MAROKO133 Hot ai: Sea urchin pandemic spreads across Canary Islands, triggering near-total

📌 MAROKO133 Breaking ai: Sea urchin pandemic spreads across Canary Islands, trigge

Sea urchins help shape marine ecosystems by controlling algae growth on reefs. By grazing on seaweed and seagrass, they protect corals and other slow-growing species.

They also support food webs as prey for fish, crustaceans, sea stars, and marine mammals.

But when urchin populations explode, especially after predators decline, reefs can suffer. Heavy grazing strips plant life from the seafloor, creating barren underwater landscapes.

Now, scientists report that a sudden and widespread collapse of sea urchins has reached the Canary Islands.

A new study shows that a previously unrecognized global sea urchin pandemic has devastated populations across the archipelago.

Researchers warn that the ecological effects could be severe and long-lasting.

“Here we show the spread and impacts of a ‘mass mortality event’ which severely hit populations of the sea urchin Diadema africanum in the Canary Islands and Madeira through 2022 and 2023,” said Iván Cano, a doctoral student at the University of La Laguna on Tenerife in the Canaries Islands, Spain.

“At approximately the same time, ther Diadema species have been observed to be dying off in the Caribbean, the Mediterranean, the Red Sea, the Sea of Oman, and the western Indian Ocean.”

Diadema africanum lives on rocky reefs in warm Atlantic waters, usually between five and 20 meters deep. In the Canary Islands, its numbers rose steadily from the mid-1960s.

Overfishing of predators and warming seas likely fueled the growth.

The population surge caused widespread urchin barrens across the islands. Between 2005 and 2019, managers tried biological controls to reduce numbers.

These efforts failed.

In February 2022, researchers noticed something different. Large numbers of D. africanum began dying near La Palma and Gomera. The die-off spread eastward through the year.

Infected urchins moved abnormally and showed little response to stimuli. They later lost spines and flesh before dying.

The symptoms matched earlier events. Similar outbreaks in 2008 and 2018 killed more than 90% of some local populations. After those events, numbers rebounded.

That recovery did not follow the 2022 outbreak. Instead, a second wave of mass mortality struck in 2023.

Historic population collapse

To measure the damage, researchers surveyed 76 sites across seven islands. They compared counts from 2022 to 2025 with historical records.

The team also collected reports from professional divers. They examined larval reproduction near eastern Tenerife during the 2023 spawning season.

“Our analyses showed that the current abundance of D. africanum across the Canary Islands is at an all-time low, with several populations nearing local extinction,” said Cano.

“Moreover, the 2022-2023 mass mortality event affected the entire population of the species across the archipelago. For example, since 2021 there has been a 74% decrease in La Palma and a 99.7 % decrease in Tenerife.”

Larval traps caught only tiny numbers of offspring. Surveys found no juveniles in shallow rocky habitats.

Disease still unknown

Scientists still do not know what caused the die-offs. Similar events elsewhere have involved parasitic ciliates in the genus ‘Philaster’.

Earlier Canary Island outbreaks linked to amoebae followed intense wave activity.

“We don’t yet know for certain which pathogen is causing these die-offs,” said Cano.

“Without a confirmed identification, we cannot say whether the agent arrived from the Caribbean by currents or shipping, or whether climate change is to blame.”

So far, the disease has not hit Diadema populations in Southeast Asia or Australia.

Researchers caution that future spread remains possible.

🔗 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