MAROKO133 Update ai: NASA Running Out of Non-Life Explanations for What Its Rover Found on

📌 MAROKO133 Hot ai: NASA Running Out of Non-Life Explanations for What Its Rover F

Last year, NASA’s Curiosity rover made a fascinating discovery after boring into a suspected ancient lake bed on Mars: long-chain organic molecules, called alkanes, that could serve as a potential chemical relic of ancient life on the Red Planet.

The molecules, researchers suggested at the time, could have derived from fatty acids, which are common building blocks of cell membranes on Earth, once again strengthening the case that Mars could’ve been teeming with life billions of years ago.

It was just another tantalizing clue in our search for extraterrestrial life, not the smoking gun we’ve all been waiting for.

Nonetheless, scientists continue to be fascinated by the finding. In a paper published in the journal Astrobiology last week, a team led by NASA Goddard Space Flight Center’s Alexander Pavlov argues that the presence of these molecules — despite the millions of years of destructive radiation that pummeled the Martian surface after it lost much of its atmosphere — “cannot be readily explained” by non-biological processes alone.

One theory is that carbon-rich dust particles and meteorites could have deposited these long-chain organic molecules on the surface, with the ancient Martian atmosphere allowing the organics to accumulate billions of years ago.

However, Pavlov and his colleagues aren’t convinced. After studying how 80 million years’ worth of pelting radiation could have affected these molecules, they concluded that prior to the loss of the planet’s atmosphere, the concentration of these alkanes was likely much higher than previously thought. To help explain their findings, they took into account other non-biological processes in an attempt to arrive at their inferred original abundance — but couldn’t, even after combining all of them.

In other words, biological processes like the ones observed on Earth are still a leading theory, even after researchers’ best efforts to find a non-life explanation.

“We argue that such high concentrations of long-chain alkanes are inconsistent with a few known abiotic sources of organic molecules on ancient Mars,” they wrote.

Nonetheless, they stopped well short of making any definitive statements about life on the Red Planet. After all, there could be still-unknown, non-biological processes we don’t know about that could have resulted in the observed concentration of long-chain carbon molecules on Mars.

“We agree with Carl Sagan’s claim that extraordinary claims require extraordinary evidence and understand that any purported detection of life on Mars will necessarily be met with intense scrutiny,” they concluded in their paper. “In addition, in practice with established norms in the field of astrobiology, we note that the certainty of a life detection beyond Earth will require multiple lines of evidence.”

Nonetheless, it’s a tantalizing waypoint in our longstanding efforts to determine whether Mars, a planet that was once covered in huge oceans, rivers, and lakes, could have supported life.

Pavlov and his colleagues are now calling for further research into how radiation degraded these intriguing molecules under Mars-like conditions to shed more light on the matter.

More on Mars: Scientists Find Evidence of Ancient Tropical Oasis on Mars

The post NASA Running Out of Non-Life Explanations for What Its Rover Found on Mars 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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