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

📌 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 Hot ai: Old Teslas Are Falling Apart Terbaru 2025

Want a used car that works? You’d be wise to not get an older Tesla.

In Consumer Reports’ latest ranking for used cars, the Elon Musk-run automaker came dead last in terms of reliability, trailing by over forty points from the top spot on a scale between 0 and 100.

CR’s tests focused on models that are between five to ten years old. Out of 26 tested brands, the two best brands, according to CR’s reliability verdict, are held by Lexus, with a score of 77, and Toyota, with a score of 73. Fellow Japanese automakers Mazda, Honda, and Acura round out the top five with scores between 58 and 53.

Things get dire when US carmakers enter the fray. All but one of the bottom ten brands in the list are American. The best of the worst was Chevrolet at a score of 40. But Tesla makes that failing grade seem respectable with its absolute rock bottom rating of 31, trailing Jeep by just one point.

The embarrassing ranking is a reflection of the automaker’s manufacturing woes and its reputation for questionable quality control. CR calculated the reliability rating based on a survey where users reported problems about their cars, which in all involved more than140,000 cars from the 2016 to 2021 model years. The problems are weighed based on their severity, CR said, and from there, the number of problems each car experiences is compared to the average number of problems for cars of that same model year.

For new cars, the reliability picture looked much different for Tesla: it jumped in CR’s ranking from 18th place to 9th, based on an analysis of the last three years of car models. Even this victory, though, came with an embarrassing wrinkle: the Cybertruck. According to CR, Tesla’s most reliable new car, the Model Y, boasted a stellar score of 81. But the infamously troubled pickup wasn’t even half that, with a rating of just 34. In other words, it’s less reliable than any other type of Tesla that’s been on the road for ten years already.

That’s hardly surprising. The Cybertruck, which Musk boasted would be “apocalypse-proof” and capable of stopping bullets, has already been recalled a staggering ten times in barely just two years of being sold. (It’s also not bulletproof.)

The issues it’s been recalled for are even more of a blemish. In March, Tesla recalled nearly every single Cybertruck ever sold at that point — which wasn’t a particularly huge number, to be fair — after it discovered that its trademark stainless steel body panels could fly off while driving because of the shoddy glue used to hold them in place. It also faced a recall for literally losing power while driving, and another for its accelerator pedal getting stuck in the down position.

CR isn’t the only automotive tester to find faults with the cars. The Cybertruck broke down in the middle of being trialed by reviewers at the car site and marketplace Edmunds, leaving them stranded in the parking lot.

More on Tesla: Driver Dies After Tesla Crashes and Bursts into Flames

The post Old Teslas Are Falling Apart appeared first on Futurism.

🔗 Sumber: futurism.com