MAROKO133 Hot ai: Light bends matter: Scientists find laser-triggered shifts in Janus 2D s

📌 MAROKO133 Hot ai: Light bends matter: Scientists find laser-triggered shifts in

Light isn’t just illuminating materials anymore, it’s moving them.

Scientists at Rice University have discovered that beams of light can physically shift atoms in a class of ultra-thin semiconductors known as Janus transition metal dichalcogenides (TMDs), unlocking a new way to tune materials for next-generation optical and quantum technologies.

The finding offers a rare glimpse into how light and matter interact at the atomic level.

When laser light hits these Janus TMDs, it doesn’t just pass through, it exerts a mechanical push on atoms inside the crystal, changing its symmetry and optical behaveior.

The phenomenon, called optostriction, could help build faster and cooler computer chips that use light instead of electricity.

“In nonlinear optics, light can be reshaped to create new colors, faster pulses, or optical switches that turn signals on and off,” said Kunyan Zhang, a Rice doctoral alumna and first author of the study.

“Two-dimensional materials, which are only a few atoms thick, make it possible to build these optical tools on a very small scale.”

Janus materials are a special subtype of TMDs, named after the two-faced Roman god of transitions.

Their top and bottom atomic layers are made of different chemical species, creating an internal imbalance that gives the crystal built-in polarity.

This asymmetry makes them particularly sensitive to light, electric fields, and mechanical strain, allowing researchers to “tune” their behavior more precisely than ordinary semiconductors.

When light pushes back

Using laser light of different colors, the Rice team studied how a two-layer Janus TMD—molybdenum sulfur selenide stacked on molybdenum disulfide—responded to illumination through a process called second harmonic generation (SHG). In SHG, a material emits light at twice the frequency of the incoming beam.

They found that when the incoming light matched the material’s resonant frequencies, the emitted SHG pattern distorted—a sign that atoms were being displaced.

“We discovered that shining light on Janus molybdenum sulfur selenide and molybdenum disulfide creates tiny, directional forces inside the material, which show up as changes in its SHG pattern,” Zhang said.

Under normal conditions, the SHG pattern looks like a six-pointed flower, mirroring the crystal’s symmetry.

But as light pushed the atoms, “this symmetry breaks—the petals of the pattern shrink unevenly,” Zhang explained.

The team traced this distortion to optostriction, where the electromagnetic field of light exerts a small but measurable mechanical force on the atoms.

Because Janus materials have uneven compositions, this push is amplified by strong interlayer coupling, making them ideal for studying and harnessing light-driven atomic motion.

Lighting the future

“Janus materials are ideal for this because their uneven composition creates an enhanced coupling between layers, which makes them more sensitive to light’s tiny forces—forces so small that it is difficult to measure directly, but we can detect them through changes in the SHG signal pattern,” Zhang said.

Such sensitivity could help create optical chips that route and process light instead of electricity, drastically improving energy efficiency.

“Such active control could help design next-generation photonic chips, ultra-sensitive detectors, or quantum light sources—technologies that use light to carry and process information instead of relying on electricity,” said Shengxi Huang, associate professor of electrical and computer engineering at Rice.

By showing that light can quite literally nudge atoms in two-dimensional semiconductors, the research opens a path toward tunable, light-responsive materials that could reshape the future of computing and sensing, one photon push at a time.

The study appears in the journal ACS Nano.

🔗 Sumber: interestingengineering.com

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

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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