ARTEMIS turns thousands of papers into reusable AI memory for regulatory science

Researchers at the U.S. Food and Drug Administration’s National Center for Toxicological Research have unveiled a framework that could change how scientists interrogate vast bodies of literature. Called ARTEMIS, short for Automated Research & Trend Extraction via Memory-based Insight Synthesis, the system transforms a chronological corpus of scientific papers into compact, reusable analytical contexts that large language models can draw on again and again. In a case study published in Discover Artificial Intelligence, the team applied ARTEMIS to more than 3,000 full-text articles on monoclonal antibodies and animal testing, and showed that the resulting synthesized memory outperformed conventional retrieval of raw text on high-level analytical questions while consuming far fewer input tokens.

The problem ARTEMIS addresses is familiar to anyone who has watched a large language model answer a specialized scientific question with confident fluency and quietly wrong facts. When the researchers asked a base model to produce analytical reports on monoclonal antibodies, the 3Rs framework of Replacement, Reduction and Refinement, and animal-use reduction, the output was polished but generic, untraceable to source evidence, and occasionally incorrect. In one striking example, the model asserted that the FDA had only 12 reviewers with specific alternative-methods expertise across all centers, a precise quantitative claim that was simply false. Static training data and the risk of hallucination make raw model knowledge unreliable in specialized domains, and even retrieval-augmented approaches that fetch raw passages often fall short when a question requires integrating findings scattered across dozens of studies.

ARTEMIS takes a different route: rather than handing the model raw documents, it first reorganizes the entire corpus into a hierarchical, evidence-grounded memory. The workflow operates at two linked levels. At the granular level, each source article is segmented into overlapping chunks of 2,000 characters, and each chunk is distilled by a language model into a concise observation capturing its most salient evidence. At the higher level, the system generates reflections by synthesizing observations from each chronological batch of five articles together with the immediately preceding reflection, so that new findings are always interpreted in light of previously accumulated knowledge. This recursive process builds an evolving memory that preserves both local evidence and broad patterns, and because the corpus is processed in publication order, the memory retains a timeline that supports historical analysis.

The scale of the compression is considerable. Processing 3,096 full-text articles produced 31,757 raw chunks, which were distilled into a memory of simple observations and 620 reflections. The median word counts tell the story: observations averaged just 24 words, reflections 599, and raw chunks 805. Crucially, the compression did not flatten the literature into a few oversimplified themes. Topic modeling with BERTopic found a similar number of topics in the reflections as in equally sized random samples of raw chunks, suggesting broad thematic coverage was preserved. But the reflections were far more conceptually convergent: their average inter-topic distance was roughly 0.2, compared with about 0.5 for raw-chunk samples, and they occupied a markedly smaller semantic volume in embedding space, approximately 0.02 versus 0.045 to 0.055. In other words, ARTEMIS does not merely shorten the literature; it reorganizes distributed evidence into a tighter, more coherent analytical representation.

To test whether this synthesized memory actually helps, the team assembled 30 high-level questions drawn from real analytical problems in the monoclonal antibody literature, including questions about reducing animal use and the evidence base for emerging alternative methods. Because these questions demand synthesis across studies, they are poorly served by retrieving isolated passages. For each question, answers were generated with the same Llama 3.3-70B model under different context conditions, and quality was scored on a 1-to-10 rubric by three independent LLM judges: GPT-4o, Claude 4.0 and Claude 4.6, each operating in isolated sessions to avoid conversational carryover. The results were consistent. ARTEMIS-based contexts produced complete answers for all 30 questions, whereas raw-chunk retrieval explicitly declared insufficient information in 5 of 30 cases. Across judges, ARTEMIS configurations received higher or comparable scores than raw-chunk retrieval, achieving 100 percent answerability versus 83 percent for the baseline.

The efficiency gains were just as notable. A context of 10 observations and 10 reflections represented only about 77.4 percent of the word count of 10 raw chunks yet matched or beat their answer quality, and a leaner ARTEMIS-lite configuration of 10 observations and 5 reflections, just 40.2 percent of the raw word count, still scored similarly or higher in many cases. Simply throwing more text at the problem did not help: when the team expanded retrieval to 50 raw chunks on a subset of questions, performance generally failed to improve and sometimes degraded, and on one item both approaches returned not-enough-information responses. The lesson, the authors argue, is that context structure and information density matter more than volume.

A series of component comparisons clarified why the combined design works. Observation-only contexts were often too compressed, failing to answer 16 of 30 questions with a mean score of 3.47. Reflection-only contexts were better but sometimes too abstract, with 8 insufficient responses and a mean score of 5.23, slightly below raw-chunk retrieval at 5.53. A single-pass summarization baseline, in which the top 10 retrieved chunks were flattened into one summary, performed poorly as well, with 14 insufficient answers and a mean score of 3.73, showing that one-shot compression without recursive accumulation is not the secret. The full ARTEMIS configurations topped the table with mean scores of 6.05 for ARTEMIS-lite and 6.28 for full ARTEMIS, and were at least as good as raw-chunk retrieval on 25 and 26 of the 30 questions respectively. The two layers, granular evidence and higher-level synthesis, are complementary, and neither alone suffices.

Perhaps the most intriguing capability is steerability. Because the reflection prompt can be modified, the same corpus can be organized under different analytical lenses without building separate evidence bases. The researchers generated three distinct sets of reflections from the identical monoclonal antibody literature using three expert personas: a 3Rs Ethical Reviewer, a Clinical Development Risk Strategist, and a Novel Alternative Methods Technology Analyst. Each persona produced distinct analyses emphasizing different signals and concerns, much as different human experts would. When the team probed the memories with a NAMs-specific query about organ-on-a-chip validation challenges, the NAMs persona’s reflections showed clearly higher cosine similarity at the top of the relevance ranking, confirming that the steering process enriches the memory with signals pertinent to the target lens. The framework also supports timeline-aware historical synthesis: using publication-ordered reflections as context, ARTEMIS generated an evidence-grounded narrative of Aducanumab’s development in four chronological phases, tracing how animal model usage evolved through the principles of Replacement, Reduction and Refinement.

The authors are careful about scope and limitations. LLM-as-a-Judge evaluations, they note, should be read as supportive rather than definitive; the judges differed in scoring stringency, with GPT-4o generally assigning higher marks than the Claude models, though relative rankings were consistent and repeated Claude 4.0 scoring in isolated sessions yielded identical results. Synthesis is also inherently lossy, and observations and reflections may omit low-frequency technical details that matter for some questions, which is why ARTEMIS is positioned as a support framework for complex literature analysis rather than a replacement for consulting source documents when full detail is required. The study centers on a single monoclonal antibody use case, and the team suggests future work could explore alternative corpus-ordering strategies, such as thematic grouping or citation-based progression, that might shape the analytical perspective embedded in the accumulated memory. Funded by the FDA and released as open access, the work arrives at a moment when regulatory agencies, drug developers and researchers alike are wrestling with literature too large and fast-moving for any human team to synthesize alone. If the approach generalizes beyond this first domain, the idea of building reusable, steerable, evidence-grounded memories from scientific corpora could become a standard layer between the world’s papers and the models asked to make sense of them.

Subject of Research: A memory-based AI framework for generating reusable analytical contexts from scientific literature for regulatory science

Article Title: ARTEMIS enables reusable analytical context generation from scientific literature

Article References: Li, D., Gong, B., Tong, W., & Xu, J. (2026). ARTEMIS enables reusable analytical context generation from scientific literature. Discover Artificial Intelligence, 6(1), Article 1465. https://doi.org/10.1007/s44163-026-02485-5

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02485-5

Keywords: ARTEMIS, large language models, regulatory science, monoclonal antibodies, retrieval-augmented generation, literature mining, 3Rs, novel alternative methods, LLM-as-a-Judge, evidence synthesis, FDA, knowledge curation