---
title: "Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.26093v1"
permalink: "https://feed.astralor.com/items/itm_ca05126635d9924ba85c73d1"
published: "2026-08-26T17:56:00.000Z"
score: 78
channel: "ai"
tags: ["AI Coding Agent","arXiv","Cell-Edge Power Control","Wireless Resource Management","Agentic Autoresearch"]
---

# Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role

## AI commentary

> What makes this paper significant is that it turns 'tuning' into 'algorithm design'. The AI agent is not merely optimizing hyperparameters but autonomously discovering provably optimal structures under complex constraints. It matters for both wireless networking and automated ML audiences, especially those hoping to move beyond handcrafted algorithms. The safeguard mechanism is worth studying as a foundation for trustworthy automated research.

## At a glance

This paper demonstrates that the entire design layer of machine learning algorithms for wireless resource management can be handed over to an autonomous agent. The autoresearch protocol lets an AI coding agent edit a training script, run a fixed-budget experiment, and accept or discard the change based on a single immutable metric, with full authority over architecture family, input representation, output parameterization, loss function, and task-sampling law. The target is sum-least-percentile-rate power control across a multicell network, a non-convex, non-smooth, strongly NP-hard problem oriented at cell-edge throughput. With safeguards including a hash-pinned evaluator, enforced inference contract, and pre-registered falsifier, the agent ran 81 unattended experiments in 26 hours, reaching 99.5% of a converged minorization-maximization reference in one fixed-cost inference pass, at roughly 600x lower inference cost, closing 94% of the gap from its first working architecture. One parameter set serves every network size and percentile target. The discovered output parameterization exactly reproduces the max-min-optimal allocation at the minimum percentile for every trained weight value.

## Key points

- Gives an AI agent full control over architecture, loss, and training strategy design for wireless resource management ML.
- On a non-convex, non-smooth, strongly NP-hard cell-edge power control task, reaches 99.5% of traditional MM reference in 26 hours / 81 experiments.
- Achieves roughly 600x lower inference cost with one parameter set for all network sizes and percentile targets.
- Recovers provable structure, reproducing max-min-optimal allocation exactly rather than tuning constants.

## Full text

Designing machine learning algorithms for wireless resource management is labour-intensive: the architecture, the loss function and the training recipe are all specified by hand. We demonstrate that this design layer can be surrendered to an autonomous agent in its entirety. We adopt the autoresearch protocol, in which an AI coding agent edits a training script, runs a fixed-budget experiment, and retains or discards the change according to a single immutable metric. We grant the agent authority over the architecture family, the input representation, the output parameterization, the loss function and the task-sampling law, and set it a target chosen for its difficulty: sum-least-percentile-rate power control across a multicell network. The formulation targets cell-edge throughput and is non-convex, non-smooth and strongly NP-hard away from its max-min vertex. Safeguards render the results trustworthy: a hash-pinned evaluator, an enforced inference contract and a pre-registered falsifier per experiment. In eighty-one unattended experiments over twenty-six hours, the agent reached $99.5\%$ of a converged minorization-maximization reference in one fixed-cost inference pass, at roughly $600\times$ lower inference cost, closing $94\%$ of the gap from its first working architecture, with one parameter set serving every network size and percentile target. It recovered provable structure rather than tuned constants: the output parameterization it discovered reproduces the exact max-min-optimal allocation at the minimum percentile, for every value of the trained weights.

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