MAROKO133 Update ai: World’s longest expressway tunnel opens in China at an altitude of 9,

📌 MAROKO133 Eksklusif ai: World’s longest expressway tunnel opens in China at an a

The Tianshan Shengli Tunnel, a 13.75-mile (22 km) long tunnel at the center of the Urumqi-Yuli Expressway, and the world’s longest, is now open in China.

Constructed over five years using innovative approaches, the tunnel halves the travel time between the regional capital, Urumqi, and the city of Korla to 3.5 hours, state media reported. 

In the past few decades, China has undertaken some of the largest infrastructure projects in the world. From the 22.5 gigawatt hydroelectric power plant at the Three Gorges Dam to the Pinglu Canal, which provides an inland-to-sea connection for large ships, China is making huge leaps in the ambition of its projects. 

Interesting Engineering has previously reported how the Asian giant is also using its deserts to field large-scale solar power plants, and the Belt and Road Initiative (BRI) looks to connect the nation with Africa and Europe through a network of new railways, ports, and roads. 

The Tianshan Shengli Tunnel might seem like a minor project compared to these mega projects. However, it is still a significant achievement for the local region and set world records during construction. 

Where is the tunnel? 

The tunnel is located in the Xinjiang Uyghur Autonomous Region and facilitates a drive through the Tianshan Mountains. The region shares borders with eight countries, including Kazakhstan, Kyrgyzstan, Tajikistan, and Pakistan, facilitating connections to Central Asia. 

Before the expressway, travelling between Urumqi and Koral would take nearly seven hours. But now the travelling time will be reduced to 3.5 hours.

Additionally, it connects the region to various economic corridors across the country, aligning it with the national ‘dual circulation’ strategy, which seeks to integrate domestic and foreign trade. 

Work on the tunnel began in April 2020, but engineers faced challenges with terrain and environmental conditions during construction. 

Innovations in its construction

The tunnel runs through the Tianshan Mountains at an altitude of 9,842 feet (~ 3,000 meters) above sea level. Temperatures at these altitudes reach a bone-chilling minus 43.6°F ( minus 42 °C).

Attempting to construct an expressway using conventional methods would have taken Chinese engineers at least a decade to complete, the chief engineer of the Xinjiang Transport Investment and Development arm told local media. 

So, the engineers used a novel “three tunnels plus four shafts” strategy during the construction. Instead of trying to build a long, deep main tunnel, the engineers dug three tunnels: the main one and two parallel ones. 

The parallel tunnels facilitated geological investigation ahead of the main bore and provided workers with access to the site and equipment. In emergencies, the third tunnel could be used to house ventilation systems and serve as an emergency escape route. 

The four shafts were vertical passages dug from the surface to the tunnel depth. Shafts nearly 2,300 feet (700 m) deep were dug and served as additional entry and exit points, allowing work to proceed in parallel rather than at just two ends.

In addition to being the world’s longest expressway tunnel, the construction also holds the world record for the world’s deepest vertical shaft for a highway tunnel.  

Reduced travel between the two regions will facilitate the flow of energy and manufactured goods to the north and agricultural goods to the south, the South China Morning Post reported. 

🔗 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