Optimal control of the future is the next frontier for AI. Current approaches to this problem are typically rooted in reinforcement learning (RL). RL is mathematically distinct from supervised learning, which has been the main workhorse for the recent achievements in AI. Moreover, RL typically operates in a stationary environment with episodic resets, limiting its utility. Here, we extend supervis
We study how reasoning evolves in a language model -- from supervised fine-tuning (SFT) to reinforcement learning (RL) -- by analyzing how a set of theoretically-inspired datasets influences language model performance in chess. We find that fine-tuning a model to directly predict the best move leads to effective RL and the strongest downstream performance -- however, the RL stage elicits \textit{u
Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes. While finetuning can create the catastrophe forgetting issue, application of meta-learning on LLMs is also limited due to its complexity and scalability. In this paper, we activate the meta-signal of $\beta$ within the SwiGLU blocks, resulting in a meta-gating mechanism that adaptively adjusts the nonlinearity of
The quadratic computational complexity of the standard attention mechanism constitutes a fundamental bottleneck for large language models in long-context inference. While existing KV cache compression methods alleviate memory pressure, they often sacrifice generation quality and fail to address the high overhead of floating-point arithmetic. This paper introduces DASH-KV, an innovative acceleratio
Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill-posed. While deep learning approaches promise efficiency, existing Convolutional Neural Networks (CNNs) and single-paradigm Neural Operators (NOs) struggle with one fundamental issue: frequency entanglement of multi-scale geological features. To addre
Capital is concentrated in a few outsized entries rather than a broad spread of rounds. The pattern points to balance-sheet scale and platform control, not a crowded seed market.
Capital is concentrated in a few outsized entries rather than a broad spread of rounds. The pattern points to balance-sheet scale and platform control, not a crowded seed market.
Capital is concentrated in a few outsized entries rather than a broad spread of rounds. The pattern points to balance-sheet scale and platform control, not a crowded seed market.
The talent desk is too thin to signal a meaningful reshuffle. Without named destination or departure details, these entries do not yet show a trajectory change.
The talent desk is too thin to signal a meaningful reshuffle. Without named destination or departure details, these entries do not yet show a trajectory change.
The talent desk is too thin to signal a meaningful reshuffle. Without named destination or departure details, these entries do not yet show a trajectory change.
Benchmark coverage tracks both general intelligence and task-specific coding surfaces. The key question is whether recent scores reflect isolated wins or a broader tightening across evaluation sets.
Benchmark coverage tracks both general intelligence and task-specific coding surfaces. The key question is whether recent scores reflect isolated wins or a broader tightening across evaluation sets.
Benchmark coverage tracks both general intelligence and task-specific coding surfaces. The key question is whether recent scores reflect isolated wins or a broader tightening across evaluation sets.