall and precision of resource recommendations for scientific experimentation.

Despite the increasing capabilities of large language model agents in web-centric tasks, automating rigorous experiment design remains a challenge due to limited data coverage and over-reliance on superficial similarity in resource retrieval. This work introduces ‘AgentExpt: Automating AI Experiment Design with LLM-based Resource Retrieval Agent’, a framework that leverages collective perception embedded in baseline and dataset citation networks to recommend relevant resources for AI research. By curating a comprehensive dataset linking over one hundred thousand papers to their utilized resources and employing interaction chain reasoning, we demonstrate significant improvements—up to +8.30% in HitRate@5—over existing methods. Could this approach unlock a new era of reliable, interpretable automation in scientific experimentation and accelerate the pace of discovery?

The Illusion of Contextual Understanding

Recommending relevant baselines and datasets is crucial for scientific progress, yet traditional methods struggle with nuanced context. Existing approaches rely on superficial similarities, overlooking the intricate relationships between research areas. This hinders knowledge discovery and slows innovation.

Simple keyword matching and basic co-occurrence analyses are insufficient to capture the complexities of research. These methods treat papers as isolated entities, failing to leverage citation networks and semantic connections. Consequently, recommendations lack precision and relevance.

This research addresses a problem requiring a novel approach to [problem domain - details missing from caption].
This research addresses a problem requiring a novel approach to [problem domain – details missing from caption].

Large language models present opportunities, but require careful integration with knowledge graphs and citation networks. While LLMs excel at semantic understanding, they are prone to hallucination and lack grounding in established knowledge. Hybrid approaches offer a promising path toward more reliable recommendations.

The map is not the territory, and every recommendation is a prophecy of what might be found, not a guarantee of what is.

Mapping the Web of Scientific Influence

Graph-based modeling provides a framework for representing relationships between papers, baselines, and datasets, exceeding the limitations of keyword-based approaches. This methodology constructs a network that captures dependencies within the research landscape.

Techniques such as Interaction Chains map how papers utilize baselines and datasets, moving beyond co-occurrence to reveal the nature of the connection. This holistic view reveals whether a baseline is central to a methodology or merely cited for comparison.

Coverage analysis reveals a temporal dependency on established experimental components, as indicated by the fraction of resources employed in year N that were introduced in preceding years.
Coverage analysis reveals a temporal dependency on established experimental components, as indicated by the fraction of resources employed in year N that were introduced in preceding years.

Collective Perception enhances understanding of baseline usage by analyzing citation contexts and embeddings, utilizing the Qwen Embedding Model. This provides a nuanced interpretation of how resources are employed, differentiating between superficial mentions and genuine methodological integration.

Automating the Search for Solutions

Supervised text classification automates resource allocation in complex problem-solving environments. By categorizing problem descriptions, systems map these to relevant datasets and baseline solutions, minimizing manual intervention.

Dense bi-encoder retrieval offers a powerful method for navigating this relational graph of problems and solutions. This technique facilitates semantic search, identifying resources based on the meaning of the problem description rather than keywords.

Advancements in retrieval methods – Textual-GCL, SymTax, SciBERT, and HAtten – demonstrate substantial improvements over existing baselines, achieving up to +8.35% improvement in HitRate@5 and +7.52% in HitRate@10.

The Inevitable Entropy of Information

Recent recommendation systems integrate with large language models to enhance agent capabilities, providing them with the ability to efficiently locate and incorporate relevant information from the web.

Evaluation on standard recommendation tasks reveals substantial performance gains, achieving a Recall@20 of 0.4523 (+7.23% over baseline), HitRate@5 (0.5933 vs 0.5476, +8.35%), and HitRate@10 (0.6938 vs 0.6453, +7.52%).

Robust evaluation benchmarks are critical for progress. New datasets, such as AgentExpt Dataset, bridge the gap between research ideas and resources. Ultimately, these systems do not solve information overload; they merely postpone the inevitable entropy of the web.

The pursuit of automated experiment design, as detailed in this work, echoes a familiar tension. Systems designed to retrieve and rerank knowledge, attempting to distill collective perception into actionable recommendations, inevitably trade flexibility for optimization. G. H. Hardy observed, “The most profound knowledge is the knowledge that one knows nothing.” This rings true; AgentExpt, while striving for precision in baseline and dataset recommendations, builds upon the inherently incomplete and evolving landscape of scientific understanding. Scalability, in this context, isn’t a measure of success, but merely the word used to justify the increasing complexity of a system destined to someday misjudge the relevance of a crucial, newly-published finding. The perfect architecture – a flawlessly comprehensive recommendation engine – remains a myth, a comforting fiction in the face of irreducible uncertainty.

What’s Next?

The pursuit of automated resource recommendation, as demonstrated by this work, invariably creates new dependencies. The system proposes baselines and datasets; it does not, however, propose an escape from the fundamental uncertainty of scientific inquiry. Each successfully retrieved resource subtly alters the landscape of future searches, creating a narrowing funnel of perceived relevance. The illusion of comprehensive knowledge deepens, while the periphery—where true novelty often resides—fades from view.

The emphasis on ‘interaction chains’ and ‘collective perception’ suggests an ambition to model the very process of scientific consensus. This is a precarious undertaking. Systems designed to reflect group thought will, inevitably, amplify existing biases and solidify prevailing paradigms. The more efficiently the system connects researchers to prior work, the less likely it is to surface genuinely disruptive ideas.

Further development will undoubtedly focus on refining retrieval algorithms and expanding the knowledge base. But the core challenge remains: how to build a system that facilitates discovery without inadvertently constructing an echo chamber? The problem is not to find more connections, but to cultivate the capacity to recognize—and even embrace—disconnection.

Original article: https://arxiv.org/pdf/2511.04921.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-10 12:11