📌 TOPINDIATOURS Update ai: Researchers from PSU and Duke introduce “Multi-Agent Sy
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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
📌 TOPINDIATOURS Update ai: Jack White Rages After Congressman Shares AI Deepfake o
Since the tragic murder of vaunted filmmaker and activist Rob Reiner, the culture warriors have been feasting. Shortly after the director’s death, president Donald Trump himself took to social media to blast him as a “tortured and struggling artist,” adding that Reiner was a “deranged person as far as Trump is concerned.”
Trump’s incendiary comments were condemned by a broad base of celebrities and government officials like, in a backlash ranging from Whoopi Goldberg to Marjorie Taylor Greene to Joe Rogan. Also getting on the action was Jack White, Detroit-born indie rock musician made famous for his role in the White Stripes.
At the time, White slammed Trump on Instagram over the comments, calling the president a “disgusting, vile, egomaniac, loser, [and] child.” As musicians go, White has a bit of a reputation for jumping into public feuds, but even he probably couldn’t have imagined what would happen next.
Two weeks after White jumped in to defend Reiner’s legacy — an eternity in the fast-moving culture wars — Tennessee congressman Tim Burchett threw himself into the mosh pit. Reposting what was clearly an AI-generated video of White calling potential fans who are also Trump supporters “fascists,” Burchett wrote on X-formerly-Twitter that “that cute little girl from the Addams Family got really ugly and angry.”
In response, White came back at Burchett, calling him one of Trump’s “lackeys and bootlicks.”
“Can you believe that a US congressman, that’s right, a CONGRESSMAN (from my state no less), a once hallowed and respected position in our society, would repost an AI generated video, containing a false comment that I never said and refuted (without researching that I might add) and like a 10 year old on a playground, add to it attempted insults to my physical appearance?” White roared on Instagram. “What kind of joke are we all living in now?”
“It’s really sad how embarrassing our leadership has become, I so wish the average American conservative could have a conversation with any intelligent people in other countries around the world, just for one brief moment, and actually see just what a joke our government (and by proxy our country) has become,” the musician continued. “All down to giving power and a soapbox to low class playground bullies the likes of trump and Congressman burchett.”
When a user on X pointed out that Burchett had fallen for an AI deepfake, the congressman attempted to brush it off, responding “you mean it’s not the girl from the Addams family?”
More on celebrities: Grok Is Making Wildly Contradictory Claims About Rob Reiner’s Death
The post Jack White Rages After Congressman Shares AI Deepfake of Him Calling Fans “Fascists” appeared first on Futurism.
🔗 Sumber: futurism.com
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