MAROKO133 Update ai: Which Agent Causes Task Failures and When?Researchers from PSU and Du

📌 MAROKO133 Update ai: Which Agent Causes Task Failures and When?Researchers from

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 1.5M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. Contact us: [email protected]

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…

Konten dipersingkat otomatis.

🔗 Sumber: syncedreview.com

📌 MAROKO133 Update ai: How Formula 1 tech turned CV90 into one of world’s fastest

Formula One isn’t just for racetracks anymore. BAE Systems has transplanted the same active-damping suspension technology that made 1990s F1 cars faster and more agile into its CV90 infantry fighting vehicle (IFV), enabling the 35-ton armored platform to race over rough terrain up to 40% faster, cut crew fatigue, and sharpen its firepower on the battlefield.

The technology senses vehicle speed and terrain and automatically adjusts the suspension to keep the vehicle level.

On rough ground, the CV90 allows travel 30% to 40% faster than existing main battle tanks, while reducing pitch acceleration by about 40%, according to BAE.

World’s fastest tank off-road

“Adapting the active damping system for the first time from a lightweight car to a heavy tracked vehicle, such as CV90, was a unique challenge for us,” BAE Systems said in a statement. 

“This advanced technology will deliver results to our customers in terms of vehicle performance and savings on through-life costs, as well as providing real benefits to the front-line soldier.”

The company said the smoother ride reduces wear on subsystems, extends component life, and cuts through-life repair costs. 

It also lessens crew fatigue and improves gunner accuracy by minimizing vertical movement.

Built by BAE Systems Land Systems Hägglunds in Sweden, the CV90 is one of the largest families of armored combat vehicles in service, operated by Norway, Finland, Denmark, and other European militaries. 

BAE says more than 1,300 CV90s have been sold to seven European nations, four of them NATO members. The vehicle has been combat-tested in Afghanistan and Liberia and is available in 15 variants.

Despite the added technology, the CV90 retains high mobility. It is powered by a fuel-efficient, high-torque V8 diesel engine, giving it a road range of up to 900 kilometers in new variants. 

The active damping system, the company said, increases off-road speed while boosting gunners’ hit probability and extending the life of subsystems.

F1 suspension

BAE touts the CV90’s survivability features as among the most advanced in the world. 

The platform offers modular protection against improvised explosive devices, anti-tank mines, shaped-charge warheads such as rocket-propelled grenades, and chemical, biological, radiological, and nuclear threats through its integrated CBRN/HVAC system. 

Additional options include a Defensive Aid Suite that detects and classifies threats, provides maneuver instructions, and deploys countermeasures and “ADAPTIV” multispectral camouflage to help the vehicle blend into its surroundings or mimic other objects.

The CV90 can be equipped with various weapons, typically a two-man turret with a 25- to 35-millimeter Bushmaster cannon. 

It supports manned and unmanned turrets, missile integration, and air-burst munitions through an advanced fire-control system to shorten the sensor-to-shooter cycle. 

A “hunter-killer” feature allows the commander to independently search for and hand off targets to the gunner.

Fully digitized and compliant with NATO’s General Vehicle Architecture standards, the CV90 integrates intelligence, surveillance, target acquisition, and reconnaissance technologies to support network-enabled operations. 

Optional upgrades such as BattleView 360 provide augmented-reality displays for 360-degree situational awareness.

BAE says these combined technologies position the CV90 as a next-generation armored fighting vehicle capable of sustained, full-spectrum operations with a reduced logistics footprint.

🔗 Sumber: interestingengineering.com