Orionnnn/hub-stats huggingface dataset:
RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic
  Sampling
Authors: name: Yang Liu, name: Jiaqi Li, name: Zilong Zheng
Published: 2025-06-10 10:31:21

Rule-based reasoning has been acknowledged as one of the fundamental problems
in reasoning, while deviations in rule formats, types, and complexity in
real-world applications pose severe challenges. Recent studies have shown that
large reasoning models (LRMs) have remarkable reasoning capabilities, and their
performance is substantially enhanced by reinforcement learning (RL). However,
it remains an open question whether small reasoning models (SRMs) can learn
rule-based reasoning effectively with robust generalization across diverse
tasks and domains. To address this, we introduce Reinforced Rule-based
Reasoning, a.k.a. RuleReasoner, a simple yet effective method to conduct
rule-based reasoning via a wide collection of curated tasks and a novel
domain-aware dynamic sampling approach. Specifically, RuleReasoner resamples
each training batch by updating the sampling weights of different domains based
on historical rewards. This facilitates domain augmentation and flexible online
learning schedules for RL, obviating the need for pre-hoc human-engineered
mix-training recipes used in existing methods. Empirical evaluations on
in-distribution (ID) and out-of-distribution (OOD) benchmarks reveal that
RuleReasoner outperforms frontier LRMs by a significant margin (Delta4.1%
average points on eight ID tasks and Delta10.4% average points on three OOD
tasks over OpenAI-o1). Notably, our approach also exhibits higher computational
efficiency compared to prior dynamic sampling methods for RL.
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand
  Better
Authors: name: Dianyi Wang, name: Wei Song, name: Yikun Wang, name: Siyuan Wang, name: Kaicheng Yu, name: Zhongyu Wei, name: Jiaqi Wang
Published: 2025-06-10 17:57:50

Typical large vision-language models (LVLMs) apply autoregressive supervision
solely to textual sequences, without fully incorporating the visual modality
into the learning process. This results in three key limitations: (1) an
inability to utilize images without accompanying captions, (2) the risk that
captions omit critical visual details, and (3) the challenge that certain
vision-centric content cannot be adequately conveyed through text. As a result,
current LVLMs often prioritize vision-to-language alignment while potentially
overlooking fine-grained visual information. While some prior works have
explored autoregressive image generation, effectively leveraging autoregressive
visual supervision to enhance image understanding remains an open challenge. In
this paper, we introduce Autoregressive Semantic Visual Reconstruction (ASVR),
which enables joint learning of visual and textual modalities within a unified
autoregressive framework. We show that autoregressively reconstructing the raw
visual appearance of images does not enhance and may even impair multimodal
understanding. In contrast, autoregressively reconstructing the semantic
representation of images consistently improves comprehension. Notably, we find
that even when models are given continuous image features as input, they can
effectively reconstruct discrete semantic tokens, resulting in stable and
consistent improvements across a wide range of multimodal understanding
benchmarks. Our approach delivers significant performance gains across varying
data scales (556k-2M) and types of LLM bacbones. Specifically, ASVR improves
LLaVA-1.5 by 5% in average scores across 14 multimodal benchmarks. The code is
available at https://github.com/AlenjandroWang/ASVR.
Mathesis: Towards Formal Theorem Proving from Natural Languages
Authors: name: Yu Xuejun, name: Jianyuan Zhong, name: Zijin Feng, name: Pengyi Zhai, name: Roozbeh Yousefzadeh, name: Wei Chong Ng, name: Haoxiong Liu, name: Ziyi Shou, name: Jing Xiong, name: Yudong Zhou, name: Claudia Beth Ong, name: Austen Jeremy Sugiarto, name: Yaoxi Zhang, name: Wai Ming Tai, name: Huan Cao, name: Dongcai Lu, name: Jiacheng Sun, name: Qiang Xu, name: Shen Xin, name: Zhenguo Li
Published: 2025-06-08 09:04:14

Recent advances in large language models show strong promise for formal
reasoning. However, most LLM-based theorem provers have long been constrained
by the need for expert-written formal statements as inputs, limiting their
applicability to real-world problems expressed in natural language. We tackle
this gap with Mathesis, the first end-to-end theorem proving pipeline
processing informal problem statements. It contributes Mathesis-Autoformalizer,
the first autoformalizer using reinforcement learning to enhance the
formalization ability of natural language problems, aided by our novel
LeanScorer framework for nuanced formalization quality assessment. It also
proposes a Mathesis-Prover, which generates formal proofs from the formalized
statements. To evaluate the real-world applicability of end-to-end formal
theorem proving, we introduce Gaokao-Formal, a benchmark of 488 complex
problems from China's national college entrance exam. Our approach is carefully
designed, with a thorough study of each component. Experiments demonstrate
Mathesis's effectiveness, with the autoformalizer outperforming the best
baseline by 22% in pass-rate on Gaokao-Formal. The full system surpasses other
model combinations, achieving 64% accuracy on MiniF2F with pass@32 and a
state-of-the-art 18% on Gaokao-Formal.
Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error
  Diagnosis in GUI Automation
Authors: name: Yuyang Wanyan, name: Xi Zhang, name: Haiyang Xu, name: Haowei Liu, name: Junyang Wang, name: Jiabo Ye, name: Yutong Kou, name: Ming Yan, name: Fei Huang, name: Xiaoshan Yang, name: Weiming Dong, name: Changsheng Xu
Published: 2025-06-05 04:12:36

In recent years, Multimodal Large Language Models (MLLMs) have been
extensively utilized for multimodal reasoning tasks, including Graphical User
Interface (GUI) automation. Unlike general offline multimodal tasks, GUI
automation is executed in online interactive environments, necessitating
step-by-step decision-making based on real-time status of the environment. This
task has a lower tolerance for decision-making errors at each step, as any
mistakes may cumulatively disrupt the process and potentially lead to
irreversible outcomes like deletions or payments. To address these issues, we
introduce a pre-operative critic mechanism that provides effective feedback
prior to the actual execution, by reasoning about the potential outcome and
correctness of actions. Specifically, we propose a Suggestion-aware Gradient
Relative Policy Optimization (S-GRPO) strategy to construct our pre-operative
critic model GUI-Critic-R1, incorporating a novel suggestion reward to enhance
the reliability of the model's feedback. Furthermore, we develop a
reasoning-bootstrapping based data collection pipeline to create a
GUI-Critic-Train and a GUI-Critic-Test, filling existing gaps in GUI critic
data. Static experiments on the GUI-Critic-Test across both mobile and web
domains reveal that our GUI-Critic-R1 offers significant advantages in critic
accuracy compared to current MLLMs. Dynamic evaluation on GUI automation
benchmark further highlights the effectiveness and superiority of our model, as
evidenced by improved success rates and operational efficiency.
Self Forcing: Bridging the Train-Test Gap in Autoregressive Video
  Diffusion
Authors: name: Xun Huang, name: Zhengqi Li, name: Guande He, name: Mingyuan Zhou, name: Eli Shechtman
Published: 2025-06-09 17:59:55

We introduce Self Forcing, a novel training paradigm for autoregressive video
diffusion models. It addresses the longstanding issue of exposure bias, where
models trained on ground-truth context must generate sequences conditioned on
their own imperfect outputs during inference. Unlike prior methods that denoise
future frames based on ground-truth context frames, Self Forcing conditions
each frame's generation on previously self-generated outputs by performing
autoregressive rollout with key-value (KV) caching during training. This
strategy enables supervision through a holistic loss at the video level that
directly evaluates the quality of the entire generated sequence, rather than
relying solely on traditional frame-wise objectives. To ensure training
efficiency, we employ a few-step diffusion model along with a stochastic
gradient truncation strategy, effectively balancing computational cost and
performance. We further introduce a rolling KV cache mechanism that enables
efficient autoregressive video extrapolation. Extensive experiments demonstrate
that our approach achieves real-time streaming video generation with sub-second
latency on a single GPU, while matching or even surpassing the generation
quality of significantly slower and non-causal diffusion models. Project
website: http://self-forcing.github.io/
Solving Inequality Proofs with Large Language Models
Authors: name: Jiayi Sheng, name: Luna Lyu, name: Jikai Jin, name: Tony Xia, name: Alex Gu, name: James Zou, name: Pan Lu
Published: 2025-06-09 16:43:38

Inequality proving, crucial across diverse scientific and mathematical
fields, tests advanced reasoning skills such as discovering tight bounds and
strategic theorem application. This makes it a distinct, demanding frontier for
large language models (LLMs), offering insights beyond general mathematical
problem-solving. Progress in this area is hampered by existing datasets that
are often scarce, synthetic, or rigidly formal. We address this by proposing an
informal yet verifiable task formulation, recasting inequality proving into two
automatically checkable subtasks: bound estimation and relation prediction.
Building on this, we release IneqMath, an expert-curated dataset of
Olympiad-level inequalities, including a test set and training corpus enriched
with step-wise solutions and theorem annotations. We also develop a novel
LLM-as-judge evaluation framework, combining a final-answer judge with four
step-wise judges designed to detect common reasoning flaws. A systematic
evaluation of 29 leading LLMs on IneqMath reveals a surprising reality: even
top models like o1 achieve less than 10% overall accuracy under step-wise
scrutiny; this is a drop of up to 65.5% from their accuracy considering only
final answer equivalence. This discrepancy exposes fragile deductive chains and
a critical gap for current LLMs between merely finding an answer and
constructing a rigorous proof. Scaling model size and increasing test-time
computation yield limited gains in overall proof correctness. Instead, our
findings highlight promising research directions such as theorem-guided
reasoning and self-refinement. Code and data are available at
https://ineqmath.github.io/.
Geopolitical biases in LLMs: what are the "good" and the "bad" countries
  according to contemporary language models
Authors: name: Mikhail Salnikov, name: Dmitrii Korzh, name: Ivan Lazichny, name: Elvir Karimov, name: Artyom Iudin, name: Ivan Oseledets, name: Oleg Y. Rogov, name: Alexander Panchenko, name: Natalia Loukachevitch, name: Elena Tutubalina
Published: 2025-06-07 10:45:17

This paper evaluates geopolitical biases in LLMs with respect to various
countries though an analysis of their interpretation of historical events with
conflicting national perspectives (USA, UK, USSR, and China). We introduce a
novel dataset with neutral event descriptions and contrasting viewpoints from
different countries. Our findings show significant geopolitical biases, with
models favoring specific national narratives. Additionally, simple debiasing
prompts had a limited effect in reducing these biases. Experiments with
manipulated participant labels reveal models' sensitivity to attribution,
sometimes amplifying biases or recognizing inconsistencies, especially with
swapped labels. This work highlights national narrative biases in LLMs,
challenges the effectiveness of simple debiasing methods, and offers a
framework and dataset for future geopolitical bias research.
Frame Guidance: Training-Free Guidance for Frame-Level Control in Video
  Diffusion Models
Authors: name: Sangwon Jang, name: Taekyung Ki, name: Jaehyeong Jo, name: Jaehong Yoon, name: Soo Ye Kim, name: Zhe Lin, name: Sung Ju Hwang
Published: 2025-06-08 14:54:41

Advancements in diffusion models have significantly improved video quality,
directing attention to fine-grained controllability. However, many existing
methods depend on fine-tuning large-scale video models for specific tasks,
which becomes increasingly impractical as model sizes continue to grow. In this
work, we present Frame Guidance, a training-free guidance for controllable
video generation based on frame-level signals, such as keyframes, style
reference images, sketches, or depth maps. For practical training-free
guidance, we propose a simple latent processing method that dramatically
reduces memory usage, and apply a novel latent optimization strategy designed
for globally coherent video generation. Frame Guidance enables effective
control across diverse tasks, including keyframe guidance, stylization, and
looping, without any training, compatible with any video models. Experimental
results show that Frame Guidance can produce high-quality controlled videos for
a wide range of tasks and input signals.
Squeeze3D: Your 3D Generation Model is Secretly an Extreme Neural
  Compressor
Authors: name: Rishit Dagli, name: Yushi Guan, name: Sankeerth Durvasula, name: Mohammadreza Mofayezi, name: Nandita Vijaykumar
Published: 2025-06-09 16:52:10

We propose Squeeze3D, a novel framework that leverages implicit prior
knowledge learnt by existing pre-trained 3D generative models to compress 3D
data at extremely high compression ratios. Our approach bridges the latent
spaces between a pre-trained encoder and a pre-trained generation model through
trainable mapping networks. Any 3D model represented as a mesh, point cloud, or
a radiance field is first encoded by the pre-trained encoder and then
transformed (i.e. compressed) into a highly compact latent code. This latent
code can effectively be used as an extremely compressed representation of the
mesh or point cloud. A mapping network transforms the compressed latent code
into the latent space of a powerful generative model, which is then conditioned
to recreate the original 3D model (i.e. decompression). Squeeze3D is trained
entirely on generated synthetic data and does not require any 3D datasets. The
Squeeze3D architecture can be flexibly used with existing pre-trained 3D
encoders and existing generative models. It can flexibly support different
formats, including meshes, point clouds, and radiance fields. Our experiments
demonstrate that Squeeze3D achieves compression ratios of up to 2187x for
textured meshes, 55x for point clouds, and 619x for radiance fields while
maintaining visual quality comparable to many existing methods. Squeeze3D only
incurs a small compression and decompression latency since it does not involve
training object-specific networks to compress an object.
Aligning Text, Images, and 3D Structure Token-by-Token
Authors: name: Aadarsh Sahoo, name: Vansh Tibrewal, name: Georgia Gkioxari
Published: 2025-06-09 17:59:37

Creating machines capable of understanding the world in 3D is essential in
assisting designers that build and edit 3D environments and robots navigating
and interacting within a three-dimensional space. Inspired by advances in
language and image modeling, we investigate the potential of autoregressive
models for a new modality: structured 3D scenes. To this end, we propose a
unified LLM framework that aligns language, images, and 3D scenes and provide a
detailed ''cookbook'' outlining critical design choices for achieving optimal
training and performance addressing key questions related to data
representation, modality-specific objectives, and more. We evaluate performance
across four core 3D tasks -- rendering, recognition, instruction-following, and
question-answering -- and four 3D datasets, synthetic and real-world. We extend
our approach to reconstruct complex 3D object shapes by enriching our 3D
modality with quantized shape encodings, and show our model's effectiveness on
real-world 3D object recognition tasks. Project webpage:
https://glab-caltech.github.io/kyvo/
MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient
  Fine-Tuning of Large Language Models
Authors: name: Jie Cao, name: Tianwei Lin, name: Hongyang He, name: Rolan Yan, name: Wenqiao Zhang, name: Juncheng Li, name: Dongping Zhang, name: Siliang Tang, name: Yueting Zhuang
Published: 2025-06-06 09:54:19

Recent studies integrate Low-Rank Adaptation (LoRA) and Mixture-of-Experts
(MoE) to further enhance the performance of parameter-efficient fine-tuning
(PEFT) methods in Large Language Model (LLM) applications. Existing methods
employ homogeneous MoE-LoRA architectures composed of LoRA experts with
either similar or identical structures and capacities. However, these
approaches often suffer from representation collapse and expert load imbalance,
which negatively impact the potential of LLMs. To address these challenges, we
propose a heterogeneous Mixture-of-Adapters (MoA) approach.
This method dynamically integrates PEFT adapter experts with diverse
structures, leveraging their complementary representational capabilities to
foster expert specialization, thereby enhancing the effective transfer of
pre-trained knowledge to downstream tasks. MoA supports two variants:
(i) Soft MoA achieves fine-grained integration by performing
a weighted fusion of all expert outputs; (ii) Sparse MoA
activates adapter experts sparsely based on their contribution, achieving this
with negligible performance degradation. Experimental results demonstrate that
heterogeneous MoA outperforms homogeneous MoE-LoRA methods in both performance
and parameter efficiency. Our project is available at
https://github.com/DCDmllm/MoA.
DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for
  Parameter-Efficient Video-Text Retrieval
Authors: name: Leqi Shen, name: Guoqiang Gong, name: Tianxiang Hao, name: Tao He, name: Yifeng Zhang, name: Pengzhang Liu, name: Sicheng Zhao, name: Jungong Han, name: Guiguang Ding
Published: 2025-06-10 15:16:40

The parameter-efficient adaptation of the image-text pretraining model CLIP
for video-text retrieval is a prominent area of research. While CLIP is focused
on image-level vision-language matching, video-text retrieval demands
comprehensive understanding at the video level. Three key discrepancies emerge
in the transfer from image-level to video-level: vision, language, and
alignment. However, existing methods mainly focus on vision while neglecting
language and alignment. In this paper, we propose Discrepancy Reduction in
Vision, Language, and Alignment (DiscoVLA), which simultaneously mitigates all
three discrepancies. Specifically, we introduce Image-Video Features Fusion to
integrate image-level and video-level features, effectively tackling both
vision and language discrepancies. Additionally, we generate pseudo image
captions to learn fine-grained image-level alignment. To mitigate alignment
discrepancies, we propose Image-to-Video Alignment Distillation, which
leverages image-level alignment knowledge to enhance video-level alignment.
Extensive experiments demonstrate the superiority of our DiscoVLA. In
particular, on MSRVTT with CLIP (ViT-B/16), DiscoVLA outperforms previous
methods by 1.5% in R@1, reaching a final score of 50.5% R@1. The code is
available at https://github.com/LunarShen/DsicoVLA.
ECoRAG: Evidentiality-guided Compression for Long Context RAG
Authors: name: Yeonseok Jeong, name: Jinsu Kim, name: Dohyeon Lee, name: Seung-won Hwang
Published: 2025-06-05 15:43:49

Large Language Models (LLMs) have shown remarkable performance in Open-Domain
Question Answering (ODQA) by leveraging external documents through
Retrieval-Augmented Generation (RAG). To reduce RAG overhead, from longer
context, context compression is necessary. However, prior compression methods
do not focus on filtering out non-evidential information, which limit the
performance in LLM-based RAG. We thus propose Evidentiality-guided RAG, or
ECoRAG framework. ECoRAG improves LLM performance by compressing retrieved
documents based on evidentiality, ensuring whether answer generation is
supported by the correct evidence. As an additional step, ECoRAG reflects
whether the compressed content provides sufficient evidence, and if not,
retrieves more until sufficient. Experiments show that ECoRAG improves LLM
performance on ODQA tasks, outperforming existing compression methods.
Furthermore, ECoRAG is highly cost-efficient, as it not only reduces latency
but also minimizes token usage by retaining only the necessary information to
generate the correct answer. Code is available at
https://github.com/ldilab/ECoRAG.
RKEFino1: A Regulation Knowledge-Enhanced Large Language Model
Authors: name: Yan Wang, name: Yueru He, name: Ruoyu Xiang, name: Jeff Zhao
Published: 2025-06-06 03:02:52

Recent advances in large language models (LLMs) hold great promise for
financial applications but introduce critical accuracy and compliance
challenges in Digital Regulatory Reporting (DRR). To address these issues, we
propose RKEFino1, a regulation knowledge-enhanced financial reasoning model
built upon Fino1, fine-tuned with domain knowledge from XBRL, CDM, and MOF. We
formulate two QA tasks-knowledge-based and mathematical reasoning-and introduce
a novel Numerical NER task covering financial entities in both sentences and
tables. Experimental results demonstrate the effectiveness and generalization
capacity of RKEFino1 in compliance-critical financial tasks. We have released
our model on Hugging Face.
Training-Free Tokenizer Transplantation via Orthogonal Matching Pursuit
Authors: name: Charles Goddard, name: Fernando Fernandes Neto
Published: 2025-06-07 00:51:27

We present a training-free method to transplant tokenizers in pretrained
large language models (LLMs) by reconstructing unseen token embeddings via
Orthogonal Matching Pursuit (OMP). Specifically, we approximate each
out-of-vocabulary token as a sparse linear combination of shared tokens, in two
phases: first, compute each new token's representation in the donor embedding
space with a small dictionary of shared anchor tokens, then transfer these same
sparse coefficients back into the base model's embedding space.
  On two challenging cross-tokenizer tasks--LlamatoMistral NeMo (12B) and
QwentoLlama (1B)--we show that OMP achieves best zero-shot preservation of
the base model's performance across multiple benchmarks, while other zero-shot
approaches degrade significantly. Compared to baselines (zero-init, mean-init,
and existing approaches like WECHSEL, FOCUS, ZETT), OMP consistently achieves
the best overall performance, effectively bridging large tokenizer
discrepancies without gradient updates. Our analysis further identifies
mismatched numerical tokenization schemes as a critical challenge for
preserving mathematical reasoning capabilities. This technique enables direct
reuse of pretrained model weights with new tokenizers, facilitating
cross-tokenizer knowledge distillation, speculative decoding, ensembling,
merging, and domain-specific vocabulary adaptations. We integrate our method
into the open-source mergekit-tokensurgeon tool for post hoc vocabulary
realignment.