MAROKO133 Eksklusif ai: Horrendous Things Happen When You Quit Zyn Cold Turkey Terbaru 202

📌 MAROKO133 Breaking ai: Horrendous Things Happen When You Quit Zyn Cold Turkey Ed

Thinking about dropping those flavorful white sachets of nicotine this resolution season? You might want to reconsider how fast you hit the off ramp.

Zyns — those tiny nicotine pouches that seem to nowinfest every urinal, bar-top, and sidewalk — have captured the hearts and mouths of many who would never consider smoking a cigarette, let alone packing ‘chaw. Since tobacco giant Philip Morris International acquired the Swedish brand for $16 billion in 2022, Zyn consumption among teens and young adults has nearly quadrupled, according to a November study by the CDC Foundation.

As it turns out, it’s much easier to start on the packs than it is to quit, especially if you go cold turkey. A horrifying profile by GQ details writer Rosecrans Baldwin’s effort to go from one 15-pouch can per day to none, virtually overnight. His experience was agonizing.

About four hours after deciding to lay off the nicotine lodes for good, Baldwin wrote that he began to experience dizziness, headaches, intense sweating, a rapid heart rate, clenching jaw, and even rolling hallucinations.

“For several hours, I’d been haunted by yellow-tinted hallucinations, sweeping in and out of my mind like searchlights, which told me I might’ve slipped between worlds,” he explained of his withdrawal experience. “The street was quiet and empty. I knew I shouldn’t stand, let alone go anywhere, in case the situation got worse.”

Little did he know at the time, Baldwin was in the throes of a panic attack, very likely brought about by acute nicotine withdrawal. “I was afraid I’d lost my mind,” he recalled.

In the US, Zyn sells its pouches in strengths varying from 3mg to 6mg. Though cigarettes contain anywhere from 10 to 12mg of nicotine, less than 2mg typically enters the body for each cigarette smoked, since a lot of the smoke drifts away. The absorption rate on nicotine pouches is much higher, as nicotine is delivered straight through the mouth’s mucous membranes and into the blood vessels.

Though Baldwin admits he’s long held a casual relationship with nicotine, he says Zyns took the habit to another level.

“Years went by when I didn’t smoke at all, and there were a few years when I smoked maybe a couple cigarettes a week,” he said. “My Zyn use turbocharged all of that, fostering a dependence I fed unwittingly, with a sachet secreting nicotine into every waking moment for more than a year.”

Baldwin’s first quitting attempt wasn’t successful, as he soon bought a pack of cigarettes and fell back into “nicotine’s grasp.” A while later, though, he decided to try again, this time by winding down his intake steadily over a few months — an effort which he says has finally freed him from the substance once and for all.

More on tobacco: FDA Working to Remove the Stuff in Cigarettes That Feels Good

The post Horrendous Things Happen When You Quit Zyn Cold Turkey appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Breaking ai: Which Agent Causes Task Failures and When?Researchers fro

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…

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🔗 Sumber: syncedreview.com