MAROKO133 Hot ai: Qwen's new Deep Research update lets you turn its reports into webp

📌 MAROKO133 Hot ai: Qwen's new Deep Research update lets you turn its reports

Chinese e-commerce giant Alibaba’s famously prolific Qwen Team of AI model researchers and engineers has introduced a major expansion to its Qwen Deep Research tool, which is available as an optional modality the user can activate on the web-based Qwen Chat (a competitor to ChatGPT).

The update lets users generate not only comprehensive research reports with well-organized citations, but also interactive web pages and multi-speaker podcasts — all within 1-2 clicks.

This functionality is part of a proprietary release, distinct from many of Qwen’s previous open-source model offerings.

While the feature relies on the open-source models Qwen3-Coder, Qwen-Image, and Qwen3-TTS to power its core capabilities, the end-to-end experience — including research execution, web deployment, and audio generation — is hosted and operated by Qwen.

This means users benefit from a managed, integrated workflow without needing to configure infrastructure. That said, developers with access to the open-source models could theoretically replicate similar functionality on private or commercial systems.

The update was announced via the team’s official X account (@Alibaba_Qwen) today, October 21, 2025, stating:

“Qwen Deep Research just got a major upgrade. It now creates not only the report, but also a live webpage and a podcast — powered by Qwen3-Coder, Qwen-Image, and Qwen3-TTS. Your insights, now visual and audible.”

Multi-Format Research Output

The core workflow begins with a user request inside the Qwen Chat interface. From there, Qwen collaborates by asking clarifying questions to shape the research scope, pulls data from the web and official sources, and analyzes or resolves any inconsistencies it finds — even generating custom code when needed.

A demo video posted by Qwen on X walks through this process on Qwen Chat using the U.S. SaaS market as an example.

In it, Qwen retrieves data from multiple industry sources, identifies discrepancies in market size estimates (e.g., $206 billion vs. $253 billion), and highlights ambiguities in the U.S. share of global figures. The assistant comments on differences in scope between sources and calculates a compound annual growth rate (CAGR) of 19.8% from 2020 to 2023, providing contextual analysis to back up the raw numbers.

Once the research is complete, users can click on the "eyeball" icon below the output result (see screenshot), which will bring up a PDF-style report in the right hand pane.

Then, when viewing the report in the right-hand pane, the user can click the "Create" button in the upper-right hand corner and select from the following two options:

1. "Web Dev" which produces a live, professional-grade web page, automatically deployed and hosted by Qwen, using Qwen3-Coder for structure and Qwen-Image for visuals.

2. "Podcast," which, as it states, produces an audio podcast, featuring dynamic, multi-speaker narration generated by Qwen3-TTS, also hosted by Qwen for easy sharing and playback.

This enables users to quickly convert a single research project into multiple forms of content — written, visual, and audible — with minimal extra input.

The website includes inline graphics generated by Qwen Image, making it suitable for use in public presentations, classrooms, or publishing.

The podcast feature allows users to select between 17 different speaker names as the host and 7 as the co-host, though I wasn't able to find a way to preview the voice outputs before selecting them. It appears designed for deep listening on the go.

There was no way to change the language output that I could see, so mine came out in English, like my reports and initial prompts, though the Qwen LLMs are multi-modal. The voices were slightly more robotic than other AI tools I've used.

Here's an example of a web page I generated on commonalities in authoritarian regimes throughout history, another one on UFO or UAP sightings, and below this paragraph, a podcast on UFO or UAP sightings.

While the website is hosted via a public link, the podcast must be downloaded by the user and can't be linked to publicly, from what I could tell in my brief usage so far.

Note the podcast is much different than the actual report — not just a straight read-through audio version of it, rather, a new format of two hosts discussing and bantering about the subject using the report as the jumping off point.

The web page versions of the report also include new graphics not found in the PDF report.

Comparisons to Google's NotebookLM

While the new capabilities have been well received by many early users, comparisons to other research assistants have surfaced — particularly Google’s NotebookLM, which recently exited beta.

AI commentator and newsletter writer Chubby (@kimmonismus) noted on X:

“I am really grateful that Qwen provides regular updates. That’s great.

But the attempt to build a NotebookLM clone inside Qwen-3-max doesn’t sound very promising compared to Google’s version.”

While NotebookLM is built around organizing and querying existing documents and web pages, Qwen Deep Research focuses more on generating new research content from scratch, aggregating sources from the open web, and presenting it across multiple modalities.

The comparison suggests that while the two tools overlap in general concept — AI-assisted research — they diverge in approach and target user experience.

Availability

Qwen Deep Research is now live and available through the Qwen Chat app. The feature can be accessed with the following URL.

No pricing details have been provided for Qwen3-Max or the specific Deep Research capabilities as of this writing.

What's Next For Qwen Deep Research?

By combining research guidance, data analysis, and multi-format content creation into a single tool, Qwen Deep Research aims to streamline the path from idea to publishable output.

The integration of code, visuals, and voice makes it especially attractive to content creators, educators, and independent analysts who want to scale their research into web- or podcast-friendly forms without switching platforms.

Still, comparisons to more specialized offerings like NotebookLM raise questions about how Qwen’s generalized approach stacks up on depth, precision, and refinement. Whether the strength of its multi-format execution outweighs those concerns may come down to user priorities — and whether they value single-click publishing over tight integration with existing notes and materials.

For now, Qwen is signaling that research doesn’t end with a document — it begins with one.

Let me know if you want this repackaged into something shorter or tailored to a particular audience — newsletter, press-style blog, internal team explainer, etc.

🔗 Sumber: venturebeat.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