Orionnnn/hub-stats huggingface dataset:
Curia: A Multi-Modal Foundation Model for Radiology
Authors: name: Corentin Dancette, name: Julien Khlaut, name: Antoine Saporta, name: Helene Philippe, name: Elodie Ferreres, name: Baptiste Callard, name: Théo Danielou, name: Léo Alberge, name: Léo Machado, name: Daniel Tordjman, name: Julie Dupuis, name: Korentin Le Floch, name: Jean Du Terrail, name: Mariam Moshiri, name: Laurent Dercle, name: Tom Boeken, name: Jules Gregory, name: Maxime Ronot, name: François Legou, name: Pascal Roux, name: Marc Sapoval, name: Pierre Manceron, name: Paul Hérent
Published: 2025-09-08 16:04:12

AI-assisted radiological interpretation is based on predominantly narrow,
single-task models. This approach is impractical for covering the vast spectrum
of imaging modalities, diseases, and radiological findings. Foundation models
(FMs) hold the promise of broad generalization across modalities and in
low-data settings. However, this potential has remained largely unrealized in
radiology. We introduce Curia, a foundation model trained on the entire
cross-sectional imaging output of a major hospital over several years, which to
our knowledge is the largest such corpus of real-world data-encompassing
150,000 exams (130 TB). On a newly curated 19-task external validation
benchmark, Curia accurately identifies organs, detects conditions like brain
hemorrhages and myocardial infarctions, and predicts outcomes in tumor staging.
Curia meets or surpasses the performance of radiologists and recent foundation
models, and exhibits clinically significant emergent properties in
cross-modality, and low-data regimes. To accelerate progress, we release our
base model's weights at https://huggingface.co/raidium/curia.
Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with
  Quantization-Aware Scheduling
Authors: name: Natalia Frumkin, name: Diana Marculescu
Published: 2025-09-01 17:09:22

Text-to-image diffusion models are computationally intensive, often requiring
dozens of forward passes through large transformer backbones. For instance,
Stable Diffusion XL generates high-quality images with 50 evaluations of a
2.6B-parameter model, an expensive process even for a single batch. Few-step
diffusion models reduce this cost to 2-8 denoising steps but still depend on
large, uncompressed U-Net or diffusion transformer backbones, which are often
too costly for full-precision inference without datacenter GPUs. These
requirements also limit existing post-training quantization methods that rely
on full-precision calibration. We introduce Q-Sched, a new paradigm for
post-training quantization that modifies the diffusion model scheduler rather
than model weights. By adjusting the few-step sampling trajectory, Q-Sched
achieves full-precision accuracy with a 4x reduction in model size. To learn
quantization-aware pre-conditioning coefficients, we propose the JAQ loss,
which combines text-image compatibility with an image quality metric for
fine-grained optimization. JAQ is reference-free and requires only a handful of
calibration prompts, avoiding full-precision inference during calibration.
Q-Sched delivers substantial gains: a 15.5% FID improvement over the FP16
4-step Latent Consistency Model and a 16.6% improvement over the FP16 8-step
Phased Consistency Model, showing that quantization and few-step distillation
are complementary for high-fidelity generation. A large-scale user study with
more than 80,000 annotations further confirms Q-Sched's effectiveness on both
FLUX.1[schnell] and SDXL-Turbo.
UMO: Scaling Multi-Identity Consistency for Image Customization via
  Matching Reward
Authors: name: Yufeng Cheng, name: Wenxu Wu, name: Shaojin Wu, name: Mengqi Huang, name: Fei Ding, name: Qian He
Published: 2025-09-08 15:54:55

Recent advancements in image customization exhibit a wide range of
application prospects due to stronger customization capabilities. However,
since we humans are more sensitive to faces, a significant challenge remains in
preserving consistent identity while avoiding identity confusion with
multi-reference images, limiting the identity scalability of customization
models. To address this, we present UMO, a Unified Multi-identity Optimization
framework, designed to maintain high-fidelity identity preservation and
alleviate identity confusion with scalability. With "multi-to-multi matching"
paradigm, UMO reformulates multi-identity generation as a global assignment
optimization problem and unleashes multi-identity consistency for existing
image customization methods generally through reinforcement learning on
diffusion models. To facilitate the training of UMO, we develop a scalable
customization dataset with multi-reference images, consisting of both
synthesised and real parts. Additionally, we propose a new metric to measure
identity confusion. Extensive experiments demonstrate that UMO not only
improves identity consistency significantly, but also reduces identity
confusion on several image customization methods, setting a new
state-of-the-art among open-source methods along the dimension of identity
preserving. Code and model: https://github.com/bytedance/UMO
Causal Attention with Lookahead Keys
Authors: name: Zhuoqing Song, name: Peng Sun, name: Huizhuo Yuan, name: Quanquan Gu
Published: 2025-09-09 00:15:23

In standard causal attention, each token's query, key, and value (QKV) are
static and encode only preceding context. We introduce CAuSal aTtention with
Lookahead kEys (CASTLE), an attention mechanism that continually updates each
token's keys as the context unfolds. We term these updated keys lookahead keys
because they belong to earlier positions yet integrate information from tokens
that appear later relative to those positions, while strictly preserving the
autoregressive property. Although the mechanism appears sequential, we derive a
mathematical equivalence that avoids explicitly materializing lookahead keys at
each position and enables efficient parallel training. On language modeling
benchmarks, CASTLE consistently outperforms standard causal attention across
model scales, reducing validation perplexity and improving performance on a
range of downstream tasks.
Reconstruction Alignment Improves Unified Multimodal Models
Authors: name: Ji Xie, name: Trevor Darrell, name: Luke Zettlemoyer, name: XuDong Wang
Published: 2025-09-08 23:59:32

Unified multimodal models (UMMs) unify visual understanding and generation
within a single architecture. However, conventional training relies on
image-text pairs (or sequences) whose captions are typically sparse and miss
fine-grained visual details--even when they use hundreds of words to describe a
simple image. We introduce Reconstruction Alignment (RecA), a
resource-efficient post-training method that leverages visual understanding
encoder embeddings as dense "text prompts," providing rich supervision without
captions. Concretely, RecA conditions a UMM on its own visual understanding
embeddings and optimizes it to reconstruct the input image with a
self-supervised reconstruction loss, thereby realigning understanding and
generation. Despite its simplicity, RecA is broadly applicable: across
autoregressive, masked-autoregressive, and diffusion-based UMMs, it
consistently improves generation and editing fidelity. With only 27 GPU-hours,
post-training with RecA substantially improves image generation performance on
GenEval (0.73rightarrow0.90) and DPGBench (80.93rightarrow88.15), while
also boosting editing benchmarks (ImgEdit 3.38rightarrow3.75, GEdit
6.94rightarrow7.25). Notably, RecA surpasses much larger open-source models
and applies broadly across diverse UMM architectures, establishing it as an
efficient and general post-training alignment strategy for UMMs
F1: A Vision-Language-Action Model Bridging Understanding and Generation
  to Actions
Authors: name: Qi Lv, name: Weijie Kong, name: Hao Li, name: Jia Zeng, name: Zherui Qiu, name: Delin Qu, name: Haoming Song, name: Qizhi Chen, name: Xiang Deng, name: Jiangmiao Pang
Published: 2025-09-08 17:58:30

Executing language-conditioned tasks in dynamic visual environments remains a
central challenge in embodied AI. Existing Vision-Language-Action (VLA) models
predominantly adopt reactive state-to-action mappings, often leading to
short-sighted behaviors and poor robustness in dynamic scenes. In this paper,
we introduce F1, a pretrained VLA framework which integrates the visual
foresight generation into decision-making pipeline. F1 adopts a
Mixture-of-Transformer architecture with dedicated modules for perception,
foresight generation, and control, thereby bridging understanding, generation,
and actions. At its core, F1 employs a next-scale prediction mechanism to
synthesize goal-conditioned visual foresight as explicit planning targets. By
forecasting plausible future visual states, F1 reformulates action generation
as a foresight-guided inverse dynamics problem, enabling actions that
implicitly achieve visual goals. To endow F1 with robust and generalizable
capabilities, we propose a three-stage training recipe on an extensive dataset
comprising over 330k trajectories across 136 diverse tasks. This training
scheme enhances modular reasoning and equips the model with transferable visual
foresight, which is critical for complex and dynamic environments. Extensive
evaluations on real-world tasks and simulation benchmarks demonstrate F1
consistently outperforms existing approaches, achieving substantial gains in
both task success rate and generalization ability.
Language Self-Play For Data-Free Training
Authors: name: Jakub Grudzien Kuba, name: Mengting Gu, name: Qi Ma, name: Yuandong Tian, name: Vijai Mohan
Published: 2025-09-09 05:51:34

Large language models (LLMs) have advanced rapidly in recent years, driven by
scale, abundant high-quality training data, and reinforcement learning. Yet
this progress faces a fundamental bottleneck: the need for ever more data from
which models can continue to learn. In this work, we propose a reinforcement
learning approach that removes this dependency by enabling models to improve
without additional data. Our method leverages a game-theoretic framework of
self-play, where a model's capabilities are cast as performance in a
competitive game and stronger policies emerge by having the model play against
itself - a process we call Language Self-Play (LSP). Experiments with
Llama-3.2-3B-Instruct on instruction-following benchmarks show that pretrained
models can not only enhance their performance on challenging tasks through
self-play alone, but can also do so more effectively than data-driven
baselines.
SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric
  Knowledge
Authors: name: Lukas Haas, name: Gal Yona, name: Giovanni D'Antonio, name: Sasha Goldshtein, name: Dipanjan Das
Published: 2025-09-09 17:53:58

We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large
Language Model (LLM) short-form factuality based on OpenAI's SimpleQA. It
addresses critical limitations in OpenAI's benchmark, including noisy and
incorrect labels, topical biases, and question redundancy. SimpleQA Verified
was created through a rigorous multi-stage filtering process involving
de-duplication, topic balancing, and source reconciliation to produce a more
reliable and challenging evaluation set, alongside improvements in the
autorater prompt. On this new benchmark, Gemini 2.5 Pro achieves a
state-of-the-art F1-score of 55.6, outperforming other frontier models,
including GPT-5. This work provides the research community with a
higher-fidelity tool to track genuine progress in parametric model factuality
and to mitigate hallucinations. The benchmark dataset, evaluation code, and
leaderboard are available at:
https://www.kaggle.com/benchmarks/deepmind/simpleqa-verified.
Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Authors: name: Tong Zheng, name: Hongming Zhang, name: Wenhao Yu, name: Xiaoyang Wang, name: Xinyu Yang, name: Runpeng Dai, name: Rui Liu, name: Huiwen Bao, name: Chengsong Huang, name: Heng Huang, name: Dong Yu
Published: 2025-09-09 17:59:35

Parallel thinking has emerged as a novel approach for enhancing the reasoning
capabilities of large language models (LLMs) by exploring multiple reasoning
paths concurrently. However, activating such capabilities through training
remains challenging, as existing methods predominantly rely on supervised
fine-tuning (SFT) over synthetic data, which encourages teacher-forced
imitation rather than exploration and generalization. Different from them, we
propose Parallel-R1, the first reinforcement learning (RL) framework
that enables parallel thinking behaviors for complex real-world reasoning
tasks. Our framework employs a progressive curriculum that explicitly addresses
the cold-start problem in training parallel thinking with RL. We first use SFT
on prompt-generated trajectories from easier tasks to instill the parallel
thinking ability, then transition to RL to explore and generalize this skill on
harder problems. Experiments on various math benchmarks, including MATH, AMC23,
and AIME, show that Parallel-R1 successfully instills parallel thinking,
leading to 8.4% accuracy improvements over the sequential thinking model
trained directly on challenging tasks with RL. Further analysis reveals a clear
shift in the model's thinking behavior: at an early stage, it uses parallel
thinking as an exploration strategy, while in a later stage, it uses the same
capability for multi-perspective verification. Most significantly, we validate
parallel thinking as a mid-training exploration scaffold, where this
temporary exploratory phase unlocks a higher performance ceiling after RL,
yielding a 42.9% improvement over the baseline on AIME25. Our model, data, and
code will be open-source at https://github.com/zhengkid/Parallel-R1.
Visual Representation Alignment for Multimodal Large Language Models
Authors: name: Heeji Yoon, name: Jaewoo Jung, name: Junwan Kim, name: Hyungyu Choi, name: Heeseong Shin, name: Sangbeom Lim, name: Honggyu An, name: Chaehyun Kim, name: Jisang Han, name: Donghyun Kim, name: Chanho Eom, name: Sunghwan Hong, name: Seungryong Kim
Published: 2025-09-09 17:59:14

Multimodal large language models (MLLMs) trained with visual instruction
tuning have achieved strong performance across diverse tasks, yet they remain
limited in vision-centric tasks such as object counting or spatial reasoning.
We attribute this gap to the prevailing text-only supervision paradigm, which
provides only indirect guidance for the visual pathway and often leads MLLMs to
discard fine-grained visual details during training. In this paper, we present
VIsual Representation ALignment (VIRAL), a simple yet effective regularization
strategy that aligns the internal visual representations of MLLMs with those of
pre-trained vision foundation models (VFMs). By explicitly enforcing this
alignment, VIRAL enables the model not only to retain critical visual details
from the input vision encoder but also to complement additional visual
knowledge from VFMs, thereby enhancing its ability to reason over complex
visual inputs. Our experiments demonstrate consistent improvements across all
tasks on widely adopted multimodal benchmarks. Furthermore, we conduct
comprehensive ablation studies to validate the key design choices underlying
our framework. We believe this simple finding opens up an important direction
for the effective integration of visual information in training MLLMs.
Staying in the Sweet Spot: Responsive Reasoning Evolution via
  Capability-Adaptive Hint Scaffolding
Authors: name: Ziheng Li, name: Zexu Sun, name: Jinman Zhao, name: Erxue Min, name: Yongcheng Zeng, name: Hui Wu, name: Hengyi Cai, name: Shuaiqiang Wang, name: Dawei Yin, name: Xu Chen, name: Zhi-Hong Deng
Published: 2025-09-08 17:36:21

Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable
success in enhancing the reasoning capabilities of large language models
(LLMs). However, existing RLVR methods often suffer from exploration
inefficiency due to mismatches between the training data's difficulty and the
model's capability. LLMs fail to discover viable reasoning paths when problems
are overly difficult, while learning little new capability when problems are
too simple. In this work, we formalize the impact of problem difficulty by
quantifying the relationship between loss descent speed and rollout accuracy.
Building on this analysis, we propose SEELE, a novel supervision-aided RLVR
framework that dynamically adjusts problem difficulty to stay within the
high-efficiency region. SEELE augments each training sample by appending a hint
(part of a full solution) after the original problem. Unlike previous
hint-based approaches, SEELE deliberately and adaptively adjusts the hint
length for each problem to achieve an optimal difficulty. To determine the
optimal hint length, SEELE employs a multi-round rollout sampling strategy. In
each round, it fits an item response theory model to the accuracy-hint pairs
collected in preceding rounds to predict the required hint length for the next
round. This instance-level, real-time difficulty adjustment aligns problem
difficulty with the evolving model capability, thereby improving exploration
efficiency. Experimental results show that SEELE outperforms Group Relative
Policy Optimization (GRPO) and Supervised Fine-tuning (SFT) by +11.8 and +10.5
points, respectively, and surpasses the best previous supervision-aided
approach by +3.6 points on average across six math reasoning benchmarks.
Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual
  Search
Authors: name: Xin Lai, name: Junyi Li, name: Wei Li, name: Tao Liu, name: Tianjian Li, name: Hengshuang Zhao
Published: 2025-09-09 17:54:21

Recent advances in large multimodal models have leveraged image-based tools
with reinforcement learning to tackle visual problems. However, existing
open-source approaches often exhibit monotonous reasoning patterns and allow
only a limited number of interaction turns, making them inadequate for
difficult tasks that require trial-and-error exploration. In this work, we
address this limitation by scaling up tool-based interactions and introduce
Mini-o3, a system that executes deep, multi-turn reasoning -- spanning tens of
steps -- and achieves state-of-the-art performance on challenging visual search
tasks. Our recipe for reproducing OpenAI o3-style behaviors comprises three key
components. First, we construct the Visual Probe Dataset, a collection of
thousands of challenging visual search problems designed for exploratory
reasoning. Second, we develop an iterative data collection pipeline to obtain
cold-start trajectories that exhibit diverse reasoning patterns, including
depth-first search, trial-and-error, and goal maintenance. Third, we propose an
over-turn masking strategy that prevents penalization of over-turn responses
(those that hit the maximum number of turns) during reinforcement learning,
thereby balancing training-time efficiency with test-time scalability. Despite
training with an upper bound of only six interaction turns, our model generates
trajectories that naturally scale to tens of turns at inference time, with
accuracy improving as the number of turns increases. Extensive experiments
demonstrate that Mini-o3 produces rich reasoning patterns and deep thinking
paths, effectively solving challenging visual search problems.
SFR-DeepResearch: Towards Effective Reinforcement Learning for
  Autonomously Reasoning Single Agents
Authors: name: Xuan-Phi Nguyen, name: Shrey Pandit, name: Revanth Gangi Reddy, name: Austin Xu, name: Silvio Savarese, name: Caiming Xiong, name: Shafiq Joty
Published: 2025-09-08 02:07:09

Equipping large language models (LLMs) with complex, interleaved reasoning
and tool-use capabilities has become a key focus in agentic AI research,
especially with recent advances in reasoning-oriented (``thinking'') models.
Such capabilities are key to unlocking a number of important applications. One
such application is Deep Research (DR), which requires extensive search and
reasoning over many sources. Our work in this paper focuses on the development
of native Autonomous Single-Agent models for DR featuring minimal web crawling
and Python tool integration. Unlike multi-agent systems, where agents take up
pre-defined roles and are told what to do at each step in a static workflow, an
autonomous single-agent determines its next action dynamically based on
context, without manual directive. While prior work has proposed training
recipes for base or instruction-tuned LLMs, we focus on continual reinforcement
learning (RL) of reasoning-optimized models to further enhance agentic skills
while preserving reasoning ability. Towards this end, we propose a simple RL
recipe with entirely synthetic data, which we apply to various open-source
LLMs. Our best variant SFR-DR-20B achieves up to 28.7% on Humanity's Last Exam
benchmark. In addition, we conduct key analysis experiments to provide more
insights into our methodologies.
Mechanistic interpretability for steering vision-language-action models
Authors: name: Bear Häon, name: Kaylene Stocking, name: Ian Chuang, name: Claire Tomlin
Published: 2025-08-30 03:01:57

Vision-Language-Action (VLA) models are a promising path to realizing
generalist embodied agents that can quickly adapt to new tasks, modalities, and
environments. However, methods for interpreting and steering VLAs fall far
short of classical robotics pipelines, which are grounded in explicit models of
kinematics, dynamics, and control. This lack of mechanistic insight is a
central challenge for deploying learned policies in real-world robotics, where
robustness and explainability are critical. Motivated by advances in
mechanistic interpretability for large language models, we introduce the first
framework for interpreting and steering VLAs via their internal
representations, enabling direct intervention in model behavior at inference
time. We project feedforward activations within transformer layers onto the
token embedding basis, identifying sparse semantic directions - such as speed
and direction - that are causally linked to action selection. Leveraging these
findings, we introduce a general-purpose activation steering method that
modulates behavior in real time, without fine-tuning, reward signals, or
environment interaction. We evaluate this method on two recent open-source
VLAs, Pi0 and OpenVLA, and demonstrate zero-shot behavioral control in
simulation (LIBERO) and on a physical robot (UR5). This work demonstrates that
interpretable components of embodied VLAs can be systematically harnessed for
control - establishing a new paradigm for transparent and steerable foundation
models in robotics.
DivMerge: A divergence-based model merging method for multi-tasking
Authors: name: Touayouch Brahim, name: Fosse Loïc, name: Damnati Géraldine, name: Lecorvé Gwénolé
Published: 2025-09-02 09:04:41

Multi-task learning (MTL) is often achieved by merging datasets before
fine-tuning, but the growing availability of fine-tuned models has led to new
approaches such as model merging via task arithmetic. A major challenge in this
setting is task interference, which worsens as the number of tasks increases.
We propose a method that merges models trained on different tasks into a single
model, maintaining strong performance across all tasks. Our approach leverages
Jensen-Shannon divergence to guide the merging process without requiring
additional labelled data, and automatically balances task importance. Unlike
existing methods, our approach remains robust as the number of tasks grows and
consistently outperforms prior work.