mlsanityData to 14 Oct 2026

BiomechAgent: AI-Assisted Biomechanical Analysis Through Code-Generating Agents

arXiv 2602.06975 ↗First posted 16 Jan 2026cs.CL cs.AIcited by 0

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Markerless motion capture is making quantitative movement analysis increasingly accessible, yet analyzing the resulting data remains a barrier for clinicians without programming expertise. We present BiomechAgent, a code-generating AI agent that enables biomechanical analysis through natural language and allows users to querying databases, generating visualizations, and even interpret data without requiring users to write code. To evaluate BiomechAgent's capabilities, we developed a systematic benchmark spanning data retrieval, visualization, activity classification, temporal segmentation, and clinical reasoning. BiomechAgent achieved robust accuracy on data retrieval and visualization tasks and demonstrated emerging clinical reasoning capabilities. We used our dataset to systematically evaluate several of our design decisions. Biomechanically-informed, domain-specific instructions significantly improved performance over generic prompts, and integrating validated specialized tools for gait event detection substantially boosted accuracy on challenging spatiotemporal analysis where the base agent struggled. We also tested BiomechAgent using a local open-weight model instead of a frontier cloud based LLM and found that perform was substantially diminished in most domains other than database retrieval. In short, BiomechAgent makes the data from accessible motion capture and much more useful and accessible to end users.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 24 of its 39 references that are in the map. First posted on arXiv 16 Jan 2026.

Builds on

24 of 39 references.

References · 24

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Visual Instruction Tuningcited by 7,869
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  5. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  6. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  7. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  8. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  9. 2021Program Synthesis with Large Language Modelscited by 3,637
  10. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  11. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,479
  12. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
12 more
  1. 2024A Survey on Large Language Models for Code Generationcited by 658
  2. 2025Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoningcited by 75
  3. 2024A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualizationcited by 33
  4. 2023DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelinescited by 27
  5. 2024Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capturecited by 18
  6. 2023Improved Trajectory Reconstruction for Markerless Pose Estimationcited by 12
  7. 2025Know When to Explore: Difficulty-Aware Certainty as a Guide for LLM Reinforcement Learningcited by 12
  8. 2022PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Researchcited by 10
  9. 2022Transforming Gait: Video-Based Spatiotemporal Gait Analysiscited by 8
  10. 2025Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphonecited by 8
  11. 2025KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capturecited by 5
  12. 2024A Causal Framework for Precision Rehabilitationcited by 2

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