Trending Papers - Hugging Face

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janchorowski

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janchorowski

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andy-yang

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andy-yang

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oriuta

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oriuta

WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report

WeMM-Embedding is a family of universal multimodal embedding models that align text, images, videos, and interleaved inputs in a shared space, achieving state-of-the-art retrieval and recommendation performance across public benchmarks and large-scale WeChat applications.

Tencent

· Published on Aug 25, 2026

WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report

WeMM-Embedding is a family of universal multimodal embedding models that align text, images, videos, and interleaved inputs in a shared space, achieving state-of-the-art retrieval and recommendation performance across public benchmarks and large-scale WeChat applications.

Prime Agent: A Self-Improving RLM Harness

Prime Agent is an open-source harness that uses recursive subagents, persistent computation, and agent-to-agent coordination to extend language models' long-horizon capabilities across coding and reasoning tasks.

Prime Agent: A Self-Improving RLM Harness

Prime Agent is an open-source harness that uses recursive subagents, persistent computation, and agent-to-agent coordination to extend language models' long-horizon capabilities across coding and reasoning tasks.

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akhaliq

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akhaliq

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akhaliq

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akhaliq

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taesiri

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taesiri

Very Large-Scale Multi-Agent Simulation in AgentScope

Enhancements to the AgentScope platform improve scalability, efficiency, and ease of use for large-scale multi-agent simulations through distributed mechanisms, flexible environments, and user-friendly tools.

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akhaliq

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akhaliq

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akhaliq

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taesiri

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taesiri

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krahets

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krahets

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zhifeixie

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zhifeixie

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Zonghao2025

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Zonghao2025

Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Mem0, a memory-centric architecture with graph-based memory, enhances long-term conversational coherence in LLMs by efficiently extracting, consolidating, and retrieving information, outperforming existing memory systems in terms of accuracy and computational efficiency.

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akhaliq

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RuofengYang

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RuofengYang

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jasonrqh

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jasonrqh

Unlimited OCR Works

Unlimited OCR introduces Reference Sliding Window Attention to eliminate growing memory consumption during long-sequence OCR tasks, enabling efficient transcription of multiple pages in a single forward pass.

BAIDU

· Published on Jun 22, 2026

Unlimited OCR Works

Unlimited OCR introduces Reference Sliding Window Attention to eliminate growing memory consumption during long-sequence OCR tasks, enabling efficient transcription of multiple pages in a single forward pass.

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Paranioar

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Paranioar

LLM-as-a-Verifier: A General-Purpose Verification Framework

LLM-as-a-Verifier introduces a probabilistic verification framework that scales across multiple dimensions to improve solution correctness assessment and agent performance across various benchmarks.

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taesiri

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happinessqq

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happinessqq

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VictorKai1996NUS

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VictorKai1996NUS

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lyhisme

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lyhisme

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taesiri

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taesiri

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ChengCui

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ChengCui

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HomieZ

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HomieZ

Kronos: A Foundation Model for the Language of Financial Markets

Kronos, a specialized pre-training framework for financial K-line data, outperforms existing models in forecasting and synthetic data generation through a unique tokenizer and autoregressive pre-training on a large dataset.

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jometeorie

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jometeorie

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andito

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andito

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LoYoT

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LoYoT

Continuous Audio Language Models

Audio Language Models (ALM) have emerged as the dominant paradigm for speech and music generation by representing audio as sequences of discrete tokens. Yet, unlike text tokens, which are invertible, audio tokens are extracted from lossy codecs with a limited bitrate. As a consequence, increasing audio quality requires generating more tokens, which imposes a trade-off between fidelity and computational cost. We address this issue by studying Continuous Audio Language Models (CALM). These models instantiate a large Transformer backbone that produces a contextual embedding at every timestep. This sequential information then conditions an MLP that generates the next continuous frame of an audio VAE through consistency modeling. By avoiding lossy compression, CALM achieves higher quality at lower computational cost than their discrete counterpart. Experiments on speech and music demonstrate improved efficiency and fidelity over state-of-the-art discrete audio language models, facilitating lightweight, high-quality audio generation. Samples are available at https://continuous-audio-language-models.github.io

Continuous Audio Language Models

Audio Language Models (ALM) have emerged as the dominant paradigm for speech and music generation by representing audio as sequences of discrete tokens. Yet, unlike text tokens, which are invertible, audio tokens are extracted from lossy codecs with a limited bitrate. As a consequence, increasing audio quality requires generating more tokens, which imposes a trade-off between fidelity and computational cost. We address this issue by studying Continuous Audio Language Models (CALM). These models instantiate a large Transformer backbone that produces a contextual embedding at every timestep. This sequential information then conditions an MLP that generates the next continuous frame of an audio VAE through consistency modeling. By avoiding lossy compression, CALM achieves higher quality at lower computational cost than their discrete counterpart. Experiments on speech and music demonstrate improved efficiency and fidelity over state-of-the-art discrete audio language models, facilitating lightweight, high-quality audio generation. Samples are available at https://continuous-audio-language-models.github.io

LongCat-Video Technical Report

LongCat-Video, a 13.6B parameter video generation model based on the Diffusion Transformer framework, excels in efficient and high-quality long video generation across multiple tasks using unified architecture, coarse-to-fine generation, and block sparse attention.

LongCat

· Published on Oct 25, 2025

LongCat-Video Technical Report

LongCat-Video, a 13.6B parameter video generation model based on the Diffusion Transformer framework, excels in efficient and high-quality long video generation across multiple tasks using unified architecture, coarse-to-fine generation, and block sparse attention.

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akhaliq

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akhaliq

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Xin64

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Xin64

VibeVoice Technical Report

VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion and a highly efficient continuous speech tokenizer, achieving superior performance and fidelity.

VibeVoice Technical Report

VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion and a highly efficient continuous speech tokenizer, achieving superior performance and fidelity.

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eric-xiang

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eric-xiang

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andito

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andito

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pshlego

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pshlego

AutoDev: Automated AI-Driven Development

AutoDev is an AI-driven software development framework that automates complex engineering tasks within a secure Docker environment, achieving high performance in code and test generation.

AutoDev: Automated AI-Driven Development

AutoDev is an AI-driven software development framework that automates complex engineering tasks within a secure Docker environment, achieving high performance in code and test generation.

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seashell11

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seashell11

Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading

The financial market is a mission-critical playground for AI agents due to its temporal dynamics and low signal-to-noise ratio. Building an effective algorithmic trading system may require a professional team to develop and test over the years. In this paper, we propose an orchestration framework for financial agents, which aims to democratize financial intelligence to the general public. We map each component of the traditional algorithmic trading system to agents, including planner, orchestrator, alpha agents, risk agents, portfolio agents, backtest agents, execution agents, audit agents, and memory agent. We present two in-house trading examples. For the stock trading task (hourly data from 04/2024 to 12/2024), our approach achieved a return of 20.42%, a Sharpe ratio of 2.63, and a maximum drawdown of -3.59%, while the S&P 500 index yielded a return of 15.97%. For the BTC trading task (minute data from 27/07/2025 to 13/08/2025), our approach achieved a return of 8.39%, a Sharpe ratio of 0.38, and a maximum drawdown of -2.80%, whereas the BTC price increased by 3.80%. Our code is available on https://github.com/Open-Finance-Lab/AgenticTrading{GitHub}.

Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading

The financial market is a mission-critical playground for AI agents due to its temporal dynamics and low signal-to-noise ratio. Building an effective algorithmic trading system may require a professional team to develop and test over the years. In this paper, we propose an orchestration framework for financial agents, which aims to democratize financial intelligence to the general public. We map each component of the traditional algorithmic trading system to agents, including planner, orchestrator, alpha agents, risk agents, portfolio agents, backtest agents, execution agents, audit agents, and memory agent. We present two in-house trading examples. For the stock trading task (hourly data from 04/2024 to 12/2024), our approach achieved a return of 20.42%, a Sharpe ratio of 2.63, and a maximum drawdown of -3.59%, while the S&P 500 index yielded a return of 15.97%. For the BTC trading task (minute data from 27/07/2025 to 13/08/2025), our approach achieved a return of 8.39%, a Sharpe ratio of 0.38, and a maximum drawdown of -2.80%, whereas the BTC price increased by 3.80%. Our code is available on https://github.com/Open-Finance-Lab/AgenticTrading{GitHub}.

On-Policy Self-Distillation in Diffusion Models

DiffusionOPSD uses on-policy self-distillation to turn image-level rewards into explicit intermediate targets for diffusion models, improving alignment efficiency and enabling separate analysis of target construction and policy fitting.

On-Policy Self-Distillation in Diffusion Models

DiffusionOPSD uses on-policy self-distillation to turn image-level rewards into explicit intermediate targets for diffusion models, improving alignment efficiency and enabling separate analysis of target construction and policy fitting.

GameWAM: A World Action Model for Video Games

GameWAM is a unified world-action model for native video-game control that jointly predicts future visuals and executable keyboard-mouse actions using block-causal flow matching, mode-specific distributions, and block-cycle replanning.

Tencent

· Published on Aug 25, 2026

GameWAM: A World Action Model for Video Games

GameWAM is a unified world-action model for native video-game control that jointly predicts future visuals and executable keyboard-mouse actions using block-causal flow matching, mode-specific distributions, and block-cycle replanning.

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Leon1207

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Leon1207