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

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

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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: Microbes may hitchhike across the solar system via astero

Microbes blasted off a planet by an asteroid strike may survive the journey to another world, including Earth, according to a new study from Johns Hopkins University.

Researchers tested whether a hardy bacterium could endure the crushing pressures generated when a space rock slams into a planet and ejects debris into space. The results suggest that life could survive the initial blast and potentially travel between planets embedded inside rock fragments.

The idea, known as lithopanspermia, proposes that life can spread through space via meteorites and planetary debris. Scientists already know that Martian meteorites have landed on Earth.

The question has been whether living organisms could endure the violent forces required to launch them off a planet in the first place.

To find out, the team recreated the intense shock pressures associated with an asteroid impact on Mars and measured whether microbes could survive.

Blasted off, still alive

The researchers chose Deinococcus radiodurans, a bacterium known for surviving extreme radiation, cold, dryness, and other harsh conditions.

It has a thick outer shell and strong DNA repair mechanisms, traits that could resemble hypothetical life on Mars.

“We do not yet know if there is life on Mars, but if there is, it is likely to have similar abilities,” said senior author K.T. Ramesh.

To simulate impact conditions, the team sandwiched the microbes between metal plates and fired a projectile at them using a gas gun. The projectile struck at speeds up to 300 mph, generating pressures between 1 and 3 gigapascals.

For comparison, pressure at the bottom of the Mariana Trench is about 0.1 gigapascals. Even the lowest pressure in the experiment exceeded that by more than tenfold.

The bacteria survived nearly all tests at 1.4 gigapascals and about 60 percent at 2.4 gigapascals. At lower pressures, the cells showed no visible damage.

At higher pressures, some membranes ruptured, and internal structures were affected, but many microbes remained viable.

“We expected it to be dead at that first pressure,” said lead author Lily Zhao. “We started shooting it faster and faster. We kept trying to kill it, but it was really hard to kill.”

Rethinking planetary protection

When large asteroids strike Mars, some debris can experience pressures near 5 gigapascals, although not all fragments are subjected to the same forces. The new findings suggest that at least some microbes could survive a significant portion of that range.

“We have shown that it is possible for life to survive large-scale impact and ejection,” Zhao said. “What that means is that life can potentially move between planets. Maybe we’re Martians!”

The results could have implications for planetary protection policies. Space agencies impose strict contamination controls when sending spacecraft to Mars and when returning samples to Earth.

However, Mars ejecta may also reach its moons, such as Phobos, under lower pressures than those required to escape to Earth.

“We might need to be very careful about which planets we visit,” Ramesh said.

The team plans to test whether repeated impacts could select for even hardier microbes and whether other organisms, including fungi, can survive similar shocks.

The study was published in the journal PNAS Nexus.

🔗 Sumber: interestingengineering.com