# You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition

arXiv: https://arxiv.org/abs/2609.16065 · HTML: https://mlsanity.com/paper/2609.16065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 13 Sep 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 5 of 5)

- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware](https://mlsanity.com/paper/2312.00425/) (2023)
- [Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO](https://mlsanity.com/paper/2311.01057/) (2023)
- [TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices](https://mlsanity.com/paper/2507.07949/) (2025)
- [Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook](https://mlsanity.com/paper/2604.02711/) (2026)

## Abstract (arXiv)

Human activity recognition (HAR) is usually framed as gradient-based training of neural networks. Agentic Heuristic Learning (AHL) Studio explores a complementary view inspired by human cognitive learning: people learn activities by remembering examples, forming rules, and repairing mistakes, not by backpropagating. This proposed tool implements AHL for HAR: a learning-time agent reasons over sensor protocols, proposes executable heuristic policies, records repair traces, and exports an LLM-free policy for edge deployment. We focus on the HAR benchmark family and provide an end-to-end workflow from dataset observation to edge-oriented export. On eleven HAR datasets evaluated so far, AHL policies reach strong executable-policy performance while remaining inspectable, editable, and replayable footnotehttps://github.com/zhaxidele/ahl-ts-studio.