MAROKO133 Eksklusif ai: Researchers from PSU and Duke introduce “Multi-Agent Systems Autom

📌 MAROKO133 Eksklusif ai: Researchers from PSU and Duke introduce “Multi-Agent Sys

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

  ---

  📌 MAROKO133 Update ai: Quantum cat qubits stay stable for over an hour, smashing p

  Quantum computing just got a stability upgrade.

  French quantum computing startup Alice & Bob has achieved a major milestone in qubit stability, potentially reshaping the path toward scalable quantum machines.

  The company on Thursday announced that its cat qubits can resist one of the main errors in quantum computers, the bit-flip, for more than one hour. This is a remarkable advance over the previous record of 430 seconds (about seven minutes) set in 2024 on Alice & Bob’s Boson 4 chip.

  Quantum computers are extremely sensitive to errors, and bit-flips are one of the two main types that can disrupt calculations.

  By extending the bit-flip lifetime to over an hour, Alice & Bob have effectively removed a significant roadblock in building practical fault-tolerant machines.

  The breakthrough was achieved on Alice & Bob’s latest qubit design, the Galvanic Cat, which also powers their 12-cat qubit chip Helium 2.

  Cat qubits defy decay

  “Being able to push the stability of our cat qubits year after year makes us confident that we will deliver on our roadmap,” said Raphael Lescanne, CTO and Co-Founder of Alice & Bob.

  “Bit-flip lifetimes are not the only metric that matters, but for cat qubits, they are foundational. The road is still long, but we are advancing fast.”

  The team’s improvements spanned software optimizations, experimental techniques, and advanced engineering, enabling bit-flip times far beyond previous levels.

  Interestingly, the lifetimes achieved now surpass typical timescales for cosmic ray impacts, suggesting some level of insensitivity by cat qubits to such events.

  At a mean photon number of 11, the researchers measured bit-flip times ranging between 33 and 60 minutes at a 95 percent confidence interval.

  They were also able to run a Z gate operation on the cat qubit with 94.2 percent fidelity in 26.5 ns, a critical step for error correction.

  Hardware needs shrink dramatically

  By virtually eliminating one of the two main error types, Alice & Bob’s cat qubits allow for more efficient error-correcting codes that require far fewer physical qubits.

  If the bit-flip protection holds during gate operations, the hardware needed for large-scale quantum computers could be reduced by up to 200 times.

  “This is a major step toward early fault-tolerant quantum computing with 100 logical qubits, which is our 2030 roadmap target,” the company said.

  The target requires a 13-minute bit-flip lifetime during two-qubit gate operations, which the new results exceed by a significant margin.

  Superconducting qubits from other companies typically achieve bit-flip times of just 25 milliseconds, making Alice & Bob’s record more than a million times longer.

  This massive leap in stability could make quantum devices more reliable and practical for materials science, cryptography, and other applications.

  The company now plans to further evaluate the performance of these cat qubits under two-qubit gates, which is a critical requirement for building functional quantum computers.

  🔗 Sumber: interestingengineering.com

  ---