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 Hot ai: Mind over machine: Neuralink’s breakthrough lets patient contr

Neuralink has claimed to have achieved a new milestone in brain-computer interface technology, with an amyotrophic lateral sclerosis (ALS) patient successfully controlling a robotic arm using only his thoughts.

In a recent demonstration shared online, Nick Wray used the arm to pick up a cup and drink from it, marking a significant step toward restoring autonomy for people with severe mobility impairments.

The breakthrough is part of Neuralink’s Food and Drug Administration (FDA) – approved “CONVOY” study, which tests how implanted brain chips enable patients to perform daily tasks independently. Wray is the eighth participant to receive the Neuralink implant.

In August, Elon Musk’s Neuralink expanded to the UK with a clinical study testing its brain chip to help paralyzed patients control devices through thought.

Mind controls machine

During three eight-hour sessions, ALS patient Nick Wray used Neuralink’s brain implant to operate a robotic arm for everyday activities.

The chip, implanted in his brain, translates neural signals into Bluetooth commands, allowing him to control external devices through thought alone. Using the system, Wray successfully performed tasks such as picking up a cup, putting on a hat, microwaving food, and opening a refrigerator, reports PC Magazine.

In one demonstration, the robotic arm lifted a cup to his mouth so he could take a drink. He also achieved record-breaking results on dexterity tests used for stroke patients, moving 39 cylinders across a table in five minutes and flipping five puzzle-like pegs in another.

Wray reportedly even completed what he called a “ridiculous trick shot,” expected to appear in a future video. In a separate trial, he managed to maneuver his wheelchair using the brain-computer interface for the first time, reports The Dallas Express.

Brain chip progress

Neuralink launched its first human trials in the US in 2024, but only after clearing a major hurdle with the U.S. Food and Drug Administration (FDA). The company had to resolve significant safety concerns that had caused the FDA to reject the bid back in 2022. Now, eight severely paralyzed individuals have received the experimental brain implant, enabling them to interact with digital devices using only their thoughts.

Among these initial participants was Noland Arbaugh, an Arizona man who became the first to get the Neuralink chip. He quickly showcased the implant’s amazing ability to restore digital independence, demonstrating that he could control a cursor and play video games just by thinking.

However, Arbaugh’s device later suffered a major technical failure: nearly 85 percent of its fine threads detached from his brain tissue. Rather than recalling the device, Neuralink rapidly deployed a software solution. The company successfully maintained the implant’s effectiveness by updating its algorithms to sharpen signal interpretation.

The implanted device, known as the N1 chip, is roughly the size of a 10-pence coin. It operates through 128 ultra-fine threads—each thinner than a human hair—that connect about 1,000 electrodes directly to the brain’s surface. These electrodes detect and transmit neural activity, translating brain signals into precise digital commands, such as cursor movements, typing, or controlling external devices.

🔗 Sumber: interestingengineering.com