Orionnnn/hub-stats huggingface dataset:
SlimMoE: Structured Compression of Large MoE Models via Expert Slimming
  and Distillation
Authors: name: Zichong Li, name: Chen Liang, name: Zixuan Zhang, name: Ilgee Hong, name: Young Jin Kim, name: Weizhu Chen, name: Tuo Zhao
Published: 2025-06-23 07:15:59

The Mixture of Experts (MoE) architecture has emerged as a powerful paradigm
for scaling large language models (LLMs) while maintaining inference
efficiency. However, their enormous memory requirements make them prohibitively
expensive to fine-tune or deploy in resource-constrained environments. To
address this challenge, we introduce SlimMoE, a multi-stage compression
framework for transforming large MoE models into much smaller, efficient
variants without incurring the prohibitive costs of training from scratch. Our
method systematically reduces parameter counts by slimming experts and
transferring knowledge through intermediate stages, effectively mitigating the
performance degradation common in one-shot pruning approaches. Using this
framework, we compress Phi 3.5-MoE (41.9B total/6.6B activated parameters) to
create Phi-mini-MoE (7.6B total/2.4B activated parameters) and Phi-tiny-MoE
(3.8B total/1.1B activated parameters) using only 400B tokens--less than 10% of
the original model's training data. These compressed models can be fine-tuned
on a single GPU (A100 for Phi-mini-MoE, A6000 for Phi-tiny-MoE), making them
highly suitable for academic and resource-limited settings. Our experiments
demonstrate that these compressed models outperform others of similar size and
remain competitive with larger models. For instance, Phi-mini-MoE achieves
similar or better performance to Phi-3-mini using only 2/3 of the activated
parameters and yields comparable MMLU scores to Llama 3.1 8B despite having
significantly lower latency. Our findings demonstrate that structured pruning
combined with staged distillation offers an effective path to creating
high-quality, compact MoE models, paving the way for broader adoption of MoE
architectures. We make our models publicly available at
https://huggingface.co/microsoft/Phi-mini-MoE-instruct and
https://huggingface.co/microsoft/Phi-tiny-MoE-instruct .
VMem: Consistent Interactive Video Scene Generation with Surfel-Indexed
  View Memory
Authors: name: Runjia Li, name: Philip Torr, name: Andrea Vedaldi, name: Tomas Jakab
Published: 2025-06-23 17:59:56

We propose a novel memory mechanism to build video generators that can
explore environments interactively. Similar results have previously been
achieved by out-painting 2D views of the scene while incrementally
reconstructing its 3D geometry, which quickly accumulates errors, or by video
generators with a short context window, which struggle to maintain scene
coherence over the long term. To address these limitations, we introduce
Surfel-Indexed View Memory (VMem), a mechanism that remembers past views by
indexing them geometrically based on the 3D surface elements (surfels) they
have observed. VMem enables the efficient retrieval of the most relevant past
views when generating new ones. By focusing only on these relevant views, our
method produces consistent explorations of imagined environments at a fraction
of the computational cost of using all past views as context. We evaluate our
approach on challenging long-term scene synthesis benchmarks and demonstrate
superior performance compared to existing methods in maintaining scene
coherence and camera control.
ReDit: Reward Dithering for Improved LLM Policy Optimization
Authors: name: Chenxing Wei, name: Jiarui Yu, name: Ying Tiffany He, name: Hande Dong, name: Yao Shu, name: Fei Yu
Published: 2025-06-23 13:36:24

DeepSeek-R1 has successfully enhanced Large Language Model (LLM) reasoning
capabilities through its rule-based reward system. While it's a ''perfect''
reward system that effectively mitigates reward hacking, such reward functions
are often discrete. Our experimental observations suggest that discrete rewards
can lead to gradient anomaly, unstable optimization, and slow convergence. To
address this issue, we propose ReDit (Reward Dithering), a method that dithers
the discrete reward signal by adding simple random noise. With this perturbed
reward, exploratory gradients are continuously provided throughout the learning
process, enabling smoother gradient updates and accelerating convergence. The
injected noise also introduces stochasticity into flat reward regions,
encouraging the model to explore novel policies and escape local optima.
Experiments across diverse tasks demonstrate the effectiveness and efficiency
of ReDit. On average, ReDit achieves performance comparable to vanilla GRPO
with only approximately 10% the training steps, and furthermore, still exhibits
a 4% performance improvement over vanilla GRPO when trained for a similar
duration. Visualizations confirm significant mitigation of gradient issues with
ReDit. Moreover, theoretical analyses are provided to further validate these
advantages.
CommVQ: Commutative Vector Quantization for KV Cache Compression
Authors: name: Junyan Li, name: Yang Zhang, name: Muhammad Yusuf Hassan, name: Talha Chafekar, name: Tianle Cai, name: Zhile Ren, name: Pengsheng Guo, name: Foroozan Karimzadeh, name: Colorado Reed, name: Chong Wang, name: Chuang Gan
Published: 2025-06-23 17:50:11

Large Language Models (LLMs) are increasingly used in applications requiring
long context lengths, but the key-value (KV) cache often becomes a memory
bottleneck on GPUs as context grows. To address this, we propose Commutative
Vector Quantization (CommVQ) to significantly reduce memory usage for
long-context LLM inference. We first introduce additive quantization with a
lightweight encoder and codebook to compress the KV cache, which can be decoded
via simple matrix multiplication. To further reduce computational costs during
decoding, we design the codebook to be commutative with Rotary Position
Embedding (RoPE) and train it using an Expectation-Maximization (EM) algorithm.
This enables efficient integration of decoding into the self-attention
mechanism. Our approach achieves high accuracy with additive quantization and
low overhead via the RoPE-commutative codebook. Experiments on long-context
benchmarks and GSM8K show that our method reduces FP16 KV cache size by 87.5%
with 2-bit quantization, while outperforming state-of-the-art KV cache
quantization methods. Notably, it enables 1-bit KV cache quantization with
minimal accuracy loss, allowing a LLaMA-3.1 8B model to run with a 128K context
length on a single RTX 4090 GPU. The source code is available at:
https://github.com/UMass-Embodied-AGI/CommVQ.
SoK: Evaluating Jailbreak Guardrails for Large Language Models
Authors: name: Xunguang Wang, name: Zhenlan Ji, name: Wenxuan Wang, name: Zongjie Li, name: Daoyuan Wu, name: Shuai Wang
Published: 2025-06-12 11:42:40

Large Language Models (LLMs) have achieved remarkable progress, but their
deployment has exposed critical vulnerabilities, particularly to jailbreak
attacks that circumvent safety mechanisms. Guardrails--external defense
mechanisms that monitor and control LLM interaction--have emerged as a
promising solution. However, the current landscape of LLM guardrails is
fragmented, lacking a unified taxonomy and comprehensive evaluation framework.
In this Systematization of Knowledge (SoK) paper, we present the first holistic
analysis of jailbreak guardrails for LLMs. We propose a novel,
multi-dimensional taxonomy that categorizes guardrails along six key
dimensions, and introduce a Security-Efficiency-Utility evaluation framework to
assess their practical effectiveness. Through extensive analysis and
experiments, we identify the strengths and limitations of existing guardrail
approaches, explore their universality across attack types, and provide
insights into optimizing defense combinations. Our work offers a structured
foundation for future research and development, aiming to guide the principled
advancement and deployment of robust LLM guardrails. The code is available at
https://github.com/xunguangwang/SoK4JailbreakGuardrails.
Phantom-Data : Towards a General Subject-Consistent Video Generation
  Dataset
Authors: name: Zhuowei Chen, name: Bingchuan Li, name: Tianxiang Ma, name: Lijie Liu, name: Mingcong Liu, name: Yi Zhang, name: Gen Li, name: Xinghui Li, name: Siyu Zhou, name: Qian He, name: Xinglong Wu
Published: 2025-06-23 17:11:56

Subject-to-video generation has witnessed substantial progress in recent
years. However, existing models still face significant challenges in faithfully
following textual instructions. This limitation, commonly known as the
copy-paste problem, arises from the widely used in-pair training paradigm. This
approach inherently entangles subject identity with background and contextual
attributes by sampling reference images from the same scene as the target
video. To address this issue, we introduce Phantom-Data, the first
general-purpose cross-pair subject-to-video consistency dataset, containing
approximately one million identity-consistent pairs across diverse categories.
Our dataset is constructed via a three-stage pipeline: (1) a general and
input-aligned subject detection module, (2) large-scale cross-context subject
retrieval from more than 53 million videos and 3 billion images, and (3)
prior-guided identity verification to ensure visual consistency under
contextual variation. Comprehensive experiments show that training with
Phantom-Data significantly improves prompt alignment and visual quality while
preserving identity consistency on par with in-pair baselines.
LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement
  Learning
Authors: name: Yuhao Wu, name: Yushi Bai, name: Zhiqiang Hu, name: Roy Ka-Wei Lee, name: Juanzi Li
Published: 2025-06-23 16:59:02

Ultra-long generation by large language models (LLMs) is a widely demanded
scenario, yet it remains a significant challenge due to their maximum
generation length limit and overall quality degradation as sequence length
increases. Previous approaches, exemplified by LongWriter, typically rely on
''teaching'', which involves supervised fine-tuning (SFT) on synthetic
long-form outputs. However, this strategy heavily depends on synthetic SFT
data, which is difficult and costly to construct, often lacks coherence and
consistency, and tends to be overly artificial and structurally monotonous. In
this work, we propose an incentivization-based approach that, starting entirely
from scratch and without relying on any annotated or synthetic data, leverages
reinforcement learning (RL) to foster the emergence of ultra-long, high-quality
text generation capabilities in LLMs. We perform RL training starting from a
base model, similar to R1-Zero, guiding it to engage in reasoning that
facilitates planning and refinement during the writing process. To support
this, we employ specialized reward models that steer the LLM towards improved
length control, writing quality, and structural formatting. Experimental
evaluations show that our LongWriter-Zero model, trained from Qwen2.5-32B,
consistently outperforms traditional SFT methods on long-form writing tasks,
achieving state-of-the-art results across all metrics on WritingBench and
Arena-Write, and even surpassing 100B+ models such as DeepSeek R1 and
Qwen3-235B. We open-source our data and model checkpoints under
https://huggingface.co/THU-KEG/LongWriter-Zero-32B
OmniGen2: Exploration to Advanced Multimodal Generation
Authors: name: Chenyuan Wu, name: Pengfei Zheng, name: Ruiran Yan, name: Shitao Xiao, name: Xin Luo, name: Yueze Wang, name: Wanli Li, name: Xiyan Jiang, name: Yexin Liu, name: Junjie Zhou, name: Ze Liu, name: Ziyi Xia, name: Chaofan Li, name: Haoge Deng, name: Jiahao Wang, name: Kun Luo, name: Bo Zhang, name: Defu Lian, name: Xinlong Wang, name: Zhongyuan Wang, name: Tiejun Huang, name: Zheng Liu
Published: 2025-06-23 17:38:54

In this work, we introduce OmniGen2, a versatile and open-source generative
model designed to provide a unified solution for diverse generation tasks,
including text-to-image, image editing, and in-context generation. Unlike
OmniGen v1, OmniGen2 features two distinct decoding pathways for text and image
modalities, utilizing unshared parameters and a decoupled image tokenizer. This
design enables OmniGen2 to build upon existing multimodal understanding models
without the need to re-adapt VAE inputs, thereby preserving the original text
generation capabilities. To facilitate the training of OmniGen2, we developed
comprehensive data construction pipelines, encompassing image editing and
in-context generation data. Additionally, we introduce a reflection mechanism
tailored for image generation tasks and curate a dedicated reflection dataset
based on OmniGen2. Despite its relatively modest parameter size, OmniGen2
achieves competitive results on multiple task benchmarks, including
text-to-image and image editing. To further evaluate in-context generation,
also referred to as subject-driven tasks, we introduce a new benchmark named
OmniContext. OmniGen2 achieves state-of-the-art performance among open-source
models in terms of consistency. We will release our models, training code,
datasets, and data construction pipeline to support future research in this
field. Project Page: https://vectorspacelab.github.io/OmniGen2; GitHub Link:
https://github.com/VectorSpaceLab/OmniGen2
ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought
  Reasoning in LLMs
Authors: name: Jiaru Zou, name: Ling Yang, name: Jingwen Gu, name: Jiahao Qiu, name: Ke Shen, name: Jingrui He, name: Mengdi Wang
Published: 2025-06-23 17:59:02

Process Reward Models (PRMs) have recently emerged as a powerful framework
for supervising intermediate reasoning steps in large language models (LLMs).
Previous PRMs are primarily trained on model final output responses and
struggle to evaluate intermediate thinking trajectories robustly, especially in
the emerging setting of trajectory-response outputs generated by frontier
reasoning models like Deepseek-R1. In this work, we introduce ReasonFlux-PRM, a
novel trajectory-aware PRM explicitly designed to evaluate the
trajectory-response type of reasoning traces. ReasonFlux-PRM incorporates both
step-level and trajectory-level supervision, enabling fine-grained reward
assignment aligned with structured chain-of-thought data. We adapt
ReasonFlux-PRM to support reward supervision under both offline and online
settings, including (i) selecting high-quality model distillation data for
downstream supervised fine-tuning of smaller models, (ii) providing dense
process-level rewards for policy optimization during reinforcement learning,
and (iii) enabling reward-guided Best-of-N test-time scaling. Empirical results
on challenging downstream benchmarks such as AIME, MATH500, and GPQA-Diamond
demonstrate that ReasonFlux-PRM-7B selects higher quality data than strong PRMs
(e.g., Qwen2.5-Math-PRM-72B) and human-curated baselines. Furthermore, our
derived ReasonFlux-PRM-7B yields consistent performance improvements, achieving
average gains of 12.1% in supervised fine-tuning, 4.5% in reinforcement
learning, and 6.3% in test-time scaling. We also release our efficient
ReasonFlux-PRM-1.5B for resource-constrained applications and edge deployment.
Projects: https://github.com/Gen-Verse/ReasonFlux
Vision as a Dialect: Unifying Visual Understanding and Generation via
  Text-Aligned Representations
Authors: name: Jiaming Han, name: Hao Chen, name: Yang Zhao, name: Hanyu Wang, name: Qi Zhao, name: Ziyan Yang, name: Hao He, name: Xiangyu Yue, name: Lu Jiang
Published: 2025-06-23 17:59:14

This paper presents a multimodal framework that attempts to unify visual
understanding and generation within a shared discrete semantic representation.
At its core is the Text-Aligned Tokenizer (TA-Tok), which converts images into
discrete tokens using a text-aligned codebook projected from a large language
model's (LLM) vocabulary. By integrating vision and text into a unified space
with an expanded vocabulary, our multimodal LLM, Tar, enables cross-modal input
and output through a shared interface, without the need for modality-specific
designs. Additionally, we propose scale-adaptive encoding and decoding to
balance efficiency and visual detail, along with a generative de-tokenizer to
produce high-fidelity visual outputs. To address diverse decoding needs, we
utilize two complementary de-tokenizers: a fast autoregressive model and a
diffusion-based model. To enhance modality fusion, we investigate advanced
pre-training tasks, demonstrating improvements in both visual understanding and
generation. Experiments across benchmarks show that Tar matches or surpasses
existing multimodal LLM methods, achieving faster convergence and greater
training efficiency. Code, models, and data are available at
https://tar.csuhan.com
Better Language Model Inversion by Compactly Representing Next-Token
  Distributions
Authors: name: Murtaza Nazir, name: Matthew Finlayson, name: John X. Morris, name: Xiang Ren, name: Swabha Swayamdipta
Published: 2025-06-20 15:53:51

Language model inversion seeks to recover hidden prompts using only language
model outputs. This capability has implications for security and accountability
in language model deployments, such as leaking private information from an
API-protected language model's system message. We propose a new method --
prompt inversion from logprob sequences (PILS) -- that recovers hidden prompts
by gleaning clues from the model's next-token probabilities over the course of
multiple generation steps. Our method is enabled by a key insight: The
vector-valued outputs of a language model occupy a low-dimensional subspace.
This enables us to losslessly compress the full next-token probability
distribution over multiple generation steps using a linear map, allowing more
output information to be used for inversion. Our approach yields massive gains
over previous state-of-the-art methods for recovering hidden prompts, achieving
2--3.5 times higher exact recovery rates across test sets, in one case
increasing the recovery rate from 17% to 60%. Our method also exhibits
surprisingly good generalization behavior; for instance, an inverter trained on
16 generations steps gets 5--27 points higher prompt recovery when we increase
the number of steps to 32 at test time. Furthermore, we demonstrate strong
performance of our method on the more challenging task of recovering hidden
system messages. We also analyze the role of verbatim repetition in prompt
recovery and propose a new method for cross-family model transfer for
logit-based inverters. Our findings show that next-token probabilities are a
considerably more vulnerable attack surface for inversion attacks than
previously known.
Watermarking Autoregressive Image Generation
Authors: name: Nikola Jovanović, name: Ismail Labiad, name: Tomáš Souček, name: Martin Vechev, name: Pierre Fernandez
Published: 2025-06-19 14:25:51

Watermarking the outputs of generative models has emerged as a promising
approach for tracking their provenance. Despite significant interest in
autoregressive image generation models and their potential for misuse, no prior
work has attempted to watermark their outputs at the token level. In this work,
we present the first such approach by adapting language model watermarking
techniques to this setting. We identify a key challenge: the lack of reverse
cycle-consistency (RCC), wherein re-tokenizing generated image tokens
significantly alters the token sequence, effectively erasing the watermark. To
address this and to make our method robust to common image transformations,
neural compression, and removal attacks, we introduce (i) a custom
tokenizer-detokenizer finetuning procedure that improves RCC, and (ii) a
complementary watermark synchronization layer. As our experiments demonstrate,
our approach enables reliable and robust watermark detection with theoretically
grounded p-values.
MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert
  Aggregation
Authors: name: Shoubin Yu, name: Yue Zhang, name: Ziyang Wang, name: Jaehong Yoon, name: Mohit Bansal
Published: 2025-06-20 16:14:13

Combining pre-trained expert models offers substantial potential for scalable
multimodal reasoning, but building a unified framework remains challenging due
to the increasing diversity of input modalities and task complexity. For
instance, medical diagnosis requires precise reasoning over structured clinical
tables, while financial forecasting depends on interpreting plot-based data to
make informed predictions. To tackle this challenge, we introduce MEXA, a
training-free framework that performs modality- and task-aware aggregation of
multiple expert models to enable effective multimodal reasoning across diverse
and distinct domains. MEXA dynamically selects expert models based on the input
modality and the task-specific reasoning demands (i.e., skills). Each expert
model, specialized in a modality task pair, generates interpretable textual
reasoning outputs. MEXA then aggregates and reasons over these outputs using a
Large Reasoning Model (LRM) to produce the final answer. This modular design
allows flexible and transparent multimodal reasoning across diverse domains
without additional training overhead. We extensively evaluate our approach on
diverse multimodal benchmarks, including Video Reasoning, Audio Reasoning, 3D
Understanding, and Medical QA. MEXA consistently delivers performance
improvements over strong multimodal baselines, highlighting the effectiveness
and broad applicability of our expert-driven selection and aggregation in
diverse multimodal reasoning tasks.
Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual
  Tokens
Authors: name: Zeyuan Yang, name: Xueyang Yu, name: Delin Chen, name: Maohao Shen, name: Chuang Gan
Published: 2025-06-20 17:59:31

Vision-language models (VLMs) excel at multimodal understanding, yet their
text-only decoding forces them to verbalize visual reasoning, limiting
performance on tasks that demand visual imagination. Recent attempts train VLMs
to render explicit images, but the heavy image-generation pre-training often
hinders the reasoning ability. Inspired by the way humans reason with mental
imagery-the internal construction and manipulation of visual cues-we
investigate whether VLMs can reason through interleaved multimodal trajectories
without producing explicit images. To this end, we present a Machine Mental
Imagery framework, dubbed as Mirage, which augments VLM decoding with latent
visual tokens alongside ordinary text. Concretely, whenever the model chooses
to ``think visually'', it recasts its hidden states as next tokens, thereby
continuing a multimodal trajectory without generating pixel-level images. Begin
by supervising the latent tokens through distillation from ground-truth image
embeddings, we then switch to text-only supervision to make the latent
trajectory align tightly with the task objective. A subsequent reinforcement
learning stage further enhances the multimodal reasoning capability.
Experiments on diverse benchmarks demonstrate that Mirage unlocks stronger
multimodal reasoning without explicit image generation.