MAROKO133 Hot ai: Researchers from PSU and Duke introduce “Multi-Agent Systems Automated F

📌 MAROKO133 Hot ai: Researchers from PSU and Duke introduce “Multi-Agent Systems A

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 2M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. 

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

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  📌 MAROKO133 Eksklusif ai: US: New AI to help scientists ‘read lips’ of fusion reac

  An international team of scientists has developed an artificial intelligence capable of creating highly detailed data inside a fusion reactor, effectively acting as a virtual sensor. 

  The AI, called Diag2Diag, is designed to improve the monitoring and control of the plasma fuel in fusion devices, a step that could lead to more reliable and economical fusion power plants.

  The technology functions by analyzing the measurements from existing sensors to generate new, synthetic data for another sensor that may be failing or too slow to capture key events.

  Using visual information

  Azarakhsh Jalalvand of Princeton University, the paper’s lead author, compares the concept to an AI watching a silent movie and generating a full soundtrack purely from the visual information. 

  “We have found a way to take the data from a bunch of sensors in a system and generate a synthetic version of the data for a different kind of sensor in that system,” Jalalvand said.

  This AI has already provided new physical insights. It supplied key evidence supporting a prominent theory on how to suppress edge-localized modes (ELMs), which are intense energy bursts that can damage reactor components. 

  The theory suggests that applying small magnetic fields creates “magnetic islands” that stabilize the plasma by flattening its temperature and density. Physical sensors could not fully observe this effect.

  “Diag2Diag provided much more details on how this happens and how it evolves,” said PPPL Principal Research Scientist Qiming Hu, who worked on the project. 

  The AI’s data clearly showed the simultaneous flattening of both properties, offering strong validation for the control method.

  Critical for commercial fusion power

  Such detailed monitoring is critical for developing commercial fusion power. While current experimental reactors can be shut down if a sensor fails, future power plants must operate continuously. 

  “If we are thinking about fusion as a source of energy, it needs to work 24/7, without interruption,” Jalalvand noted.

  The technology also offers significant economic and design benefits. By reducing the need for numerous physical sensors, future fusion reactors could become more compact, simpler, and less expensive to build and maintain. 

  “Diag2Diag is kind of giving your diagnostics a boost without spending hardware money,” concluded Egemen Kolemen, the project’s principal investigator.

  The team suggests the AI could also be applied to other fields, such as enhancing data from sensors on spacecraft or in robotic surgery.

  Using AI to enhance fusion tech

  The use of AI in improving nuclear fusion tech has increased exponentially across the world. Recently, researchers in China created two new AI systems to improve the safety and efficiency of fusion energy experiments by predicting plasma failures with extreme accuracy. 

  In tests, the system reached a 94% success rate in early detection, issuing warnings about 137 milliseconds before disruptions occur – giving operators valuable time to act.

  In another development, scientists at the Lawrence Livermore National Laboratory (LLNL) used an AI model to predict the outcome of a nuclear fusion experiment with an impressive 74% accuracy.

  Besides, a team of researchers from Commonwealth Fusion Systems (CFS), the US Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory has developed a new AI-powered method, called HEAT-ML, that can protect fusion reactors from the extreme heat generated by plasma.

  🔗 Sumber: interestingengineering.com

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