February 2026

You’ve probably been there: you want to trade some crypto, so you log into your go-to exchange, check the rate, and hit go. Simple enough, right? Here’s the catch – that rate you just accepted might not be the best one available. In fact, it almost certainly isn’t. Different exchanges price the same crypto pairs differently, sometimes by a fraction of a percent, sometimes by a lot more. And unless you’re manually checking five different platforms before every […]

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Author(s): Mandar Karhade, MD. PhD. Originally published on Towards AI. Why Sequoia and the Tech World Are Betting the House on Voice Agents Another day another news cycle dominated by a valuation number that looks like a typo. But here’s the thing: this one might actually make sense. If this is not it… then what is? We are witnessing the coronation of the first true platform giant of the generative voice era.The article discusses ElevenLabs’ impressive rise, fueled […]

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Every time a new wave of automation appears, the same fear resurfaces: “This time is different. This time, jobs are really gone.” AI feels like that moment for software development. But what’s actually happening is far less dramatic — and far more interesting. ==Programming is not disappearing. Its economic role is changing.== For a long time, software teams scaled linearly. More features meant more people, more ideas meant more engineers. That made sense when writing code was the bottleneck. Today, it […]

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Conditional representation learning aims to extract criterion-specific features for customized tasks. Recent studies project universal features onto the conditional feature subspace spanned by an LLM-generated text basis to obtain conditional representations. However, such methods face two key limitations: sensitivity to subspace basis and vulnerability to inter-subspace interference. To address these challenges, we propose OD-CRL, a novel framework integrating Adaptive Orthogonal Basis Optimization (AOBO) and Null-Space Denoising Projection (NSDP). Specifically, AOBO constructs orthogonal semantic bases via singular value decomposition […]

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The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effectively applied knowledge transfer learning and co-training regularization from large to small models in LSL. However, such training strategies are computationally intensive and prone to local optima, thereby increasing the difficulty of finding the optimal solution. This has prompted us to investigate the feasibility of integrating three low-cost scenarios for text mining tasks: limited labeled supervision, […]

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Federated Learning (FL) aims to train a collaborative model while preserving data privacy. However, the distributed nature of this approach still raises privacy and security issues, such as the exposure of sensitive data due to inference attacks and the influence of Byzantine behaviors on the trained model. In particular, achieving both secure aggregation and Byzantine resilience remains challenging, as existing solutions often address these aspects independently. In this work, we propose to address these challenges through a novel […]

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Federated Learning (FL) aims to train a collaborative model while preserving data privacy. However, the distributed nature of this approach still raises privacy and security issues, such as the exposure of sensitive data due to inference attacks and the influence of Byzantine behaviors on the trained model. In particular, achieving both secure aggregation and Byzantine resilience remains challenging, as existing solutions often address these aspects independently. In this work, we propose to address these challenges through a novel […]

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As Large Language Models (LLMs) achieve remarkable empirical success through scaling model and data size, pretraining has become increasingly critical yet computationally prohibitive, hindering rapid development. Despite the availability of numerous pretrained LLMs developed at significant computational expense, a fundamental real-world question remains underexplored: textit{Can we leverage existing small pretrained models to accelerate the training of larger models?} In this paper, we propose a Late-to-Early Training (LET) paradigm that enables LLMs to explicitly learn later knowledge in earlier […]

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