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

📌 MAROKO133 Breaking ai: Which Agent Causes Task Failures and When?Researchers fro

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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 Eksklusif ai: Elon Musk’s Boring Company marks first airport service w

The Boring Company has taken another step toward linking Las Vegas’s airport with its underground transit network.

The company has begun limited Vegas Loop service to Harry Reid International Airport, marking the first time Loop vehicles can legally access airport curbs.

Airport officials confirmed that regulators approved The Boring Company’s Automated Vehicle Identification permit last week.

That approval allows Loop vehicles to operate on airport property under controlled conditions.

For now, the service supports departures only. Vehicles drop passengers at the departures curb between 10 a.m. and 9 p.m. each day.

Airport staff will allow pick-ups only after every Loop vehicle receives an installed transponder.

Once that step is complete, passengers will board from Zero Level areas at Terminals 1 and 3. Those locations currently handle limousines and shuttle services.

Only departures for now

Vegas Loop’s ticketing site already lists airport trips. Prices sit near US$12 per ride.

Passengers can travel from Resorts World Las Vegas or Westgate to either Terminal 1 or Terminal 3.

Each trip combines tunnel travel with surface driving. The Loop does not yet run beneath the airport.

Vehicles must exit tunnels and finish the trip on city streets.

State regulators approved that setup earlier this year.

The Nevada Transportation Authority limited surface travel to four miles per trip. Each route must still include tunnel segments.

Even with those restrictions, airport access marks a notable expansion.

Until now, the Vegas Loop served convention areas and nearby resorts only.

Current Loop footprint

The Vegas Loop currently includes more than 10 miles of completed tunnels. About four miles operate with active passenger service.

Stations now operate at Encore, Resorts World, Westgate, and several locations across the Las Vegas Convention Center campus.

All sit within four miles of the airport, which helped enable early airport access.

The Boring Company says the current setup represents a transition phase rather than a finished product.

Bigger changes coming

Construction continues on the 2.25-mile Airport Connector twin tunnels. The company aims to open that segment in the first quarter of 2026.

Once complete, most airport trips will move underground. That shift should reduce surface traffic and improve travel times.

The Airport Connector will link into the University Center Loop under construction beneath Paradise Road.

Planned stops include areas near UNLV, Virgin Hotels Las Vegas, and the Sphere district. Plans also include a future apartment complex owned by The Boring Company.

At full build-out, the Vegas Loop aims to span 68 miles with 104 stations.

The system would connect the Strip, downtown Las Vegas, Chinatown, Allegiant Stadium, and the airport into one network.

The airport launch arrives just ahead of CES, which brings a surge of visitors each January.

While service remains limited, it offers an early glimpse of how Loop travel could reshape airport-to-Strip trips in the near future.

🔗 Sumber: interestingengineering.com