Orionnnn/hub-stats huggingface dataset:
Beyond Next-Token: Next-X Prediction for Autoregressive Visual
  Generation
Authors: name: Sucheng Ren, name: Qihang Yu, name: Ju He, name: Xiaohui Shen, name: Alan Yuille, name: Liang-Chieh Chen
Published: 2025-02-27 18:59:08

Autoregressive (AR) modeling, known for its next-token prediction paradigm,
underpins state-of-the-art language and visual generative models.
Traditionally, a ``token'' is treated as the smallest prediction unit, often a
discrete symbol in language or a quantized patch in vision. However, the
optimal token definition for 2D image structures remains an open question.
Moreover, AR models suffer from exposure bias, where teacher forcing during
training leads to error accumulation at inference. In this paper, we propose
xAR, a generalized AR framework that extends the notion of a token to an entity
X, which can represent an individual patch token, a cell (a ktimes k
grouping of neighboring patches), a subsample (a non-local grouping of distant
patches), a scale (coarse-to-fine resolution), or even a whole image.
Additionally, we reformulate discrete token classification as
continuous entity regression, leveraging flow-matching methods at each
AR step. This approach conditions training on noisy entities instead of ground
truth tokens, leading to Noisy Context Learning, which effectively alleviates
exposure bias. As a result, xAR offers two key advantages: (1) it enables
flexible prediction units that capture different contextual granularity and
spatial structures, and (2) it mitigates exposure bias by avoiding reliance on
teacher forcing. On ImageNet-256 generation benchmark, our base model, xAR-B
(172M), outperforms DiT-XL/SiT-XL (675M) while achieving 20times faster
inference. Meanwhile, xAR-H sets a new state-of-the-art with an FID of 1.24,
running 2.2times faster than the previous best-performing model without
relying on vision foundation modules (\eg, DINOv2) or advanced guidance
interval sampling.
On Relation-Specific Neurons in Large Language Models
Authors: name: Yihong Liu, name: Runsheng Chen, name: Lea Hirlimann, name: Ahmad Dawar Hakimi, name: Mingyang Wang, name: Amir Hossein Kargaran, name: Sascha Rothe, name: François Yvon, name: Hinrich Schütze
Published: 2025-02-24 17:33:18

In large language models (LLMs), certain neurons can store distinct pieces of
knowledge learned during pretraining. While knowledge typically appears as a
combination of relations and entities, it remains unclear whether some neurons
focus on a relation itself -- independent of any entity. We hypothesize such
neurons detect a relation in the input text and guide generation involving such
a relation. To investigate this, we study the Llama-2 family on a chosen set of
relations with a statistics-based method. Our experiments demonstrate the
existence of relation-specific neurons. We measure the effect of selectively
deactivating candidate neurons specific to relation r on the LLM's ability to
handle (1) facts whose relation is r and (2) facts whose relation is a
different relation r' neq r. With respect to their capacity for encoding
relation information, we give evidence for the following three properties of
relation-specific neurons. (i) Neuron cumulativity. The neurons for
r present a cumulative effect so that deactivating a larger portion of them
results in the degradation of more facts in r. (ii) Neuron
versatility. Neurons can be shared across multiple closely related as well as
less related relations. Some relation neurons transfer across languages.
(iii) Neuron interference. Deactivating neurons specific to one
relation can improve LLM generation performance for facts of other relations.
We will make our code publicly available at
https://github.com/cisnlp/relation-specific-neurons.
Guardians of the Agentic System: Preventing Many Shots Jailbreak with
  Agentic System
Authors: name: Saikat Barua, name: Mostafizur Rahman, name: Md Jafor Sadek, name: Rafiul Islam, name: Shehnaz Khaled, name: Ahmedul Kabir
Published: 2025-02-23 23:35:15

The autonomous AI agents using large language models can create undeniable
values in all span of the society but they face security threats from
adversaries that warrants immediate protective solutions because trust and
safety issues arise. Considering the many-shot jailbreaking and deceptive
alignment as some of the main advanced attacks, that cannot be mitigated by the
static guardrails used during the supervised training, points out a crucial
research priority for real world robustness. The combination of static
guardrails in dynamic multi-agent system fails to defend against those attacks.
We intend to enhance security for LLM-based agents through the development of
new evaluation frameworks which identify and counter threats for safe
operational deployment. Our work uses three examination methods to detect rogue
agents through a Reverse Turing Test and analyze deceptive alignment through
multi-agent simulations and develops an anti-jailbreaking system by testing it
with GEMINI 1.5 pro and llama-3.3-70B, deepseek r1 models using tool-mediated
adversarial scenarios. The detection capabilities are strong such as 94\%
accuracy for GEMINI 1.5 pro yet the system suffers persistent vulnerabilities
when under long attacks as prompt length increases attack success rates (ASR)
and diversity metrics become ineffective in prediction while revealing multiple
complex system faults. The findings demonstrate the necessity of adopting
flexible security systems based on active monitoring that can be performed by
the agents themselves together with adaptable interventions by system admin as
the current models can create vulnerabilities that can lead to the unreliable
and vulnerable system. So, in our work, we try to address such situations and
propose a comprehensive framework to counteract the security issues.
Training Consistency Models with Variational Noise Coupling
Authors: name: Gianluigi Silvestri, name: Luca Ambrogioni, name: Chieh-Hsin Lai, name: Yuhta Takida, name: Yuki Mitsufuji
Published: 2025-02-25 13:38:04

Consistency Training (CT) has recently emerged as a promising alternative to
diffusion models, achieving competitive performance in image generation tasks.
However, non-distillation consistency training often suffers from high variance
and instability, and analyzing and improving its training dynamics is an active
area of research. In this work, we propose a novel CT training approach based
on the Flow Matching framework. Our main contribution is a trained
noise-coupling scheme inspired by the architecture of Variational Autoencoders
(VAE). By training a data-dependent noise emission model implemented as an
encoder architecture, our method can indirectly learn the geometry of the
noise-to-data mapping, which is instead fixed by the choice of the forward
process in classical CT. Empirical results across diverse image datasets show
significant generative improvements, with our model outperforming baselines and
achieving the state-of-the-art (SoTA) non-distillation CT FID on CIFAR-10, and
attaining FID on par with SoTA on ImageNet at 64 times 64 resolution in
2-step generation. Our code is available at https://github.com/sony/vct .
Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via
  Sparse Time-Variant Attribute Modeling
Authors: name: Hanyang Kong, name: Xingyi Yang, name: Xinchao Wang
Published: 2025-02-27 18:53:06

Rendering dynamic scenes from monocular videos is a crucial yet challenging
task. The recent deformable Gaussian Splatting has emerged as a robust solution
to represent real-world dynamic scenes. However, it often leads to heavily
redundant Gaussians, attempting to fit every training view at various time
steps, leading to slower rendering speeds. Additionally, the attributes of
Gaussians in static areas are time-invariant, making it unnecessary to model
every Gaussian, which can cause jittering in static regions. In practice, the
primary bottleneck in rendering speed for dynamic scenes is the number of
Gaussians. In response, we introduce Efficient Dynamic Gaussian Splatting
(EDGS), which represents dynamic scenes via sparse time-variant attribute
modeling. Our approach formulates dynamic scenes using a sparse anchor-grid
representation, with the motion flow of dense Gaussians calculated via a
classical kernel representation. Furthermore, we propose an unsupervised
strategy to efficiently filter out anchors corresponding to static areas. Only
anchors associated with deformable objects are input into MLPs to query
time-variant attributes. Experiments on two real-world datasets demonstrate
that our EDGS significantly improves the rendering speed with superior
rendering quality compared to previous state-of-the-art methods.
Building Interactable Replicas of Complex Articulated Objects via
  Gaussian Splatting
Authors: name: Yu Liu, name: Baoxiong Jia, name: Ruijie Lu, name: Junfeng Ni, name: Song-Chun Zhu, name: Siyuan Huang
Published: 2025-02-26 10:25:32

Building articulated objects is a key challenge in computer vision. Existing
methods often fail to effectively integrate information across different object
states, limiting the accuracy of part-mesh reconstruction and part dynamics
modeling, particularly for complex multi-part articulated objects. We introduce
ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient
representation to address these issues. Our method incorporates canonical
Gaussians with coarse-to-fine initialization and updates for aligning
articulated part information across different object states, and employs a
skinning-inspired part dynamics modeling module to improve both part-mesh
reconstruction and articulation learning. Extensive experiments on both
synthetic and real-world datasets, including a new benchmark for complex
multi-part objects, demonstrate that ArtGS achieves state-of-the-art
performance in joint parameter estimation and part mesh reconstruction. Our
approach significantly improves reconstruction quality and efficiency,
especially for multi-part articulated objects. Additionally, we provide
comprehensive analyses of our design choices, validating the effectiveness of
each component to highlight potential areas for future improvement.
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language
  Models (VLMs) via Reinforcement Learning
Authors: name: Jiazhen Pan, name: Che Liu, name: Junde Wu, name: Fenglin Liu, name: Jiayuan Zhu, name: Hongwei Bran Li, name: Chen Chen, name: Cheng Ouyang, name: Daniel Rueckert
Published: 2025-02-26 23:57:34

Reasoning is a critical frontier for advancing medical image analysis, where
transparency and trustworthiness play a central role in both clinician trust
and regulatory approval. Although Medical Visual Language Models (VLMs) show
promise for radiological tasks, most existing VLMs merely produce final answers
without revealing the underlying reasoning. To address this gap, we introduce
MedVLM-R1, a medical VLM that explicitly generates natural language reasoning
to enhance transparency and trustworthiness. Instead of relying on supervised
fine-tuning (SFT), which often suffers from overfitting to training
distributions and fails to foster genuine reasoning, MedVLM-R1 employs a
reinforcement learning framework that incentivizes the model to discover
human-interpretable reasoning paths without using any reasoning references.
Despite limited training data (600 visual question answering samples) and model
parameters (2B), MedVLM-R1 boosts accuracy from 55.11% to 78.22% across MRI,
CT, and X-ray benchmarks, outperforming larger models trained on over a million
samples. It also demonstrates robust domain generalization under
out-of-distribution tasks. By unifying medical image analysis with explicit
reasoning, MedVLM-R1 marks a pivotal step toward trustworthy and interpretable
AI in clinical practice.
Multimodal Representation Alignment for Image Generation: Text-Image
  Interleaved Control Is Easier Than You Think
Authors: name: Liang Chen, name: Shuai Bai, name: Wenhao Chai, name: Weichu Xie, name: Haozhe Zhao, name: Leon Vinci, name: Junyang Lin, name: Baobao Chang
Published: 2025-02-27 15:08:39

The field of advanced text-to-image generation is witnessing the emergence of
unified frameworks that integrate powerful text encoders, such as CLIP and T5,
with Diffusion Transformer backbones. Although there have been efforts to
control output images with additional conditions, like canny and depth map, a
comprehensive framework for arbitrary text-image interleaved control is still
lacking. This gap is especially evident when attempting to merge concepts or
visual elements from multiple images in the generation process. To mitigate the
gap, we conducted preliminary experiments showing that large multimodal models
(LMMs) offer an effective shared representation space, where image and text can
be well-aligned to serve as a condition for external diffusion models. Based on
this discovery, we propose Dream Engine, an efficient and unified framework
designed for arbitrary text-image interleaved control in image generation
models. Building on powerful text-to-image models like SD3.5, we replace the
original text-only encoders by incorporating versatile multimodal information
encoders such as QwenVL. Our approach utilizes a two-stage training paradigm,
consisting of joint text-image alignment and multimodal interleaved instruction
tuning. Our experiments demonstrate that this training method is effective,
achieving a 0.69 overall score on the GenEval benchmark, and matching the
performance of state-of-the-art text-to-image models like SD3.5 and FLUX.
NeoBERT: A Next-Generation BERT
Authors: name: Lola Le Breton, name: Quentin Fournier, name: Mariam El Mezouar, name: Sarath Chandar
Published: 2025-02-26 22:00:22

Recent innovations in architecture, pre-training, and fine-tuning have led to
the remarkable in-context learning and reasoning abilities of large
auto-regressive language models such as LLaMA and DeepSeek. In contrast,
encoders like BERT and RoBERTa have not seen the same level of progress despite
being foundational for many downstream NLP applications. To bridge this gap, we
introduce NeoBERT, a next-generation encoder that redefines the capabilities of
bidirectional models by integrating state-of-the-art advancements in
architecture, modern data, and optimized pre-training methodologies. NeoBERT is
designed for seamless adoption: it serves as a plug-and-play replacement for
existing base models, relies on an optimal depth-to-width ratio, and leverages
an extended context length of 4,096 tokens. Despite its compact 250M parameter
footprint, it achieves state-of-the-art results on the massive MTEB benchmark,
outperforming BERT large, RoBERTa large, NomicBERT, and ModernBERT under
identical fine-tuning conditions. In addition, we rigorously evaluate the
impact of each modification on GLUE and design a uniform fine-tuning and
evaluation framework for MTEB. We release all code, data, checkpoints, and
training scripts to accelerate research and real-world adoption.
Lean and Mean: Decoupled Value Policy Optimization with Global Value
  Guidance
Authors: name: Chenghua Huang, name: Lu Wang, name: Fangkai Yang, name: Pu Zhao, name: Zhixu Li, name: Qingwei Lin, name: Dongmei Zhang, name: Saravan Rajmohan, name: Qi Zhang
Published: 2025-02-24 08:11:33

Proximal Policy Optimization (PPO)-based Reinforcement Learning from Human
Feedback (RLHF) is essential for aligning large language models (LLMs) with
human preferences. It requires joint training of an actor and critic with a
pretrained, fixed reward model for guidance. This approach increases
computational complexity and instability due to actor-critic interdependence.
Additionally, PPO lacks access to true environment rewards in LLM tasks,
limiting its adaptability. Under such conditions, pretraining a value model or
a reward model becomes equivalent, as both provide fixed supervisory signals
without new ground-truth feedback. To address these issues, we propose
Decoupled Value Policy Optimization (DVPO), a lean framework that
replaces traditional reward modeling with a pretrained global value model
(GVM). The GVM is conditioned on policy trajectories and predicts token-level
return-to-go estimates. By decoupling value model from policy training (via
frozen GVM-driven RL objectives), DVPO eliminates actor-critic interdependence,
reducing GPU memory usage by 40\% and training time by 35\% compared to
conventional RLHF. Experiments across benchmarks show DVPO outperforms
efficient RLHF methods (e.g., DPO) while matching state-of-the-art PPO in
performance.
FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through
  Reflective Puzzle Solving
Authors: name: Guizhen Chen, name: Weiwen Xu, name: Hao Zhang, name: Hou Pong Chan, name: Chaoqun Liu, name: Lidong Bing, name: Deli Zhao, name: Anh Tuan Luu, name: Yu Rong
Published: 2025-02-27 16:23:25

Many challenging reasoning tasks require not just rapid, intuitive responses,
but a more deliberate, multi-step approach. Recent progress in large language
models (LLMs) highlights an important shift from the "System 1" way of quick
reactions to the "System 2" style of reflection-and-correction problem solving.
However, current benchmarks heavily rely on the final-answer accuracy, leaving
much of a model's intermediate reasoning steps unexamined. This fails to assess
the model's ability to reflect and rectify mistakes within the reasoning
process. To bridge this gap, we introduce FINEREASON, a logic-puzzle benchmark
for fine-grained evaluation of LLMs' reasoning capabilities. Each puzzle can be
decomposed into atomic steps, making it ideal for rigorous validation of
intermediate correctness. Building on this, we introduce two tasks: state
checking, and state transition, for a comprehensive evaluation of how models
assess the current situation and plan the next move. To support broader
research, we also provide a puzzle training set aimed at enhancing performance
on general mathematical tasks. We show that models trained on our state
checking and transition data demonstrate gains in math reasoning by up to 5.1%
on GSM8K.
Mobius: Text to Seamless Looping Video Generation via Latent Shift
Authors: name: Xiuli Bi, name: Jianfei Yuan, name: Bo Liu, name: Yong Zhang, name: Xiaodong Cun, name: Chi-Man Pun, name: Bin Xiao
Published: 2025-02-27 17:33:51

We present Mobius, a novel method to generate seamlessly looping videos from
text descriptions directly without any user annotations, thereby creating new
visual materials for the multi-media presentation. Our method repurposes the
pre-trained video latent diffusion model for generating looping videos from
text prompts without any training. During inference, we first construct a
latent cycle by connecting the starting and ending noise of the videos. Given
that the temporal consistency can be maintained by the context of the video
diffusion model, we perform multi-frame latent denoising by gradually shifting
the first-frame latent to the end in each step. As a result, the denoising
context varies in each step while maintaining consistency throughout the
inference process. Moreover, the latent cycle in our method can be of any
length. This extends our latent-shifting approach to generate seamless looping
videos beyond the scope of the video diffusion model's context. Unlike previous
cinemagraphs, the proposed method does not require an image as appearance,
which will restrict the motions of the generated results. Instead, our method
can produce more dynamic motion and better visual quality. We conduct multiple
experiments and comparisons to verify the effectiveness of the proposed method,
demonstrating its efficacy in different scenarios. All the code will be made
available.
FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality
  Samples with Less Compute
Authors: name: Sotiris Anagnostidis, name: Gregor Bachmann, name: Yeongmin Kim, name: Jonas Kohler, name: Markos Georgopoulos, name: Artsiom Sanakoyeu, name: Yuming Du, name: Albert Pumarola, name: Ali Thabet, name: Edgar Schönfeld
Published: 2025-02-27 14:16:56

Despite their remarkable performance, modern Diffusion Transformers are
hindered by substantial resource requirements during inference, stemming from
the fixed and large amount of compute needed for each denoising step. In this
work, we revisit the conventional static paradigm that allocates a fixed
compute budget per denoising iteration and propose a dynamic strategy instead.
Our simple and sample-efficient framework enables pre-trained DiT models to be
converted into flexible ones -- dubbed FlexiDiT -- allowing them to
process inputs at varying compute budgets. We demonstrate how a single
flexible model can generate images without any drop in quality, while
reducing the required FLOPs by more than 40\% compared to their static
counterparts, for both class-conditioned and text-conditioned image generation.
Our method is general and agnostic to input and conditioning modalities. We
show how our approach can be readily extended for video generation, where
FlexiDiT models generate samples with up to 75\% less compute without
compromising performance.
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning
  Learning
Authors: name: Minggui He, name: Yilun Liu, name: Shimin Tao, name: Yuanchang Luo, name: Hongyong Zeng, name: Chang Su, name: Li Zhang, name: Hongxia Ma, name: Daimeng Wei, name: Weibin Meng, name: Hao Yang, name: Boxing Chen, name: Osamu Yoshie
Published: 2025-02-27 03:57:00

Despite recent breakthroughs in reasoning-enhanced large language models
(LLMs) like DeepSeek-R1, incorporating inference-time reasoning into machine
translation (MT), where human translators naturally employ structured,
multi-layered reasoning chain-of-thoughts (CoTs), is yet underexplored.
Existing methods either design a fixed CoT tailored for a specific MT sub-task
(e.g., literature translation), or rely on synthesizing CoTs unaligned with
humans and supervised fine-tuning (SFT) prone to catastrophic forgetting,
limiting their adaptability to diverse translation scenarios. This paper
introduces R1-Translator (R1-T1), a novel framework to achieve inference-time
reasoning for general MT via reinforcement learning (RL) with human-aligned
CoTs comprising six common patterns. Our approach pioneers three innovations:
(1) extending reasoning-based translation beyond MT sub-tasks to six languages
and diverse tasks (e.g., legal/medical domain adaptation, idiom resolution);
(2) formalizing six expert-curated CoT templates that mirror hybrid human
strategies like context-aware paraphrasing and back translation; and (3)
enabling self-evolving CoT discovery and anti-forgetting adaptation through RL
with KL-constrained rewards. Experimental results indicate a steady translation
performance improvement in 21 languages and 80 translation directions on
Flores-101 test set, especially on the 15 languages unseen from training, with
its general multilingual abilities preserved compared with plain SFT.