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

📌 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 Eksklusif ai: Rivers Turn Bright Orange in Alaska Hari Ini

Some of Alaska’s scenic rivers and streams look downright apocalyptic this year because they turned a flagrant orange color — but it’s not due to local pollution, according to scientists at the National Oceanic and Atmospheric Administration (NOAA).

In actuality, the orange tinted water is rust, released as the frozen ground in Alaska thaws out due to unchecked greenhouse gasses driving global warming. And it’s leaking into the state’s waterways, according to NOAA’s annual report on the Arctic region, where it’s posing a danger to local wildlife, residents and commercial fisheries.

The day-glo rivers are also a bright orange flag that the Arctic is warming faster than the rest of the world. The massive defrosting is also anticipated to increase sea levels and screw up weather patterns, according to scientists who talked to NPR.

“When the Arctic thaws and warms, it’s having an impact on the global climate,” Matthew Druckenmiller, lead author of the report and senior scientist with the Boulder, Colorado-based National Snow and Ice Data Center, told the broadcaster.

The planet is already showing signs of distress from global warming, such as large-scale forest fires and extreme summer temperatures outside the Arctic, which Druckenmiller described as a giant fridge for the planet.

“The Arctic is warming several times faster than Earth as a whole, reshaping the northern landscapes, ecosystems, and livelihoods of Arctic peoples,” reads the NOAA report. “Also transforming are the roles the Arctic plays in the global climate, economic, and societal systems.”

Zooming back to Alaska, people started noticing the orange waterways in 2018, according to NPR.

“ We heard from people who live in the region — pilots who are often flying over, people in the national parks,”  US Geological Survey research hydrologist Josh Koch told the broadcaster.

As temperatures heats up in the most remote parts of Alaska, permafrost — ground that usually stays continuously frozen — is melting, and that’s unlocking iron in the soil, which oxidizes from exposure to water and air, causing rivers and streams to turn orange. Surveys revealed that this contamination is far reaching, covering hundreds of miles of terrain in Alaska.

“It’s often not orange until it reaches the stream, and then all the iron and other metals can precipitate and create this iron staining,” Koch added.

It’s not clear if residents are being harmed from the polluted water, but local scientists are monitoring the situation, NPR reports.

The other problem with these rusty rivers is that they increase the acidity level in the water, according to the NOAA report, and this may harm fish like Dolly Varden char, whose juvenile offspring have experienced a sharp decrease in numbers most likely due to iron in its aquatic habitat. And that’s pretty bad for everybody in Alaska.

“The food chain is connected to the lives of people living in the Arctic,” Druckenmiller said.

More on climate change: Melting Glacier in Alaska Floods State Capital

The post Rivers Turn Bright Orange in Alaska appeared first on Futurism.

🔗 Sumber: futurism.com