Orionnnn/hub-stats huggingface dataset:
Pro3D-Editor : A Progressive-Views Perspective for Consistent and
  Precise 3D Editing
Authors: name: Yang Zheng, name: Mengqi Huang, name: Nan Chen, name: Zhendong Mao
Published: 2025-05-31 11:11:55

Text-guided 3D editing aims to precisely edit semantically relevant local 3D
regions, which has significant potential for various practical applications
ranging from 3D games to film production. Existing methods typically follow a
view-indiscriminate paradigm: editing 2D views indiscriminately and projecting
them back into 3D space. However, they overlook the different cross-view
interdependencies, resulting in inconsistent multi-view editing. In this study,
we argue that ideal consistent 3D editing can be achieved through a
progressive-views paradigm, which propagates editing semantics from
the editing-salient view to other editing-sparse views. Specifically, we
propose Pro3D-Editor, a novel framework, which mainly includes
Primary-view Sampler, Key-view Render, and Full-view Refiner. Primary-view
Sampler dynamically samples and edits the most editing-salient view as the
primary view. Key-view Render accurately propagates editing semantics from the
primary view to other key views through its Mixture-of-View-Experts Low-Rank
Adaption (MoVE-LoRA). Full-view Refiner edits and refines the 3D object based
on the edited multi-views. Extensive experiments demonstrate that our method
outperforms existing methods in editing accuracy and spatial consistency.
EarthMind: Towards Multi-Granular and Multi-Sensor Earth Observation
  with Large Multimodal Models
Authors: name: Yan Shu, name: Bin Ren, name: Zhitong Xiong, name: Danda Pani Paudel, name: Luc Van Gool, name: Begum Demir, name: Nicu Sebe, name: Paolo Rota
Published: 2025-06-02 13:36:05

Large Multimodal Models (LMMs) have demonstrated strong performance in
various vision-language tasks. However, they often struggle to comprehensively
understand Earth Observation (EO) data, which is critical for monitoring the
environment and the effects of human activity on it. In this work, we present
EarthMind, a novel vision-language framework for multi-granular and
multi-sensor EO data understanding. EarthMind features two core components: (1)
Spatial Attention Prompting (SAP), which reallocates attention within the LLM
to enhance pixel-level understanding; and (2) Cross-modal Fusion, which aligns
heterogeneous modalities into a shared space and adaptively reweighs tokens
based on their information density for effective fusion. To facilitate
multi-sensor fusion evaluation, we propose EarthMind-Bench, a comprehensive
benchmark with over 2,000 human-annotated multi-sensor image-question pairs,
covering a wide range of perception and reasoning tasks. Extensive experiments
demonstrate the effectiveness of EarthMind. It achieves state-of-the-art
performance on EarthMind-Bench, surpassing GPT-4o despite being only 4B in
scale. Moreover, EarthMind outperforms existing methods on multiple public EO
benchmarks, showcasing its potential to handle both multi-granular and
multi-sensor challenges in a unified framework.
Think Again! The Effect of Test-Time Compute on Preferences, Opinions,
  and Beliefs of Large Language Models
Authors: name: George Kour, name: Itay Nakash, name: Ateret Anaby-Tavor, name: Michal Shmueli-Scheuer
Published: 2025-05-26 07:41:21

As Large Language Models (LLMs) become deeply integrated into human life and
increasingly influence decision-making, it's crucial to evaluate whether and to
what extent they exhibit subjective preferences, opinions, and beliefs. These
tendencies may stem from biases within the models, which may shape their
behavior, influence the advice and recommendations they offer to users, and
potentially reinforce certain viewpoints. This paper presents the Preference,
Opinion, and Belief survey (POBs), a benchmark developed to assess LLMs'
subjective inclinations across societal, cultural, ethical, and personal
domains. We applied our benchmark to evaluate leading open- and closed-source
LLMs, measuring desired properties such as reliability, neutrality, and
consistency. In addition, we investigated the effect of increasing the
test-time compute, through reasoning and self-reflection mechanisms, on those
metrics. While effective in other tasks, our results show that these mechanisms
offer only limited gains in our domain. Furthermore, we reveal that newer model
versions are becoming less consistent and more biased toward specific
viewpoints, highlighting a blind spot and a concerning trend. POBS:
https://ibm.github.io/POBS
zip2zip: Inference-Time Adaptive Vocabularies for Language Models via
  Token Compression
Authors: name: Saibo Geng, name: Nathan Ranchin, name: Yunzhen yao, name: Maxime Peyrard, name: Chris Wendler, name: Michael Gastpar, name: Robert West
Published: 2025-06-01 17:03:02

Tokenization efficiency plays a critical role in the performance and cost of
large language models (LLMs), yet most models rely on static tokenizers
optimized for general-purpose corpora. These tokenizers' fixed vocabularies
often fail to adapt to domain- or language-specific inputs, leading to longer
token sequences and higher computational costs. We introduce zip2zip, a
framework that enables LLMs to dynamically adjust token vocabulary at inference
time, allowing for fewer generated tokens and thus faster inference. zip2zip
consists of three key components: (1) a tokenizer based on Lempel-Ziv-Welch
(LZW) compression that incrementally compresses tokens into reusable
"hypertokens" on the fly; (2) an embedding layer that computes embeddings for
newly formed hypertokens at runtime; and (3) a causal language modeling variant
that trains the model to operate on hypertokenized, compressed sequences. We
show that an existing LLM can be zip2zip-fied in 10 GPU-hours via
parameter-efficient finetuning. The resulting zip2zip LLMs effectively learn to
use hypertokens at inference time, reducing input and output sequence length by
20-60\%, with significant improvements in inference latency.
CodeV-R1: Reasoning-Enhanced Verilog Generation
Authors: name: Yaoyu Zhu, name: Di Huang, name: Hanqi Lyu, name: Xiaoyun Zhang, name: Chongxiao Li, name: Wenxuan Shi, name: Yutong Wu, name: Jianan Mu, name: Jinghua Wang, name: Yang Zhao, name: Pengwei Jin, name: Shuyao Cheng, name: Shengwen Liang, name: Xishan Zhang, name: Rui Zhang, name: Zidong Du, name: Qi Guo, name: Xing Hu, name: Yunji Chen
Published: 2025-05-30 03:51:06

Large language models (LLMs) trained via reinforcement learning with
verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit,
automatable verification, such as software programming and mathematical
problems. Extending RLVR to electronic design automation (EDA), especially
automatically generating hardware description languages (HDLs) like Verilog
from natural-language (NL) specifications, however, poses three key challenges:
the lack of automated and accurate verification environments, the scarcity of
high-quality NL-code pairs, and the prohibitive computation cost of RLVR. To
this end, we introduce CodeV-R1, an RLVR framework for training Verilog
generation LLMs. First, we develop a rule-based testbench generator that
performs robust equivalence checking against golden references. Second, we
propose a round-trip data synthesis method that pairs open-source Verilog
snippets with LLM-generated NL descriptions, verifies code-NL-code consistency
via the generated testbench, and filters out inequivalent examples to yield a
high-quality dataset. Third, we employ a two-stage "distill-then-RL" training
pipeline: distillation for the cold start of reasoning abilities, followed by
adaptive DAPO, our novel RLVR algorithm that can reduce training cost by
adaptively adjusting sampling rate. The resulting model, CodeV-R1-7B, achieves
68.6% and 72.9% pass@1 on VerilogEval v2 and RTLLM v1.1, respectively,
surpassing prior state-of-the-art by 12~20%, while matching or even exceeding
the performance of 671B DeepSeek-R1. We will release our model, training
pipeline, and dataset to facilitate research in EDA and LLM communities.
Massively Multilingual Adaptation of Large Language Models Using
  Bilingual Translation Data
Authors: name: Shaoxiong Ji, name: Zihao Li, name: Jaakko Paavola, name: Indraneil Paul, name: Hengyu Luo, name: Jörg Tiedemann
Published: 2025-05-31 08:37:17

This paper investigates a critical design decision in the practice of
massively multilingual continual pre-training -- the inclusion of parallel
data. Specifically, we study the impact of bilingual translation data for
massively multilingual language adaptation of the Llama3 family of models to
500 languages. To this end, we construct the MaLA bilingual translation corpus,
containing data from more than 2,500 language pairs. Subsequently, we develop
the EMMA-500 Llama 3 suite of four massively multilingual models -- continually
pre-trained from the Llama 3 family of base models extensively on diverse data
mixes up to 671B tokens -- and explore the effect of continual pre-training
with or without bilingual translation data. Comprehensive evaluation across 7
tasks and 12 benchmarks demonstrates that bilingual data tends to enhance
language transfer and performance, particularly for low-resource languages. We
open-source the MaLA corpus, EMMA-500 Llama 3 suite artefacts, code, and model
generations.
AReaL: A Large-Scale Asynchronous Reinforcement Learning System for
  Language Reasoning
Authors: name: Wei Fu, name: Jiaxuan Gao, name: Xujie Shen, name: Chen Zhu, name: Zhiyu Mei, name: Chuyi He, name: Shusheng Xu, name: Guo Wei, name: Jun Mei, name: Jiashu Wang, name: Tongkai Yang, name: Binhang Yuan, name: Yi Wu
Published: 2025-05-30 07:18:25

Reinforcement learning (RL) has become a trending paradigm for training large
language models (LLMs), particularly for reasoning tasks. Effective RL for LLMs
requires massive parallelization and poses an urgent need for efficient
training systems. Most existing large-scale RL systems for LLMs are synchronous
by alternating generation and training in a batch setting, where the rollouts
in each training batch are generated by the same (or latest) model. This
stabilizes RL training but suffers from severe system-level inefficiency.
Generation must wait until the longest output in the batch is completed before
model update, resulting in GPU underutilization. We present AReaL, a
fully asynchronous RL system that completely decouples generation from
training. Rollout workers in AReaL continuously generate new outputs without
waiting, while training workers update the model whenever a batch of data is
collected. AReaL also incorporates a collection of system-level optimizations,
leading to substantially higher GPU utilization. To stabilize RL training,
AReaL balances the workload of rollout and training workers to control data
staleness, and adopts a staleness-enhanced PPO variant to better handle
outdated training samples. Extensive experiments on math and code reasoning
benchmarks show that AReaL achieves up to 2.57times training
speedup compared to the best synchronous systems with the same number of GPUs
and matched or even improved final performance. The code of AReaL is available
at https://github.com/inclusionAI/AReaL/.
Cascading Adversarial Bias from Injection to Distillation in Language
  Models
Authors: name: Harsh Chaudhari, name: Jamie Hayes, name: Matthew Jagielski, name: Ilia Shumailov, name: Milad Nasr, name: Alina Oprea
Published: 2025-05-30 17:41:58

Model distillation has become essential for creating smaller, deployable
language models that retain larger system capabilities. However, widespread
deployment raises concerns about resilience to adversarial manipulation. This
paper investigates vulnerability of distilled models to adversarial injection
of biased content during training. We demonstrate that adversaries can inject
subtle biases into teacher models through minimal data poisoning, which
propagates to student models and becomes significantly amplified. We propose
two propagation modes: Untargeted Propagation, where bias affects multiple
tasks, and Targeted Propagation, focusing on specific tasks while maintaining
normal behavior elsewhere. With only 25 poisoned samples (0.25% poisoning
rate), student models generate biased responses 76.9% of the time in targeted
scenarios - higher than 69.4% in teacher models. For untargeted propagation,
adversarial bias appears 6x-29x more frequently in student models on unseen
tasks. We validate findings across six bias types (targeted advertisements,
phishing links, narrative manipulations, insecure coding practices), various
distillation methods, and different modalities spanning text and code
generation. Our evaluation reveals shortcomings in current defenses -
perplexity filtering, bias detection systems, and LLM-based autorater
frameworks - against these attacks. Results expose significant security
vulnerabilities in distilled models, highlighting need for specialized
safeguards. We propose practical design principles for building effective
adversarial bias mitigation strategies.
Stepsize anything: A unified learning rate schedule for
  budgeted-iteration training
Authors: name: Anda Tang, name: Yiming Dong, name: Yutao Zeng, name: zhou Xun, name: Zhouchen Lin
Published: 2025-05-30 10:38:03

The expanding computational costs and limited resources underscore the
critical need for budgeted-iteration training, which aims to achieve optimal
learning within predetermined iteration budgets.While learning rate schedules
fundamentally govern the performance of different networks and tasks,
particularly in budgeted-iteration scenarios, their design remains largely
heuristic, lacking theoretical foundations.In addition, the optimal learning
rate schedule requires extensive trial-and-error selection, making the training
process inefficient.In this work, we propose the Unified Budget-Aware (UBA)
schedule, a theoretically grounded learning rate schedule that consistently
outperforms commonly-used schedules among diverse architectures and tasks under
different constrained training budgets.First, we bridge the gap by constructing
a novel training budget-aware optimization framework, which explicitly accounts
for the robustness to landscape curvature variations.From this framework, we
derive the UBA schedule, controlled by a single hyper-parameter varphi that
provides a trade-off between flexibility and simplicity, eliminating the need
for per-network numerical optimization. Moreover, we establish a theoretical
connection between varphi and the condition number, adding interpretation
and justification to our approach. Besides, we prove the convergence for
different values of varphi.We offer practical guidelines for its selection
via theoretical analysis and empirical results.xtensive experimental results
show that UBA consistently surpasses the commonly-used schedules
across diverse vision and language tasks, spanning network architectures (e.g.,
ResNet, OLMo) and scales, under different training-iteration budgets.
MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity
  Reconstruction and Generation
Authors: name: Yakun Song, name: Jiawei Chen, name: Xiaobin Zhuang, name: Chenpeng Du, name: Ziyang Ma, name: Jian Wu, name: Jian Cong, name: Dongya Jia, name: Zhuo Chen, name: Yuping Wang, name: Yuxuan Wang, name: Xie Chen
Published: 2025-05-31 04:31:02

Neural audio codecs have made significant strides in efficiently mapping raw
audio waveforms into discrete token representations, which are foundational for
contemporary audio generative models. However, most existing codecs are
optimized primarily for reconstruction quality, often at the expense of the
downstream modelability of the encoded tokens. Motivated by the need to
overcome this bottleneck, we introduce MagiCodec, a novel
single-layer, streaming Transformer-based audio codec. MagiCodec is designed
with a multistage training pipeline that incorporates Gaussian noise injection
and latent regularization, explicitly targeting the enhancement of semantic
expressiveness in the generated codes while preserving high reconstruction
fidelity. We analytically derive the effect of noise injection in the frequency
domain, demonstrating its efficacy in attenuating high-frequency components and
fostering robust tokenization. Extensive experimental evaluations show that
MagiCodec surpasses state-of-the-art codecs in both reconstruction quality and
downstream tasks. Notably, the tokens produced by MagiCodec exhibit Zipf-like
distributions, as observed in natural languages, thereby improving
compatibility with language-model-based generative architectures. The code and
pre-trained models are available at https://github.com/Ereboas/MagiCodec.
ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and
  Understanding
Authors: name: Junliang Ye, name: Zhengyi Wang, name: Ruowen Zhao, name: Shenghao Xie, name: Jun Zhu
Published: 2025-06-02 16:40:50

Recently, the powerful text-to-image capabilities of ChatGPT-4o have led to
growing appreciation for native multimodal large language models. However, its
multimodal capabilities remain confined to images and text. Yet beyond images,
the ability to understand and generate 3D content is equally crucial. To
address this gap, we propose ShapeLLM-Omni-a native 3D large language model
capable of understanding and generating 3D assets and text in any sequence.
First, we train a 3D vector-quantized variational autoencoder (VQVAE), which
maps 3D objects into a discrete latent space to achieve efficient and accurate
shape representation and reconstruction. Building upon the 3D-aware discrete
tokens, we innovatively construct a large-scale continuous training dataset
named 3D-Alpaca, encompassing generation, comprehension, and editing, thus
providing rich resources for future research and training. Finally, by
performing instruction-based training of the Qwen-2.5-vl-7B-Instruct model on
the 3D-Alpaca dataset. Our work provides an effective attempt at extending
multimodal models with basic 3D capabilities, which contributes to future
research in 3D-native AI. Project page:
https://github.com/JAMESYJL/ShapeLLM-Omni
LoHoVLA: A Unified Vision-Language-Action Model for Long-Horizon
  Embodied Tasks
Authors: name: Yi Yang, name: Jiaxuan Sun, name: Siqi Kou, name: Yihan Wang, name: Zhijie Deng
Published: 2025-05-31 06:01:03

Real-world embodied agents face long-horizon tasks, characterized by
high-level goals demanding multi-step solutions beyond single actions.
Successfully navigating these requires both high-level task planning (i.e.,
decomposing goals into sub-tasks) and low-level motion control (i.e.,
generating precise robot actions). While existing vision language action (VLA)
models and hierarchical architectures offer potential in embodied tasks, the
former often falter in planning, and the latter can suffer from coordination
issues, both hampering performance. We introduce a new unified VLA framework
for long-horizon tasks, dubbed LoHoVLA, to overcome these limitations. LoHoVLA
leverages a large pretrained vision language model (VLM) as the backbone to
jointly generate language and action tokens for sub-task generation and robot
action prediction, respectively. This shared representation promotes better
generalization across tasks. Additionally, LoHoVLA embraces a hierarchical
closed-loop control mechanism to mitigate errors originating from both
high-level planning and low-level control. To train LoHoVLA, we introduce
LoHoSet, a dataset built on the Ravens simulator, containing 20 long-horizon
tasks, each with 1,000 expert demonstrations composed of visual observations,
linguistic goals, sub-tasks, and robot actions. Experimental results show that
LoHoVLA significantly surpasses both hierarchical and standard VLA approaches
on long-horizon embodied tasks in the Ravens simulator. These findings
underscore the promise of unified architectures for advancing generalizable
embodied intelligence.
MiCRo: Mixture Modeling and Context-aware Routing for Personalized
  Preference Learning
Authors: name: Jingyan Shen, name: Jiarui Yao, name: Rui Yang, name: Yifan Sun, name: Feng Luo, name: Rui Pan, name: Tong Zhang, name: Han Zhao
Published: 2025-05-30 17:44:28

Reward modeling is a key step in building safe foundation models when
applying reinforcement learning from human feedback (RLHF) to align Large
Language Models (LLMs). However, reward modeling based on the Bradley-Terry
(BT) model assumes a global reward function, failing to capture the inherently
diverse and heterogeneous human preferences. Hence, such oversimplification
limits LLMs from supporting personalization and pluralistic alignment.
Theoretically, we show that when human preferences follow a mixture
distribution of diverse subgroups, a single BT model has an irreducible error.
While existing solutions, such as multi-objective learning with fine-grained
annotations, help address this issue, they are costly and constrained by
predefined attributes, failing to fully capture the richness of human values.
In this work, we introduce MiCRo, a two-stage framework that enhances
personalized preference learning by leveraging large-scale binary preference
datasets without requiring explicit fine-grained annotations. In the first
stage, MiCRo introduces context-aware mixture modeling approach to capture
diverse human preferences. In the second stage, MiCRo integrates an online
routing strategy that dynamically adapts mixture weights based on specific
context to resolve ambiguity, allowing for efficient and scalable preference
adaptation with minimal additional supervision. Experiments on multiple
preference datasets demonstrate that MiCRo effectively captures diverse human
preferences and significantly improves downstream personalization.
Temporal In-Context Fine-Tuning for Versatile Control of Video Diffusion
  Models
Authors: name: Kinam Kim, name: Junha Hyung, name: Jaegul Choo
Published: 2025-06-01 12:57:43

Recent advances in text-to-video diffusion models have enabled high-quality
video synthesis, but controllable generation remains challenging, particularly
under limited data and compute. Existing fine-tuning methods for conditional
generation often rely on external encoders or architectural modifications,
which demand large datasets and are typically restricted to spatially aligned
conditioning, limiting flexibility and scalability. In this work, we introduce
Temporal In-Context Fine-Tuning (TIC-FT), an efficient and versatile approach
for adapting pretrained video diffusion models to diverse conditional
generation tasks. Our key idea is to concatenate condition and target frames
along the temporal axis and insert intermediate buffer frames with
progressively increasing noise levels. These buffer frames enable smooth
transitions, aligning the fine-tuning process with the pretrained model's
temporal dynamics. TIC-FT requires no architectural changes and achieves strong
performance with as few as 10-30 training samples. We validate our method
across a range of tasks, including image-to-video and video-to-video
generation, using large-scale base models such as CogVideoX-5B and Wan-14B.
Extensive experiments show that TIC-FT outperforms existing baselines in both
condition fidelity and visual quality, while remaining highly efficient in both
training and inference. For additional results, visit
https://kinam0252.github.io/TIC-FT/