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

📌 MAROKO133 Update 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

📌 MAROKO133 Hot ai: Catalyst breakthrough lets chemists build bioactive materials

Natural gas powers much of the world, but its core components, methane, ethane, and propane, have resisted direct conversion into useful chemicals.

Their molecular stability makes them resistant to transformation under extreme conditions.

That limitation has prevented the chemical industry from utilizing these inexpensive hydrocarbons as sustainable feedstocks. A research team in Spain now claims a breakthrough that could shift that landscape.

Turning gas into value

A team led by Martín Fañanás at the Center for Research in Biological Chemistry and Molecular Materials (CiQUS) has developed a method to convert methane and other natural gas components into adaptable chemical “building blocks.”

The method enables direct synthesis of high-value products, including pharmaceutical ingredients.

The team validated its approach by making a real drug ingredient. They created dimestrol, a non-steroidal estrogen used in hormone therapy, starting from methane.

This marks the first time researchers have built a bioactive compound directly from this abundant gas.

The result suggests that natural gas could support broader chemical manufacturing without relying on complex refinery steps.

Scientists have chased this idea for decades. They sought to use natural gas as a low-carbon feedstock, but reactivity barriers stalled progress.

Methane’s strong bonds make it difficult to functionalize without generating waste or unwanted byproducts.

Many attempts produced chlorinated residues or required harsh operating conditions. The CiQUS method aims to bypass those pitfalls.

Radical control challenge

The team built its strategy around a reaction called allylation. This process attaches an allyl group to the hydrocarbon, giving chemists a handle for later transformations.

That flexibility matters. With the handle in place, researchers can build diverse molecules, including drug scaffolds and everyday industrial chemicals.

Earlier attempts failed because catalysts created excessive chlorination byproducts.

These side reactions killed yields and limited practical use. Fañanás and colleagues tackled the issue by designing a supramolecular catalyst that tunes radical behavior inside the reaction.

“The core of this breakthrough lies in designing a catalyst based on a tetrachloroferrate anion stabilized by collidinium cations,” Fañanás says. He adds that the system “effectively modulates the reactivity of the radical species generated in the reaction medium.”

The tailored catalyst acts like a control mechanism, guiding reactive intermediates toward the allylation step.

Their system also avoids extreme temperatures or pressures. It works under comparatively mild conditions, which could make scaling easier. The approach supports different gas substrates, not only methane.

That versatility raises the prospect of converting larger fractions of natural gas streams into valuable intermediates.

Researchers say the work points toward a future where natural gas feeds higher-value manufacturing rather than combustion.

Methane remains abundant and cheap, but its climate impact is severe. Transforming it into stable chemicals could reduce emissions and support a circular chemical economy.

The CiQUS team continues to test new molecules and refine catalyst designs. They argue that the method offers a blueprint for cleaner industrial chemistry. Industry groups are also watching.

A direct pathway from natural gas to drug ingredients could reshape supply chains and cut processing steps.

The study is published in the journal Science Advances.

🔗 Sumber: interestingengineering.com