MAROKO133 Eksklusif ai: You Will Le Cringe When You Hear the Louvre Video Surveillance Sys

📌 MAROKO133 Eksklusif ai: You Will Le Cringe When You Hear the Louvre Video Survei

On October 19, criminals used a truck-mounted ladder to pull off an astonishing heist, gaining access to the Louvre’s Apollo Gallery in Paris to steal diamond and sapphire-encrusted jewelry that once belonged to royalty.

The robbery drew widespread disbelief, especially considering the thieves’ low-tech approach. How did a mechanical lift allow them to break into the Louvre, steal invaluable objects, and make off on motorcycles in broad daylight?

As French newspaper Libération reported over the weekend, the iconic museum’s security sounds seriously lacking. Perhaps most glaringly, the paper obtained internal documents that date back to 2014, suggesting that the Louvre’s video surveillance server password was — we are not kidding — “Louvre.” While it’s unclear if the password has since been updated, it’s nonetheless an enormous IT oversight that suggests the world-class museum may be suffering from some serious gaps in its security.

Experts at the French Cybersecurity Agency easily got into the poorly secured network at the time to manipulate video surveillance and could even change who could access the system.

However, the thieves likely didn’t even attempt to get into the video surveillance network, considering the museum’s camera systems recorded plenty of footage of them breaking into the building and using angle grinders to laboriously cut open glass cases protecting the jewelry.

Per Libération, a 40-page audit by the National Institute for Advanced Studies in Security and Justice concluded in 2017 that the Louvre’s security had “serious shortcomings” and “poorly managed” visitor flow. The institute also found that rooftops were easily accessible while the museum was under construction, and that it was working with outdated and malfunctioning security systems.

Things didn’t get better over the last ten years, with 2025 documents suggesting the Louvre was using security software it had purchased in 2003, running on hardware using the long-obsolete operating system Windows Server 2003.

Police have since identified four suspects, in some cases using DNA recovered from the crime scene. Ironically, as CNN reports, none of them have any association with organized crime — and instead appear to be local petty criminals that already have an established record for previous robberies.

More on data security: Programmers Using AI Create Way More Glaring Security Issues, Data Shows

The post You Will Le Cringe When You Hear the Louvre Video Surveillance System’s Actual Password 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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