MAROKO133 Breaking ai: Alarming Research Finds People Hooked on AI Far Are More Likely to

📌 MAROKO133 Breaking ai: Alarming Research Finds People Hooked on AI Far Are More

We’ve heard story after story about people becoming obsessed with AI chatbots and experiencing profound breaks with reality.

But there was always an ambiguity: was AI use pushing people into psychosis, or were people already suffering from mental health problems seeking out AI as a means to cope?

Now, new research has provided an important clue. As PsyPost reports, scientists have found that people who use AI chatbots tend to experience higher levels of psychological distress compared to individuals who don’t. It doesn’t quite establish causation, but it’s a glaring sign of correlation — and the implications could be deep.

“Humans have an innate need to build and maintain meaningful relationships, and in today’s digital world, many of these connections increasingly unfold through technology,” author and Tampere University senior research fellow Iina Savolainen told PsyPost.

“With the rise of AI, more people are using social chatbots to explore new forms of communication or to seek companionship, emotional support, or simply, everyday interaction,” she continued. “Yet despite this growing trend, surprisingly few empirical studies have examined who uses these tools and how such usage relates to well-being.”

As detailed in a new paper published in the Journal of Social and Personal Relationships, Savolainen and her colleagues at Tampere analyzed data from a 2023 longitudinal study. The data included responses from 5,663 adults across six European countries — including Finland, France, Germany, Ireland, Italy, and Poland — who filled out an online survey assessing their use of “chatbot friends” services offered by companies like Replika and My AI.

The team measured the level of psychological distress using a 38-item measure of mental well-being called the Mental Health Inventory, and compared outcomes between those who reported using the AI chatbots and those who didn’t.

It was quickly clear that there was a correlation.

“The cross-cultural consistency was striking; social chatbot use was related to poorer mental well-being in all six countries,” Savolainen told PsyPost.

“Taken together, these results suggest that social chatbot use may emerge as a response to emotional or social challenges rather than as a tool that inherently improves well-being,” she added. “This does not mean chatbots can’t be helpful, but it reminds us that the dynamics of use are complex and may reflect underlying needs that technology alone cannot fully address.”

Importantly, the researchers admitted that the study only allowed them to “discuss associations” and not infer a cause-and-effect relationship. In other words, while it’s an intriguing link, we still don’t know if social chatbot usage actually causes negative mental health outcomes, or if the explanation is something more complex.

Another limitation of the study is the age of the dataset itself. The original survey was conducted in late 2023, less than a year after the launch of OpenAI’s ChatGPT, raising the possibility that trends may have shifted since then as more people have been drawn to the tech.

“Future research is needed to make more robust conclusions on social chatbot usage and their potential in supporting human health and well-being,” the paper reads. “Moreover, the possibility that these technologies are of particular interest to those who are already in a vulnerable position warrants investigation.”

As researchers continue to study the subject, they’re warning of the potential dangers of social chatbot usage, particularly among younger people.

In extreme cases, we’ve already found that an infatuation with AI models has played a role in a growing number of deaths, leading to lawsuits aimed at companies including Character.AI and OpenAI that are still playing out in court.

“We are living a unique time of human-computer interaction, as different technologies are becoming more sophisticated and culturally embedded,” Savolainen told PsyPost.

“As chatbots diversify and become more personalized, understanding the evolving nature of these relationships will be crucial,” she added.

More on AI chatbot use: A Startling Proportion of Teens Now Prefer Talking to AI Over a Real Person

The post Alarming Research Finds People Hooked on AI Far Are More Likely to Experience Mental Distress appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Hot ai: Researchers from PSU and Duke introduce “Multi-Agent Systems A

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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