In a world where “bigger is better” has become the default maxim in AI, Samsung’s recent paper turns that narrative on its head. Their Tiny Recursive Model (TRM), with just 7 million parameters—orders of magnitude smaller than today’s sprawling foundation models—achieves state‑of‑the‑art results on some of the hardest reasoning benchmarks. It’s a provocative demonstration that smarter architecture, not brute force scaling, might be the next frontier.

The Scale Trap: Why Big Models Still Struggle with Reasoning

Over the past few years, the AI arms race has fixated on parameter counts. Models with hundreds of billions—and soon trillions—of parameters have become the norm, enabling fluent language generation, multimodal reasoning, and general-purpose capabilities. Yet, when it comes to multi‑step reasoning—solving puzzles, planning paths, logical deduction—these behemoths remain brittle. A single misstep early in generation can compound errors, leading to invalid conclusions.

To compensate, researchers introduced methods like chain-of-thought prompting, which encourages models to “think aloud” through intermediate steps. However, these methods come with costs: they increase computational load, require specialized prompting or training, and still don’t guarantee flawless logic.

Enter TRM—a model that targets reasoning directly with a recursive architecture built to self-correct, rather than relying on sheer scale or brute force.

The Tiny Recursive Model (TRM): A Minimalist with a Punch

The core insight behind TRM is deceptively simple: use recursion and self‑refinement to incrementally polish both the reasoning trace and the answer itself. The model receives the problem prompt, an initial guess at the answer, and a latent reasoning vector. It then cycles—up to 16 times—through a two-stage process: first, it updates the latent reasoning vector based on the prompt, current answer, and prior reasoning. Second, it uses the updated reasoning to propose an improved answer.

Rather than relying on fixed-point convergence theorems, TRM is trained by backpropagating through the full recursive process. Surprisingly, the researchers found that a shallow two‑layer network version of TRM outperformed a deeper four‑layer variant. Intuitively, restricting capacity may help avoid overfitting and force more generalizable reasoning patterns.

Blowing Benchmarks Out of the Water

The results are striking. On tasks where training data is sparse and reasoning precision is critical, TRM posts significant gains. On the Sudoku-Extreme benchmark, TRM hits 87.4 percent accuracy, compared to a baseline of around 56.5 percent using hierarchical reasoning models (HRMs) with more parameters and longer training. On Maze-Hard, which involves pathfinding in large 30×30 grids, TRM achieves 85.3 percent accuracy, significantly outperforming HRM’s 74.5 percent.

Most dramatically, on the Abstraction and Reasoning Corpus (ARC-AGI) benchmarks—designed to test fluid, general intelligence—TRM’s 7 million-parameter version achieves 44.6 percent on ARC-AGI-1 and 7.8 percent on ARC-AGI-2. These numbers not only beat HRMs with 27 million parameters but also surpass the performance of some of the largest commercial LLMs, such as Gemini 2.5 Pro, which scores around 4.9 percent on ARC-AGI-2.

These gains come without extravagant compute. TRM introduces an adaptive stopping mechanism (ACT) to decide when recursion is sufficient, reducing wasteful extra forward passes during training and inference.

Implications: Architectures Over Scale?

If TRM’s performance holds across broader benchmarks, this work could mark a pivotal shift in how we build AI.

Efficiency and sustainability become much more viable when you can achieve state-of-the-art results without expensive hardware or massive data centers. A 7 million-parameter model that outperforms giants in key reasoning tasks is a stark counterexample to the “bigger is always better” mindset.

Rather than forcing a gigantic general-purpose model to master every task, future systems might combine tiny, specialized reasoning modules with larger generative backbones. You might call a TRM-like module only when precise logic is needed.

ARC-AGI was created to test general fluid intelligence—the ability to solve new, abstract problems. That TRM does well here suggests that architectural cleverness may matter more than scale when it comes to true intelligence, not just pattern matching.

Caveats and Open Questions

TRM’s promise is compelling, but there are several caveats. The benchmarks used—Sudoku, Maze, ARC—are highly structured and well-defined. Real-world reasoning often involves ambiguity, commonsense, and incomplete information.

TRM’s recursion depth is fixed and bounded; some problems might require more flexible or unbounded reasoning chains. It also remains to be seen how TRM-style modules integrate with large language models and whether similar strategies scale to multimodal or open-ended tasks.

Conclusion
Samsung’s Tiny Recursive Model points toward a bold alternative to the current scaling regime: leaner, smarter architectures that recursively self-correct rather than relying on mind-boggling parameter counts. If this approach generalizes, we may be witnessing the dawn of an AI paradigm where efficiency and elegance outstrip brute force.

In the rapidly evolving world of generative AI, text-to-video has become the new frontier. The release of OpenAI’s Sora 2 and Google DeepMind’s Veo 3 has ignited fresh debate over which model currently leads the charge. Both promise cinematic-quality video from text prompts, yet their strengths—and limitations—reveal very different approaches to solving the same problem. So, which one is truly pushing the envelope in AI-generated video? Let’s take a closer look.

The Shape of a New Medium

Sora 2 and Veo 3 aren’t just iterative updates; they represent a leap forward in AI’s ability to understand, simulate, and visualize the physical world. Veo 3, unveiled as part of Google’s Gemini ecosystem, emphasizes realism, cinematic polish, and high-fidelity audio. Sora 2, OpenAI’s successor to its original Sora model, doubles down on deep physics simulation, coherence across time, and intelligent prompt understanding.

Both models target similar creative workflows—commercials, short films, visual storytelling—but their design choices show stark contrasts in how they get there.

Visual Realism and Cinematic Quality

On first impression, both Sora 2 and Veo 3 impress with sharp resolution, consistent lighting, and smooth transitions. Veo 3, in particular, demonstrates a clear edge in cinematic effects: seamless camera movement, depth-of-field rendering, and visually stunning transitions that mimic professional film work. Veo’s ability to replicate human-directed cinematography stands out.

Sora 2, by contrast, leans harder into realistic physics and object behavior. Where Veo 3 dazzles with filmic beauty, Sora 2 seems more intent on ensuring that what happens on screen makes sense. Vehicles move with believable momentum, liquids splash and flow realistically, and characters interact with their environment in ways that respect gravity and friction. This physics-aware realism may not always be as visually glossy as Veo 3, but it adds a layer of believability that matters for narrative coherence.

Temporal Coherence and Scene Continuity

A major weakness of early video generators was temporal inconsistency: objects morphing frame-to-frame, faces flickering, or scene geometry drifting. Sora 2 makes significant strides in solving this. Across 10-second (and sometimes longer) videos, objects remain stable, actions continue naturally, and the scene retains structural integrity.

Veo 3 also shows improvement here, but with caveats. While its short clips (typically 4–8 seconds) hold together well, subtle issues can emerge in complex motion sequences or rapid cuts. In side-by-side prompts involving a person dancing through a rainstorm or a dog running through a forest, Sora 2 often preserves object integrity and movement more effectively over time.

However, Veo 3’s strength in lighting and composition can sometimes make its videos appear more polished—even when inconsistencies are present.

Audio Integration and Lip Sync

Here’s where Veo 3 pulls ahead decisively. Veo 3 not only generates realistic visuals but also supports synchronized audio, including ambient noise, sound effects, and even lip-synced speech. This makes it uniquely suited for use cases like video ads, dialogue scenes, and social media content that require full audiovisual immersion.

Sora 2 has made progress in audio generation, but lip-sync remains rudimentary in current versions. While OpenAI has demonstrated Sora’s ability to match ambient sounds to visuals (like footsteps or weather effects), it has not yet caught up to Veo in producing realistic spoken dialogue.

For creators working in multimedia formats, Veo 3’s audio capabilities are a game-changer.

Prompt Control and Creative Flexibility

Controllability—how much influence users have over the generated output—is key to unlocking creative potential. Veo 3 offers a relatively straightforward prompting system, often yielding high-quality results with minimal fine-tuning. However, it sometimes sacrifices precision for polish; complex multi-step prompts or shot-specific instructions can be hard to achieve.

Sora 2, in contrast, supports a more nuanced form of instruction. It appears better at following detailed, layered prompts involving camera angles, character action, and scene transitions. This makes it especially appealing to storytellers or developers who want fine-grained control over the output.

If you’re crafting a multi-part scene with shifting perspectives and nuanced interactions, Sora 2 often delivers a more controllable, logically grounded result.

Limitations and Access

Despite their power, both models remain gated behind layers of access control. Veo 3 is currently integrated into Google’s suite of tools and remains limited to selected creators, while Sora 2 is available through invite-only access via OpenAI’s platform.

Sora 2 also enforces stricter prompt filtering—especially around violence, celebrities, and copyrighted characters—making it less permissive in some creative contexts. Veo 3, while still governed by safety policies, appears slightly more lenient in some edge cases, though this can change with updates.

Both models are also computationally intensive, and neither is fully accessible via open API or commercial licensing at scale yet.

Final Verdict: Different Strengths, Different Futures

If you’re choosing between Sora 2 and Veo 3, the best answer may not be “which is better?” but “which is better for you?”

• Choose Veo 3 if your priority is audiovisual polish, cinematic beauty, and natural soundscapes. It’s ideal for creators looking to generate short, eye-catching content with minimal post-processing.
• Choose Sora 2 if your work demands physical realism, temporal stability, or precise narrative control. It’s a better fit for complex scenes, storytelling, and simulation-heavy tasks.

Both are leading the charge into a future where the boundary between imagination and reality blurs further with every frame. As the models continue to evolve, the true winners will be the creators who learn to harness their distinct strengths.

The Future of Video Starts Here

In a world saturated with generative content tools, few innovations truly reset the creative landscape. But Luma AI’s latest model, Ray3, just might be one of them.

Touted as the world’s first reasoning-capable video generation model, Ray3 doesn’t just turn text into moving images—it thinks, plans, and refines. And for filmmakers, designers, animators, and creators across the board, it promises something most AI tools still can’t deliver: control, quality, and cinematic depth.

What Makes Ray3 Different

Unlike typical AI video generators that fire off a single clip from your prompt and hope for the best, Ray3 is built to reason. It operates more like a creative collaborator—reading your input, breaking it down into visual tasks, checking its work, and upgrading the result to cinematic quality.

This “thinking before rendering” architecture means you get:

• Smarter scenes: with better alignment between prompt, motion, and story.
• Cleaner drafts: that evolve into hi-fi, high dynamic range (HDR) final cuts.
• Real-time visual feedback: draw on a frame to guide the camera or movement.

Ray3 even allows creators to sketch annotations—like arrows for motion or curves for a camera path—and have the model understand and execute them. This isn’t just text-to-video; it’s direction-to-video.

HDR Native, Studio-Ready

One of Ray3’s most impressive feats is its ability to generate video natively in HDR, supporting 10-, 12-, and 16-bit color depths. For anyone working in film, advertising, or visual effects, this is more than a feature—it’s a lifeline.

With EXR and ACES export support, you can finally drop AI-generated footage directly into professional post-production workflows without conversion or quality loss. The footage is not just pretty—it’s usable, flexible, and cinematic.

This is especially important for:

• Colorists who demand dynamic range and tonal control.
• VFX artists who need footage to integrate seamlessly with rendered scenes.
• Agencies that require brand-safe, edit-ready assets.

Built for Iteration, Not Guesswork

Ray3 introduces a draft and refine workflow. You can quickly explore ideas in lightweight draft mode—low latency, faster feedback—and then promote your favorite version to full high-fidelity output. This dramatically shortens the feedback loop and puts creative control back into the hands of the user.

Behind the scenes, Ray3 continuously evaluates its own output: Is the shot on target? Is the movement fluid? Does the light hit right? It loops through generations until the result feels polished—so you don’t have to waste time regenerating manually.

More Than a Generator—A Creative Partner

While many generative tools feel like black boxes, Ray3 invites interaction. Prompt it, sketch over frames, revise outputs, and guide its choices. The combination of natural language, visual annotation, and cinematic intelligence makes Ray3 a new kind of AI: one that collaborates instead of guessing.

For creators, this unlocks a new tier of control:

• Want to simulate a dolly zoom or pan? Sketch the camera path.
• Need to maintain a character’s appearance across scenes? Ray3 tracks identity.
• Trying to hit a visual beat or dramatic moment? Refine and direct like on a set.

Why You Should Try Ray3 Now

If you’re a creative looking to break into AI-driven video, Ray3 offers the most professional, flexible, and intuitive workflow to date. You no longer have to choose between speed and quality or creativity and control. Ray3 gives you all of it—cinema-quality video with real creative direction.

Whether you’re building a storyboard, visualizing a scene, crafting an ad, or just exploring visual storytelling, Ray3 invites you to create faster, better, and with far more control than ever before.

This isn’t just the next step in AI video. It’s a leap.