Too much evidence, too little time: From text to actionable recommendations through multi-objective evidence reasoning
Summary
SCEPTER is a framework that converts clinical case descriptions into evidence-based recommendations by combining PubMed retrieval, PubMedBERT ranking, LLM claim extraction, contradiction detection, and Pareto-optimal claim selection, achieving a 192:1 compression ratio while maintaining high evidence diversity.
View Cached Full Text
Cached at: 07/28/26, 06:25 AM
# Too much evidence, too little time: From text to actionable recommendations through multi-objective evidence reasoning Source: [https://arxiv.org/abs/2607.22574](https://arxiv.org/abs/2607.22574) [View PDF](https://arxiv.org/pdf/2607.22574) > Abstract:Evidence\-based clinical decision making requires specialists to identify, evaluate and synthesize relevant scientific literature\. However, PubMed searches for complex clinical cases often return hundreds of publications that cannot be reviewed manually under time constraints\. This study proposes SCEPTER \(Single\-Case Evidence\-driven PubMed\-To\-rEcommendation Reasoner\), a framework for transforming clinical case descriptions into evidence\-based recommendations\. SCEPTER combines PubMed retrieval, PubMedBERT semantic ranking, large language model \(LLM\)\-based claim extraction, evidence\-level weighting, contradiction detection, consensus analysis and multi\-objective Pareto claim selection\. The framework generates structured evidence syntheses and grounded actionable recommendations\. A Paper Q&A module further enables interactive exploration of selected publications\. The proposed framework introduces multi\-objective reasoning model that integrates literature support, contradiction analysis and interactive literature interrogation into a unified clinical decision\-support pipeline\. Evaluation on 150 case studies demonstrated that the framework reduced an average search space of 576 papers to 53 retained papers, 7 Pareto\-optimal claims and 3 final recommendations, corresponding to an overall compression ratio of 192:1\. Despite this reduction, the retained evidence maintained high diversity \(entropy=0\.901\)\. The ablation study showed that Pareto\-based selection increased evidence diversity and recommendation utility compared with conventional ranking approaches\. ## Submission history From: Simona\-Vasilica Oprea \[[view email](https://arxiv.org/show-email/55e19940/2607.22574)\] **\[v1\]**Fri, 5 Jun 2026 03:53:28 UTC \(1,087 KB\)
Similar Articles
Turning scattered evidence into discovery decisions for life sciences
OpenAI’s new life-science model “GPT-Rosalind” inside Codex autonomously ranks asthma drug targets by orchestrating specialist sub-agents that merge genetics, transcriptomics, safety and IP data into a single evidence-backed decision.
Evidence-RL: Towards Evidence-intensive Visual Reasoning
This paper introduces Counterfactual Evidence Disentanglement (CED), a training-time method that makes vision-language models rely on concrete image evidence rather than language priors or shortcuts, improving visual reasoning grounding across benchmarks.
PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
PathoSage introduces a three-stage framework for pathology multimodal reasoning that separates knowledge retrieval, evidence collection, and evidence adjudication to reduce hallucinations and handle conflicting evidence, featuring a training-free Beta-Bernoulli experience system for modeling tool reliability.
CalBrief: A Pilot Diagnostic Benchmark for Evidence-Calibrated Scientific Briefing with Large Language Models
The paper presents CalBrief, a pilot diagnostic benchmark of 16 evidence packages and 96 human-verified takeaways for evaluating whether large language models can generate evidence-calibrated scientific briefings. The study finds that structured organization improves reasoning but explicit strength-calibration policies are overly conservative, with most conservatism arising from expanded label spaces rather than signal injection.
MEDSYN: Benchmarking Multi-Evidence Synthesis in Complex Clinical Cases for Multimodal Large Language Models
MEDSYN is a multilingual multimodal benchmark for evaluating MLLMs on complex clinical cases with up to 7 distinct visual evidence types per case. The study reveals that while frontier models match human experts on differential diagnosis generation, all MLLMs show significant gaps in final diagnosis selection due to poor synthesis of heterogeneous clinical evidence.