closed-loop

This paper applies meta-learning and pretraining to improve neural stimulation response modeling, reducing catastrophic forecast failures and enhancing prediction accuracy in non-human primate studies.

LLM4LLM introduces a deployment-aware closed-loop optimization framework to bridge kernel benchmarks and real LLM inference, achieving up to 6.98x speedups on H100 GPUs.

This paper evaluates large language models as autonomous co-pilots for digital agriculture, using a 49-channel phytosensor network and closed-loop control to optimize plant growth, reduce production cycles by 35%, and achieve significant energy savings.

Introduces CMU-Drive, a closed-loop benchmark for cooperative multi-agent autonomous driving, and V2V-VLA, a vision-language-action model that jointly generates driving actions, waypoints, reasoning, and communication policies. This provides the first benchmark and baseline for cooperative VLA driving.

Introduces PIRL (Policy Improvement Reinforcement Learning) and its practical implementation PIPO, a closed-loop framework that verifies policy updates by comparing performance with a historical anchor, enabling correction or reinforcement of previous updates. Experiments show consistent gains in mathematical reasoning, code generation, tool use, and self-distillation when applied on top of existing RL algorithms like PPO and GRPO.

This paper scales a closed-loop LLM-based channel configuration search to 250 candidates per cycle, showing positive accuracy trends and improved parameter efficiency on CIFAR-100, and revealing architectural regularities in LLM-generated channel priors.

This paper presents TANS-FO, a research prototype for closed-loop generative design of customized foot orthoses using text-aligned neural surrogates and graph neural networks to predict plantar stress. The system achieves surrogate-predicted peak-pressure reduction of 34.7% over parametric CAD but requires human review and has not received regulatory clearance.

This paper presents an LLM-driven pipeline using GPT-5, GPT-4o, and Claude Sonnet 4 to automatically design neural network architectures for cross-lingual handwritten OCR, achieving over 93% accuracy across Arabic, English, and Persian scripts without human intervention.

An interview-based study across nine companies in six countries examining current autonomous driving system testing practices, challenges, and future trends, proposing an evidence-centered closed-loop testing framework.

LingBot-VA 2.0 is a video-action foundation model trained from scratch for robot control, achieving 225 Hz closed-loop execution with 13B parameters (1.9B active per token) and outperforming prior models on RoboTwin 2.0.

Introduces AdaJEPA, an adaptive world model that continuously learns and updates its latent representation during deployment, enabling agents to adjust their plans based on real-world observations without retraining or memory tricks.

Introduces AdaJEPA, an adaptive world model that continuously learns from perception, planning, and action in a closed loop.

Presents Embodied CAD, a closed-loop framework that grounds LLM agents in a CAD execution environment for parametric B-Rep assembly modeling, using solver feedback for planning and refinement.

Introduces the capability slice, a unit for linking evaluation failures to data interventions in LLMs, enabling a closed-loop process that diagnoses and fixes model weaknesses. Demonstrated on two case studies, showing recovery from training regression and significant math reasoning improvements.

ENPIRE is a framework that enables autonomous robot policy self-improvement in the real world through a closed-loop system of environment feedback, policy refinement, and evolutionary code optimization, achieving 99% success on dexterous manipulation tasks.

This paper introduces PersonaDrive, a pipeline that conditions a vision-language-action (VLA) driving agent on retrieved demonstrations from a style-instructed human driving dataset, enabling style-diverse non-ego agents for closed-loop simulation and improving driving scores on Bench2Drive.

A developer shares how visualizing failure clusters across many agent runs changed their debugging approach, emphasizing the need for a feedback loop so agents learn from past mistakes rather than treating failures as isolated bugs. The post highlights manual workarounds and a platform called BentoLabs that implements closed-loop improvement.

The article argues that using LLMs for research requires a closed-loop system like Karpathy's LLM Wiki or the Recall AI knowledge base to prevent hallucinations, ensuring all outputs are grounded in trusted source documents.

Proposes Reason-Imagine-Act (RIA), a closed-loop framework coupling an LLM reasoner with an action-conditioned world model for online safety verification in autonomous driving, achieving 80.05% route completion and 0.20% collision rate in CARLA simulations.

SEAL proposes a closed-loop framework for jointly evolving LLM agents and their training environments, using diagnosis-guided labels to align both sides. It achieves substantial gains in multi-turn tool-use tasks with only 400 training samples, demonstrating improved robustness and out-of-distribution transfer.