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

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

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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: Novel Pulled From Shelves After Author Is Accused of Using

A prominent publisher is pulling a horror novel after the author was widely accused of using AI to help write the book, The New York Times reports.

Hachette Book Group, one of the largest publishing houses in the US, said it will discontinue selling “Shy Girl” by Mia Ballard in the UK, where it was released last fall and sold 1,800 print copies, per data cited in the reporting. It will also cancel plans to publish a US edition, which was slated to release this spring through its Orbit imprint. 

“Hachette remains committed to protecting original creative expression and storytelling,” a Hachette spokeswoman said.

The spokesperson noted that Hachette requires all submissions to be original to the authors and that the authors disclose whether AI is used during the writing process, suggesting it views alleged AI usage not merely as an affront to creative principles, but as a contractual violation. (Several publishers have released books whose marketing explicitly mentioned experimenting with AI.)

In a statement to the Wall Street Journal, the Hachette spokeswoman said that both its US and UK imprints conducted a “lengthy investigation in recent weeks” before the decision was made not to publish. 

Ballard, the author, denies personally using AI, claiming that an editor she hired to go over the book when she originally self-published it was responsible for using AI instead.

“This controversy has changed my life in many ways and my mental health is at an all time low and my name is ruined for something I didn’t even personally do,” Ballard wrote in a statement to the NYT.

Ballard said she couldn’t provide more details on how the book was edited with AI because she was pursuing legal action.

The novel’s cancellation is illustrative of a mounting anti-AI sentiment that has especially taken a hold in the arts, and is one of the first examples of a major publisher dropping a book deal over accusations of AI usage.  

Accusations of AI usage have swirled around “Shy Girl” since it became a minor hit in the self-published world last year, picking up steam among readers on TikTok. 

Initial glowing praise soon gave way to suspicion. In January, a Reddit post from a user who claimed to be a book editor generated significant discussion around “Shy Girl,” flagging its prose for having the hallmarks of a large language model. (Some in the discussion also accused Ballard of using AI to write her responses in a written interview.)

In a YouTube video essay that’s since racked up over 1.2 million views, a reviewer thoroughly unpacked, over the course of nearly three hours, how the book appeared to be AI written. “i’m pretty sure this book is ai slop,” read the video title. Max Spero, the founder and CEO of the AI detection software Pangram, conducted his own test and found evidence that 78 percent of the book is AI-generated.

While both the publisher and readers both seem to feel they have a strong case that the book is AI written, the incident raises tough questions about how the industry will handle AI’s rapid but sneaky invasion into books. The self-publishing world, which major publishers are increasingly turning to find social-media-ready diamonds in the rough, is already rife with low effort, AI-generated dreck.

Given the tech’s popularity, it’s inevitable that other authors will use AI in their writing process, too. The question becomes whether it’s sustainable for a publisher to respond in dramatic fashion each time evidence of AI usage pops up as it’s done here. Will AI be off the table entirely for a major book deal, or will authors merely be encouraged to be upfront about using ChatGPT? We’re years into the AI boom, but are still just beginning to grapple with these problems.

More on AI: Encyclopedia Britannica Hits OpenAI With Scary Lawsuit

The post Novel Pulled From Shelves After Author Is Accused of Using AI appeared first on Futurism.

🔗 Sumber: futurism.com