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

📌 MAROKO133 Breaking ai: Which Agent Causes Task Failures and When?Researchers fro

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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 Update ai: New study unlocks mystery behind why some ancient people li

Why do people in Italy, specifically, live longer than most? Well, according to DNA samples, it’s their genetic ancestry.

A recent study reached into the prehistoric past to uncover why people currently 100 or older are still living.

Previous studies identified specific genes related to a longer life span. However, the recent study in GeroScience ventured deeper into the subject by investigating whether ancient populations factor into the complex equation, where genetics, environment, and lifestyle all play a role in how long we live.

The centenarians in Italy, as a country with the highest percentage of centenarians in the world, share a genetic link: they all descend from Western Hunter-Gatherers, the first inhabitants of Europe after the Ice Age.

So, after the harshest conditions, the most inopportune circumstances, these prehistoric Europeans evolved to survive to the point of impacting time by passing down these genes that are enabling modern-day Italians to live past 100 years old.

Europeans live longer

If you’ve ever gone through trials and tribulations, as they say, what doesn’t kill you might make you stronger — and everyone else down the line.

Researchers analyzed 333 Italian centenarians and compared their genetic composition to 103 ancient genomes, according to the study, related to past European ancestries, “…to investigate whether, and to what extent, ancient ancestral populations may have contributed to the genetic basis of human longevity. 

Fortunately, today, researchers explained that recent advancements in paleogenomics have permitted this cross-examination between modern genomes and ancient DNA, Archaeology Mag reports.

It might come as no surprise that the genes belonging to this group of centenarians varied. However, researchers did find a striking similarity between them. They had a stronger genetic link with Western Hunter-Gatherers, and that connection permeates the larger Italian population.

“In fact, for every small increase in hunter-gatherer DNA, a person’s odds of becoming a centenarian rose by 38 percent,” according to Phys. And the potent effect even favors women.

What doesn’t kill you makes you stronger

It had nothing to do with diet, it turns out, as one could joke about how Italians eat pasta and cheese and yet live until 100. Though lifestyle does factor into it, according to the researchers, it might just come down to the genes.

Furthermore, as they penetrated the genes into the individual chromosomes, researchers found that it isn’t even related to the demographic but rather the mechanisms that developed, in their evolutionary process, as these groups exited the Ice Age, and it changed their genetics.

These ancient groups had to evolve because they faced adversity. The human body had to strengthen its defense mechanisms and energy output, as per Archaeology Mag. And, as a result, they passed down these more robust genes to the future generations, perhaps inspiring, in considering that the toughest times that we might face could pass down a positive, rather than the trauma of the negative.”

It is all about perspective and the almost unbelievable human ability to withstand the worst and prolong life for thousands of years. That’s quite an effect that the Ice Age had.

Read the study in GeroScience.

🔗 Sumber: interestingengineering.com