Orionnnn/hub-stats huggingface dataset:
Inpainting-Guided Policy Optimization for Diffusion Large Language
  Models
Authors: name: Siyan Zhao, name: Mengchen Liu, name: Jing Huang, name: Miao Liu, name: Chenyu Wang, name: Bo Liu, name: Yuandong Tian, name: Guan Pang, name: Sean Bell, name: Aditya Grover, name: Feiyu Chen
Published: 2025-09-12 16:44:31

Masked diffusion large language models (dLLMs) are emerging as promising
alternatives to autoregressive LLMs, offering competitive performance while
supporting unique generation capabilities such as inpainting. We explore how
inpainting can inform RL algorithm design for dLLMs. Aligning LLMs with
reinforcement learning faces an exploration challenge: sparse reward signals
and sample waste when models fail to discover correct solutions. While this
inefficiency affects LLMs broadly, dLLMs offer a distinctive opportunity--their
inpainting ability can guide exploration. We introduce IGPO (Inpainting Guided
Policy Optimization), an RL framework that strategically inserts partial
ground-truth reasoning traces during online sampling. Unlike providing full
solutions, inpainting steers exploration toward promising trajectory spaces
while preserving self-generated reasoning, bridging supervised fine-tuning and
reinforcement learning. We apply IGPO to group-based optimization methods such
as GRPO, where exploration failures cause zero advantages and gradients. IGPO
restores meaningful gradients while improving sample efficiency. We also
propose supervised fine-tuning on synthetically rewritten concise traces that
better align with dLLM generation patterns. With additional techniques
including entropy-based filtering, our training recipe yields substantial gains
across three mathematical benchmarks--GSM8K, Math500, and AMC--achieving new
state-of-the-art results for full-attention masked dLLMs.
CMHG: A Dataset and Benchmark for Headline Generation of Minority
  Languages in China
Authors: name: Guixian Xu, name: Zeli Su, name: Ziyin Zhang, name: Jianing Liu, name: XU Han, name: Ting Zhang, name: Yushuang Dong
Published: 2025-09-12 06:18:44

Minority languages in China, such as Tibetan, Uyghur, and Traditional
Mongolian, face significant challenges due to their unique writing systems,
which differ from international standards. This discrepancy has led to a severe
lack of relevant corpora, particularly for supervised tasks like headline
generation. To address this gap, we introduce a novel dataset, Chinese Minority
Headline Generation (CMHG), which includes 100,000 entries for Tibetan, and
50,000 entries each for Uyghur and Mongolian, specifically curated for headline
generation tasks. Additionally, we propose a high-quality test set annotated by
native speakers, designed to serve as a benchmark for future research in this
domain. We hope this dataset will become a valuable resource for advancing
headline generation in Chinese minority languages and contribute to the
development of related benchmarks.
HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented
  Generation for Multi-hop Question Answering
Authors: name: Duolin Sun, name: Dan Yang, name: Yue Shen, name: Yihan Jiao, name: Zhehao Tan, name: Jie Feng, name: Lianzhen Zhong, name: Jian Wang, name: Peng Wei, name: Jinjie Gu
Published: 2025-09-08 06:22:38

The Retrieval-Augmented Generation (RAG) approach enhances question-answering
systems and dialogue generation tasks by integrating information retrieval (IR)
technologies with large language models (LLMs). This strategy, which retrieves
information from external knowledge bases to bolster the response capabilities
of generative models, has achieved certain successes. However, current RAG
methods still face numerous challenges when dealing with multi-hop queries. For
instance, some approaches overly rely on iterative retrieval, wasting too many
retrieval steps on compound queries. Additionally, using the original complex
query for retrieval may fail to capture content relevant to specific
sub-queries, resulting in noisy retrieved content. If the noise is not managed,
it can lead to the problem of noise accumulation. To address these issues, we
introduce HANRAG, a novel heuristic-based framework designed to efficiently
tackle problems of varying complexity. Driven by a powerful revelator, HANRAG
routes queries, decomposes them into sub-queries, and filters noise from
retrieved documents. This enhances the system's adaptability and noise
resistance, making it highly capable of handling diverse queries. We compare
the proposed framework against other leading industry methods across various
benchmarks. The results demonstrate that our framework obtains superior
performance in both single-hop and multi-hop question-answering tasks.
LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised
  Learning in Open-World Scenarios
Authors: name: Jiahao Chen, name: Zhiyuan Huang, name: Yurou Liu, name: Bing Su
Published: 2025-09-12 02:28:32

Long-tailed learning has garnered increasing attention due to its wide
applicability in real-world scenarios. Among existing approaches, Long-Tailed
Semi-Supervised Learning (LTSSL) has emerged as an effective solution by
incorporating a large amount of unlabeled data into the imbalanced labeled
dataset. However, most prior LTSSL methods are designed to train models from
scratch, which often leads to issues such as overconfidence and low-quality
pseudo-labels. To address these challenges, we extend LTSSL into the foundation
model fine-tuning paradigm and propose a novel framework: LoFT (Long-tailed
semi-supervised learning via parameter-efficient Fine-Tuning). We demonstrate
that fine-tuned foundation models can generate more reliable pseudolabels,
thereby benefiting imbalanced learning. Furthermore, we explore a more
practical setting by investigating semi-supervised learning under open-world
conditions, where the unlabeled data may include out-of-distribution (OOD)
samples. To handle this problem, we propose LoFT-OW (LoFT under Open-World
scenarios) to improve the discriminative ability. Experimental results on
multiple benchmarks demonstrate that our method achieves superior performance
compared to previous approaches, even when utilizing only 1\% of the unlabeled
data compared with previous works.
Virtual Agent Economies
Authors: name: Nenad Tomasev, name: Matija Franklin, name: Joel Z. Leibo, name: Julian Jacobs, name: William A. Cunningham, name: Iason Gabriel, name: Simon Osindero
Published: 2025-09-12 11:20:11

The rapid adoption of autonomous AI agents is giving rise to a new economic
layer where agents transact and coordinate at scales and speeds beyond direct
human oversight. We propose the "sandbox economy" as a framework for analyzing
this emergent system, characterizing it along two key dimensions: its origins
(emergent vs. intentional) and its degree of separateness from the established
human economy (permeable vs. impermeable). Our current trajectory points toward
a spontaneous emergence of a vast and highly permeable AI agent economy,
presenting us with opportunities for an unprecedented degree of coordination as
well as significant challenges, including systemic economic risk and
exacerbated inequality. Here we discuss a number of possible design choices
that may lead to safely steerable AI agent markets. In particular, we consider
auction mechanisms for fair resource allocation and preference resolution, the
design of AI "mission economies" to coordinate around achieving collective
goals, and socio-technical infrastructure needed to ensure trust, safety, and
accountability. By doing this, we argue for the proactive design of steerable
agent markets to ensure the coming technological shift aligns with humanity's
long-term collective flourishing.
VStyle: A Benchmark for Voice Style Adaptation with Spoken Instructions
Authors: name: Jun Zhan, name: Mingyang Han, name: Yuxuan Xie, name: Chen Wang, name: Dong Zhang, name: Kexin Huang, name: Haoxiang Shi, name: DongXiao Wang, name: Tengtao Song, name: Qinyuan Cheng, name: Shimin Li, name: Jun Song, name: Xipeng Qiu, name: Bo Zheng
Published: 2025-09-09 14:28:58

Spoken language models (SLMs) have emerged as a unified paradigm for speech
understanding and generation, enabling natural human machine interaction.
However, while most progress has focused on semantic accuracy and instruction
following, the ability of SLMs to adapt their speaking style based on spoken
instructions has received limited attention. We introduce Voice Style
Adaptation (VSA), a new task that examines whether SLMs can modify their
speaking style, such as timbre, prosody, or persona following natural language
spoken commands. To study this task, we present VStyle, a bilingual (Chinese &
English) benchmark covering four categories of speech generation: acoustic
attributes, natural language instruction, role play, and implicit empathy. We
also introduce the Large Audio Language Model as a Judge (LALM as a Judge)
framework, which progressively evaluates outputs along textual faithfulness,
style adherence, and naturalness, ensuring reproducible and objective
assessment. Experiments on commercial systems and open source SLMs demonstrate
that current models face clear limitations in controllable style adaptation,
highlighting both the novelty and challenge of this task. By releasing VStyle
and its evaluation toolkit, we aim to provide the community with a foundation
for advancing human centered spoken interaction. The dataset and code are
publicly available at
https://junzhan2000.github.io/VStyle.github.io/{project's homepage}.
QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading
Authors: name: Fei Xiong, name: Xiang Zhang, name: Aosong Feng, name: Siqi Sun, name: Chenyu You
Published: 2025-09-12 06:35:40

Recent advances in Large Language Models (LLMs) have demonstrated impressive
capabilities in financial reasoning and market understanding. Multi-agent LLM
frameworks such as TradingAgent and FINMEM augment these models to long-horizon
investment tasks, leveraging fundamental and sentiment-based inputs for
strategic decision-making. However, such systems are ill-suited for the
high-speed, precision-critical demands of High-Frequency Trading (HFT). HFT
requires rapid, risk-aware decisions based on structured, short-horizon
signals, including technical indicators, chart patterns, and trend-based
features, distinct from the long-term semantic reasoning typical of traditional
financial LLM applications. To this end, we introduce QuantAgent, the first
multi-agent LLM framework explicitly designed for high-frequency algorithmic
trading. The system decomposes trading into four specialized agents, Indicator,
Pattern, Trend, and Risk, each equipped with domain-specific tools and
structured reasoning capabilities to capture distinct aspects of market
dynamics over short temporal windows. In zero-shot evaluations across ten
financial instruments, including Bitcoin and Nasdaq futures, QuantAgent
demonstrates superior performance in both predictive accuracy and cumulative
return over 4-hour trading intervals, outperforming strong neural and
rule-based baselines. Our findings suggest that combining structured financial
priors with language-native reasoning unlocks new potential for traceable,
real-time decision systems in high-frequency financial markets.
MCP-AgentBench: Evaluating Real-World Language Agent Performance with
  MCP-Mediated Tools
Authors: name: Zikang Guo, name: Benfeng Xu, name: Chiwei Zhu, name: Wentao Hong, name: Xiaorui Wang, name: Zhendong Mao
Published: 2025-09-10 14:08:40

The Model Context Protocol (MCP) is rapidly emerging as a pivotal open
standard, designed to enhance agent-tool integration and interoperability, and
is positioned to unlock a new era of powerful, interconnected, and genuinely
utilitarian agentic AI. However, despite MCP's growing adoption, existing
benchmarks often fail to capture real-world agent performance within this new
paradigm, leading to a distorted perception of their true operational value and
an inability to reliably differentiate proficiencies. To bridge this critical
evaluation gap, we introduce MCP-AgentBench -- a comprehensive benchmark
specifically engineered to rigorously assess language agent capabilities in
MCP-mediated tool interactions. Core contributions of MCP-AgentBench include:
the establishment of a robust MCP testbed comprising 33 operational servers
with 188 distinct tools; the development of a benchmark featuring 600
systematically designed queries distributed across 6 distinct categories of
varying interaction complexity; and the introduction of MCP-Eval, a novel
outcome-oriented evaluation methodology prioritizing real-world task success.
Through extensive empirical evaluation of leading language agents, we provide
foundational insights. MCP-AgentBench aims to equip the research community with
a standardized and reliable framework to build, validate, and advance agents
capable of fully leveraging MCP's transformative benefits, thereby accelerating
progress toward truly capable and interoperable AI systems.
The Illusion of Diminishing Returns: Measuring Long Horizon Execution in
  LLMs
Authors: name: Akshit Sinha, name: Arvindh Arun, name: Shashwat Goel, name: Steffen Staab, name: Jonas Geiping
Published: 2025-09-11 17:59:34

Does continued scaling of large language models (LLMs) yield diminishing
returns? Real-world value often stems from the length of task an agent can
complete. We start this work by observing the simple but counterintuitive fact
that marginal gains in single-step accuracy can compound into exponential
improvements in the length of a task a model can successfully complete. Then,
we argue that failures of LLMs when simple tasks are made longer arise from
mistakes in execution, rather than an inability to reason. We propose isolating
execution capability, by explicitly providing the knowledge and plan needed to
solve a long-horizon task. We find that larger models can correctly execute
significantly more turns even when small models have 100\% single-turn
accuracy. We observe that the per-step accuracy of models degrades as the
number of steps increases. This is not just due to long-context limitations --
curiously, we observe a self-conditioning effect -- models become more likely
to make mistakes when the context contains their errors from prior turns.
Self-conditioning does not reduce by just scaling the model size. In contrast,
recent thinking models do not self-condition, and can also execute much longer
tasks in a single turn. We conclude by benchmarking frontier thinking models on
the length of task they can execute in a single turn. Overall, by focusing on
the ability to execute, we hope to reconcile debates on how LLMs can solve
complex reasoning problems yet fail at simple tasks when made longer, and
highlight the massive benefits of scaling model size and sequential test-time
compute for long-horizon tasks.
Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings
  for Improved Diffusion Generation
Authors: name: Sung-Lin Tsai, name: Bo-Lun Huang, name: Yu Ting Shen, name: Cheng Yu Yeo, name: Chiang Tseng, name: Bo-Kai Ruan, name: Wen-Sheng Lien, name: Hong-Han Shuai
Published: 2025-09-12 08:44:22

Accurate color alignment in text-to-image (T2I) generation is critical for
applications such as fashion, product visualization, and interior design, yet
current diffusion models struggle with nuanced and compound color terms (e.g.,
Tiffany blue, lime green, hot pink), often producing images that are misaligned
with human intent. Existing approaches rely on cross-attention manipulation,
reference images, or fine-tuning but fail to systematically resolve ambiguous
color descriptions. To precisely render colors under prompt ambiguity, we
propose a training-free framework that enhances color fidelity by leveraging a
large language model (LLM) to disambiguate color-related prompts and guiding
color blending operations directly in the text embedding space. Our method
first employs a large language model (LLM) to resolve ambiguous color terms in
the text prompt, and then refines the text embeddings based on the spatial
relationships of the resulting color terms in the CIELAB color space. Unlike
prior methods, our approach improves color accuracy without requiring
additional training or external reference images. Experimental results
demonstrate that our framework improves color alignment without compromising
image quality, bridging the gap between text semantics and visual generation.
InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis
Authors: name: Tao Han, name: Wanghan Xu, name: Junchao Gong, name: Xiaoyu Yue, name: Song Guo, name: Luping Zhou, name: Lei Bai
Published: 2025-09-12 17:48:57

Arbitrary resolution image generation provides a consistent visual experience
across devices, having extensive applications for producers and consumers.
Current diffusion models increase computational demand quadratically with
resolution, causing 4K image generation delays over 100 seconds. To solve this,
we explore the second generation upon the latent diffusion models, where the
fixed latent generated by diffusion models is regarded as the content
representation and we propose to decode arbitrary resolution images with a
compact generated latent using a one-step generator. Thus, we present the
InfGen, replacing the VAE decoder with the new generator, for
generating images at any resolution from a fixed-size latent without retraining
the diffusion models, which simplifies the process, reducing computational
complexity and can be applied to any model using the same latent space.
Experiments show InfGen is capable of improving many models into the arbitrary
high-resolution era while cutting 4K image generation time to under 10 seconds.
MachineLearningLM: Continued Pretraining Language Models on Millions of
  Synthetic Tabular Prediction Tasks Scales In-Context ML
Authors: name: Haoyu Dong, name: Pengkun Zhang, name: Mingzhe Lu, name: Yanzhen Shen, name: Guolin Ke
Published: 2025-09-08 15:38:31

Large language models (LLMs) possess broad world knowledge and strong
general-purpose reasoning ability, yet they struggle to learn from many
in-context examples on standard machine learning (ML) tasks, that is, to
leverage many-shot demonstrations purely via in-context learning (ICL) without
gradient descent. We introduce MachineLearningLM, a portable
continued-pretraining framework that equips a general-purpose LLM with robust
in-context ML capability while preserving its general knowledge and reasoning
for broader chat workflows.
  Our pretraining procedure synthesizes ML tasks from millions of structural
causal models (SCMs), spanning shot counts up to 1,024. We begin with a
random-forest teacher, distilling tree-based decision strategies into the LLM
to strengthen robustness in numerical modeling. All tasks are serialized with a
token-efficient prompt, enabling 3x to 6x more examples per context window and
delivering up to 50x amortized throughput via batch inference.
  Despite a modest setup (Qwen-2.5-7B-Instruct with LoRA rank 8),
MachineLearningLM outperforms strong LLM baselines (e.g., GPT-5-mini) by an
average of about 15% on out-of-distribution tabular classification across
finance, physics, biology, and healthcare domains. It exhibits a striking
many-shot scaling law: accuracy increases monotonically as in-context
demonstrations grow from 8 to 1,024. Without any task-specific training, it
attains random-forest-level accuracy across hundreds of shots. General chat
capabilities, including knowledge and reasoning, are preserved: it achieves
75.4% on MMLU.
Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View
  Scenes
Authors: name: Mohsen Gholami, name: Ahmad Rezaei, name: Zhou Weimin, name: Yong Zhang, name: Mohammad Akbari
Published: 2025-09-08 01:08:41

Understanding 3D spatial relationships remains a major limitation of current
Vision-Language Models (VLMs). Prior work has addressed this issue by creating
spatial question-answering (QA) datasets based on single images or indoor
videos. However, real-world embodied AI agents such as robots and self-driving
cars typically rely on ego-centric, multi-view observations. To this end, we
introduce Ego3D-Bench, a new benchmark designed to evaluate the spatial
reasoning abilities of VLMs using ego-centric, multi-view outdoor data.
Ego3D-Bench comprises over 8,600 QA pairs, created with significant involvement
from human annotators to ensure quality and diversity. We benchmark 16 SOTA
VLMs, including GPT-4o, Gemini1.5-Pro, InternVL3, and Qwen2.5-VL. Our results
reveal a notable performance gap between human level scores and VLM
performance, highlighting that current VLMs still fall short of human level
spatial understanding. To bridge this gap, we propose Ego3D-VLM, a
post-training framework that enhances 3D spatial reasoning of VLMs. Ego3D-VLM
generates cognitive map based on estimated global 3D coordinates, resulting in
12% average improvement on multi-choice QA and 56% average improvement on
absolute distance estimation. Ego3D-VLM is modular and can be integrated with
any existing VLM. Together, Ego3D-Bench and Ego3D-VLM offer valuable tools for
advancing toward human level spatial understanding in real-world, multi-view
environments.
AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs
Authors: name: Sidharth Surapaneni, name: Hoang Nguyen, name: Jash Mehta, name: Aman Tiwari, name: Oluwanifemi Bamgbose, name: Akshay Kalkunte, name: Sai Rajeswar, name: Sathwik Tejaswi Madhusudhan
Published: 2025-09-09 15:30:40

Large Audio Language Models (LALMs) are rapidly advancing, but evaluating
them remains challenging due to inefficient toolkits that limit fair comparison
and systematic assessment. Current frameworks suffer from three critical
issues: slow processing that bottlenecks large-scale studies, inconsistent
prompting that hurts reproducibility, and narrow task coverage that misses
important audio reasoning capabilities. We introduce AU-Harness, an efficient
and comprehensive evaluation framework for LALMs. Our system achieves a speedup
of up to 127% over existing toolkits through optimized batch processing and
parallel execution, enabling large-scale evaluations previously impractical. We
provide standardized prompting protocols and flexible configurations for fair
model comparison across diverse scenarios. Additionally, we introduce two new
evaluation categories: LLM-Adaptive Diarization for temporal audio
understanding and Spoken Language Reasoning for complex audio-based cognitive
tasks. Through evaluation across 380+ tasks, we reveal significant gaps in
current LALMs, particularly in temporal understanding and complex spoken
language reasoning tasks. Our findings also highlight a lack of standardization
in instruction modality existent across audio benchmarks, which can lead up
performance differences up to 9.5 absolute points on the challenging complex
instruction following downstream tasks. AU-Harness provides both practical
evaluation tools and insights into model limitations, advancing systematic LALM
development.