MAROKO133 Hot ai: Which Agent Causes Task Failures and When?Researchers from PSU and Duke

📌 MAROKO133 Hot ai: Which Agent Causes Task Failures and When?Researchers from PSU

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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…

Konten dipersingkat otomatis.

🔗 Sumber: syncedreview.com

📌 MAROKO133 Hot ai: World’s largest offshore solar farm could meet power needs of

China has commissioned the world’s largest open-sea offshore solar power plant, bringing a 1-gigawatt (GW) photovoltaic (PV) installation fully online off the coast of Dongying in Shandong province.

Developed by Guohua Investment, a unit of China Energy Investment Corp (CHN Energy), a state-owned energy firm, the offshore solar farm achieved full-capacity grid connection in late December 2025.

Located nearly 4.9 miles (eight kilometers) from shore the HG14 installation spans about 1,223 hectares of shallow coastal waters, with depths ranging from 3.2 to 13.1 feet (one to four meters).

The farm is the first gigawatt-scale fixed-pile offshore PV project ever completed. It features a total of 2,934 platforms anchored by offshore steel truss structures.

Supplying the grid

According to CHN Energy, the Dongying project‘s fixed-pile design is engineered to withstand waves, tides, strong winds and seasonal sea ice. It is supported by a total of 11,736 steel steel piles.

“This not only withstands a strong gale of force 11 and winter ice conditions but also reduces steel usage by over 10 percent, providing valuable experience for future offshore solar farm construction,” Zhang Bo, Guohua Energy Investment deputy manager of the Kenli project, said.

Each of its platforms measures about 196 feet (60 meters) in length and 114 feet (35 meters) in width. Additionally, the project marks the nation’s first use of a 66-kilovolt (kV) offshore cable. It is combined with an onshore cable for high-capacity, long-distance PV power transmission.

The massive farm uses more than 2.3 million high-power 710-watt (W) n-type bifacial solar panels mounted at a 15-degree tilt. Its offshore location improves performance, with cooler air and reflected sunlight increasing power generation.

“The structure features a ‘four-pile foundation plus solar platform’ design, with panels tilted at a precisely calibrated 15 degrees,” Bo explained.

According to project data, electricity generation efficiency is five to 15 percent higher than comparable onshore solar plants. The 66-kV subsea cable network carries power generated offshore to land. An onshore substation then steps the power up to 220 kV.

Engineering at sea

HG14 also integrates a 100-megawatt (MW)/200-megawatt-hour (MWh) energy storage system. This improves grid stability and enables smoother power delivery.

The solar farm’s combined transmission and storage design increases effective capacity by around 20 percent. It also cuts unit costs by about 15 percent. Once fully operational, the farm will generate around 1.78 terawatt-hours (TWh) of power per year.

This output could meet the needs of approximately 2.67 million urban residents in China. “This is equivalent to saving an estimated 503,800 tons of standard coal and reducing carbon dioxide emissions by 1.3447 million tons,” the company revealed in a press release.

As per the firm, the project strengthens regional energy security and supports China’s low-carbon transition. “Additionally, the project utilizes an integrated fishing and PV development model, combining fish farming with PV power generation to enhance the comprehensive utilization of the marine area,” CHN Energy concluded.

🔗 Sumber: interestingengineering.com