"MiSO: Optimizing brain stimulation to create neural population activity states", Minai et al 2024
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MIT engineers design an aerial microrobot that can fly as fast as a bumblebee
With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
( 8
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Helping power-system planners prepare for an unknown future
Macro, a modeling tool developed by the MIT Energy Initiative, enables energy-system planners to explore options for developing infrastructure to support decarbonized, reliable, and low-cost power grids.
( 7
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Mixture of Experts Powers the Most Intelligent Frontier AI Models, Runs 10x Faster on NVIDIA Blackwell NVL72
The top 10 most intelligent open-source models all use a mixture-of-experts architecture. Kimi K2 Thinking, DeepSeek-R1, Mistral Large 3 and others run 10x faster on NVIDIA GB200 NVL72. A look under the hood of virtually any frontier model today will reveal a mixture-of-experts (MoE) model architecture that mimics the efficiency of the human brain. Just
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( 10
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Risk-Sensitive Q-Learning in Continuous Time with Application to Dynamic Portfolio Selection
arXiv:2512.02386v1 Announce Type: new
Abstract: This paper studies the problem of risk-sensitive reinforcement learning (RSRL) in continuous time, where the environment is characterized by a controllable stochastic differential equation (SDE) and the objective is a potentially nonlinear functional of cumulative rewards. We prove that when the functional is an optimized certainty equivalent (OCE), the optimal policy is Markovian with respect to an augmented environment. We also propose \textit{CT-RS-q}, a risk-sensitive q-learning algorithm based on a novel martingale characterization approach. Finally, we run a simulation study on a dynamic portfolio selection problem and illustrate the effectiveness of our algorithm.
( 2
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Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data
arXiv:2512.02489v1 Announce Type: new
Abstract: I present an application of established machine learning techniques to NHANES health survey data for predicting diabetes status. I compare baseline models (logistic regression, random forest, XGBoost) with a hybrid approach that uses an XGBoost feature encoder and a lightweight multilayer perceptron (MLP) head. Experiments show the hybrid model attains improved AUC and balanced accuracy compared to baselines on the processed NHANES subset. I release code and reproducible scripts to encourage replication.
( 2
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A Concise Review of Hallucinations in LLMs and their Mitigation
arXiv:2512.02527v1 Announce Type: cross
Abstract: Traditional language models face a challenge from hallucinations. Their very presence casts a large, dangerous shadow over the promising realm of natural language processing. It becomes crucial to understand the various kinds of hallucinations that occur nowadays, their origins, and ways of reducing them. This document provides a concise and straightforward summary of that. It serves as a one-stop resource for a general understanding of hallucinations and how to mitigate them.
( 2
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Convergent Reinforcement Learning Algorithms for Stochastic Shortest Path Problem
arXiv:2508.13963v2 Announce Type: replace
Abstract: In this paper we propose two algorithms in the tabular setting and an algorithm for the function approximation setting for the Stochastic Shortest Path (SSP) problem. SSP problems form an important class of problems in Reinforcement Learning (RL), as other types of cost-criteria in RL can be formulated in the setting of SSP. We show asymptotic almost-sure convergence for all our algorithms. We observe superior performance of our tabular algorithms compared to other well-known convergent RL algorithms. We further observe reliable performance of our function approximation algorithm compared to other algorithms in the function approximation setting.
( 2
min )
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
arXiv:2009.04413v2 Announce Type: replace-cross
Abstract: SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models. In this paper, we formulate a framework for word embedding, referred to as Word-Context Classification (WCC), that generalizes SGN to a wide family of models. The framework, which uses some ``noise examples'', is justified through theoretical analysis. The impact of noise distribution on the learning of the WCC embedding models is studied experimentally, suggesting that the best noise distribution is, in fact, the data distribution, in terms of both the embedding performance and the speed of convergence during training. Along our way, we discover several novel embedding models that outperform existing WCC models.
( 2
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Decentralized Projection-free Online Upper-Linearizable Optimization with Applications to DR-Submodular Optimization
arXiv:2501.18183v2 Announce Type: replace-cross
Abstract: We introduce a novel framework for decentralized projection-free optimization, extending projection-free methods to a broader class of upper-linearizable functions. Our approach leverages decentralized optimization techniques with the flexibility of upper-linearizable function frameworks, effectively generalizing traditional DR-submodular function optimization. We obtain the regret of $O(T^{1-\theta/2})$ with communication complexity of $O(T^{\theta})$ and number of linear optimization oracle calls of $O(T^{2\theta})$ for decentralized upper-linearizable function optimization, for any $0\le \theta \le 1$. This approach allows for the first results for monotone up-concave optimization with general convex constraints and non-monotone up-concave optimization with general convex constraints. Further, the above results for first order feedback are extended to zeroth order, semi-bandit, and bandit feedback.
( 2
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GRAFT: GRaPH and Table Reasoning for Textual Alignment -- A Benchmark for Structured Instruction Following and Visual Reasoning
arXiv:2508.15690v4 Announce Type: replace-cross
Abstract: GRAFT is a structured multimodal benchmark designed to probe how well LLMs handle instruction following, visual reasoning, and tasks requiring tight visual textual alignment. The dataset is built around programmatically generated charts and synthetically rendered tables, each paired with a carefully constructed, multi step analytical question that depends solely on what can be inferred from the image itself. Responses are formatted in structured outputs such as JSON or YAML, enabling consistent and fine grained evaluation of both reasoning processes and adherence to output specifications. The benchmark further introduces a taxonomy of reasoning operations ranging from comparison and trend identification to ranking, aggregation, proportional estimation, and anomaly detection to support a comprehensive assessment of model capabilities. Taken together, GRAFT provides a unified and scalable framework for evaluating multimodal LLMs on visually grounded, structured reasoning tasks, offering a more rigorous standard for future benchmarking efforts.
( 2
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Open
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
arXiv:2009.04413v2 Announce Type: replace-cross
Abstract: SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models. In this paper, we formulate a framework for word embedding, referred to as Word-Context Classification (WCC), that generalizes SGN to a wide family of models. The framework, which uses some ``noise examples'', is justified through theoretical analysis. The impact of noise distribution on the learning of the WCC embedding models is studied experimentally, suggesting that the best noise distribution is, in fact, the data distribution, in terms of both the embedding performance and the speed of convergence during training. Along our way, we discover several novel embedding models that outperform existing WCC models.
( 2
min )
Decentralized Projection-free Online Upper-Linearizable Optimization with Applications to DR-Submodular Optimization
arXiv:2501.18183v2 Announce Type: replace-cross
Abstract: We introduce a novel framework for decentralized projection-free optimization, extending projection-free methods to a broader class of upper-linearizable functions. Our approach leverages decentralized optimization techniques with the flexibility of upper-linearizable function frameworks, effectively generalizing traditional DR-submodular function optimization. We obtain the regret of $O(T^{1-\theta/2})$ with communication complexity of $O(T^{\theta})$ and number of linear optimization oracle calls of $O(T^{2\theta})$ for decentralized upper-linearizable function optimization, for any $0\le \theta \le 1$. This approach allows for the first results for monotone up-concave optimization with general convex constraints and non-monotone up-concave optimization with general convex constraints. Further, the above results for first order feedback are extended to zeroth order, semi-bandit, and bandit feedback.
( 2
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Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 satellite data
arXiv:2509.18228v2 Announce Type: replace-cross
Abstract: We present a new 10-meter map of dominant tree species in Swedish forests accompanied by pixel-level uncertainty estimates. The tree species classification is based on spatiotemporal metrics derived from Sentinel-1 and Sentinel-2 satellite data, combined with field observations from the Swedish National Forest Inventory. We apply an extreme gradient boosting model with Bayesian optimization to relate field observations to satellite-derived features and generate the final species map. Classification uncertainty is quantified using Shannon's entropy of the predicted class probabilities, which provide a spatially explicit measure of model confidence. The final model achieved an overall accuracy of 85% (F1 score = 0.82, Matthews correlation coefficient = 0.81), and mapped species distributions showed strong agreement with official forest statistics (Spearman's rho = 0.94).
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