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 Breaking ai: Days After Mass Bricking Event, Waymo Fleet Shuts Down Ag

Waymo has hit another bump in the road.

Just days after its robotaxi fleet crumbled into complete disarray because of a city power outage in San Francisco, the company was forced to suspend all rides again in the Bay Area on Thursday in anticipation of a nasty storm, CNBC reported.

Customers received word of the measure via the company’s ride-hailing app.

“Service temporarily paused due to National Weather Service flash flood warning,” a notification read.

While you can hardly fault Waymo for erring on the side of caution, the measure comes as its self-driving cabs’ ability to handle conditions that fall outside its typical programming is being called into question.

The San Francisco incident, for instance, left its robotaxis stranded and helpless on public roads. Footage showed the cabs piling up at intersections, where they appeared confused about what to do without traffic lights to guide them. Rather than taking initiative or moving themselves out of the way, the cabs stayed idling in the middle of the roads, blocking motorists and even other Waymos. One video showed at least five of the cabs stuck in the same intersection. Frightened animals have a fight-or-flight response; Waymos have a stand-dumbfoundedly-in-place response. 

Waymo has been offering fully autonomous rides to the public in San Francisco since 2024, with at least 800 robotaxis in the area. And though it boasts an impressive safety record, the cars have been a controversial presence with locals, whose complaints about the vehicles’ safety reached new heights last month when one of the cabs ran over and killed a beloved bodega cat.

Beyond tragedies like those, the cabs have been spotted more than occasionally driving down the wrong side of the road and committing blunders like getting stuck in a roundabout.

Other incidents like careening through an active police standoff and becoming paralyzed by a parade have exposed that the cars still struggle to handle the diversity of offbeat road scenarios one might be expected to encounter in a bustling city like San Francisco. Waymo can’t be blamed for the power outage last week, but that it seemingly didn’t equip its cars to handle a scenario like that is concerning.

“I think we need to be asking ‘what is a reasonable number of [autonomous vehicles] to have on city streets, by time of day, by geography and weather?’” former CEO of San Francisco’s Municipal Transit Authority, Jeffrey Tumlin told CNBC.

More on self-driving cabs: The Number of Robotaxis Tesla Is Actually Running Will Make You Snort Out of Your Nose With Pure Derision

The post Days After Mass Bricking Event, Waymo Fleet Shuts Down Again appeared first on Futurism.

🔗 Sumber: futurism.com