MAROKO133 Breaking ai: Alzheimer’s Fully Reversed in Mice, Scientists Say Hari Ini

📌 MAROKO133 Update ai: Alzheimer’s Fully Reversed in Mice, Scientists Say Wajib Ba

A team of American scientists claim they have done something miraculous: they “cured” lab mice suffering from Alzheimer’s disease, which has robbed more than seven million Americans, typically 65 years old and up, of their identity and cognitive ability.

The researchers achieved this feat by administering the rodents with the powerful compound P7C3-A20, which they announced in a new paper in the journal Cell Reports Medicine. Scientists from Ohio’s Case Western Reserve University (CWRU), University Hospitals, and the Louis Stokes Cleveland VA Medical Center undertook the study.

“The key takeaway is a message of hope — the effects of Alzheimer’s disease may not be inevitably permanent,” said Andrew A. Pieper, the study’s principal investigator and a CWRU neuroscience professor, in a statement about the research. “The damaged brain can, under some conditions, repair itself and regain function.”

This research is part of a growing wave of very promising lab studies that point to a tantalizing future where Alzheimer’s and other neurological issues could be a thing of the past. Besides this P7C3-A20 research, others have scored remarkable lab results using different compounds and treatments.

This has made normally cautious scientists so excited that they are making bold predictions. University of Edinburgh neuroscience professor Tara Spires-Jones, who wasn’t part of this P7C3-A20 study, told the BBC this month she thinks scientists are closer than ever to a “truly life-changing” treatment — in as little as five to 10 years; instead of a slow death where people lose themselves, she forecasts that new tests will detect the condition early and innovative treatments will “really make your life normal.”

Scientists are also closer to understanding what causes Alzheimer’s, which seems to be sparked by different factors such as genetics, environment and other stressors — which means that future patients may receive personalized cocktail of anti-Alzheimer’s treatment and drugs suited for their own situation.

Regardless of the cause, previous research has suggested that Alzheimer’s is a form of inflammation. That means lessening or zeroing out inflammation in the brain would be key rather than managing symptoms.

In the P7C3-A20 study, the scientists focused on the impact of the crucial molecule NAD+, a coenzyme important for driving cellular metabolism and which decreases as we age, according to the study. Patients with Alzheimer’s suffer from a significant decrease of NAD+ in the brain, and hence their brain cells have trouble maintaining normal functionality, staving off inflammation, and canceling other physical hallmarks of the disease.

For the study, the team first took two types of lab mice that have been genetically bred to be predisposed to Alzheimer’s; one cohort had mutations for the amyloid protein and the other had tau protein mutations. Both proteins are important to cellular function, but they can become dangerous if they accrete in the brain in the form of amyloid plaques and tau tangles — causing a breakdown in normal cellular processes.

The team injected P7C3-A20 into both mice cohorts at two months of age, later finding out that this treatment successfully prevented them from developing the disease. But the big news was when they injected the compound into another batch of lab mice, who were suffering from a relatively advanced stage of Alzheimer’s at six months of age; after getting injections, these mice completely recovered their cognitive ability and NAD+ levels were restored to homeostasis leve;s

“We were very excited and encouraged by our results,” said Pieper in the statement. “Restoring the brain’s energy balance achieved pathological and functional recovery in both lines of mice with advanced Alzheimer’s. Seeing this effect in two very different animal models, each driven by different genetic causes, strengthens the new idea that recovery from advanced disease might be possible in people with AD when the brain’s NAD+ balance is restored.”

What’s also good about this study is that P7C3-A20 offers an alternative pathway to boosting NAD+ levels versus taking over-the-counter chemical precursors for NAD+, which can can raise NAD+ to such toxic levels that people could develop cancer, Pieper said. Supplements to boost NAD+ are just a click away on your cellphone, which should be worrying for anybody concerned about cancer.

The team wants to move to human clinical trials but some people are clearly not waiting; if you search online on how to obtain P7C3-A20 for yourself, numerous websites selling vials of the compound will pop up.

More on Alzheimer’s disease: Scientists Identify Possible Game Changing Treatment for Alzheimer’s Disease That Could Control It Like High Cholesterol

The post Alzheimer’s Fully Reversed in Mice, Scientists Say appeared first on Futurism.

🔗 Sumber: futurism.com

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

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 1.5M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. Contact us: [email protected]

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…

Konten dipersingkat otomatis.

🔗 Sumber: syncedreview.com