A Rapid Pipeline for Training and Deploying ML Models on WeBe Band

Ehsan Kourkchi; Asmita Asmita; Houman Homayoun; Mahdi Eslamimehr

Home/Archive/arXiv

arXiv · 2609.29084

A Rapid Pipeline for Training and Deploying ML Models on WeBe Band

Ehsan Kourkchi·Asmita Asmita·Houman Homayoun·Mahdi Eslamimehr

View original source ↗Read source document ↗Export metadata ↓

Abstract

Developing optimized machine-learning algorithms for edge devices with limited computational and memory resources is challenging, time-consuming, and highly dependent on device-specific constraints. In this work, we streamline an edge ML workflow to enable rapid development, optimization, and deployment of machine-learning (ML) models directly on the WeBe Band, a wrist-worn wearable device designed for multimodal physiological data monitoring. The proposed system automatically generates hardware-efficient ML models that can be easily integrated into the WeBe core firmware, supporting AutoML, hardware-aware quantization, and performance profiling to build models that meet desired latency targets while remaining compatible with device memory and power limitations. The proposed framework tightly integrates the open-source Piccolo AI ecosystem with an automated pipeline that generates deployable firmware artifacts, performs hardware-aware model compilation, and supports over-the-air (OTA) deployment. The system supports multiple lightweight model classes, including classical machine-learning algorithms and neural networks, and provides built-in on-device profiling tools to evaluate inference latency and memory footprint under realistic execution conditions. Experimental results demonstrate clear trade-offs between model complexity and deployability on a microcontroller, showing that classical models offer strong real-time performance while lightweight neural networks require careful resource management. Rather than proposing new learning architectures, the current work mainly focuses on system-level automation, deployability, and enabling researchers and developers to rapidly iterate on models and evaluate them directly on target hardware. Although demonstrated on the WeBe Band platform, the workflow is designed to be extensible to other ML-powered edge devices.

Explore related subjects

cs.AI cs.LG cs.PF

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ehsan Kourkchi, Asmita Asmita, Houman Homayoun, Mahdi Eslamimehr. 2026-09-24. A Rapid Pipeline for Training and Deploying ML Models on WeBe Band. https://arxiv.org/abs/2609.29084

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork

In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with unknown partners is required-remains unexplored. To rigorously evaluate this, we introduce a large-scale benchmark ICRL4AHT, built upon a high-throughput JAX implementation of Overcooked-V2. Our benchmark includes a large, diverse teammate suite spanning both RL and heuristic policies, enabling controlled train-test shifts, and provides a reproducible end-to-end pipeline for teammate generation, learning-history collection, dataset construction, and online multi-episode evaluation. We evaluate representative history-conditioned ICRL algorithms, including Algorithm Distillation (AD) and Decision-Pretrained Transformer (DPT), across millions of transitions. Results reveal notable limitations: contrary to their success in single-agent domains, these baselines fail to exhibit robust test-time adaptation in multi-agent settings. Specifically, these methods frequently underperform random baselines across both unseen teammate and unseen layout tracks, with no clear in-context improvement over long horizons. These findings highlight the challenges of strategic inference under partial observability within the OvercookedV2 AHT protocol, establishing our benchmark as a critical testbed for next-generation coordination algorithms.

cs.AI↗

arXiv2026

Planning Takes More Than Token Prediction: Causal Plan for Benchmarking and Building Physically Grounded Embodied Reasoners

Current benchmarks for embodied vision-language planning inadvertently favor linguistic next-token prediction over physically grounded next-state reasoning. This rewards models that mimic statistical language priors rather than track true causal dependencies, reducing complex physical planning to shallow sequence modeling. Hence, achieving genuine physical autonomy requires a fundamental shift from linguistically grounded token prediction toward physically grounded causal reasoning. To this end, we introduce Causal-Plan-Bench, a high-fidelity diagnostic suite spanning four causal dimensions, curated via multi-stage verification. To endow models with this capability, a four-stage annotation pipeline extracts structured interaction records from egocentric videos to construct Causal-Plan-1M, a dense million-scale corpus of explicit causal reasoning traces. Extensive evaluation reveals a striking gap: leading models struggle to demonstrate genuine physical agency -- even GPT-6-astra scores only 43.04. In contrast, our tailored training recipe enables Causal Planner to internalize the complex physical logic required for accurate next-state estimation. Built upon Qwen3-VL-8B, Causal Planner raises its backbone's score from 33.23 to 45.28, a 36.3% relative gain, and improves on three external benchmarks without benchmark-specific adaptation. We further observe an empirical Causal-Supervision Scaling Trend. Paired no-vision controls also reveal substantial visual dependence, while cross-judge comparisons and human scoring assess the reliability of automated evaluation. More importantly, we initiate the first effort to turn agents from superficial token predictors into physically grounded causal reasoners, bridging language modeling and world modeling.

cs.AI↗

arXiv2026

Omni-Decision: Evidence-Ledger Planning for Omni-Modal Agents

Omni-modal agents must seek evidence across video, audio, web pages, and computation to answer questions. Their main bottleneck is planning: noisy multimodal observations accumulate in conversation history and disrupt later decisions, while multimodal models have limited capacity for multi-step planning. Controlled backend replacements support this diagnosis: replacing the planner causes a much larger performance loss than replacing the perception backend. We present Omni-Decision, an omni-modal agent built on evidence-ledger planning: it replaces the growing dialogue history with an explicit evidence ledger that records what evidence is still missing, what has been confirmed, and where records conflict. A critic reads each noisy observation and passes only the usable content to the ledger, discarding the rest, so the planner works from a compact context throughout the task. Each run records the state, action, and verdict at every step, and supervised fine-tuning and decision-level reinforcement learning on these trajectories further improve the planner. Omni-Decision achieves state-of-the-art accuracy of 81.4% on OmniGAIA at approximately 43% of Gemini-3.1-Pro's cost per question, and 65.0% on WorldSense long-video understanding, level with the strongest end-to-end model.

cs.AI↗