MAROKO133 Hot ai: Japan unveils ‘world’s first’ hydrogen-powered driverless tractor to tac

📌 MAROKO133 Eksklusif ai: Japan unveils ‘world’s first’ hydrogen-powered driverles

Japanese multinational corporation Kubota has unveiled the ‘world’s first’ hydrogen fuel cell tractor with a self-driving function. The machine was presented to the viewers at the World Expo 2025 in Osaka, Japan, on Monday this week.

The tractor will remain on display at the event until Thursday. Kubota has unveiled this tractor to support their decarbonization efforts and boost labor efficiency, which are well-known global challenges in agriculture.

The tractor combines AI-driven autonomous driving with zero-emission hydrogen power to address labor shortages and sustainability in agriculture.

Decoding the system

Kubota has introduced the 100-horsepower hydrogen-powered tractor that runs on a fuel cell stack, providing farmers nearly half a day of uninterrupted operation per refueling. This runtime is critical for agricultural use, where long working hours and reliability are essential.

Coming to its dimensions, the tractor is 4.4 meters (14.4 feet) long, 2.2 meters (7.22 feet) wide, and 2.3 meters (7.5 feet) tall. It has no driver’s seat but can be controlled remotely from anywhere within network range.

Kubota’s hydrogen model also offers a faster turnaround between refueling, higher power output, and zero CO2 emissions. Beyond performance, the tractor has advanced technology such as AI-powered cameras capable of detecting people or obstacles in the field and stopping automatically to ensure safety.

It also supports remote operation, enabling off-site monitoring and control. Japan’s farming sector faces worker shortages and an aging population, and Kubota’s new tractor offers a smart fix. It’s efficient, eco-friendly, and packed with modern tech to help farmers stay productive.

Inspired by the past

The unmanned version of the tractor is inspired by its manned fuel cell-powered counterpart that was presented last year.

This hydrogen-powered tractor delivered around 60 horsepower with three tanks above the cab, enabling four hours of quiet, low-vibration operation after a quick 10-minute refuel. It was designed with fuel cell tech like Toyota’s Mirai and tested in real farm tasks such as plowing.

More about hydrogen fuel cells

Hydrogen fuel cells are remarkable pieces of engineering that generate electricity by directly combining hydrogen and oxygen, producing only water and heat as byproducts.

This makes them a clean and sustainable energy source with zero emissions. Recent advancements have pushed the technology forward with high-durability platinum catalysts that extend lifespan, thinner membranes that improve efficiency, and advanced bipolar plates that enhance performance.

A promising future

The Japanese company is planning to test the new model in the fields.

The company says that unlike battery-electric tractors, Kubota’s hydrogen model provides higher power output and longer uptime.

“We will soon conduct a demonstration experiment and continue development towards practical application,” said Isamu Kazama, one of Kubota’s lead developers.

Kubota’s latest unveiling shows how hydrogen fuel cells and autonomous systems can come together to reshape farming. If successful in field trials, this tractor could mark a turning point in sustainable, tech-driven agriculture worldwide.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Update ai: Researchers from PSU and Duke introduce “Multi-Agent System

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 2M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. 

Meet the author
Institutions: Penn State University, Duke University, Google DeepMind, University of Washington, Meta, Nanyang Technological University, and Oregon State University. The co-first authors are Shaokun Zhang of Penn State University and Ming Yin of Duke University.

In recent years, LLM Multi-Agent systems have garnered widespread attention for their collaborative approach to solving complex problems. However, it’s a common scenario for these systems to fail at a task despite a flurry of activity. This leaves developers with a critical question: which agent, at what point, was responsible for the failure? Sifting through vast interaction logs to pinpoint the root cause feels like finding a needle in a haystack—a time-consuming and labor-intensive effort.
 
This is a familiar frustration for developers. In increasingly complex Multi-Agent systems, failures are not only common but also incredibly difficult to diagnose due to the autonomous nature of agent collaboration and long information chains. Without a way to quickly identify the source of a failure, system iteration and optimization grind to a halt.
 
To address this challenge, researchers from Penn State University and Duke University, in collaboration with institutions including Google DeepMind, have introduced the novel research problem of “Automated Failure Attribution.” They have constructed the first benchmark dataset for this task, Who&When, and have developed and evaluated several automated attribution methods. This work not only highlights the complexity of the task but also paves a new path toward enhancing the reliability of LLM Multi-Agent systems.
The paper has been accepted as a Spotlight presentation at the top-tier machine learning conference, ICML 2025, and the code and dataset are now fully open-source.

Paper：https://arxiv.org/pdf/2505.00212
Code：https://github.com/mingyin1/Agents_Failure_Attribution
Dataset：https://huggingface.co/datasets/Kevin355/Who_and_When
 
 
Research Background and Challenges
LLM-driven Multi-Agent systems have demonstrated immense potential across many domains. However, these systems are fragile; errors by a single agent, misunderstandings between agents, or mistakes in information transmission can lead to the failure of the entire task.

Currently, when a system fails, developers are often left with manual and inefficient methods for debugging:
Manual Log Archaeology : Developers must manually review lengthy interaction logs to find the source of the problem.
Reliance on Expertise : The debugging process is highly dependent on the developer’s deep understanding of the system and the task at hand.
 
This “needle in a haystack” approach to debugging is not only inefficient but also severely hinders rapid system iteration and the improvement of system reliability. There is an urgent need for an automated, systematic method to pinpoint the cause of failures, effectively bridging the gap between “evaluation results” and “system improvement.”

Core Contributions
This paper makes several groundbreaking contributions to address the challenges above:
1. Defining a New Problem: The paper is the first to formalize “automated failure attribution” as a specific research task. This task is defined by identifying the

2. failure-responsible agent and the decisive error step that led to the task’s failure.

Constructing the First Benchmark Dataset: Who&When : This dataset includes a wide range of failure logs collected from 127 LLM Multi-Agent systems, which were either algorithmically generated or hand-crafted by experts to ensure realism and diversity. Each failure log is accompanied by fine-grained human annotations for:
Who: The agent responsible for the failure.
When: The specific interaction step where the decisive error occurred.
Why: A natural language explanation of the cause of the failure.

3. Exploring Initial “Automated Attribution” Methods : Using the Who&When dataset, the paper designs and assesses three distinct methods for automated failure attribution:
All-at-Once: This method provides the LLM with the user query and the complete failure log, asking it to identify the responsible agent and the decisive error step in a single pass. While cost-effective, it may struggle to pinpoint precise errors in long contexts.
Step-by-Step: This approach mimics manual debugging by having the LLM review the interaction log sequentially, making a judgment at each step until the error is found. It is more precise at locating the error step but incurs higher costs and risks accumulating errors.
Binary Search: A compromise between the first two methods, this strategy repeatedly divides the log in half, using the LLM to determine which segment contains the error. It then recursively searches the identified segment, offering a balance of cost and performance.
 
Experimental Results and Key Findings

Experiments were conducted in two settings: one where the LLM knows the ground truth answer to the problem the Multi-Agent system is trying to solve (With Ground Truth) and one where it does not (Without Ground Truth). The primary model used was GPT-4o, though other models were also tested. The systematic evaluation of these methods on the Who&When dataset yielded several important insights:

• A Long Way to Go: Current methods are far from perfect. Even the best-performing single method achieved an accuracy of only about 53.5% in identifying the responsible agent and a mere 14.2% in pinpointing the exact error step. Some methods performed even worse than random guessing, underscoring the difficulty of the task.
• No “All-in-One” Solution: Different methods excel at different aspects of the problem. The All-at-Once method is better at identifying “Who,” while the Step-by-Step method is more effective at determining “When.” The Binary Search method provides a middle-ground performance.

• Hybrid Approaches Show Promise but at a High Cost: The researchers found that combining different methods, such as using the All-at-Once approach to identify a potential agent and then applying the Step-by-Step method to find the error, can improve overall performance. However, this comes with a significant increase in computational cost.

• State-of-the-Art Models Struggle: Surprisingly, even the most advanced reasoning m…

  Konten dipersingkat otomatis.

  🔗 Sumber: syncedreview.com

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