mlsanityData to 13 Oct 2026

The Verbose Context Problem in Medical Records

arXiv 2606.29503 ↗First posted 28 Jun 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

The verbose context problem occurs when structured concepts have token-inefficient textual representations. This bottleneck is acute in population health: cohort-level analysis of longitudinal patient records requires reasoning over thousands of medically-coded events, often exceeding 400K tokens in total. We present PopMedQA, a benchmark isolating this problem through computational tasks on groups of longitudinal patient records. We construct the benchmark using neopatient, a new library for language-controlled generation of artificial patient records. Through extensive ablations -- including prompting strategies, prompt compression, and agentic decomposition -- we find that domain-independent methods fail to alleviate the verbose context problem. There remains significant opportunity to exploit domain-specific structure in language model inputs for population-scale reasoning.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 28 Jun 2026.

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19 of 34 references.

References · 19

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,376
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,898
  3. 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,386
  4. 2020Long Range Arena: A Benchmark for Efficient Transformerscited by 697
  5. 2025GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningcited by 489
  6. 2024LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitaskscited by 369
  7. 2024∞Bench: Extending Long Context Evaluation Beyond 100K Tokenscited by 361
  8. 2019On the "steerability" of generative adversarial networkscited by 247
  9. 2022Extracting Latent Steering Vectors from Pretrained Language Modelscited by 221
  10. 2025DeepSeek-OCR: Contexts Optical Compressioncited by 178
  11. 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Modelscited by 103
  12. 2024Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queriescited by 86
7 more
  1. 2025Cartridges: Lightweight and general-purpose long context representations via self-studycited by 75
  2. 2023MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Recordscited by 56
  3. 2025Glyph: Scaling Context Windows via Visual-Text Compressioncited by 49
  4. 2023EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Recordscited by 44
  5. 2024Multimodal Table Understandingcited by 43
  6. 2024Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?cited by 28
  7. 2025MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summariescited by 4

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  1. 2025Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction
  2. 2026Generated Context versus Governed State: Functional Conditions for Accountable Longitudinal Clinical Reasoning
  3. 2023Coupling Symbolic Reasoning with Language Modeling for Efficient Longitudinal Understanding of Unstructured Electronic Medical Records
  4. 2026GraphWalker: Patient Analogy Meets Information Gain for Clinical Reasoning with Large Language Models
  5. 2026ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
  6. 2026Think Fast, Talk Smart: Partitioning Deterministic and Neural Computation for Structured Health Text Generation
  7. 2026Medical Reasoning with Large Language Models: A Survey and MR-Bench
  8. 2025TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records