📌 TOPINDIATOURS Update ai: After Outcry, Firefox Promises “Kill Switch” That Turns

The backlash against AI invading almost every aspect of the computing experience is growing by the day.

Particularly as an onslaught of lazy AI slop subsuming news feeds, the tech is starting to feel like a massive distraction — and huge parts of the internet are disillusioned or even fuming in anger.

For instance, a vast number of Windows users refused to upgrade after Microsoft announced it would turn the operating system into a so-called “agentic OS.”

Even household names in the open-source industry aren’t safe. After being appointed as the new CEO of open-source software company Mozilla, whose Firefox browser has long been lauded as a compelling alternative to Google’s Chrome and Apple’s Safari, Anthony Enzor-DeMeo announced that it would be tripling down on AI.

In a December 16 blog post, Enzor-DeMeo announced that Firefox would become a “modern AI browser and support a portfolio of new and trusted software additions.”

But a ringing backlash quickly forced the company into damage control mode.

“I’ve never seen a company so astoundingly out of touch with the people who want to use its software,” one disillusioned user tweeted in response to the news.

“I switched back to Firefox late last year BECAUSE it was the last AI-free browser,” another lamented. “I shoulda known.”

“Please don’t turn Firefox into an AI browser,” yet another begged. “That’s a great way to push us to alternatives.”

The outcry was formidable enough for Mozilla to clarify the company’s new CEO’s comments.

“Something that hasn’t been made clear: Firefox will have an option to completely disable all AI features,” the company wrote in an update on Mastodon. “We’ve been calling it the AI kill switch internally. I’m sure it’ll ship with a less murderous name, but that’s how seriously and absolutely we’re taking this.”

An open letter posted to the Firefox subreddit took issue with Enzor-DeMeo’s new direction.

“Ironically, in a post announcing this new direction and highlighting ‘agency and choice,’ there was little mention of user input or feedback,” the letter reads. “This highlights a disconnect that many of us experience daily: Mozilla has a pattern of struggling to implement and support basic features, and much of the time fails to even acknowledge serious user feedback.”

“Firefox doesn’t need to become Google or Microsoft to succeed by both business and user standards,” the letter goes on. “It’s beloved precisely because it’s not. I hope that distinction isn’t lost as Mozilla enters its ‘next chapter’ as part of a ‘broader ecosystem of trusted software.’”

In an apparent effort to reassure the company’s most diehard fans, Enzor-DeMeo took to the comments.

“Rest assured, Firefox will always remain a browser built around user control,” he wrote. “That includes AI. You will have a clear way to turn AI features off. A real kill switch is coming in Q1 of 2026.”

However, his attempts to calm the situation ended up fanning the flames even further.

“If a ‘kill switch’ is the official control for this, then the entire organization needs to stop referring to your ‘AI’ features as ‘opt-in,’” one user responded. “This is clearly opt-out.”

“If Mozilla can’t agree to that basic definition, I don’t see how users are supposed to trust it’ll actually work,” the user added.

Interestingly, the competing browser company Vivaldi, whose browser is based on Google’s open-source Chromium project, has taken a dramatically different approach.

In an August blog post, Vivaldi CEO Jon von Tetzchner accused other companies like Google and Microsoft of “reshaping the address bar into an assistant prompt, turning the joy of exploring into inactive spectatorship.”

“We will continue building a browser for curious minds, power users, researchers, and anyone who values autonomy,” von Tetzchner wrote.

“If AI contributes to that goal without stealing intellectual property, compromising privacy or the open web, we will use it,” he added. “If it turns people into passive consumers, we will not.”

More on AI slop software: Vast Number of Windows Users Refusing to Upgrade After Microsoft’s Embrace of AI Slop

The post After Outcry, Firefox Promises “Kill Switch” That Turns Off All AI Features appeared first on Futurism.

🔗 Sumber: futurism.com

📌 TOPINDIATOURS Hot ai: Researchers from PSU and Duke introduce “Multi-Agent Syste

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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