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…

  Konten dipersingkat otomatis.

  🔗 Sumber: syncedreview.com

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  📌 MAROKO133 Hot ai: NVIDIA cleared to sell advanced AI chips to China under Trump’

  The U.S. is easing restrictions on advanced AI chip exports to China, opening a path for NVIDIA and Advanced Micro Devices to seek approval to sell high-performance processors under revised federal rules.

  Under new regulations released Tuesday by the U.S. Commerce Department, chipmakers can apply to ship certain advanced computing processors to Chinese customers on a case-by-case basis.

  The move marks a clear departure from Washington’s earlier stance, which presumed rejection of nearly all such requests.

  The updated rule places NVIDIA’s H200 and AMD’s MI325X under a new licensing framework overseen by the Commerce Department’s Bureau of Industry and Security.

  Instead of blanket denials, applications will now be reviewed individually, provided companies meet a series of strict conditions.

  One key requirement is that exporters must demonstrate there is no shortage of the processors in the U.S. and certify that shipments to China will not divert manufacturing capacity away from domestic customers.

  The revised policy also applies to exports bound for Macau.

  Eligibility is tightly defined by performance thresholds, with only chips operating below specific limits qualifying for case-by-case review. These include processors with a total processing performance below 21,000 and total DRAM bandwidth under 6,500 gigabytes per second.

  The Commerce Department made clear that the easing does not apply to military or sensitive uses.

  Export approvals will be denied if the chips are intended for military, military-intelligence, nuclear, missile, or chemical and biological weapons applications, or if restricted entities are involved.

  Any transaction linked to prohibited end users remains blocked under existing export control rules.

  Together, the conditions are intended to prevent advanced U.S. AI hardware from strengthening China’s defense or intelligence capabilities, even as commercial access is partially restored.

  Export controls loosen carefully

  The revised framework also limits the scale of AI chip shipments to China.

  Companies will be allowed to export not more than 50 percent of the total volume of eligible processors shipped to customers in the U.S. market, ensuring domestic supply remains protected.

  Exporters must also implement rigorous Know Your Customer procedures to prevent unauthorized use or remote access to the technology.

  In addition, all approved chips will be required to undergo independent, third-party testing within the U.S. before they can be shipped.

  The regulation follows President Donald Trump’s decision last month to allow U.S. chipmakers to resume limited sales of advanced AI processors to China.

  It marks a significant shift from export controls introduced in 2022, which aimed to block Beijing from accessing the most powerful US semiconductor technologies.

  China access resumes cautiously

  If approved, NVIDIA’s H200 would become the most advanced AI chip legally exported to Chinese customers. Introduced more than two years ago, the processor sits below NVIDIA’s newer Blackwell generation, which remains restricted to the U.S. and allied markets.

  NVIDIA is preparing to transition to an even faster chip family named after astronomer Vera Rubin. Those future processors are expected to remain off-limits to China under existing export control rules.

  While the revised policy does not guarantee approvals, it lowers the barrier for U.S. companies seeking licenses to sell AI hardware into China’s massive technology market. Each application will be reviewed individually, giving regulators greater flexibility while retaining tight oversight.

  For U.S. chipmakers, the change opens a limited commercial channel back into China. For Washington, it reflects a careful recalibration of export controls aimed at balancing economic interests with national security concerns.

  🔗 Sumber: interestingengineering.com

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