Hi everyone, I’m a Senior Machine Learning Engineer planning a focused transition into Reinforcement Learning research over the next 12–18 months, and I’d really value advice from people who’ve done this alongside full-time work. Background (brief): • B.Tech + M.Tech (strong math/PDEs) • ~2+ years in ML/DS (forecasting, optimization, CNNs) • Currently building LLM-based agents & multi-agent systems in fintech (orchestration, tools, OpenAI/Anthropic, knowledge graphs),via AI automation I’m comfortable with Python, PyTorch, probability, linear algebra, and optimization. Why […]

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I’ve been working on developing foundation models for massively multimodal datasets (around 30-40 different modalities on 1 dataset, you can kind of think of it like robot with a lot of different sensors). I think most scientific papers I see from the last couple years use Perceiver, which I feel is a really intuitive and elegant solution (like you literally just slap on name of modality + the data and let it handle the rest). However, it is […]

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arXiv:2601.19026v1 Announce Type: new Abstract: Microscaling data formats leverage per-block tensor quantization to enable aggressive model compression with limited loss in accuracy. Unlocking their potential for efficient training and inference necessitates hardware-friendly implementations that handle matrix multiplications in a native format and adopt efficient error-mitigation strategies. Herein, we report the emergence of a surprising behavior associated with microscaling quantization, whereas the output of a quantized model degrades as block size is decreased below a given threshold. This behavior […]

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In late 2025, the Reuters Institute for the Study of Journalism reported that [global publishers have already lost ]()**33% of search referral traffic year over year with media leaders projecting an additional *40%+ decline* as AI-generated answers replace traditional discovery paths. That data point matters far beyond journalism. It confirms something B2B marketers are now feeling in real time: buyers are no longer navigating the web, they are prompting AI systems to do the thinking for them. Call […]

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arXiv:2601.04213v1 Announce Type: new Abstract: Large language models (LLMs) are becoming central to natural language processing education, yet materials showing their mechanics are sparse. We present AnimatedLLM, an interactive web application that provides step-by-step visualizations of a Transformer language model. AnimatedLLM runs entirely in the browser, using pre-computed traces of open LLMs applied on manually curated inputs. The application is available at https://animatedllm.github.io, both as a teaching aid and for self-educational purposes.

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