📌 TOPINDIATOURS Hot 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

📌 TOPINDIATOURS Hot ai: Polymarket CEO Known for Yelling at His Employees, Attendi

Polymarket CEO Shayne Coplan’s social media went silent in the aftermath of a dawn raid on his Manhattan penthouse late in 2024. FBI agents came in force early one Wednesday morning, and though they hadn’t arrested Coplan or even issued a subpoena, they did seize his phone. His laptop, too.

As the then-26-year-old would later learn, the raid was part of a high-level investigation into his company. A prediction market platform allowing users to gamble on real-life events, Polymarket wasn’t cleared for use by US citizens, though that didn’t stop them from placing prop bets on the presidential election anyway — a fact federal regulators believed Coplan was keenly aware of.

Finally, at 4pm the day of the raid, Coplan issued his much-awaited statement on X-formerly-Twitter: “new phone, who dis?”

What looked like just another smarmy startup founder was actually a perfect pitch for Polymarket’s core audience: people who believe regulation is for losers, and that pithy tech bros are the funniest guys in the world.

And whether calculated or compulsive, Coplan’s public persona was about to become central to Polymarket’s story.

Ruling like a petty tyrant from the company’s headquarters in lower Manhattan, Coplan isn’t an easy boss to work with, according to new reporting by the Wall Street Journal. The 20-something CEO is said to frequently holler at his employees, and occasionally shows up to company Zoom calls shirtless.

At first, “a lot of people wouldn’t invest because they thought Shayne was nuts,” Polymarket investor Samir Vasavada told the WSJ. “It was to an extreme the amount he believed in himself.”

However put off investors may have been as Coplan got a taste for the founders’ life, it didn’t stop Polymarket from ballooning into an $8 billion company, or its CEO becoming the youngest billionaire on earth. Aided by the Trump administration’s dismissal of two federal probes — including the one that sent FBI agents to bust down his door — not to mention a handsome investment from Donald Trump Jr. himself, the company is now a dominant player in America’s fast-growing gambling industry.

And as Coplan’s net worth has gone through the stratosphere, so too has his confidence, taking his ambitions far beyond the confines of the humble crypto casino. Per the WSJ, Coplan envisions a Polymarket with billions of users, one where anonymous bookies inform government policy and become the go-to source to fact check information.

“The vision that I know that my team and I want to build has not come to life fully yet,” he told the paper. “We still have a long way to go.”

More on Polymarket: The Venezuela Polymarket Scandal Is Looking Really Bad

The post Polymarket CEO Known for Yelling at His Employees, Attending Meetings Shirtless appeared first on Futurism.

🔗 Sumber: futurism.com