📌 TOPINDIATOURS Hot ai: Researchers from PSU and Duke introduce “Multi-Agent Syste

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 2M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. 

Meet the author
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

2. failure-responsible agent and the decisive error step that led to the task’s failure.

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 m…

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  🔗 Sumber: syncedreview.com

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  📌 TOPINDIATOURS Hot ai: Researchers Just Discovered Something Extremely Unflatteri

  What makes a conspiracy theorist? Is it poor education, an overactive imagination, a tinfoil hat? According to one recent study, it isn’t any of that stuff, but instead something psychologically revealing: a deep insecurity about the world they inhabit.

  The study, published in the journal Applied Cognitive Psychology, examined 14 variables in demographics, ideology, and personality for links to conspiratorial thinking. Some 253 adults were recruited to participate in the study, primarily from the UK, US, Canada, and South Africa, with an average age of 49 years old.

  Specifically, the researchers were interested in exploring factors that might influence whether a person has “beliefs about cover-ups.” This is the blanket idea that powerful organizations or collectives are concealing the truth from the entire world, which only the conspiracy-minded can see through.

  “I have long been interested in conspiracy theories, having published around 20 papers on the topic over the past decade or so,” the study’s lead author and professor of psychology at the Norwegian Business School, Adrian Furnham, told PsyPost. “Few, if any, researchers have taken into account the ‘cover up’ perspective of conspiracy believers.”

  To consistently measure conspiratorial thinking, the researchers created a 10-point scale, corresponding to responses to statements such as “politicians usually do not tell us the true motives for their decisions” and “government agencies closely monitor all citizens.” Participants also took a personality assessment called the High Potential Trait Indicator (HTPI), which measures six traits including competitiveness and tolerance of ambiguity.

  When all was said and done, one of the main factors researchers found a strong correlation between endorsement of conspiracy theories and an intriguing characteristic: a low tolerance of ambiguity.

  In other words, people who feel insecure or uncomfortable when they don’t know all the answers, or who can’t grasp that some situations are complex, multidimensional, and puzzling even to experts. Presented with a complicated set of issues or events, these folks were more likely to sign on to a hare-brained conspiracy theory that offered an easy answer, even if it was wrong, or an oversimplification.

  There was also a significant correlation between those who believe that the world is fundamentally unfair — from a “human nature” point of view — and those who subscribe to far-fetched theories. Those who believe in an “unjust world,” the researchers found, were also more inclined to believe in shadowy groups pulling the strings.

  Contrary to what many may think, the researchers found no correlation between a person’s level of education and their capacity to believe in absurd conspiracies. Basically, this suggests that intelligence isn’t a factor determining whether a person falls into a conspiracy rabbithole, which paints a far different picture of conspiracy theorists than the one we typically see.

  Going forward, more research with a much larger sample size is needed to expand the findings. Still, it’s a fascinating glimpse at the factors that could shape conspiratorial thinking — concrete evidence that when faced with uncertainty, some of us really do prefer simple lies to complicated truths.

  More on conspiracies: A Strange Conspiracy Theory Is Reportedly Spreading Inside OpenAI
  

  The post Researchers Just Discovered Something Extremely Unflattering About People Who Believe Conspiracy Theories appeared first on Futurism.

  🔗 Sumber: futurism.com

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