MAROKO133 Breaking ai: Researchers from PSU and Duke introduce “Multi-Agent Systems Automa

📌 MAROKO133 Eksklusif ai: Researchers from PSU and Duke introduce “Multi-Agent Sys

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 2M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. 

Meet the author
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

2. failure-responsible agent and the decisive error step that led to the task’s failure.

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 m…

  Konten dipersingkat otomatis.

  🔗 Sumber: syncedreview.com

  ---

  📌 MAROKO133 Eksklusif ai: Engineered biochar harnesses sunlight to boost chemical

  Biochar engineered with artificial humic substances can dramatically amplify sunlight-driven chemical reactions, offering a new way to tackle environmental pollution using solar energy.

  Researchers have shown that carefully tuning the chemistry of biochar allows it to actively drive reduction reactions that affect metal cycling and contaminant behavior in natural environments.

  The strategy centers on combining biochar with artificially synthesized humic substances, materials that normally form slowly in nature through the breakdown of organic matter.

  By recreating and accelerating this process in the lab, the team produced hybrid materials that respond strongly to sunlight and transfer electrons far more efficiently than conventional biochar.

  The artificial humic substances were created from pine sawdust using a controlled hydrothermal process.

  By adjusting treatment temperatures, the researchers were able to fine-tune the molecular structure of the resulting material, directly influencing how it behaves under light exposure.

  Higher temperatures produced materials with stronger electron-donating properties, a key factor behind their improved performance.

  Biochar is widely used in soil improvement and pollution control, but its photochemical behavior has remained poorly understood.

  Natural humic substances, meanwhile, play a major role in environmental redox reactions but are difficult to study due to their complex composition and slow formation. The new approach bridges that gap by creating engineered systems that mimic and enhance natural processes.

  “Our work shows that it is possible to precisely design biochar-based materials with controllable redox activity by co-engineering them with artificial humic substances,” the study’s corresponding authors said.

  “This approach allows us to accelerate natural humification processes and create materials that actively respond to sunlight.”

  Sunlight as catalyst

  To test the engineered materials, the researchers used silver ion reduction as a model reaction.

  The results were striking. Materials produced at higher hydrothermal temperatures showed far stronger photochemical activity than those synthesized at lower temperatures.

  In particular, samples treated at 340 degrees Celsius achieved reduction efficiencies more than nineteen times higher.

  The performance boost comes from changes in lignin-derived molecules during hydrothermal treatment.

  Higher temperatures increase the concentration of phenolic functional groups, which act as powerful electron donors. Under sunlight, these groups generate reactive superoxide radicals that drive reduction reactions and enable ligand-to-metal charge transfer.

  Beyond enhanced performance, the team uncovered an unexpected dynamic behavior. When exposed to sunlight, hydrochar partially dissolves, releasing dissolved organic molecules into the surrounding environment.

  These molecules further intensify photochemical activity, revealing that biochar-based materials can evolve and interact with their surroundings over time.

  “Our findings highlight that biochar is not just a passive sorbent,” the authors explained. “It can dynamically transform under sunlight and participate in complex photochemical reactions that affect pollutant behavior and metal cycling.”

  Beyond passive carbon

  The discovery opens new possibilities for designing solar-responsive remediation systems for contaminated soils and waters.

  By engineering biochar to actively participate in light-driven chemistry, researchers could develop low-energy solutions for transforming pollutants and controlling metal mobility in natural environments.

  The materials also offer a sustainable advantage. The artificial humic substances are derived from waste biomass, aligning with efforts to develop carbon-negative technologies and circular bioeconomy pathways.

  Future studies will focus on testing the materials against a broader range of pollutants and under real-world environmental conditions.

  The study was published in the journal Biochar.

  🔗 Sumber: interestingengineering.com

  ---