Trending Research
Olympus: A Universal Task Router for Computer Vision Tasks
We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks.
RARE: Retrieval-Augmented Reasoning Modeling
Domain-specific intelligence demands specialized knowledge and sophisticated reasoning for problem-solving, posing significant challenges for large language models (LLMs) that struggle with knowledge hallucination and inadequate reasoning capabilities under constrained parameter budgets.
Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens
Recent advancements in large language models (LLMs) have driven significant progress in zero-shot text-to-speech (TTS) synthesis.
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation
We introduce a new model - Segment any Text (SaT) - to solve this problem.
TrackOcc: Camera-based 4D Panoptic Occupancy Tracking
tsinghua-mars-lab/trackocc
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In this work, we introduce a brand-new task, Camera-based 4D Panoptic Occupancy Tracking, which simultaneously addresses panoptic occupancy segmentation and object tracking from camera-only input.
Efficient Memory Management for Large Language Model Serving with PagedAttention
On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage.
Optimal Stepsize for Diffusion Sampling
Diffusion models achieve remarkable generation quality but suffer from computational intensive sampling due to suboptimal step discretization.
InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX.
Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving
The task of issue resolving is to modify a codebase to generate a patch that addresses a given issue.
