MAROKO133 Breaking ai: Canada’s attack submarines could feel like ‘five-star hotels’ as So

📌 MAROKO133 Breaking ai: Canada’s attack submarines could feel like ‘five-star hot

South Korea has pitched a dozen ‘five-star hotel’ attack submarines to Canada, in a bold bid to secure a multi-billion-dollar contract that could reshape the nation’s defense industry and help it break into NATO markets.

The East Asian country sent a high-level delegation to Canada, led by the presidential chief of staff Kang Hoon-sik. The group is lobbying for a program to replace the Royal Canadian Navy’s aging submarine fleet, which dates back to the 1990s.

The project is expected to cover about 12 diesel-powered attack submarines and is estimated by industry sources to be worth more than USD 12 billion. It brings together government officials and key industrial players, including Hanwha, HD Hyundai, and Hyundai Motor Group.

“They are designed and built with the mindset that my own sons and daughters would be aboard,” Kang revealed in a Facebook post on January 29. “That is why we aim to create them like a ‘five-star hotel.’”

Luxurious attack subs

The proposal stresses Seoul’s ambition to deepen Western defense partnerships. Kang stated he delivered a personal letter from South Korea’s President Lee Jae Myung to Canadian Prime Minister Mark Carney and met with senior officials to discuss the submarine projects and future defense-industrial cooperation.

“They are designed and built with the mindset that my own sons and daughters would be aboard,” Kang noted, commenting on the submarines. “If we imagine our daughters and sons aboard, it is only natural that even in emergencies, they should not be injured and have spaces to rest comfortably.”

Kang cited positive reactions from Canadian officials, noting that Defense Minister David McGuinty voiced no safety concerns after inspecting a submarine under construction at Hanwha Shipyard last year. “At that moment, I wanted to take it back to Canada immediately,” the chief of staff added.

He said Canada sees the submarine procurement project as a pivotal opportunity to reshape its industrial and security policies. “All high-level officials consistently emphasized that this is not merely about purchasing new weapons,” Kang stated.

Canadian representatives said that the procurement is about long-term industrial cooperation, domestic job creation, as well as strengthening Arctic and undersea surveillance capabilities along the world’s longest coastline.

Targeting Western markets

The proposal pits South Korea against Germany’s Thyssenkrupp Marine Systems (TKMS). The company, one of the world’s leading providers of integrated system solutions in maritime defense tech, offered its own investment-heavy bid backed by European partners.

The Korean delegation has framed the proposal as part of a broader economic partnership. South Korean and Canadian companies have already signed six cooperation agreements spanning steel, artificial intelligence (AI), rare earths, satellites and sensors.

Five of them involve Hanwha affiliates. Hanwha, a global leader in security and surveillance systems has publicly stated that it aims to create a significant number of jobs in Canada by 2040 through cross-sector collaboration, including naval platforms.

“This project will also be a leap forward for South Korea’s defense industry,” Kang said. “If successful, it will mark the largest-ever entry into Western markets and is expected to pave the way for full-scale entry into NATO markets.”

As per Reuters, the submarine project would rank among South Korea’s largest defense procurements. It would generate more than USD 27.6 billion (KRW 40 trillion in economic benefits and create about 20,000 jobs.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Eksklusif ai: Which Agent Causes Task Failures and When?Researchers fr

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Meet the authors
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 failure-responsible agent and the decisive error step that led to the task’s failure.
2. 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 models, like OpenAI o1 and DeepSeek R1, find this task challenging.- This h…

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🔗 Sumber: syncedreview.com