While 10B-level industrial foundation models have pushed the boundaries of image inpainting, their prohibitive computational costs severely hinder practical dep...

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Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-wor...

Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engi...

Stochastic momentum methods such as heavy ball (HB), Nesterov momentum, and variants of Accelerated SGD (ASGD) [Kidambi et al., 2018] are widely used in modern ...

We introduce Dango, a 1.8B-parameter large language model designed for controlled studies of L1-to-L2 (Japanese-to-English) transfer in second language acquisit...

Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, ...

Giving a diagnosis is the first step in treating a patient. Once a diagnosis is established, the challenge becomes managing a health condition over time — track...

AudioLLMs enable speech recognition conditioned on textual prompts such as domain descriptions or entity lists. However, it remains unclear whether these models...

Dynamic 3D hand reconstruction from egocentric videos is essential for next-generation computing platforms such as AR/VR and AI glasses. Despite its importance,...

Valuable critique of generative image models within visual culture and the humanities has emphasized the role of datasets in shaping the images they produce. Ye...

The remarkable success of Transformer-based models in natural language processing stems from architectural scaling, which leads to a large number of parameters ...

Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approach...

This paper develops local certificates for population-risk increments around a current model. For a local candidate set (mathcal D), the certificate is a two-si...

Dynamical systems are fundamental to modeling the natural world, yet modeling them involves a persistent trade-off: manually prescribed mechanistic models are i...

Current conversational AI systems have made significant progress in language generation, personalization, and long-context interaction. However, most existing m...

Accurate survival prediction is essential for personalized treatment planning in head and neck cancer, yet remains challenging due to the heterogeneous and high...

Automatic Handwritten Text Recognition (HTR) is inherently a challenging task, and its complexity is further increased when dealing with cursive scripts. Althou...

Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions ty...

We present Pareto Q-Learning with Reward Machines (PQLRM), a multi-objective reinforcement learning algorithm for tasks whose reward structure is specified by a...

Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clien...

On-policy self-distillation (OPSD) trains a model on its own rollouts and uses a frozen copy to provide dense token-level targets conditioned on a reference tar...

Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across ...

Team science holds that leadership is contingent: it helps only under specific conditions, and capable, autonomous teams may need none at all. We ask the analog...

an LLM application. Ok, the first thought that comes to your mind is: let’s build a powerful agent! But immediately you ask yourself: which agent framework shou...

Research methods are essential carriers of knowledge contribution in academic papers. Automatic multi-label classification of research methods can support knowl...

Pre-training Large Language Models (LLMs) typically demands large-scale infrastructure with tightly coupled hardware accelerators. While increasing model and da...

Diffusion models have become a promising alternative to autoregressive models. Among these, uniform diffusion language models (UDLMs) permit any token to be upd...

Reinforcement learning (RL) post-training of Diffusion Transformers (DiTs) is prohibitively expensive, requiring thousands of high-end GPUs. Existing works expl...

Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization i...

Automated assessment in software engineering education has advanced significantly for code grading and essay scoring. However, reviewing software architecture d...

part of the question-parsing brick of Enterprise Document Intelligencehttps://towardsdatascience.com/document-intelligence-a-series-on-building-rag-brick-by-bri...

OpenAI’s work in science is motivated by a simple belief: advanced AI can become a powerful partner for scientists, helping them explore more ideas, connect dis...

A network of oscillators that synchronizes perfectly computes nothing further, so an attention architecture built from synchronization must locate its computati...

An auto factory worker can remember the storage bin where she left a partly assembled component the night before, and quickly return to that spot to pick it up....

The advent of agentic vulnerability detection is already becoming a watershed moment for software security. Audits conducted entirely by autonomous LLM agents a...

3D Gaussian Splatting (3DGS) enables high-fidelity and real-time 3D scene reconstruction, but scaling training to large-scale scenes requires optimizing hundred...

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Agentic AI systems are becoming increasingly capable of performing scientific tasks. However, their usefulness to life science researchers depends on how well t...

Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world chall...

In May, the Initiative for New Manufacturing INM marked its first anniversary with MIT Manufacturing Week, four days of events that attracted more than 800 regi...

Distributed stochastic gradient descent (SGD) is limited by communication rather than computation, since each iteration requires an AllReduce across processes. ...

Scientific workflow management systems (WMS) support scalable and reproducible execution of complex pipelines, but workflow design, implementation, and debuggin...

Forecasting the evolution of dynamic environments is crucial for autonomous agents. While generative world models have recently achieved high photorealism in 2D...

Unified Multimodal Modeling aims to integrate visual understanding and generation within a single system. However, existing approaches typically rely on two dis...

Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. ...

Scaling model size, specifically depth and width, has driven significant progress in transformer-based language models. However, most architectures maintain a c...

Collaborative human-object interaction shows dynamic and complex movements that require mutual anticipation and continuous adjustment between participants and t...