Orionnnn/hub-stats huggingface dataset:
Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for
  RLVR
Authors: name: Jiakang Wang, name: Runze Liu, name: Fuzheng Zhang, name: Xiu Li, name: Guorui Zhou
Published: 2025-07-21 16:34:01

Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective
post-training method for improving the reasoning abilities of Large Language
Models (LLMs), mainly by shaping higher-order behaviors such as reflection and
planning. However, previous RLVR algorithms often apply uniform training
signals to all tokens, without considering the different roles of low-entropy
knowledge-related tokens and high-entropy reasoning-related tokens. Some recent
methods try to separate these token types by gradient masking or asynchronous
updates, but these approaches may break semantic dependencies in the model
output and hinder effective learning. In this work, we propose Archer, an
entropy-aware RLVR approach with dual-token constraints and synchronous
updates. Specifically, our method applies weaker KL regularization and higher
clipping thresholds to reasoning tokens to encourage exploration, while using
stronger constraints on knowledge tokens to maintain factual knowledge.
Experimental results on several mathematical reasoning and code generation
benchmarks show that our approach significantly outperforms previous RLVR
methods, reaching or exceeding state-of-the-art performance among models of
comparable size. The code is available at
https://github.com/wizard-III/ArcherCodeR.
WebShaper: Agentically Data Synthesizing via Information-Seeking
  Formalization
Authors: name: Zhengwei Tao, name: Jialong Wu, name: Wenbiao Yin, name: Junkai Zhang, name: Baixuan Li, name: Haiyang Shen, name: Kuan Li, name: Liwen Zhang, name: Xinyu Wang, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Published: 2025-07-20 17:53:37

The advent of Large Language Model (LLM)-powered agents has revolutionized
artificial intelligence by enabling solutions to complex, open-ended tasks
through web-based information-seeking (IS) capabilities. The scarcity of
high-quality training data has limited the development of IS agents. Existing
approaches typically adopt an information-driven paradigm that first collects
web data and then generates questions based on the retrieval. However, this may
lead to inconsistency between information structure and reasoning structure,
question and answer. To mitigate, we propose a formalization-driven IS data
synthesis framework WebShaper to construct a dataset. WebShaper systematically
formalizes IS tasks through set theory. Central to the formalization is the
concept of Knowledge Projections (KP), which enables precise control over
reasoning structure by KP operation compositions. During synthesis, we begin by
creating seed tasks, then use a multi-step expansion process. At each step, an
agentic Expander expands the current formal question more complex with
retrieval and validation tools based on our formalization. We train our model
on the synthesized dataset. Experiment results demonstrate that WebShaper
achieves state-of-the-art performance among open-sourced IS agents on GAIA and
WebWalkerQA benchmarks.
GUI-G^2: Gaussian Reward Modeling for GUI Grounding
Authors: name: Fei Tang, name: Zhangxuan Gu, name: Zhengxi Lu, name: Xuyang Liu, name: Shuheng Shen, name: Changhua Meng, name: Wen Wang, name: Wenqi Zhang, name: Yongliang Shen, name: Weiming Lu, name: Jun Xiao, name: Yueting Zhuang
Published: 2025-07-21 17:53:42

Graphical User Interface (GUI) grounding maps natural language instructions
to precise interface locations for autonomous interaction. Current
reinforcement learning approaches use binary rewards that treat elements as
hit-or-miss targets, creating sparse signals that ignore the continuous nature
of spatial interactions. Motivated by human clicking behavior that naturally
forms Gaussian distributions centered on target elements, we introduce GUI
Gaussian Grounding Rewards (GUI-G^2), a principled reward framework that
models GUI elements as continuous Gaussian distributions across the interface
plane. GUI-G^2 incorporates two synergistic mechanisms: Gaussian point
rewards model precise localization through exponentially decaying distributions
centered on element centroids, while coverage rewards assess spatial alignment
by measuring the overlap between predicted Gaussian distributions and target
regions. To handle diverse element scales, we develop an adaptive variance
mechanism that calibrates reward distributions based on element dimensions.
This framework transforms GUI grounding from sparse binary classification to
dense continuous optimization, where Gaussian distributions generate rich
gradient signals that guide models toward optimal interaction positions.
Extensive experiments across ScreenSpot, ScreenSpot-v2, and ScreenSpot-Pro
benchmarks demonstrate that GUI-G^2, substantially outperforms
state-of-the-art method UI-TARS-72B, with the most significant improvement of
24.7% on ScreenSpot-Pro. Our analysis reveals that continuous modeling provides
superior robustness to interface variations and enhanced generalization to
unseen layouts, establishing a new paradigm for spatial reasoning in GUI
interaction tasks.
OpenBEATs: A Fully Open-Source General-Purpose Audio Encoder
Authors: name: Shikhar Bharadwaj, name: Samuele Cornell, name: Kwanghee Choi, name: Satoru Fukayama, name: Hye-jin Shim, name: Soham Deshmukh, name: Shinji Watanabe
Published: 2025-07-18 17:57:46

Masked token prediction has emerged as a powerful pre-training objective
across language, vision, and speech, offering the potential to unify these
diverse modalities through a single pre-training task. However, its application
for general audio understanding remains underexplored, with BEATs being the
only notable example. BEATs has seen limited modifications due to the absence
of open-source pre-training code. Furthermore, BEATs was trained only on
AudioSet, restricting its broader downstream applicability. To address these
gaps, we present OpenBEATs, an open-source framework that extends BEATs via
multi-domain audio pre-training. We conduct comprehensive evaluations across
six types of tasks, twenty five datasets, and three audio domains, including
audio reasoning tasks such as audio question answering, entailment, and
captioning. OpenBEATs achieves state-of-the-art performance on six bioacoustics
datasets, two environmental sound datasets and five reasoning datasets,
performing better than models exceeding a billion parameters at one-fourth
their parameter size. These results demonstrate the effectiveness of
multi-domain datasets and masked token prediction task to learn general-purpose
audio representations. To promote further research and reproducibility, we
release all pre-training and evaluation code, pretrained and fine-tuned
checkpoints, and training logs at https://shikhar-s.github.io/OpenBEATs
Inverse Reinforcement Learning Meets Large Language Model Post-Training:
  Basics, Advances, and Opportunities
Authors: name: Hao Sun, name: Mihaela van der Schaar
Published: 2025-07-17 14:22:24

In the era of Large Language Models (LLMs), alignment has emerged as a
fundamental yet challenging problem in the pursuit of more reliable,
controllable, and capable machine intelligence. The recent success of reasoning
models and conversational AI systems has underscored the critical role of
reinforcement learning (RL) in enhancing these systems, driving increased
research interest at the intersection of RL and LLM alignment. This paper
provides a comprehensive review of recent advances in LLM alignment through the
lens of inverse reinforcement learning (IRL), emphasizing the distinctions
between RL techniques employed in LLM alignment and those in conventional RL
tasks. In particular, we highlight the necessity of constructing neural reward
models from human data and discuss the formal and practical implications of
this paradigm shift. We begin by introducing fundamental concepts in RL to
provide a foundation for readers unfamiliar with the field. We then examine
recent advances in this research agenda, discussing key challenges and
opportunities in conducting IRL for LLM alignment. Beyond methodological
considerations, we explore practical aspects, including datasets, benchmarks,
evaluation metrics, infrastructure, and computationally efficient training and
inference techniques. Finally, we draw insights from the literature on
sparse-reward RL to identify open questions and potential research directions.
By synthesizing findings from diverse studies, we aim to provide a structured
and critical overview of the field, highlight unresolved challenges, and
outline promising future directions for improving LLM alignment through RL and
IRL techniques.
Mitigating Object Hallucinations via Sentence-Level Early Intervention
Authors: name: Shangpin Peng, name: Senqiao Yang, name: Li Jiang, name: Zhuotao Tian
Published: 2025-07-16 17:55:43

Multimodal large language models (MLLMs) have revolutionized cross-modal
understanding but continue to struggle with hallucinations - fabricated content
contradicting visual inputs. Existing hallucination mitigation methods either
incur prohibitive computational costs or introduce distribution mismatches
between training data and model outputs. We identify a critical insight:
hallucinations predominantly emerge at the early stages of text generation and
propagate through subsequent outputs. To address this, we propose **SENTINEL**
(**S**entence-level **E**arly i**N**tervention **T**hrough **IN**-domain
pr**E**ference **L**earning), a framework that eliminates dependency on human
annotations. Specifically, we first bootstrap high-quality in-domain preference
pairs by iteratively sampling model outputs, validating object existence
through cross-checking with two open-vocabulary detectors, and classifying
sentences into hallucinated/non-hallucinated categories. Subsequently, we use
context-coherent positive samples and hallucinated negative samples to build
context-aware preference data iteratively. Finally, we train models using a
context-aware preference loss (C-DPO) that emphasizes discriminative learning
at the sentence level where hallucinations initially manifest. Experimental
results show that SENTINEL can reduce hallucinations by over 90\% compared to
the original model and outperforms the previous state-of-the-art method on both
hallucination benchmarks and general capabilities benchmarks, demonstrating its
superiority and generalization ability. The models, datasets, and code are
available at https://github.com/pspdada/SENTINEL.
A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges
  in Russian Speech Generative Models
Authors: name: Kirill Borodin, name: Nikita Vasiliev, name: Vasiliy Kudryavtsev, name: Maxim Maslov, name: Mikhail Gorodnichev, name: Oleg Rogov, name: Grach Mkrtchian
Published: 2025-07-17 22:41:40

Russian speech synthesis presents distinctive challenges, including vowel
reduction, consonant devoicing, variable stress patterns, homograph ambiguity,
and unnatural intonation. This paper introduces Balalaika, a novel dataset
comprising more than 2,000 hours of studio-quality Russian speech with
comprehensive textual annotations, including punctuation and stress markings.
Experimental results show that models trained on Balalaika significantly
outperform those trained on existing datasets in both speech synthesis and
enhancement tasks. We detail the dataset construction pipeline, annotation
methodology, and results of comparative evaluations.
CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models
Authors: name: Quang-Binh Nguyen, name: Minh Luu, name: Quang Nguyen, name: Anh Tran, name: Khoi Nguyen
Published: 2025-07-18 14:45:48

Disentangling content and style from a single image, known as content-style
decomposition (CSD), enables recontextualization of extracted content and
stylization of extracted styles, offering greater creative flexibility in
visual synthesis. While recent personalization methods have explored the
decomposition of explicit content style, they remain tailored for diffusion
models. Meanwhile, Visual Autoregressive Modeling (VAR) has emerged as a
promising alternative with a next-scale prediction paradigm, achieving
performance comparable to that of diffusion models. In this paper, we explore
VAR as a generative framework for CSD, leveraging its scale-wise generation
process for improved disentanglement. To this end, we propose CSD-VAR, a novel
method that introduces three key innovations: (1) a scale-aware alternating
optimization strategy that aligns content and style representation with their
respective scales to enhance separation, (2) an SVD-based rectification method
to mitigate content leakage into style representations, and (3) an Augmented
Key-Value (K-V) memory enhancing content identity preservation. To benchmark
this task, we introduce CSD-100, a dataset specifically designed for
content-style decomposition, featuring diverse subjects rendered in various
artistic styles. Experiments demonstrate that CSD-VAR outperforms prior
approaches, achieving superior content preservation and stylization fidelity.
The Generative Energy Arena (GEA): Incorporating Energy Awareness in
  Large Language Model (LLM) Human Evaluations
Authors: name: Carlos Arriaga, name: Gonzalo Martínez, name: Eneko Sendin, name: Javier Conde, name: Pedro Reviriego
Published: 2025-07-17 17:11:14

The evaluation of large language models is a complex task, in which several
approaches have been proposed. The most common is the use of automated
benchmarks in which LLMs have to answer multiple-choice questions of different
topics. However, this method has certain limitations, being the most
concerning, the poor correlation with the humans. An alternative approach, is
to have humans evaluate the LLMs. This poses scalability issues as there is a
large and growing number of models to evaluate making it impractical (and
costly) to run traditional studies based on recruiting a number of evaluators
and having them rank the responses of the models. An alternative approach is
the use of public arenas, such as the popular LM arena, on which any user can
freely evaluate models on any question and rank the responses of two models.
The results are then elaborated into a model ranking. An increasingly important
aspect of LLMs is their energy consumption and, therefore, evaluating how
energy awareness influences the decisions of humans in selecting a model is of
interest. In this paper, we present GEA, the Generative Energy Arena, an arena
that incorporates information on the energy consumption of the model in the
evaluation process. Preliminary results obtained with GEA are also presented,
showing that for most questions, when users are aware of the energy
consumption, they favor smaller and more energy efficient models. This suggests
that for most user interactions, the extra cost and energy incurred by the more
complex and top-performing models do not provide an increase in the perceived
quality of the responses that justifies their use.
Quantitative Risk Management in Volatile Markets with an Expectile-Based
  Framework for the FTSE Index
Authors: name: Abiodun Finbarrs Oketunji
Published: 2025-07-16 08:24:14

This research presents a framework for quantitative risk management in
volatile markets, specifically focusing on expectile-based methodologies
applied to the FTSE 100 index. Traditional risk measures such as Value-at-Risk
(VaR) have demonstrated significant limitations during periods of market
stress, as evidenced during the 2008 financial crisis and subsequent volatile
periods. This study develops an advanced expectile-based framework that
addresses the shortcomings of conventional quantile-based approaches by
providing greater sensitivity to tail losses and improved stability in extreme
market conditions. The research employs a dataset spanning two decades of FTSE
100 returns, incorporating periods of high volatility, market crashes, and
recovery phases. Our methodology introduces novel mathematical formulations for
expectile regression models, enhanced threshold determination techniques using
time series analysis, and robust backtesting procedures. The empirical results
demonstrate that expectile-based Value-at-Risk (EVaR) consistently outperforms
traditional VaR measures across various confidence levels and market
conditions. The framework exhibits superior performance during volatile
periods, with reduced model risk and enhanced predictive accuracy. Furthermore,
the study establishes practical implementation guidelines for financial
institutions and provides evidence-based recommendations for regulatory
compliance and portfolio management. The findings contribute significantly to
the literature on financial risk management and offer practical tools for
practitioners dealing with volatile market environments.
Franca: Nested Matryoshka Clustering for Scalable Visual Representation
  Learning
Authors: name: Shashanka Venkataramanan, name: Valentinos Pariza, name: Mohammadreza Salehi, name: Lukas Knobel, name: Spyros Gidaris, name: Elias Ramzi, name: Andrei Bursuc, name: Yuki M. Asano
Published: 2025-07-18 17:59:55

We present Franca (pronounced Fran-ka): free one; the first fully open-source
(data, code, weights) vision foundation model that matches and in many cases
surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2,
CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training
pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and
a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in
SSL clustering methods. While modern models rely on assigning image features to
large codebooks via clustering algorithms like Sinkhorn-Knopp, they fail to
account for the inherent ambiguity in clustering semantics. To address this, we
introduce a parameter-efficient, multi-head clustering projector based on
nested Matryoshka representations. This design progressively refines features
into increasingly fine-grained clusters without increasing the model size,
enabling both performance and memory efficiency. Additionally, we propose a
novel positional disentanglement strategy that explicitly removes positional
biases from dense representations, thereby improving the encoding of semantic
content. This leads to consistent gains on several downstream benchmarks,
demonstrating the utility of cleaner feature spaces. Our contributions
establish a new standard for transparent, high-performance vision models and
open a path toward more reproducible and generalizable foundation models for
the broader AI community. The code and model checkpoints are available at
https://github.com/valeoai/Franca.
The Devil behind the mask: An emergent safety vulnerability of Diffusion
  LLMs
Authors: name: Zichen Wen, name: Jiashu Qu, name: Dongrui Liu, name: Zhiyuan Liu, name: Ruixi Wu, name: Yicun Yang, name: Xiangqi Jin, name: Haoyun Xu, name: Xuyang Liu, name: Weijia Li, name: Chaochao Lu, name: Jing Shao, name: Conghui He, name: Linfeng Zhang
Published: 2025-07-15 08:44:46

Diffusion-based large language models (dLLMs) have recently emerged as a
powerful alternative to autoregressive LLMs, offering faster inference and
greater interactivity via parallel decoding and bidirectional modeling.
However, despite strong performance in code generation and text infilling, we
identify a fundamental safety concern: existing alignment mechanisms fail to
safeguard dLLMs against context-aware, masked-input adversarial prompts,
exposing novel vulnerabilities. To this end, we present DIJA, the first
systematic study and jailbreak attack framework that exploits unique safety
weaknesses of dLLMs. Specifically, our proposed DIJA constructs adversarial
interleaved mask-text prompts that exploit the text generation mechanisms of
dLLMs, i.e., bidirectional modeling and parallel decoding. Bidirectional
modeling drives the model to produce contextually consistent outputs for masked
spans, even when harmful, while parallel decoding limits model dynamic
filtering and rejection sampling of unsafe content. This causes standard
alignment mechanisms to fail, enabling harmful completions in alignment-tuned
dLLMs, even when harmful behaviors or unsafe instructions are directly exposed
in the prompt. Through comprehensive experiments, we demonstrate that DIJA
significantly outperforms existing jailbreak methods, exposing a previously
overlooked threat surface in dLLM architectures. Notably, our method achieves
up to 100% keyword-based ASR on Dream-Instruct, surpassing the strongest prior
baseline, ReNeLLM, by up to 78.5% in evaluator-based ASR on JailbreakBench and
by 37.7 points in StrongREJECT score, while requiring no rewriting or hiding of
harmful content in the jailbreak prompt. Our findings underscore the urgent
need for rethinking safety alignment in this emerging class of language models.
Code is available at https://github.com/ZichenWen1/DIJA.
Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal
  Large Language Models
Authors: name: Gen Luo, name: Wenhan Dou, name: Wenhao Li, name: Zhaokai Wang, name: Xue Yang, name: Changyao Tian, name: Hao Li, name: Weiyun Wang, name: Wenhai Wang, name: Xizhou Zhu, name: Yu Qiao, name: Jifeng Dai
Published: 2025-07-16 18:31:23

This paper focuses on monolithic Multimodal Large Language Models (MLLMs),
which integrate visual encoding and language decoding into a single model.
Existing structures and pre-training strategies for monolithic MLLMs often
suffer from unstable optimization and catastrophic forgetting. To address these
challenges, our key idea is to embed a new visual parameter space into a
pre-trained LLM, enabling stable learning of visual knowledge from noisy data
via delta tuning. Based on this principle, we first introduce Mono-InternVL, an
advanced monolithic MLLM that incorporates a set of visual experts through a
multimodal mixture-of-experts architecture. In addition, we design an
innovative Endogenous Visual Pre-training (EViP) for Mono-InternVL to maximize
its visual capabilities via progressive learning. Mono-InternVL achieves
competitive performance against existing MLLMs but also leads to relatively
expensive data cost. Therefore, we further present Mono-InternVL-1.5, a cheaper
and stronger monolithic MLLM equipped with an improved EViP (EViP++). EViP++
introduces additional visual attention experts to Mono-InternVL-1.5 and
re-organizes the pre-training process in an efficient manner. During inference,
it includes a fused CUDA kernel to speed up its MoE operations. With these
designs, Mono-InternVL-1.5 significantly reduces training and inference costs,
while still maintaining competitive performance with Mono-InternVL. To evaluate
our approach, we conduct extensive experiments across 15 benchmarks. Results
demonstrate that Mono-InternVL outperforms existing monolithic MLLMs on 12 out
of 15 benchmarks, e.g., +114-point improvement over Emu3 on OCRBench. Compared
to its modular counterpart, i.e., InternVL-1.5, Mono-InternVL-1.5 achieves
similar multimodal performance while reducing first-token latency by up to 69%.
Code and models are released at https://github.com/OpenGVLab/Mono-InternVL.