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Effective reasoning is crucial for solving complex problems in fields such as mathematics and programming, and LLMs have demonstrated significant improvements through long-chain-of-thought reasoning. However, transformer-based models face limitations due to their quadratic computational complexity and linear memory requirements, making it challenging to process long sequences efficiently. While techniques such as Chain of Thought (CoT) reasoning and adaptive compute allocation have helped boost model performance, these methods also increase computational costs. Additionally, generating multiple outputs and selecting the best one has been explored as a way to enhance reasoning accuracy. However, such methods still depend on transformer-based architectures, which struggle with scalability in large-batch, long-context tasks. To address these challenges, alternatives to the transformer architecture have been explored, including RNN-based models, state space models (SSMs), and linear atten...

AI systems are becoming increasingly dependent on real-time interactions with external data sources and operational tools. These systems are now expected to perform dynamic actions, make decisions in changing environments, and access live information streams. To enable such capabilities, AI architectures are evolving to incorporate standardized interfaces that connect models with services and datasets, thereby facilitating seamless integration. One of the most significant advancements in this area is the adoption of protocols that allow AI to move beyond static prompts and directly interface with cloud platforms, development environments, and remote tools. As AI becomes more autonomous and embedded in critical enterprise infrastructure, the importance of controlling and securing these interaction channels has grown immensely. With these capabilities, however, comes a significant security burden. When AI is empowered to execute tasks or make decisions based on input from various extern...

MLLMs have recently advanced in handling fine-grained, pixel-level visual understanding, thereby expanding their applications to tasks such as precise region-based editing and segmentation. Despite their effectiveness, most existing approaches rely heavily on complex architectures composed of separate components such as vision encoders (e.g., CLIP), segmentation networks, and additional fusion or decoding modules. This reliance on modular systems increases system complexity and limits scalability, especially when adapting to new tasks. Inspired by unified architectures that jointly learn visual and textual features using a single transformer, recent efforts have explored more simplified designs that avoid external components while still enabling strong performance in tasks requiring detailed visual grounding and language interaction. Historically, vision-language models have evolved from contrastive learning approaches, such as CLIP and ALIGN, progressing toward large-scale models tha...

The Challenge of Data Selection in LLM Pretraining Developing large language models entails substantial computational investment, especially when experimenting with alternative pretraining corpora. Comparing datasets at full scale—on the order of billions of parameters and hundreds of billions of tokens—can consume hundreds of thousands of GPU hours per run. Consequently, practitioners resort to smaller‐scale experiments as proxies for large‐model behavior. Yet these “pilot” studies are rarely published, producing a fragmented landscape in which each laboratory repeats similar small‐scale tests without shared benchmarks or methodologies . This opacity impedes reproducibility, underutilizes collective insights, and obscures the true trade‑offs between development compute and final model performance. DataDecide To address these limitations, the Allen Institute for AI (AI2), in collaboration with the University of Washington and the University of Pennsylvania, today releases DataDec...

​Today, OpenAI introduced two new reasoning models— OpenAI o3 and o4-mini —marking a significant advancement in integrating multimodal inputs into AI reasoning processes.​ OpenAI o3: Advanced Reasoning with Multimodal Integration The OpenAI o3 model represents a substantial enhancement over its predecessors, particularly in handling complex tasks across domains such as mathematics, coding, and scientific analysis. A notable feature of o3 is its ability to incorporate visual inputs directly into its reasoning chain. This means that when provided with images—such as diagrams or handwritten notes—the model doesn’t merely process them superficially but integrates the visual information into its analytical workflow, enabling more nuanced and context-aware responses. This capability is facilitated by the model’s support for tools like image analysis and manipulation, allowing operations such as zooming and rotating images as part of its reasoning process. o4-mini: Efficient Reasoning fo...

Biophysical modeling serves as a valuable tool for understanding brain function by linking neural dynamics at the cellular level with large-scale brain activity. These models are governed by biologically interpretable parameters, many of which can be directly measured through experiments. However, some parameters remain unknown and must be tuned to align simulations with empirical data, such as resting-state fMRI. Traditional optimization approaches—including exhaustive search, gradient descent, evolutionary algorithms, and Bayesian optimization—require repeated numerical integration of complex differential equations, making them computationally intensive and difficult to scale for models involving numerous parameters or brain regions. As a result, many studies simplify the problem by tuning only a few parameters or assuming uniform properties across regions, which limits biological realism. More recent efforts aim to enhance biological plausibility by accounting for spatial heterogen...

Diffusion models have demonstrated significant success across various generative tasks, including image synthesis, 3D scene creation, video generation, and human motion modeling. However, their typical training on fixed-domain datasets limits their adaptability to varied formats and complex data structures. To overcome this, recent research has explored the collaborative use of multiple diffusion models by synchronizing their generation processes. These methods often rely on simple heuristics, such as averaging the predicted noise across trajectories, to align generations. While this approach can yield compelling results in tasks like panoramic image synthesis or optical illusions, it lacks task-specific customization and a theoretical explanation for why these strategies work. This leads to inconsistent performance and requires extensive trial-and-error for new tasks, limiting scalability and generalization. Existing works like SyncTweedies and Visual Anagrams have shown the potentia...

The transformer architecture has revolutionized natural language processing, enabling models like GPT to predict the next token in a sequence efficiently. However, these models suffer from a fundamental limitation of performing a one-pass projection of all previous tokens to predict the next token, which restricts their capacity for iterative refinement. Transformers apply constant computational effort regardless of the complexity or ambiguity of the predicted token, lacking mechanisms to reconsider or refine their predictions. Traditional neural networks, including transformers, map input sequences to predict in a single forward pass, processing inputs through multiple layers to refine internal representations. Universal Transformers introduced the recurrent application of transformer layers to capture short-term and long-term dependencies by iteratively refining representations. However, experiments were limited to smaller models and datasets rather than large-scale language models ...

Tabular data is widely utilized in various fields, including scientific research, finance, and healthcare. Traditionally, machine learning models such as gradient-boosted decision trees have been preferred for analyzing tabular data due to their effectiveness in handling heterogeneous and structured datasets. Despite their popularity, these methods have notable limitations, particularly in terms of performance on unseen data distributions, transferring learned knowledge between datasets, and integration challenges with neural network-based models because of their non-differentiable nature. Researchers from the University of Freiburg, Berlin Institute of Health, Prior Labs, and ELLIS Institute have introduced a novel approach named Tabular Prior-data Fitted Network (TabPFN). TabPFN leverages transformer architectures to address common limitations associated with traditional tabular data methods. The model significantly surpasses gradient-boosted decision trees in both classification a...

Transformers have emerged as foundational tools in machine learning , underpinning models that operate on sequential and structured data. One critical challenge in this setup is enabling the model to understand the position of tokens or inputs since Transformers inherently lack a mechanism for encoding order. Rotary Position Embedding (RoPE) became a popular solution, especially in language and vision tasks, because it efficiently encodes absolute positions to facilitate relative spatial understanding. As these models grow in complexity and application across modalities, enhancing the expressiveness and dimensional flexibility of RoPE has become increasingly significant. A significant challenge arises when scaling RoPE, from handling simple 1D sequences to processing multidimensional spatial data. The difficulty lies in preserving two essential features: relativity—enabling the model to distinguish positions relative to one another—and reversibility—ensuring unique recovery of origina...

Multimodal artificial intelligence faces fundamental challenges in effectively integrating and processing diverse data types simultaneously. Current methodologies predominantly rely on late-fusion strategies, where separately pre-trained unimodal models are grafted together, such as attaching vision encoders to language models. This approach, while convenient, raises critical questions about optimality for true multimodal understanding. The inherent biases from unimodal pre-training potentially limit the model’s ability to capture essential cross-modality dependencies. Also, scaling these composite systems introduces significant complexity, as each component brings its hyperparameters, pre-training requirements, and distinct scaling properties. The allocation of computational resources across modalities becomes increasingly difficult with this rigid architectural paradigm, hampering efficient scaling and potentially limiting performance in tasks requiring deep multimodal reasoning and ...

As language models continue to grow in size and complexity, so do the resource requirements needed to train and deploy them. While large-scale models can achieve remarkable performance across a variety of benchmarks, they are often inaccessible to many organizations due to infrastructure limitations and high operational costs. This gap between capability and deployability presents a practical challenge, particularly for enterprises seeking to embed language models into real-time systems or cost-sensitive environments. In recent years, small language models (SLMs) have emerged as a potential solution, offering reduced memory and compute requirements without entirely compromising on performance. Still, many SLMs struggle to provide consistent results across diverse tasks, and their design often involves trade-offs that limit generalization or usability. ServiceNow AI Releases Apriel-5B: A Step Toward Practical AI at Scale To address these concerns, ServiceNow AI has released Apriel-5...

Foundation models, often massive neural networks trained on extensive text and image data, have significantly shifted how artificial intelligence systems handle language and vision tasks. These models are not designed for a single task but generalize across a wide variety by leveraging their pretraining knowledge. Once trained, they can generate coherent responses, classify images, or solve problems without needing new task-specific training. Their scalability and reuse across domains make them a cornerstone of AI development. Despite their broad capabilities, a persistent issue lies in how these models are adapted for new, unseen tasks. In most scenarios, achieving strong performance requires providing them with handcrafted prompts or labeled examples that guide the model on how to behave. This process, however, introduces overhead, as crafting prompts involves trial and error, and collecting labeled examples can be expensive and time-consuming. Moreover, in real-world applications, ...