MAROKO133 Hot ai: Researchers from PSU and Duke introduce “Multi-Agent Systems Automated F

📌 MAROKO133 Breaking ai: Researchers from PSU and Duke introduce “Multi-Agent Syst

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…

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  🔗 Sumber: syncedreview.com

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  📌 MAROKO133 Eksklusif ai: Meta’s SPICE framework lets AI systems teach themselves

  Researchers at Meta FAIR and the National University of Singapore have developed a new reinforcement learning framework for self-improving AI systems.

  Called Self-Play In Corpus Environments (SPICE), the framework pits two AI agents against each other, creating its own challenges and gradually improving without human supervision.

  While currently a proof-of-concept, this self-play mechanism could provide a basis for future AI systems that can dynamically adapt to their environments, making them more robust against the unpredictability of real-world applications.

  The challenge of self-improving AI

  The goal of self-improving AI is to create systems that can enhance their capabilities by interacting with their environment.

  A common approach is reinforcement learning with verifiable rewards (RLVR), where models are rewarded for providing the correct answers to problems. This is often limited by its reliance on human-curated problem sets and domain-specific reward engineering, which makes it difficult to scale.

  Self-play, where a model improves by competing against itself, is another promising paradigm. But existing self-play methods for language models are often limited by two critical factors.

  1. Factual errors in generated questions and answers compound, leading to a feedback loop of hallucinations.

  2. When the problem generator and solver have information symmetry (i.e., share the same knowledge base) they fail to generate genuinely new challenges and fall into repetitive patterns. 

  As the researchers note in their paper, “These systematic empirical failures indicate that self-improvement requires interaction with an external source providing diverse, verifiable feedback, rather than closed-loop pure introspection.”

  How SPICE works

  SPICE is a self-play framework where a single model acts in two distinct roles.

  • A "Challenger" constructs a curriculum of challenging problems from a large corpus of documents.

  • A "Reasoner" then attempts to solve these problems without access to the source documents.

  This setup breaks the information symmetry that limits other self-play methods, as the Reasoner does not have access to the documents and knowledge that the Challenger uses to generate the problems.

  Grounding the tasks in a vast and diverse corpus of documents prevents hallucination by anchoring questions and answers in real-world content. This is important because for AI systems to reliably self-improve, they need external grounding sources. Therefore, LLM agents should learn from interactions with humans and the real world, not just their own outputs, to avoid compounding errors.

  The adversarial dynamic between the two roles creates an automatic curriculum.

  The Challenger is rewarded for generating problems that are both diverse and at the frontier of the Reasoner's capability (not too easy and also not impossible).

  The Reasoner is rewarded for answering correctly. This symbiotic interaction pushes both agents to continuously discover and overcome new challenges. 

  Because the system uses raw documents instead of pre-defined question-answer pairs, it can generate diverse task formats, such as multiple-choice and free-form questions.

  This flexibility allows SPICE to be applied to any domain, breaking the bottleneck that has confined previous methods to narrow fields like math and code. It also reduces dependence on expensive human-curated datasets for specialized domains like legal or medical analysis.

  SPICE in action

  The researchers evaluated SPICE on several base models, including Qwen3-4B-Base and OctoThinker-3B-Hybrid-Base.

  They compared its performance against baselines such as the base model with no training, a Reasoner model trained with a fixed "Strong Challenger" (Qwen3-32B-Instruct), and pure self-play methods like R-Zero and Absolute Zero. The evaluation covered a wide range of mathematical and general reasoning benchmarks.

  Across all models, SPICE consistently outperformed the baselines, delivering significant improvements in both mathematical and general reasoning tasks.

  The results show that the reasoning capabilities developed through corpus-grounded self-play transfer broadly across different models, thanks to the diverse external knowledge corpus they used.

  A key finding is that the adversarial dynamic creates an effective automatic curriculum. As training progresses, the Challenger learns to generate increasingly difficult problems.

  In one experiment, the Reasoner's pass rate on a fixed set of problems increased from 55% to 85% over time, showing its improved capabilities.

  Meanwhile, later versions of the Challenger were able to generate questions that dropped the pass rate of an early-stage Reasoner from 55% to 35%, confirming that both roles co-evolve successfully.

  The researchers conclude that this approach presents a paradigm shift in self-improving reasoning methods from “closed-loop self-play that often stagnates due to hallucination drift, to open-ended improvement through interaction with the vast, verifiable knowledge embedded in web document corpora.”

  Currently, the corpus used for SPICE represents human experience captured in text. The ultimate goal is for self-improving systems to generate questions based on interactions with reality, including the physical world, the internet, and human interactions across multiple modalities like video, audio, and sensor data.

  🔗 Sumber: venturebeat.com

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