Orionnnn/hub-stats huggingface dataset:
Voila: Voice-Language Foundation Models for Real-Time Autonomous
  Interaction and Voice Role-Play
Authors: name: Yemin Shi, name: Yu Shu, name: Siwei Dong, name: Guangyi Liu, name: Jaward Sesay, name: Jingwen Li, name: Zhiting Hu
Published: 2025-05-05 15:05:01

A voice AI agent that blends seamlessly into daily life would interact with
humans in an autonomous, real-time, and emotionally expressive manner. Rather
than merely reacting to commands, it would continuously listen, reason, and
respond proactively, fostering fluid, dynamic, and emotionally resonant
interactions. We introduce Voila, a family of large voice-language foundation
models that make a step towards this vision. Voila moves beyond traditional
pipeline systems by adopting a new end-to-end architecture that enables
full-duplex, low-latency conversations while preserving rich vocal nuances such
as tone, rhythm, and emotion. It achieves a response latency of just 195
milliseconds, surpassing the average human response time. Its hierarchical
multi-scale Transformer integrates the reasoning capabilities of large language
models (LLMs) with powerful acoustic modeling, enabling natural, persona-aware
voice generation -- where users can simply write text instructions to define
the speaker's identity, tone, and other characteristics. Moreover, Voila
supports over one million pre-built voices and efficient customization of new
ones from brief audio samples as short as 10 seconds. Beyond spoken dialogue,
Voila is designed as a unified model for a wide range of voice-based
applications, including automatic speech recognition (ASR), Text-to-Speech
(TTS), and, with minimal adaptation, multilingual speech translation. Voila is
fully open-sourced to support open research and accelerate progress toward
next-generation human-machine interactions.
A Survey on Inference Engines for Large Language Models: Perspectives on
  Optimization and Efficiency
Authors: name: Sihyeong Park, name: Sungryeol Jeon, name: Chaelyn Lee, name: Seokhun Jeon, name: Byung-Soo Kim, name: Jemin Lee
Published: 2025-05-03 02:47:43

Large language models (LLMs) are widely applied in chatbots, code generators,
and search engines. Workloads such as chain-of-thought, complex reasoning, and
agent services significantly increase the inference cost by invoking the model
repeatedly. Optimization methods such as parallelism, compression, and caching
have been adopted to reduce costs, but the diverse service requirements make it
hard to select the right method. Recently, specialized LLM inference engines
have emerged as a key component for integrating the optimization methods into
service-oriented infrastructures. However, a systematic study on inference
engines is still lacking. This paper provides a comprehensive evaluation of 25
open-source and commercial inference engines. We examine each inference engine
in terms of ease-of-use, ease-of-deployment, general-purpose support,
scalability, and suitability for throughput- and latency-aware computation.
Furthermore, we explore the design goals of each inference engine by
investigating the optimization techniques it supports. In addition, we assess
the ecosystem maturity of open source inference engines and handle the
performance and cost policy of commercial solutions. We outline future research
directions that include support for complex LLM-based services, support of
various hardware, and enhanced security, offering practical guidance to
researchers and developers in selecting and designing optimized LLM inference
engines. We also provide a public repository to continually track developments
in this fast-evolving field:
https://github.com/sihyeong/Awesome-LLM-Inference-Engine
SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from
  Sparse and Noisy Demonstrations
Authors: name: Runyi Yu, name: Yinhuai Wang, name: Qihan Zhao, name: Hok Wai Tsui, name: Jingbo Wang, name: Ping Tan, name: Qifeng Chen
Published: 2025-05-04 13:00:29

We address a fundamental challenge in Reinforcement Learning from Interaction
Demonstration (RLID): demonstration noise and coverage limitations. While
existing data collection approaches provide valuable interaction
demonstrations, they often yield sparse, disconnected, and noisy trajectories
that fail to capture the full spectrum of possible skill variations and
transitions. Our key insight is that despite noisy and sparse demonstrations,
there exist infinite physically feasible trajectories that naturally bridge
between demonstrated skills or emerge from their neighboring states, forming a
continuous space of possible skill variations and transitions. Building upon
this insight, we present two data augmentation techniques: a Stitched
Trajectory Graph (STG) that discovers potential transitions between
demonstration skills, and a State Transition Field (STF) that establishes
unique connections for arbitrary states within the demonstration neighborhood.
To enable effective RLID with augmented data, we develop an Adaptive Trajectory
Sampling (ATS) strategy for dynamic curriculum generation and a historical
encoding mechanism for memory-dependent skill learning. Our approach enables
robust skill acquisition that significantly generalizes beyond the reference
demonstrations. Extensive experiments across diverse interaction tasks
demonstrate substantial improvements over state-of-the-art methods in terms of
convergence stability, generalization capability, and recovery robustness.
Agentic Reasoning and Tool Integration for LLMs via Reinforcement
  Learning
Authors: name: Joykirat Singh, name: Raghav Magazine, name: Yash Pandya, name: Akshay Nambi
Published: 2025-04-28 10:42:49

Large language models (LLMs) have achieved remarkable progress in complex
reasoning tasks, yet they remain fundamentally limited by their reliance on
static internal knowledge and text-only reasoning. Real-world problem solving
often demands dynamic, multi-step reasoning, adaptive decision making, and the
ability to interact with external tools and environments. In this work, we
introduce ARTIST (Agentic Reasoning and Tool Integration in Self-improving
Transformers), a unified framework that tightly couples agentic reasoning,
reinforcement learning, and tool integration for LLMs. ARTIST enables models to
autonomously decide when, how, and which tools to invoke within multi-turn
reasoning chains, leveraging outcome-based RL to learn robust strategies for
tool use and environment interaction without requiring step-level supervision.
Extensive experiments on mathematical reasoning and multi-turn function calling
benchmarks show that ARTIST consistently outperforms state-of-the-art
baselines, with up to 22% absolute improvement over base models and strong
gains on the most challenging tasks. Detailed studies and metric analyses
reveal that agentic RL training leads to deeper reasoning, more effective tool
use, and higher-quality solutions. Our results establish agentic RL with tool
integration as a powerful new frontier for robust, interpretable, and
generalizable problem-solving in LLMs.
RM-R1: Reward Modeling as Reasoning
Authors: name: Xiusi Chen, name: Gaotang Li, name: Ziqi Wang, name: Bowen Jin, name: Cheng Qian, name: Yu Wang, name: Hongru Wang, name: Yu Zhang, name: Denghui Zhang, name: Tong Zhang, name: Hanghang Tong, name: Heng Ji
Published: 2025-05-05 06:11:12

Reward modeling is essential for aligning large language models (LLMs) with
human preferences, especially through reinforcement learning from human
feedback (RLHF). To provide accurate reward signals, a reward model (RM) should
stimulate deep thinking and conduct interpretable reasoning before assigning a
score or a judgment. However, existing RMs either produce opaque scalar scores
or directly generate the prediction of a preferred answer, making them struggle
to integrate natural language critiques, thus lacking interpretability.
Inspired by recent advances of long chain-of-thought (CoT) on
reasoning-intensive tasks, we hypothesize and validate that integrating
reasoning capabilities into reward modeling significantly enhances RM's
interpretability and performance. In this work, we introduce a new class of
generative reward models -- Reasoning Reward Models (ReasRMs) -- which
formulate reward modeling as a reasoning task. We propose a reasoning-oriented
training pipeline and train a family of ReasRMs, RM-R1. The training consists
of two key stages: (1) distillation of high-quality reasoning chains and (2)
reinforcement learning with verifiable rewards. RM-R1 improves LLM rollouts by
self-generating reasoning traces or chat-specific rubrics and evaluating
candidate responses against them. Empirically, our models achieve
state-of-the-art or near state-of-the-art performance of generative RMs across
multiple comprehensive reward model benchmarks, outperforming much larger
open-weight models (e.g., Llama3.1-405B) and proprietary ones (e.g., GPT-4o) by
up to 13.8%. Beyond final performance, we perform thorough empirical analysis
to understand the key ingredients of successful ReasRM training. To facilitate
future research, we release six ReasRM models along with code and data at
https://github.com/RM-R1-UIUC/RM-R1.
MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset
  via Attention Routing
Authors: name: Zinan Guo, name: Pengze Zhang, name: Yanze Wu, name: Chong Mou, name: Songtao Zhao, name: Qian He
Published: 2025-05-05 17:50:24

Current multi-subject customization approaches encounter two critical
challenges: the difficulty in acquiring diverse multi-subject training data,
and attribute entanglement across different subjects. To bridge these gaps, we
propose MUSAR - a simple yet effective framework to achieve robust
multi-subject customization while requiring only single-subject training data.
Firstly, to break the data limitation, we introduce debiased diptych learning.
It constructs diptych training pairs from single-subject images to facilitate
multi-subject learning, while actively correcting the distribution bias
introduced by diptych construction via static attention routing and dual-branch
LoRA. Secondly, to eliminate cross-subject entanglement, we introduce dynamic
attention routing mechanism, which adaptively establishes bijective mappings
between generated images and conditional subjects. This design not only
achieves decoupling of multi-subject representations but also maintains
scalable generalization performance with increasing reference subjects.
Comprehensive experiments demonstrate that our MUSAR outperforms existing
methods - even those trained on multi-subject dataset - in image quality,
subject consistency, and interaction naturalness, despite requiring only
single-subject dataset.
TEMPURA: Temporal Event Masked Prediction and Understanding for
  Reasoning in Action
Authors: name: Jen-Hao Cheng, name: Vivian Wang, name: Huayu Wang, name: Huapeng Zhou, name: Yi-Hao Peng, name: Hou-I Liu, name: Hsiang-Wei Huang, name: Kuang-Ming Chen, name: Cheng-Yen Yang, name: Wenhao Chai, name: Yi-Ling Chen, name: Vibhav Vineet, name: Qin Cai, name: Jenq-Neng Hwang
Published: 2025-05-02 21:00:17

Understanding causal event relationships and achieving fine-grained temporal
grounding in videos remain challenging for vision-language models. Existing
methods either compress video tokens to reduce temporal resolution, or treat
videos as unsegmented streams, which obscures fine-grained event boundaries and
limits the modeling of causal dependencies. We propose TEMPURA (Temporal Event
Masked Prediction and Understanding for Reasoning in Action), a two-stage
training framework that enhances video temporal understanding. TEMPURA first
applies masked event prediction reasoning to reconstruct missing events and
generate step-by-step causal explanations from dense event annotations, drawing
inspiration from effective infilling techniques. TEMPURA then learns to perform
video segmentation and dense captioning to decompose videos into
non-overlapping events with detailed, timestamp-aligned descriptions. We train
TEMPURA on VER, a large-scale dataset curated by us that comprises 1M training
instances and 500K videos with temporally aligned event descriptions and
structured reasoning steps. Experiments on temporal grounding and highlight
detection benchmarks demonstrate that TEMPURA outperforms strong baseline
models, confirming that integrating causal reasoning with fine-grained temporal
segmentation leads to improved video understanding.
SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based
  Image Editing
Authors: name: Ming Li, name: Xin Gu, name: Fan Chen, name: Xiaoying Xing, name: Longyin Wen, name: Chen Chen, name: Sijie Zhu
Published: 2025-05-05 05:19:40

Due to the challenges of manually collecting accurate editing data, existing
datasets are typically constructed using various automated methods, leading to
noisy supervision signals caused by the mismatch between editing instructions
and original-edited image pairs. Recent efforts attempt to improve editing
models through generating higher-quality edited images, pre-training on
recognition tasks, or introducing vision-language models (VLMs) but fail to
resolve this fundamental issue. In this paper, we offer a novel solution by
constructing more effective editing instructions for given image pairs. This
includes rectifying the editing instructions to better align with the
original-edited image pairs and using contrastive editing instructions to
further enhance their effectiveness. Specifically, we find that editing models
exhibit specific generation attributes at different inference steps,
independent of the text. Based on these prior attributes, we define a unified
guide for VLMs to rectify editing instructions. However, there are some
challenging editing scenarios that cannot be resolved solely with rectified
instructions. To this end, we further construct contrastive supervision signals
with positive and negative instructions and introduce them into the model
training using triplet loss, thereby further facilitating supervision
effectiveness. Our method does not require the VLM modules or pre-training
tasks used in previous work, offering a more direct and efficient way to
provide better supervision signals, and providing a novel, simple, and
effective solution for instruction-based image editing. Results on multiple
benchmarks demonstrate that our method significantly outperforms existing
approaches. Compared with previous SOTA SmartEdit, we achieve 9.19%
improvements on the Real-Edit benchmark with 30x less training data and 13x
smaller model size.
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement
  Learning
Authors: name: Yi-Fan Zhang, name: Xingyu Lu, name: Xiao Hu, name: Chaoyou Fu, name: Bin Wen, name: Tianke Zhang, name: Changyi Liu, name: Kaiyu Jiang, name: Kaibing Chen, name: Kaiyu Tang, name: Haojie Ding, name: Jiankang Chen, name: Fan Yang, name: Zhang Zhang, name: Tingting Gao, name: Liang Wang
Published: 2025-05-05 17:59:50

Multimodal Reward Models (MRMs) play a crucial role in enhancing the
performance of Multimodal Large Language Models (MLLMs). While recent
advancements have primarily focused on improving the model structure and
training data of MRMs, there has been limited exploration into the
effectiveness of long-term reasoning capabilities for reward modeling and how
to activate these capabilities in MRMs. In this paper, we explore how
Reinforcement Learning (RL) can be used to improve reward modeling.
Specifically, we reformulate the reward modeling problem as a rule-based RL
task. However, we observe that directly applying existing RL algorithms, such
as Reinforce++, to reward modeling often leads to training instability or even
collapse due to the inherent limitations of these algorithms. To address this
issue, we propose the StableReinforce algorithm, which refines the training
loss, advantage estimation strategy, and reward design of existing RL methods.
These refinements result in more stable training dynamics and superior
performance. To facilitate MRM training, we collect 200K preference data from
diverse datasets. Our reward model, R1-Reward, trained using the
StableReinforce algorithm on this dataset, significantly improves performance
on multimodal reward modeling benchmarks. Compared to previous SOTA models,
R1-Reward achieves a 8.4% improvement on the VL Reward-Bench and a 14.3%
improvement on the Multimodal Reward Bench. Moreover, with more inference
compute, R1-Reward's performance is further enhanced, highlighting the
potential of RL algorithms in optimizing MRMs.
Think on your Feet: Adaptive Thinking via Reinforcement Learning for
  Social Agents
Authors: name: Minzheng Wang, name: Yongbin Li, name: Haobo Wang, name: Xinghua Zhang, name: Nan Xu, name: Bingli Wu, name: Fei Huang, name: Haiyang Yu, name: Wenji Mao
Published: 2025-05-04 15:39:58

Effective social intelligence simulation requires language agents to
dynamically adjust reasoning depth, a capability notably absent in current
approaches. While existing methods either lack this kind of reasoning
capability or enforce uniform long chain-of-thought reasoning across all
scenarios, resulting in excessive token usage and inappropriate social
simulation. In this paper, we propose Adaptive Mode
Learning (AML) that strategically selects from four
thinking modes (intuitive reaction rightarrow deep contemplation) based on
real-time context. Our framework's core innovation, the Adaptive
Mode Policy Optimization (AMPO)
algorithm, introduces three key advancements over existing methods: (1)
Multi-granular thinking mode design, (2) Context-aware mode switching across
social interaction, and (3) Token-efficient reasoning via depth-adaptive
processing. Extensive experiments on social intelligence tasks confirm that AML
achieves 15.6% higher task performance than state-of-the-art methods. Notably,
our method outperforms GRPO by 7.0% with 32.8% shorter reasoning chains. These
results demonstrate that context-sensitive thinking mode selection, as
implemented in AMPO, enables more human-like adaptive reasoning than GRPO's
fixed-depth approach
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive
  Streaming Speech Synthesis
Authors: name: Qingkai Fang, name: Yan Zhou, name: Shoutao Guo, name: Shaolei Zhang, name: Yang Feng
Published: 2025-05-05 12:53:09

Real-time, intelligent, and natural speech interaction is an essential part
of the next-generation human-computer interaction. Recent advancements have
showcased the potential of building intelligent spoken chatbots based on large
language models (LLMs). In this paper, we introduce LLaMA-Omni 2, a series of
speech language models (SpeechLMs) ranging from 0.5B to 14B parameters, capable
of achieving high-quality real-time speech interaction. LLaMA-Omni 2 is built
upon the Qwen2.5 series models, integrating a speech encoder and an
autoregressive streaming speech decoder. Despite being trained on only 200K
multi-turn speech dialogue samples, LLaMA-Omni 2 demonstrates strong
performance on several spoken question answering and speech instruction
following benchmarks, surpassing previous state-of-the-art SpeechLMs like
GLM-4-Voice, which was trained on millions of hours of speech data.
Ming-Lite-Uni: Advancements in Unified Architecture for Natural
  Multimodal Interaction
Authors: name: Biao Gong, name: Cheng Zou, name: Dandan Zheng, name: Hu Yu, name: Jingdong Chen, name: Jianxin Sun, name: Junbo Zhao, name: Jun Zhou, name: Kaixiang Ji, name: Lixiang Ru, name: Libin Wang, name: Qingpei Guo, name: Rui Liu, name: Weilong Chai, name: Xinyu Xiao, name: Ziyuan Huang
Published: 2025-05-05 08:56:12

We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a
newly designed unified visual generator and a native multimodal autoregressive
model tailored for unifying vision and language. Specifically, this project
provides an open-source implementation of the integrated MetaQueries and
M2-omni framework, while introducing the novel multi-scale learnable tokens and
multi-scale representation alignment strategy. By leveraging a fixed MLLM and a
learnable diffusion model, Ming-Lite-Uni enables native multimodal AR models to
perform both text-to-image generation and instruction based image editing
tasks, expanding their capabilities beyond pure visual understanding. Our
experimental results demonstrate the strong performance of Ming-Lite-Uni and
illustrate the impressive fluid nature of its interactive process. All code and
model weights are open-sourced to foster further exploration within the
community. Notably, this work aligns with concurrent multimodal AI milestones -
such as ChatGPT-4o with native image generation updated in March 25, 2025 -
underscoring the broader significance of unified models like Ming-Lite-Uni on
the path toward AGI. Ming-Lite-Uni is in alpha stage and will soon be further
refined.
FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language
  Models
Authors: name: Zhouliang Yu, name: Ruotian Peng, name: Keyi Ding, name: Yizhe Li, name: Zhongyuan Peng, name: Minghao Liu, name: Yifan Zhang, name: Zheng Yuan, name: Huajian Xin, name: Wenhao Huang, name: Yandong Wen, name: Ge Zhang, name: Weiyang Liu
Published: 2025-05-05 15:37:00

Formal mathematical reasoning remains a critical challenge for artificial
intelligence, hindered by limitations of existing benchmarks in scope and
scale. To address this, we present FormalMATH, a large-scale Lean4 benchmark
comprising 5,560 formally verified problems spanning from high-school Olympiad
challenges to undergraduate-level theorems across diverse domains (e.g.,
algebra, applied mathematics, calculus, number theory, and discrete
mathematics). To mitigate the inefficiency of manual formalization, we
introduce a novel human-in-the-loop autoformalization pipeline that integrates:
(1) specialized large language models (LLMs) for statement autoformalization,
(2) multi-LLM semantic verification, and (3) negation-based disproof filtering
strategies using off-the-shelf LLM-based provers. This approach reduces expert
annotation costs by retaining 72.09% of statements before manual verification
while ensuring fidelity to the original natural-language problems. Our
evaluation of state-of-the-art LLM-based theorem provers reveals significant
limitations: even the strongest models achieve only 16.46% success rate under
practical sampling budgets, exhibiting pronounced domain bias (e.g., excelling
in algebra but failing in calculus) and over-reliance on simplified automation
tactics. Notably, we identify a counterintuitive inverse relationship between
natural-language solution guidance and proof success in chain-of-thought
reasoning scenarios, suggesting that human-written informal reasoning
introduces noise rather than clarity in the formal reasoning settings. We
believe that FormalMATH provides a robust benchmark for benchmarking formal
mathematical reasoning.