DSD: A Distributed Speculative Decoding Solution for Edge-Cloud Agile Large Model Serving

A new benchmark called CaptionQA has been introduced to evaluate the utility of model-generated captions in supporting downstream tasks across various domains, including Natural, Document, E-commerce, and Embodied AI. This benchmark consists of 33,027 annotated multiple-choice questions that require visual information to answer, aiming to assess whether captions can effectively replace images in multimodal systems.

Inferix has been introduced as a next-generation inference engine that utilizes a block-diffusion decoding paradigm, merging diffusion and autoregressive methods to enhance video generation capabilities. This innovation aims to create long, interactive, and high-quality videos, which are essential for applications in agentic AI, embodied AI, and gaming.

MUSE, a new framework for emotional synthesis in images, has been introduced, addressing inefficiencies in current Image Emotional Synthesis (IES) methods by integrating emotional generation and editing tasks. This approach leverages Test-Time Scaling, allowing for stable synthesis guidance without the need for additional model updates or specialized datasets.

Recent advancements in Large Language Models (LLMs) have led to the development of a multi-reward Group Relative Policy Optimization (GRPO) framework aimed at enhancing the stability and prosody of single-codebook text-to-speech (TTS) systems. This framework integrates various rule-based rewards to optimize token generation policies, addressing issues such as unstable prosody and speaker drift that have plagued existing models.

A new framework has been developed for generating safety-critical scenarios in autonomous driving, utilizing a conditional variational autoencoder (CVAE) and a large language model (LLM). This approach addresses the challenges posed by rare long-tail events and complex multi-agent interactions, which are crucial for safety validation but often underrepresented in real-world data. The integration allows for the creation of realistic and risk-sensitive scenarios.

Augur has introduced a novel framework for time series forecasting that leverages large language models (LLMs) to identify and utilize directed causal associations among covariates. This two-stage architecture involves a teacher LLM that infers a causal graph and a student agent that refines this graph for improved forecasting accuracy.

A recent study evaluates the ability of models to generalize attribute knowledge across unrelated categories, such as identifying shared attributes between dogs and chairs. This research introduces innovative train-test split strategies to assess the robustness of attribute prediction tasks under conditions of reduced correlation between training and test sets.

The REFLEX paradigm has been introduced as a self-refining approach to automated fact-checking, addressing the challenges of misinformation on social media by leveraging internal knowledge from large language models (LLMs) to enhance both accuracy and explanation quality. This innovative method reformulates fact-checking into a role-play dialogue, allowing for joint training of verdict prediction and explanation generation.