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

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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

📌 TOPINDIATOURS Hot ai: Engineers develop AI-powered wearable that turns everyday

A new wearable system can read your gestures so accurately that you can control a robot while sprinting, bouncing in a car, or drifting through choppy ocean waves.

And for the first time, the motion noise that usually ruins these signals no longer matters.

Engineers have built a next-generation human–machine interface that works in real-world conditions. The breakthrough brings gesture-based control closer to everyday use, from medical rehab to underwater robotics.

Developed at the University of California San Diego, the system pairs soft, stretchable sensors with a deep-learning engine that cleans noisy data in real time, yielding a reliable interface that interprets natural arm gestures under nearly any disturbance.

Wearable gesture sensors typically fail when the user moves too much.

“Wearable technologies with gesture sensors work fine when a user is sitting still, but the signals start to fall apart under excessive motion noise,” said co-first author Xiangjun Chen. “Our system overcomes this limitation.”

Kills motion noise

This technology could transform how people interact with machines in high-motion or unpredictable environments.

Patients with limited mobility could use simple gestures to control robotic aids without precise finger movement.

Industrial workers and first responders could operate tools or robots hands-free in hazardous settings. Even divers or remote operators might command underwater robots despite turbulent currents.

Consumer gadgets could also benefit, enabling gesture controls that stay reliable during everyday motion, including walking, riding in a car, or exercising.

The project is the result of collaboration between the labs of professors Sheng Xu and Joseph Wang at UC San Diego.

 According to the researchers, this is the first wearable human-machine interface that consistently performs across such a broad range of motion disturbances.

The soft electronic patch, glued onto a cloth armband, integrates motion sensors, muscle sensors, a Bluetooth microcontroller, and a stretchable battery into a thin, multilayered package.

It collects signals from the arm and feeds them to a specialized deep-learning model that strips out interference and identifies the intended gesture.

“This advancement brings us closer to intuitive and robust human-machine interfaces that can be deployed in daily life,” Chen said.

Built for real life

The team stress-tested the system in extreme conditions. Participants used it to control a robotic arm while running, while exposed to high-frequency vibrations, and under combinations of disruptive motions.

To push the limits further, the researchers validated it in simulated ocean scenarios inside the Scripps Ocean-Atmosphere Research Simulator.

The tank recreated both lab-generated and real sea motion, and the wearable still delivered accurate, low-latency performance.

The original inspiration came from military divers who need ways to control underwater robots. But the researchers quickly realized the challenge was universal.

Motion interference plagues nearly all wearable technology, limiting accuracy in real life.

“This work establishes a new method for noise tolerance in wearable sensors,” Chen said. “It paves the way for next-generation wearable systems that are not only stretchable and wireless, but also capable of learning from complex environments and individual users.”

The study appears in the journal Nature Sensors.

🔗 Sumber: interestingengineering.com