Roelof Botha joins SpaceX’s board of directors
The former Sequoia Capital leader is filling an “existing vacancy” on SpaceX’s board, days after the company went public in the largest IPO ever.
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Robotic Assembly Using Deep Reinforcement Learning
Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]
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SubSearch: Intermediate Rewards for Unsupervised Guided Reasoning in Complex Retrieval
arXiv:2604.07415v1 Announce Type: new Abstract: Large language models (LLMs) are probabilistic in nature and perform more reliably when augmented with external information. As complex queries often require multi-step reasoning over the retrieved information, with no clear or predetermined reasoning path, they remain challenging. Recent approaches train models using reinforcement learning on the model’s outcome, showing promise in improving how models handle complex information. We introduce SubSearch, a specialized framework that shifts from outcome-only supervision to intermediate reward signals […]
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Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos
arXiv:2602.18466v1 Announce Type: new Abstract: K-12 science classrooms are rich sites of inquiry where students coordinate phenomena, evidence, and explanatory models through discourse; yet, the multimodal complexity of these interactions has made automated analysis elusive. Existing benchmarks for classroom discourse focus primarily on mathematics and rely solely on transcripts, overlooking the visual artifacts and model-based reasoning emphasized by the Next Generation Science Standards (NGSS). We address this gap with SciIBI, the first video benchmark for analyzing science classroom […]
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Split-and-Conquer: Distributed Factor Modeling for High-Dimensional Matrix-Variate Time Series
arXiv:2601.11091v1 Announce Type: new Abstract: In this paper, we propose a distributed framework for reducing the dimensionality of high-dimensional, large-scale, heterogeneous matrix-variate time series data using a factor model. The data are first partitioned column-wise (or row-wise) and allocated to node servers, where each node estimates the row (or column) loading matrix via two-dimensional tensor PCA. These local estimates are then transmitted to a central server and aggregated, followed by a final PCA step to obtain the global […]
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Towards Latent Diffusion Suitable For Text
arXiv:2601.16220v1 Announce Type: cross Abstract: Language diffusion models aim to improve sampling speed and coherence over autoregressive LLMs. We introduce Neural Flow Diffusion Models for language generation, an extension of NFDM that enables the straightforward application of continuous diffusion models to discrete state spaces. NFDM learns a multivariate forward process from the data, ensuring that the forward process and generative trajectory are a good fit for language modeling. Our model substantially reduces the likelihood gap with autoregressive models […]
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REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
arXiv:2603.22750v1 Announce Type: new Abstract: Active learning reduces labeling costs by selecting samples that maximize information gain. A dominant framework, Query-by-Committee (QBC), typically relies on perturbation-based diversity by inducing model disagreement through random feature subsetting or data blinding. While this approximates one notion of epistemic uncertainty, it sacrifices direct characterization of the plausible hypothesis space. We propose the complementary approach: Rashomon Ensembled Active Learning (REAL) which constructs a committee by exhaustively enumerating the Rashomon Set of all near-optimal […]
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[Discussion] Using a supervised price predictor as an auxiliary signal in RL-based portfolio trading — does it actually help?
I am working on an RL-based trading system where the agent does more than just predict price direction — it learns portfolio allocation across 6 assets, stop-losses, take-profits, and other trade management decisions. I have been thinking about adding a second model, maybe a transformer or some other suitable architecture, trained on the same 1-hour OHLCV data and possibly auxiliary features, but with a much simpler job: predict only the next price move or just up/down direction. Then […]
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“I Just Don’t Want My Work Being Fed Into The AI Blender”: Queer Artists on Refusing and Resisting Generative AI
arXiv:2604.14266v1 Announce Type: new Abstract: Art-making is a collective social activity through which queer people engage in political resistance, develop identities, archive queer memory, and form community. However, in recent years, generative AI has disrupted queer artistic communities. Through 15 semi-structured interviews, we examine how queer artists are making sense of the encroachment of GenAI into their art worlds. Our findings surface significant tensions between the relationality of our participants’ queer art practices and the perceived anti-relationality of […]
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A Stein Identity for q-Gaussians with Bounded Support
arXiv:2603.03673v1 Announce Type: cross Abstract: Stein’s identity is a fundamental tool in machine learning with applications in generative models, stochastic optimization, and other problems involving gradients of expectations under Gaussian distributions. Less attention has been paid to problems with non-Gaussian expectations. Here, we consider the class of bounded-support $q$-Gaussians and derive a new Stein identity leading to gradient estimators which have nearly identical forms to the Gaussian ones, and which are similarly easy to implement. We do this […]
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