BED-LLM effectively applies the sequential Bayesian experimental design (BED) framework to the interactive information collection problem with LLMs.

Compared with the LLaMA model and other state-of-the-art baseline models, REFRAG achieves significant speedup without loss of accuracy.

As a general and lightweight solution, ATE enhances the practicality of deploying VLA models to new robotic platforms and tasks.

MoC provides a new blueprint for the next generation of scalable and controllable long-term video generation models.

The TiG framework enables LLMs to develop procedural understanding by interacting directly with the game environment while retaining their inherent reasoning and interpretation capabilities.

LOVON aims to leverage large language models for hierarchical task planning in conjunction with an open vocabulary visual detection model.

MP1 is able to directly generate motion trajectories within a single network function evaluation.

Meta-rater aims to integrate the four dimensions of expertise, readability, reasoning, and cleanliness with existing quality indicators by learning optimal weights.

MaCP aims to achieve excellent performance in fine-tuning large base models with minimal parameter and memory overhead.

Contextual engineering marks a paradigm upgrade in LLM practice from “prompt engineering” to systematic “contextual engineering”.

Imitation learning acquires strategies by learning from expert demonstrations

POET is a novel reparameterized training algorithm

NSA combines algorithmic innovation and hardware optimization to achieve efficient long-context modeling.

Embodied navigation optimizes navigation routes by interacting with the physical world through moving objects.

DiC is a diffusion model architecture baseline that combines speed and performance

PCEvolve is a novel API-assisted algorithm

EBTs are a promising new paradigm that can simultaneously expand the learning and thinking capabilities of models.

D-MoLE is a novel method designed for continuous multimodal instruction fine-tuning

M+ significantly improves the ability to retain long-term information

AI Flow improves the intelligence, responsiveness and accessibility of AI services

SparseMM prioritizes and preserves visual semantics during decoding

MAS is a computing system consisting of multiple agents interacting in an environment.

CTC is a loss function and modeling method widely used in sequence-to-sequence learning tasks.

The shared knowledge set of the search tree is a search algorithm proposed by Google DeepMind

BED-LLM effectively applies the sequential Bayesian experimental design (BED) framework to the interactive information collection problem with LLMs.

Compared with the LLaMA model and other state-of-the-art baseline models, REFRAG achieves significant speedup without loss of accuracy.

As a general and lightweight solution, ATE enhances the practicality of deploying VLA models to new robotic platforms and tasks.

MoC provides a new blueprint for the next generation of scalable and controllable long-term video generation models.

The TiG framework enables LLMs to develop procedural understanding by interacting directly with the game environment while retaining their inherent reasoning and interpretation capabilities.

LOVON aims to leverage large language models for hierarchical task planning in conjunction with an open vocabulary visual detection model.

MP1 is able to directly generate motion trajectories within a single network function evaluation.

Meta-rater aims to integrate the four dimensions of expertise, readability, reasoning, and cleanliness with existing quality indicators by learning optimal weights.

MaCP aims to achieve excellent performance in fine-tuning large base models with minimal parameter and memory overhead.

Contextual engineering marks a paradigm upgrade in LLM practice from “prompt engineering” to systematic “contextual engineering”.

Imitation learning acquires strategies by learning from expert demonstrations

POET is a novel reparameterized training algorithm

NSA combines algorithmic innovation and hardware optimization to achieve efficient long-context modeling.

Embodied navigation optimizes navigation routes by interacting with the physical world through moving objects.

DiC is a diffusion model architecture baseline that combines speed and performance

PCEvolve is a novel API-assisted algorithm

EBTs are a promising new paradigm that can simultaneously expand the learning and thinking capabilities of models.

D-MoLE is a novel method designed for continuous multimodal instruction fine-tuning

M+ significantly improves the ability to retain long-term information

AI Flow improves the intelligence, responsiveness and accessibility of AI services

SparseMM prioritizes and preserves visual semantics during decoding

MAS is a computing system consisting of multiple agents interacting in an environment.

CTC is a loss function and modeling method widely used in sequence-to-sequence learning tasks.

The shared knowledge set of the search tree is a search algorithm proposed by Google DeepMind