Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remains consistent with observed regions. In particular, zero-shot approaches that leverage a pretrained diffusion model, without any retraining, have been shown to achieve highly effective reconstructions. However, state-of-t
Adversarial Imitation Learning (AIL) is a dominant framework in imitation learning that infers rewards from expert demonstrations to guide policy optimization. Although providing more expert demonstrations typically leads to improved performance and greater stability, collecting such demonstrations can be challenging in certain scenarios. Inspired by the success of diffusion models in data generat
An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy and Gemini Deep Think. Existing selection methods often rely on external reward models, which requires training a strong reward model and introduces additional computation overhead. As an alternative, previous approaches have explored intrinsic sign
To usher in the next round of client AI innovation, there is an urgent need to enable efficient, lossless inference of high-accuracy large language models (LLMs) and vision language models (VLMs), jointly referred to as xLMs, on client systems. To address this, we present pipelined sharding, a novel, benchmark-profile-guided CPU-GPU hybrid scheduling technique to achieve efficient, VRAM-constraine
Predictive applications of machine learning often rely on small (sub 1 Bn parameter) specialized models tuned to particular domains or modalities. Such models often achieve excellent performance, but lack flexibility. LLMs and VLMs offer versatility, but typically underperform specialized predictors, especially on non-traditional modalities and long-tail domains. We propose MARVIS (Modality Adapti
Capital is concentrating in a few outsized balance-sheet stories rather than a broad spread of startup rounds. The OpenAI nonprofit valuation anchors the cycle, alongside large corporate and financial-system allocations.
Capital is concentrating in a few outsized balance-sheet stories rather than a broad spread of startup rounds. The OpenAI nonprofit valuation anchors the cycle, alongside large corporate and financial-system allocations.
Capital is concentrating in a few outsized balance-sheet stories rather than a broad spread of startup rounds. The OpenAI nonprofit valuation anchors the cycle, alongside large corporate and financial-system allocations.
Named movement is sparse, but the OpenAI-related entries show the organization still drawing senior legal and founding attention. The desk matters here because even a small set of role changes can reshape control, governance, and execution.
Named movement is sparse, but the OpenAI-related entries show the organization still drawing senior legal and founding attention. The desk matters here because even a small set of role changes can reshape control, governance, and execution.
Named movement is sparse, but the OpenAI-related entries show the organization still drawing senior legal and founding attention. The desk matters here because even a small set of role changes can reshape control, governance, and execution.
Fresh scores land across vision and reasoning surfaces, with one model setting a new high on an arena-style visual evaluation and another posting results on broad reasoning and knowledge tests. The signal is less about one leaderboard and more about continued pressure across multiple evaluation axes.
Fresh scores land across vision and reasoning surfaces, with one model setting a new high on an arena-style visual evaluation and another posting results on broad reasoning and knowledge tests. The signal is less about one leaderboard and more about continued pressure across multiple evaluation axes.
Fresh scores land across vision and reasoning surfaces, with one model setting a new high on an arena-style visual evaluation and another posting results on broad reasoning and knowledge tests. The signal is less about one leaderboard and more about continued pressure across multiple evaluation axes.