Trending Research
TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools
It selects tools based on task objectives and executes structured function calls to solve therapeutic tasks that require clinical reasoning and cross-source validation.
Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
prs-eth/thera
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We present a novel way to design neural fields such that points can be queried with an adaptive Gaussian PSF, so as to guarantee correct anti-aliasing at any desired output resolution.
Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question Answering
Recently, reinforcement learning (RL) has been shown to greatly enhance the reasoning capabilities of large language models (LLMs), and RL-based approaches have been progressively applied to visual multimodal tasks.
Agent S: An Open Agentic Framework that Uses Computers Like a Human
We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks.
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.
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeling and are limited to fixed-length generation.
YOLOE: Real-Time Seeing Anything
THU-MIG/yoloe
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Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios.
FoundationStereo: Zero-Shot Stereo Matching
However, achieving strong zero-shot generalization - a hallmark of foundation models in other computer vision tasks - remains challenging for stereo matching.
ReasonGraph: Visualisation of Reasoning Paths
Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools.
Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k
With this model, we demonstrate that the cost of training a top-performing video generation model is highly controllable.
