MAROKO133 Hot ai: Exceptional 500 medieval cannonballs discovered in Belgium stun archaeol

📌 MAROKO133 Breaking ai: Exceptional 500 medieval cannonballs discovered in Belgiu

The construction of a new administrative building in Nieuwpoort, Belgium, took an unexpected and humorous turn. Archaeologists have announced the discovery of what was thought to be a remarkable cache of 500 medieval cannonballs.

Plans for the new government building are set to begin, with the laying of foundations scheduled for the end of 2026. In preparation for the construction project, the city conducted archaeological excavations led by the Group Van Vooren.

As the construction site lies between the city hall, which dates back to the Middle Ages, and the town’s medieval defenses, archaeologists expected to uncover historical relics. As the city mayor stated in a press release, Nieuwpoort is a city “where history is literally everywhere.”

Unearthing a new side of the dreary Middle Ages

By peeling back layers of time, they discovered old walls and floors, but they have yet to determine their original purpose. Still, archaeologists suspect these structures had an administrative function due to their location in the civic center.

While these discoveries were exciting, they paled in comparison to what became the most talked-about find: the supposed 500 medieval cannonballs. The high-quality finish led archaeologists to believe they were intended for firearms. The cannonballs varied in size, suggesting that Nieuwpoort had a diverse arsenal. A cannon is even depicted on a map drawn by Antonius Sanderus in 1641, close to the excavation site.

“The most talked-about discovery is undoubtedly a hoard containing many dozens of natural stone cannonballs. Such cannonballs were used between approximately 1350 and 1600 and could be fired from cannons as well as catapults or trebuchets,” a press release explained.

Their placement suggests that they were stored in this location, rather than used in battle, according to Heritage Daily. As they were kept close to the southern city wall, they might have been kept there for defense purposes.

Furthermore, Fox News continued, they uncovered an unexploded shell from the First World War. “The DOVO demining service was immediately notified and arrived on site to secure and remove the ammunition.”

“This find serves as a reminder of the destruction of 1914–1918, during which Nieuwpoort was completely wiped off the map,” as per the press release.

Overall, the military was the overarching theme encompassing the excavation.

1,000 years of history

The findings exceeded their expectations: “from medieval building structures and an exceptional cannonball hoard to military relics pointing to our past as a front-line city,” the press release continued.

Mayor Kris Vandecasteele called “every construction phase in Nieuwpoort is also a journey of discovery into our own history, a past that is far from having revealed all its secrets.”

“It is particularly symbolic that we are giving the go-ahead for the construction of our new Administrative Centre on this historic ground this year. In doing so, we connect Nieuwpoort’s rich past with future-oriented services for our residents.”

“This site was situated in a strategic location near the city wall and the Town Hall and clearly played a role in the well-being and protection of our residents.”

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Hot ai: Which Agent Causes Task Failures and When?Researchers from PSU

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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