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

📌 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

📌 MAROKO133 Eksklusif ai: Pope Slams Elon Musk for Obscene Greed Wajib Baca

In the months following the 2025 conclave that elected him, Pope Leo XIV warned that the astronomical and still widening gap between the rich and the poor means that “we’re in big trouble.”

In remarks to Catholic newspaper Crux in July, which were published over the weekend, the Pope reflected on the “continuously wider gap between the income levels of the working class and the money that the wealthiest receive.”

“Yesterday [there was] the news that Elon Musk is going to be the first trillionaire in the world,” he told the newspaper.

The Pope was seemingly referring to estimates that the mercurial CEO’s net worth could reach 13 figures in the coming years.

“What does that mean, and what’s that about?” the Pope asked. “If that is the only thing that has any value any more, then we are in big trouble.”

His comments evoke a similar progressive stance to his predecessor, Pope Francis, who condemned capitalism and the “idolatry of money” months after being named the Pope in 2013.

In retrospect, Pope Leo’s July remarks were prescient. Weeks after he sat down with Crux, the board of Musk’s EV maker, Tesla, proposed a new pay package — potentially worth around $1 trillion, depending on the company’s future performance — which could boost the richest man in the world’s wealth to unprecedented heights.

The Pope’s comments touch on an alarming trend as global inequality continues to climb. It’s an especially troubling situation in the United States, which has seen its income gap grow for more than 30 years now. The richest 0.01 percent of Americans are enjoying massively accelerated income growth, with the top 12,000 households growing almost 27 times as fast as the income of the bottom 20 percent of earners.

The tech industry, in particular, has made high-ranking executives obscenely wealthy in a relatively short period of time. It’s a trend that was kicked into overdrive as the hype surrounding artificial intelligence keeps growing, minting an entirely new class of billionaires at a record pace.

And the latest head of the Catholic church seems deeply concerned.

“CEOs that 60 years ago might have been making four to six times what the workers are receiving, the last figure I saw, it’s 600 times what average workers are receiving,” the sovereign of the Vatican told Crux.

For now, the 70-year-old Chicago-born is still adjusting to his new life as Pope.

“I’ve followed current affairs for many, many years,” he added. “I’ve always tried to stay up on the news, but the role of Pope is certainly new to me. I’m learning a lot and feeling very challenged, but not overwhelmed.”

More on the Pope: The New Pope Is Deeply Skeptical of AI

The post Pope Slams Elon Musk for Obscene Greed appeared first on Futurism.

🔗 Sumber: futurism.com