MAROKO133 Breaking ai: World’s first sodium-ion portable power station unveiled, offers 1,

📌 MAROKO133 Hot ai: World’s first sodium-ion portable power station unveiled, offe

Chinese energy storage and portable power system maker Bluetti has unveiled what it calls the “world’s first” sodium-ion portable power station. Called the Pioneer Na, the system will be available for purchase globally from around mid-October 2025.

First unveiled at the Innovation for All (IFA) conference in Berlin last week, the new system is essentially a large, portable rechargeable battery. Featuring AC and DC outputs, instead of the usual lithium-ion/LFP (lithium iron phosphate) batteries, it uses sodium-ion (Na-ion) batteries.

Bluetti also unveiled some other new products at IFA, including its FridgePower Portable Power Station, a 2,016 Wh, 1,800 W, slim design for appliances. They also unveiled their Apex 300, a 2,764.8 Wh / 3,840 W, expandable to 58,000 Wh (can run a house for days).

Bluetti’s RVSolar 48V System, an expandable to 122 kWh, quick install, for RVs/off-grid homes, also made an appearance.

Portable power for cold climates

According to reports, the Pioneer Na has a 900-watt-hour (Wh) capacity, which is sufficient to power laptops, small appliances, or serve as a backup power source. The system has a standard output of 1,500 watts, with a “Power Lifting” mode for up to 2,250 W (for short bursts or heavy resistive devices like heaters).

The system can be recharged using solar power up to 1,900 W, and has a lifecycle of around 4,000 charge cycles. It is essential to note that the system is approximately 20–25% heavier than its LFP equivalents, with a total weight of 35 pounds (16 kg).

One of the system’s main selling points is its great performance in cold environments. According to reports, it can charge at 5 °F (–15 °C) and discharge at -13 °F (–25 °C).

At the lower end, still delivers 80% discharge capacity (most lithium batteries would shut down or degrade heavily). Even at around 14 °F (–10 °C), it can still recharge to 60% capacity.

To put that into perspective, most LFP batteries usually cannot charge at temperatures below the freezing point. To this end, Bluetti is marketing its new system for cold regions and expeditions (they’re even supplying one to an Antarctic explorer).

Due for release in October 2025

The choice of sodium-ion is a wise one, as sodium is far more abundant and cheaper than lithium. It also performs much better in sub-zero conditions. Battery systems based on it are also potentially safer (less prone to thermal runaway, may handle punctures better).

But, these benefits come with some drawbacks too, such as its relatively lower energy density. This is part of the reason why the system is so bulky and heavy. It is also important to note that it is still new tech, so long-term performance in real-world freeze-thaw cycles isn’t proven yet.

Bluetti’s Pioneer Na is the first real commercial test of sodium-ion batteries in portable power. It’s heavier and slightly less energy-dense than lithium, but it works in deep cold where lithium packs fail, has decent cycle life, and could be safer.

Bluetti is positioning it for households in cold climates and adventurers/explorers as a reliable off-grid backup.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Eksklusif ai: Researchers from PSU and Duke introduce “Multi-Agent Sys

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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