MAROKO133 Update ai: AI Slop Now Invading Spotify’s Discover Weekly Lists Terbaru 2025

📌 MAROKO133 Breaking ai: AI Slop Now Invading Spotify’s Discover Weekly Lists Edis

Spotify is continuing to grapple with a tidal wave of AI slop that’s frustrating its users.

While the company recently promised to address the issue of AI impersonators and content farms that “push ‘slop’ into the ecosystem, and interfere with authentic artists working to build their careers,” the platform’s paying subscribers are increasingly fed up with being recommended slop.

A quick search on social media reveals that slop continues to find its way into users’ Discover Weekly, which are personalized playlists that refresh every Monday to serve them new music based on their listening habits.

“Discover Weekly is unusable now cause it is just full of AI slop,” one user complained on X-formerly-Twitter.

“Dear Spotify, please stop putting AI music in my Discover Weekly. Sincerely, everyone,” another wrote.

“I had no idea how bad it had gotten, since none of it was being shown to me,” one user wrote on the forum Hacker News. “Then a friend sent me his AI music on Spotify, I listened, and my recommendations were all AI for my usual genres suddenly.”

According to an August 2024 forum post on Spotify’s Community page, the issue has been around for well over a year now.

“It has gotten really bad lately,” a subscriber wrote in a December post. “This week’s Discover Weekly has four [AI-generated] songs in the first five entries. It makes me very sad that this garbage penetrates the recommended playlists of users.”

Many users threatened to jump ship and leave Spotify altogether as a result.

“Six songs out of 30 on my Discover Weekly playlist on Spotify were AI this week,” one user wrote in a Bluesky post. “Ridiculous that Spotify is pushing this crap on us. Looks like that’s it for Spotify.”

It’s a damning trend, with Spotify’s algorithm actively recommending slop to users who are paying monthly for a Premium subscription.

“Spotify has been recommending me so much AI music in my Discover Weekly that I can’t even rely on it for finding new songs anymore,” another Bluesky user lamented.

In the face of it all, Spotify has refused to implement a blanket ban on AI slop. In late September, the company announced a slew of new policies to protect artists against “spam, impersonation, and deception,” including a new “filter” that it says can detect common tactics used by spammers to game its royalties system.

The company also promised to establish “AI disclosures for music with industry-standard credits.”

However, Spotify fell short of forbidding AI tunes entirely, arguing that “music has always been shaped by technology” and that “at its best, AI is unlocking incredible new ways for artists to create music and for listeners to discover it.”

“This journey isn’t new to us,” the company wrote in its announcement at the time. “We’ve invested massively in fighting spam over the past decade. In fact, in the past 12 months alone, a period marked by the explosion of generative AI tools, we’ve removed over 75 million spammy tracks from Spotify.”

While browsing their Discover Weekly playlists, users are finding “songs with weird, all-caps artists with AI pictures in them.” Others have pointed out artists with “no text in bios” and “ten plus releases each in 2025 alone.”

Besides polluting Discover Weekly playlists, we’ve also come across AI-generated impersonations showing up in Spotify’s New Releases playlists, which are designed to automatically surface new music by known artists.

Earlier this year, a self-proclaimed “indie rock band” called The Velvet Sundown made headlines after accumulating millions of streams. The “band” was later revealed to be an “ongoing artistic provocation designed to challenge the boundaries of authorship, identity, and the future of music itself in the age of AI.”

Spotify has also been caught populating the profiles of long-dead artists with AI-generated songs imitating their style.

As reporter Kieran Press-Reynolds argued in a recent column for Pitchfork, the company likely isn’t particularly incentivized to remove AI tracks from its platform.

“Spotify won’t prohibit this music — not because it thinks it’s innovative or ushering in a new era of technological futurism (the platform has never cared about culture) but simply because it’s generating streams,” Press-Reynolds wrote. “If the company actually wants to be transparent and control spam, they need to go way harder.”

More on Spotify: Bon Iver Side Project’s Spotify Page Features an AI Slop Song

The post AI Slop Now Invading Spotify’s Discover Weekly Lists appeared first on Futurism.

🔗 Sumber: futurism.com

📌 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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