MAROKO133 Hot ai: Researchers report first evidence of solar neutrinos flipping carbon int

📌 MAROKO133 Breaking ai: Researchers report first evidence of solar neutrinos flip

Scientists have recorded a rare interaction between solar neutrinos and carbon for the first time.

The result marks an important step in understanding how low-energy neutrinos behave inside matter.

The SNO+ detector in Canada captured the elusive signal after more than a year of data collection.

The Oxford-led team used the SNO+ detector at SNOLAB, located two kilometres (1.24 miles) underground in a working mine in Sudbury, Canada.

The depth shields the experiment from cosmic rays and background noise.

That protection allowed researchers to isolate extremely faint signals produced when neutrinos strike atomic nuclei.

Neutrinos remain among the most mysterious particles in the universe. They rarely interact with matter. Trillions pass through the human body each second.

They emerge from nuclear reactions, including those inside the Sun.

Detecting them requires patience, precision, and enormous shielding.

The SNO+ team focused on interactions with carbon-13, a rare form of carbon present in the detector’s liquid scintillator.

When a high-energy solar neutrino hits carbon-13, it can transform the atom into nitrogen-13.

The new nucleus decays about ten minutes later.

Researchers relied on a delayed-coincidence technique. They looked for an initial flash from the neutrino strike.

They then searched for a second flash minutes later as nitrogen-13 decayed.

That paired pattern helps distinguish genuine events from background signals.

The analysis identified 5.6 such events over 231 days from May 2022 to June 2023.

The number aligns with the 4.7 solar neutrino events expected during that period.

Rare reaction confirmed

Lead author Gulliver Milton, a PhD student at Oxford, called the detection a major achievement.

“Capturing this interaction is an extraordinary achievement. Despite the rarity of the carbon isotope, we were able to observe its interaction with neutrinos, which were born in the Sun’s core and travelled vast distances to reach our detector.”

The result also builds on decades of neutrino research. Co-author Professor Steven Biller noted the history behind the work.

“Solar neutrinos themselves have been an intriguing subject of study for many years, and the measurements of these by our predecessor experiment, SNO, led to the 2015 Nobel Prize in physics.”

He added that understanding has deepened so much that researchers can now use solar neutrinos as a “test beam” for rare atomic reactions.

Foundation for future studies

SNO+ repurposes the earlier SNO experiment, which first proved that neutrinos shift between three types as they travel from the Sun to Earth.

Dr Christine Kraus, a staff scientist at SNOLAB, highlighted how the team used the natural carbon-13 in the target material to measure this specific reaction.

“To our knowledge, these results represent the lowest energy observation of neutrino interactions on carbon-13 nuclei to date and provides the first direct cross-section measurement for this specific nuclear reaction to the ground state of the resulting nitrogen-13 nucleus.”

Researchers say the achievement opens new opportunities to study rare neutrino interactions.

It may also guide future detector designs as scientists push to understand how these ghostlike particles shape nuclear processes and the wider universe.

The study is published in the journal Physical Review Letters.

🔗 Sumber: interestingengineering.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…

  Konten dipersingkat otomatis.

  🔗 Sumber: syncedreview.com

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