📌 TOPINDIATOURS Eksklusif ai: Which Agent Causes Task Failures and When?Researcher

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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…

Konten dipersingkat otomatis.

🔗 Sumber: syncedreview.com

📌 TOPINDIATOURS Update ai: Watch: Autonomous drones now manage freezer inventories

Corvus Robotics has launched an autonomous drone system designed to manage inventory inside some of the harshest warehouse environments: industrial freezers.

Called Corvus One for Cold Chain, the system operates continuously in temperatures as low as minus-20 degrees Fahrenheit, eliminating the need for human workers to conduct manual inventory checks in sub-zero conditions.

The system is built for logistics and warehousing operations where frozen goods face strict shelf-life limits, FIFO requirements, and growing SKU complexity.

By performing frequent, autonomous inventory scans, the drones provide operators with near real-time visibility into pallet locations and dwell times.

Cold storage facilities have long struggled to automate inventory management due to frost, airflow, condensation, and glare, all of which degrade conventional sensors and scanning systems.

Corvus One for Cold Chain is engineered to function despite those challenges, maintaining flight stability and barcode accuracy without modifying warehouse infrastructure.

Corvus Robotics says the system can operate during active warehouse shifts without disrupting workflows.

It does not rely on Wi-Fi, localization markers, special lighting, or modified barcodes, allowing freezer blowers and doors to function normally during operation.

“Operating autonomous aerial systems continuously in freezer environments is an engineering challenge most robotics platforms were never designed to handle,” said Jackie Wu, Chief Executive Officer, at Corvus Robotics.

“Corvus One for Cold Chain required re-architecting thermal management, sensing, flight stability, and onboard perception so the system could maintain autonomy and accuracy despite frost, glare, airflow, and extreme temperature swings.”

Built for extreme cold

Corvus One for Cold Chain uses industrial-grade barcode scanners with precise control over focus and exposure, enabling reliable label capture even when barcodes are frosted, damaged, or low contrast.

The drones automatically adapt scanning parameters based on environmental conditions while stabilizing flight to counter strong airflow inside freezer aisles.

The system is already operating in live commercial environments.

A leading national grocer, Kroger, is currently using Corvus One for Cold Chain in active freezer facilities, where it is reducing reliance on manual cycle counts and improving inventory accuracy in sub-zero conditions.

Freezer operations typically require specialized protective gear, shorter shifts, and strict exposure limits, all of which drive higher labor costs.

By removing the need for workers to enter freezer aisles for routine inventory checks, Corvus Robotics says the system improves worker safety while lowering operational overhead.

Corvus One for Cold Chain is delivered through the company’s Robots-as-a-Service model, which includes automated battery management and device health monitoring to maintain continuous uptime without on-site operators.

Automation without warehouse changes

Unlike many automation systems, Corvus One for Cold Chain does not require changes to warehouse layouts or workflows.

The drones fly autonomously alongside normal operations, adapting to airflow from blowers and door activity while continuing to capture inventory data.

The company says this infrastructure-free approach allows operators to deploy the system quickly across existing cold storage facilities.

The platform is built on Corvus Robotics’ AI world model, enabling autonomous navigation and perception without human intervention.

Corvus Robotics is positioning the system for large-scale cold chain operations seeking tighter inventory control, reduced write-offs, and improved space optimization.

As frozen supply chains continue to expand, autonomous systems designed specifically for extreme environments may become essential rather than optional.

🔗 Sumber: interestingengineering.com