MAROKO133 Update ai: Meta researchers open the LLM black box to repair flawed AI reasoning

📌 MAROKO133 Eksklusif ai: Meta researchers open the LLM black box to repair flawed

Researchers at Meta FAIR and the University of Edinburgh have developed a new technique that can predict the correctness of a large language model's (LLM) reasoning and even intervene to fix its mistakes. Called Circuit-based Reasoning Verification (CRV), the method looks inside an LLM to monitor its internal “reasoning circuits” and detect signs of computational errors as the model solves a problem.

Their findings show that CRV can detect reasoning errors in LLMs with high accuracy by building and observing a computational graph from the model's internal activations. In a key breakthrough, the researchers also demonstrated they can use this deep insight to apply targeted interventions that correct a model’s faulty reasoning on the fly.

The technique could help solve one of the great challenges of AI: Ensuring a model’s reasoning is faithful and correct. This could be a critical step toward building more trustworthy AI applications for the enterprise, where reliability is paramount.

Investigating chain-of-thought reasoning

Chain-of-thought (CoT) reasoning has been a powerful method for boosting the performance of LLMs on complex tasks and has been one of the key ingredients in the success of reasoning models such as the OpenAI o-series and DeepSeek-R1. 

However, despite the success of CoT, it is not fully reliable. The reasoning process itself is often flawed, and several studies have shown that the CoT tokens an LLM generates is not always a faithful representation of its internal reasoning process.

Current remedies for verifying CoT fall into two main categories. “Black-box” approaches analyze the final generated token or the confidence scores of different token options. “Gray-box” approaches go a step further, looking at the model's internal state by using simple probes on its raw neural activations. 

But while these methods can detect that a model’s internal state is correlated with an error, they can't explain why the underlying computation failed. For real-world applications where understanding the root cause of a failure is crucial, this is a significant gap.

A white-box approach to verification

CRV is based on the idea that models perform tasks using specialized subgraphs, or "circuits," of neurons that function like latent algorithms. So if the model’s reasoning fails, it is caused by a flaw in the execution of one of these algorithms. This means that by inspecting the underlying computational process, we can diagnose the cause of the flaw, similar to how developers examine execution traces to debug traditional software.

To make this possible, the researchers first make the target LLM interpretable. They replace the standard dense layers of the transformer blocks with trained "transcoders." A transcoder is a specialized deep learning component that forces the model to represent its intermediate computations not as a dense, unreadable vector of numbers, but as a sparse and meaningful set of features. Transcoders are similar to the sparse autoencoders (SAE) used in mechanistic interpretability research with the difference that they also preserve the functionality of the network they emulate. This modification effectively installs a diagnostic port into the model, allowing researchers to observe its internal workings.

With this interpretable model in place, the CRV process unfolds in a few steps. For each reasoning step the model takes, CRV constructs an "attribution graph" that maps the causal flow of information between the interpretable features of the transcoder and the tokens it is processing. From this graph, it extracts a "structural fingerprint" that contains a set of features describing the graph's properties. Finally, a “diagnostic classifier” model is trained on these fingerprints to predict whether the reasoning step is correct or not.

At inference time, the classifier monitors the activations of the model and provides feedback on whether the model’s reasoning trace is on the right track.

Finding and fixing errors

The researchers tested their method on a Llama 3.1 8B Instruct model modified with the transcoders, evaluating it on a mix of synthetic (Boolean and Arithmetic) and real-world (GSM8K math problems) datasets. They compared CRV against a comprehensive suite of black-box and gray-box baselines.

The results provide strong empirical support for the central hypothesis: the structural signatures in a reasoning step's computational trace contain a verifiable signal of its correctness. CRV consistently outperformed all baseline methods across every dataset and metric, demonstrating that a deep, structural view of the model's computation is more powerful than surface-level analysis.

Interestingly, the analysis revealed that the signatures of error are highly domain-specific. This means failures in different reasoning tasks (formal logic versus arithmetic calculation) manifest as distinct computational patterns. A classifier trained to detect errors in one domain does not transfer well to another, highlighting that different types of reasoning rely on different internal circuits. In practice, this means that you might need to train a separate classifier for each task (though the transcoder remains unchanged).

The most significant finding, however, is that these error signatures are not just correlational but causal. Because CRV provides a transparent view of the computation, a predicted failure can be traced back to a specific component. In one case study, the model made an order-of-operations error. CRV flagged the step and identified that a "multiplication" feature was firing prematurely. The researchers intervened by manually suppressing that single feature, and the model immediately corrected its path and solved the problem correctly. 

This work represents a step toward a more rigorous science of AI interpretability and control. As the paper concludes, “these findings establish CRV as a proof-of-concept for mechanistic analysis, showing that shifting from opaque activations to interpretable computational structure enables a causal understanding of how and why LLMs fail to reason correctly.” To support further research, the team plans to release its datasets and trained transcoders to the public.

Why it’s important

While CRV is a research proof-of-concept, its results hint at a significant future for AI development. AI models learn internal algorithms, or "circuits," for different tasks. But because these models are opaque, we can't debug them like standard computer programs by tracing bugs to specific steps in the computation. Attribution graphs are the closest thing we have to an execution trace, showing how an output is derived from intermediate steps.

This research suggests that attribution graphs could be the foundation for a new class of AI model debuggers. Such tools would allow developers to understand the root cause of failures, whether it's insufficient training data or interference between competing tasks. This would enable precise mitigations, like targeted fine-tuning or even direct model editing, instead of c…

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🔗 Sumber: venturebeat.com

📌 MAROKO133 Update ai: Why IT leaders should pay attention to Canva’s ‘imagination

The rise of AI marks a critical shift away from decades defined by information-chasing and a push for more and more compute power. 

Canva co-founder and CPO Cameron Adams refers to this dawning time as the “imagination era.” Meaning: Individuals and enterprises must be able to turn creativity into action with AI.  

Canva hopes to position itself at the center of this shift with a sweeping new suite of tools. The company’s new Creative Operating System (COS) integrates AI across every layer of content creation, creating a single, comprehensive creativity platform rather than a simple, template-based design tool.

“We’re entering a new era where we need to rethink how we achieve our goals,” said Adams. “We’re enabling people’s imagination and giving them the tools they need to take action.”

An 'engine' for creativity

Adams describes Canva’s platform as a three-layer stack: The top Visual Suite layer containing designs, images and other content; a collaborative Canva AI plane at center; and a foundational proprietary model holding it all up. 

At the heart of Canva’s strategy is its Creative Operating System (COS) underlying. This “engine,” as Adams describes it, integrates documents, websites, presentations, sheets, whiteboards, videos, social content, hundreds of millions of photos, illustrations, a rich sound library, and numerous templates, charts, and branded elements.

The COS is getting a 2.0 upgrade, but the crucial advance is the “middle, crucial layer” that fully integrates AI and makes it accessible throughout various workflows, Adams explained. This gives creative and technical teams a single dashboard for generating, editing and launching all types of content.

The underlying model is trained to understand the “complexity of design” so the platform can build out various elements — such as photos, videos, textures, or 3D graphics — in real time, matching branding style without the need for manual adjustments. It also supports live collaboration, meaning teams across departments can co-create. 

With a unified dashboard, a user working on a specific design, for instance, can create a new piece of content (say, a presentation) within the same workflow, without having to switch to another window or platform. Also, if they generate an image and aren’t pleased with it, they don’t have to go back and create from scratch; they can immediately begin editing, changing colors or tone. 

Another new capability in COS, “Ask Canva,” provides direct design advice. Users can tag @Canva to get copy suggestions and smart edits; or, they can highlight an image and direct the AI assistant to modify it or generate variants. 

“It’s a really unique interaction,” said Adams, noting that this AI design partner is always present. “It’s a real collaboration between people and AI, and we think it’s a revolutionary change.”

Other new features include a 2.0 video editor and interactive form and email design with drag-and-drop tools. Further, Canva is now incorporated with Affinity, its unified app for pro designers incorporating vector, pixel and layer workflows, and Affinity is “free forever.” 

Automating intelligence, supporting marketing

Branding is critical for enterprise; Canva has introduced new tools to help organizations consistently showcase theirs across platforms. The new Canva Grow engine integrates business objectives into the creative process so teams can workshop, create, distribute and refine ads and other materials. 

As Adams explained: “It automatically scans your website, figures out who your audience is, what assets you use to promote your products, the message it needs to send out, the formats you want to send it out in, makes a creative for you, and you can deploy it directly to the platform without having to leave Canva.”

Marketing teams can now design and launch ads across platforms like Meta, track insights as they happen and refine future content based on performance metrics. “Your brand system is now available inside the AI you’re working with,” Adams noted. 

Success metrics and enterprise adoption

The impact of Canva’s COS is reflected in notable user metrics: More than 250 million people use Canva every month, just over 29 million of which are paid subscribers. Adams reports that 41 billion designs have been created on Canva since launch, which equates to 1 billion each month. 

“If you break that down, it turns into the crazy number of 386 designs being created every single second,” said Adams. Whereas in the early days, it took roughly an hour for users to create a single design. 

Canva customers include Walmart, Disney, Virgin Voyages, Pinterest, FedEx, Expedia and eXp Realty. DocuSign, for one, reported that it unlocked more than 500 hours of team capacity and saved $300,000-plus in design hours by fully integrating Canva into its content creation. Disney, meanwhile, uses translation capabilities for its internationalization work, Adams said. 

Competitors in the design space

Canva plays in an evolving landscape of professional design tools including Adobe Express and Figma; AI-powered challengers led by Microsoft Designer; and direct consumer alternatives like Visme and Piktochart.

Adobe Express (starting at $9.99 a month for premium features) is known for its ease of use and integration with the broader Adobe Creative Cloud ecosystem. It features professional-grade templates and access to Adobe’s extensive stock library, and has incorporated Google's Gemini 2.5 Flash image model and other gen AI features so that designers can create graphics via natural language prompts. Users with some design experience say they prefer its interface, controls and technical advantages over Canva (such as the ability to import high-fidelity PDFs). 

Figma (starting at $3 a month for professional plans) is touted for its real-time collaboration, advanced prototyping capabilities and deep integration with dev workflows; however, some say it has a steeper learning curve and higher-precision design tools, making it preferable for professional designers, developers and product teams working on more complex projects. 

Microsoft Designer (free version available; although a Microsoft 365 subscription starting at $9.99 a month unlocks additional features) benefits from its integration with Microsoft’s AI capabilities, Copilot layout and text generation and Dall-E powered image generation. The platform’s “Inspire Me” and “New Ideas” buttons provide design variations, and users can also import data from Excel, add 3D models from PowerPoint and access images from OneDrive. 

However, users report that its stock photos and template and image libraries are limited compared to Canva's extensive collection, and its visuals can come across as outdated. 

Canva’s advantage seems to be in its extensive template library (more than 600,000 ready-to-use) and asset library (141 million-plus stock photos, videos, graphics, and audio elements).​ Its platform is also praised for its ease of use and interface friendly to non-designers, allowing them to begin quickly without training. 

Canva has also expanded into a variety of content types — documents, websites, presentations, whiteboards, videos, and more — making its platform a comprehensive visual suite than just a graphics tool. 

Canva has <a href="https://www.canva.com…

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🔗 Sumber: venturebeat.com