Orionnnn/hub-stats huggingface dataset:
What Characterizes Effective Reasoning? Revisiting Length, Review, and
  Structure of CoT
Authors: name: Yunzhen Feng, name: Julia Kempe, name: Cheng Zhang, name: Parag Jain, name: Anthony Hartshorn
Published: 2025-09-23 17:50:54

Large reasoning models (LRMs) spend substantial test-time compute on long
chain-of-thought (CoT) traces, but what *characterizes* an effective CoT
remains unclear. While prior work reports gains from lengthening CoTs and
increasing review (revisiting earlier steps) via appended *wait* tokens, recent
studies suggest that shorter thinking can outperform longer traces. We
therefore conduct a systematic evaluation across ten LRMs on math and
scientific reasoning. Contrary to the "longer-is-better" narrative, we find
that both naive CoT lengthening and increased review are associated with
*lower* accuracy.
  As CoT unfolds step by step, token-level metrics can conflate verbosity with
process quality. We introduce a graph view of CoT to extract structure and
identify a single statistic-the *Failed-Step Fraction (FSF)*, the fraction of
steps in abandoned branches-that consistently outpredicts length and review
ratio for correctness across models. To probe causality, we design two
interventions. First, we rank candidate CoTs by each metric at test time, where
FSF yields the largest pass@1 gains; second, we edit CoTs to remove failed
branches, which significantly improves accuracy, indicating that failed
branches bias subsequent reasoning. Taken together, these results characterize
effective CoTs as those that *fail less* and support *structure-aware*
test-time scaling over indiscriminately generating long CoT.
Reinforcement Learning on Pre-Training Data
Authors: name: Siheng Li, name: Kejiao Li, name: Zenan Xu, name: Guanhua Huang, name: Evander Yang, name: Kun Li, name: Haoyuan Wu, name: Jiajia Wu, name: Zihao Zheng, name: Chenchen Zhang, name: Kun Shi, name: Kyrierl Deng, name: Qi Yi, name: Ruibin Xiong, name: Tingqiang Xu, name: Yuhao Jiang, name: Jianfeng Yan, name: Yuyuan Zeng, name: Guanghui Xu, name: Jinbao Xue, name: Zhijiang Xu, name: Zheng Fang, name: Shuai Li, name: Qibin Liu, name: Xiaoxue Li, name: Zhuoyu Li, name: Yangyu Tao, name: Fei Gao, name: Cheng Jiang, name: Bo Chao Wang, name: Kai Liu, name: Jianchen Zhu, name: Wai Lam, name: Wayyt Wang, name: Bo Zhou, name: Di Wang
Published: 2025-09-23 17:10:40

The growing disparity between the exponential scaling of computational
resources and the finite growth of high-quality text data now constrains
conventional scaling approaches for large language models (LLMs). To address
this challenge, we introduce Reinforcement Learning on Pre-Training data
(RLPT), a new training-time scaling paradigm for optimizing LLMs. In contrast
to prior approaches that scale training primarily through supervised learning,
RLPT enables the policy to autonomously explore meaningful trajectories to
learn from pre-training data and improve its capability through reinforcement
learning (RL). While existing RL strategies such as reinforcement learning from
human feedback (RLHF) and reinforcement learning with verifiable rewards (RLVR)
rely on human annotation for reward construction, RLPT eliminates this
dependency by deriving reward signals directly from pre-training data.
Specifically, it adopts a next-segment reasoning objective, rewarding the
policy for accurately predicting subsequent text segments conditioned on the
preceding context. This formulation allows RL to be scaled on pre-training
data, encouraging the exploration of richer trajectories across broader
contexts and thereby fostering more generalizable reasoning skills. Extensive
experiments on both general-domain and mathematical reasoning benchmarks across
multiple models validate the effectiveness of RLPT. For example, when applied
to Qwen3-4B-Base, RLPT yields absolute improvements of 3.0, 5.1, 8.1,
6.0, 6.6, and 5.3 on MMLU, MMLU-Pro, GPQA-Diamond, KOR-Bench, AIME24, and
AIME25, respectively. The results further demonstrate favorable scaling
behavior, suggesting strong potential for continued gains with more compute. In
addition, RLPT provides a solid foundation, extending the reasoning boundaries
of LLMs and enhancing RLVR performance.
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and
  Training Recipe
Authors: name: Tianyu Yu, name: Zefan Wang, name: Chongyi Wang, name: Fuwei Huang, name: Wenshuo Ma, name: Zhihui He, name: Tianchi Cai, name: Weize Chen, name: Yuxiang Huang, name: Yuanqian Zhao, name: Bokai Xu, name: Junbo Cui, name: Yingjing Xu, name: Liqing Ruan, name: Luoyuan Zhang, name: Hanyu Liu, name: Jingkun Tang, name: Hongyuan Liu, name: Qining Guo, name: Wenhao Hu, name: Bingxiang He, name: Jie Zhou, name: Jie Cai, name: Ji Qi, name: Zonghao Guo, name: Chi Chen, name: Guoyang Zeng, name: Yuxuan Li, name: Ganqu Cui, name: Ning Ding, name: Xu Han, name: Yuan Yao, name: Zhiyuan Liu, name: Maosong Sun
Published: 2025-09-16 19:41:48

Multimodal Large Language Models (MLLMs) are undergoing rapid progress and
represent the frontier of AI development. However, their training and inference
efficiency have emerged as a core bottleneck in making MLLMs more accessible
and scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B
parameter model designed for high efficiency and strong performance. We
introduce three core improvements in model architecture, data strategy and
training method: a unified 3D-Resampler model architecture for highly compact
encoding over images and videos, a unified learning paradigm for document
knowledge and text recognition without heavy data engineering, and a hybrid
reinforcement learning strategy for proficiency in both short and long
reasoning modes. Comprehensive experimental results in OpenCompass evaluation
show that MiniCPM-V 4.5 surpasses widely used proprietary models such as
GPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL
72B. Notably, the strong performance is achieved with remarkable efficiency.
For example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 achieves
state-of-the-art performance among models under 30B size, using just 46.7\% GPU
memory cost and 8.7\% inference time of Qwen2.5-VL 7B.
Hyper-Bagel: A Unified Acceleration Framework for Multimodal
  Understanding and Generation
Authors: name: Yanzuo Lu, name: Xin Xia, name: Manlin Zhang, name: Huafeng Kuang, name: Jianbin Zheng, name: Yuxi Ren, name: Xuefeng Xiao
Published: 2025-09-23 09:12:46

Unified multimodal models have recently attracted considerable attention for
their remarkable abilities in jointly understanding and generating diverse
content. However, as contexts integrate increasingly numerous interleaved
multimodal tokens, the iterative processes of diffusion denoising and
autoregressive decoding impose significant computational overhead. To address
this, we propose Hyper-Bagel, a unified acceleration framework designed to
simultaneously speed up both multimodal understanding and generation tasks. Our
approach uses a divide-and-conquer strategy, employing speculative decoding for
next-token prediction and a multi-stage distillation process for diffusion
denoising. The framework delivers substantial performance gains, achieving over
a 2x speedup in multimodal understanding. For generative tasks, our resulting
lossless 6-NFE model yields a 16.67x speedup in text-to-image generation and a
22x speedup in image editing, all while preserving the high-quality output of
the original model. We further develop a highly efficient 1-NFE model that
enables near real-time interactive editing and generation. By combining
advanced adversarial distillation with human feedback learning, this model
achieves ultimate cost-effectiveness and responsiveness, making complex
multimodal interactions seamless and instantaneous.
Zero-Shot Multi-Spectral Learning: Reimagining a Generalist Multimodal
  Gemini 2.5 Model for Remote Sensing Applications
Authors: name: Ganesh Mallya, name: Yotam Gigi, name: Dahun Kim, name: Maxim Neumann, name: Genady Beryozkin, name: Tomer Shekel, name: Anelia Angelova
Published: 2025-09-23 14:40:52

Multi-spectral imagery plays a crucial role in diverse Remote Sensing
applications including land-use classification, environmental monitoring and
urban planning. These images are widely adopted because their additional
spectral bands correlate strongly with physical materials on the ground, such
as ice, water, and vegetation. This allows for more accurate identification,
and their public availability from missions, such as Sentinel-2 and Landsat,
only adds to their value. Currently, the automatic analysis of such data is
predominantly managed through machine learning models specifically trained for
multi-spectral input, which are costly to train and support. Furthermore,
although providing a lot of utility for Remote Sensing, such additional inputs
cannot be used with powerful generalist large multimodal models, which are
capable of solving many visual problems, but are not able to understand
specialized multi-spectral signals.
  To address this, we propose a training-free approach which introduces new
multi-spectral data in a Zero-Shot-only mode, as inputs to generalist
multimodal models, trained on RGB-only inputs. Our approach leverages the
multimodal models' understanding of the visual space, and proposes to adapt to
inputs to that space, and to inject domain-specific information as instructions
into the model. We exemplify this idea with the Gemini2.5 model and observe
strong Zero-Shot performance gains of the approach on popular Remote Sensing
benchmarks for land cover and land use classification and demonstrate the easy
adaptability of Gemini2.5 to new inputs. These results highlight the potential
for geospatial professionals, working with non-standard specialized inputs, to
easily leverage powerful multimodal models, such as Gemini2.5, to accelerate
their work, benefiting from their rich reasoning and contextual capabilities,
grounded in the specialized sensor data.
Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model
  Self-Distillation
Authors: name: Sherwin Bahmani, name: Tianchang Shen, name: Jiawei Ren, name: Jiahui Huang, name: Yifeng Jiang, name: Haithem Turki, name: Andrea Tagliasacchi, name: David B. Lindell, name: Zan Gojcic, name: Sanja Fidler, name: Huan Ling, name: Jun Gao, name: Xuanchi Ren
Published: 2025-09-23 17:58:01

The ability to generate virtual environments is crucial for applications
ranging from gaming to physical AI domains such as robotics, autonomous
driving, and industrial AI. Current learning-based 3D reconstruction methods
rely on the availability of captured real-world multi-view data, which is not
always readily available. Recent advancements in video diffusion models have
shown remarkable imagination capabilities, yet their 2D nature limits the
applications to simulation where a robot needs to navigate and interact with
the environment. In this paper, we propose a self-distillation framework that
aims to distill the implicit 3D knowledge in the video diffusion models into an
explicit 3D Gaussian Splatting (3DGS) representation, eliminating the need for
multi-view training data. Specifically, we augment the typical RGB decoder with
a 3DGS decoder, which is supervised by the output of the RGB decoder. In this
approach, the 3DGS decoder can be purely trained with synthetic data generated
by video diffusion models. At inference time, our model can synthesize 3D
scenes from either a text prompt or a single image for real-time rendering. Our
framework further extends to dynamic 3D scene generation from a monocular input
video. Experimental results show that our framework achieves state-of-the-art
performance in static and dynamic 3D scene generation.
FocalCodec-Stream: Streaming Low-Bitrate Speech Coding via Causal
  Distillation
Authors: name: Luca Della Libera, name: Cem Subakan, name: Mirco Ravanelli
Published: 2025-09-19 17:57:13

Neural audio codecs are a fundamental component of modern generative audio
pipelines. Although recent codecs achieve strong low-bitrate reconstruction and
provide powerful representations for downstream tasks, most are non-streamable,
limiting their use in real-time applications. We present FocalCodec-Stream, a
hybrid codec based on focal modulation that compresses speech into a single
binary codebook at 0.55 - 0.80 kbps with a theoretical latency of 80 ms. Our
approach combines multi-stage causal distillation of WavLM with targeted
architectural improvements, including a lightweight refiner module that
enhances quality under latency constraints. Experiments show that
FocalCodec-Stream outperforms existing streamable codecs at comparable
bitrates, while preserving both semantic and acoustic information. The result
is a favorable trade-off between reconstruction quality, downstream task
performance, latency, and efficiency. Code and checkpoints will be released at
https://github.com/lucadellalib/focalcodec.
DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
Authors: name: Hao-Shu Fang, name: Branden Romero, name: Yichen Xie, name: Arthur Hu, name: Bo-Ruei Huang, name: Juan Alvarez, name: Matthew Kim, name: Gabriel Margolis, name: Kavya Anbarasu, name: Masayoshi Tomizuka, name: Edward Adelson, name: Pulkit Agrawal
Published: 2025-09-04 17:57:13

We introduce perioperation, a paradigm for robotic data collection that
sensorizes and records human manipulation while maximizing the transferability
of the data to real robots. We implement this paradigm in DEXOP, a passive hand
exoskeleton designed to maximize human ability to collect rich sensory (vision
+ tactile) data for diverse dexterous manipulation tasks in natural
environments. DEXOP mechanically connects human fingers to robot fingers,
providing users with direct contact feedback (via proprioception) and mirrors
the human hand pose to the passive robot hand to maximize the transfer of
demonstrated skills to the robot. The force feedback and pose mirroring make
task demonstrations more natural for humans compared to teleoperation,
increasing both speed and accuracy. We evaluate DEXOP across a range of
dexterous, contact-rich tasks, demonstrating its ability to collect
high-quality demonstration data at scale. Policies learned with DEXOP data
significantly improve task performance per unit time of data collection
compared to teleoperation, making DEXOP a powerful tool for advancing robot
dexterity. Our project page is at https://dex-op.github.io.
D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language
  Models
Authors: name: Satyapriya Krishna, name: Andy Zou, name: Rahul Gupta, name: Eliot Krzysztof Jones, name: Nick Winter, name: Dan Hendrycks, name: J. Zico Kolter, name: Matt Fredrikson, name: Spyros Matsoukas
Published: 2025-09-22 15:59:40

The safety and alignment of Large Language Models (LLMs) are critical for
their responsible deployment. Current evaluation methods predominantly focus on
identifying and preventing overtly harmful outputs. However, they often fail to
address a more insidious failure mode: models that produce benign-appearing
outputs while operating on malicious or deceptive internal reasoning. This
vulnerability, often triggered by sophisticated system prompt injections,
allows models to bypass conventional safety filters, posing a significant,
underexplored risk. To address this gap, we introduce the Deceptive Reasoning
Exposure Suite (D-REX), a novel dataset designed to evaluate the discrepancy
between a model's internal reasoning process and its final output. D-REX was
constructed through a competitive red-teaming exercise where participants
crafted adversarial system prompts to induce such deceptive behaviors. Each
sample in D-REX contains the adversarial system prompt, an end-user's test
query, the model's seemingly innocuous response, and, crucially, the model's
internal chain-of-thought, which reveals the underlying malicious intent. Our
benchmark facilitates a new, essential evaluation task: the detection of
deceptive alignment. We demonstrate that D-REX presents a significant challenge
for existing models and safety mechanisms, highlighting the urgent need for new
techniques that scrutinize the internal processes of LLMs, not just their final
outputs.
Accurate and Efficient Low-Rank Model Merging in Core Space
Authors: name: Aniello Panariello, name: Daniel Marczak, name: Simone Magistri, name: Angelo Porrello, name: Bartłomiej Twardowski, name: Andrew D. Bagdanov, name: Simone Calderara, name: Joost van de Weijer
Published: 2025-09-22 13:48:15

In this paper, we address the challenges associated with merging low-rank
adaptations of large neural networks. With the rise of parameter-efficient
adaptation techniques, such as Low-Rank Adaptation (LoRA), model fine-tuning
has become more accessible. While fine-tuning models with LoRA is highly
efficient, existing merging methods often sacrifice this efficiency by merging
fully-sized weight matrices. We propose the Core Space merging framework, which
enables the merging of LoRA-adapted models within a common alignment basis,
thereby preserving the efficiency of low-rank adaptation while substantially
improving accuracy across tasks. We further provide a formal proof that
projection into Core Space ensures no loss of information and provide a
complexity analysis showing the efficiency gains. Extensive empirical results
demonstrate that Core Space significantly improves existing merging techniques
and achieves state-of-the-art results on both vision and language tasks while
utilizing a fraction of the computational resources. Codebase is available at
https://github.com/apanariello4/core-space-merging.
V2V-GoT: Vehicle-to-Vehicle Cooperative Autonomous Driving with
  Multimodal Large Language Models and Graph-of-Thoughts
Authors: name: Hsu-kuang Chiu, name: Ryo Hachiuma, name: Chien-Yi Wang, name: Yu-Chiang Frank Wang, name: Min-Hung Chen, name: Stephen F. Smith
Published: 2025-09-22 17:27:29

Current state-of-the-art autonomous vehicles could face safety-critical
situations when their local sensors are occluded by large nearby objects on the
road. Vehicle-to-vehicle (V2V) cooperative autonomous driving has been proposed
as a means of addressing this problem, and one recently introduced framework
for cooperative autonomous driving has further adopted an approach that
incorporates a Multimodal Large Language Model (MLLM) to integrate cooperative
perception and planning processes. However, despite the potential benefit of
applying graph-of-thoughts reasoning to the MLLM, this idea has not been
considered by previous cooperative autonomous driving research. In this paper,
we propose a novel graph-of-thoughts framework specifically designed for
MLLM-based cooperative autonomous driving. Our graph-of-thoughts includes our
proposed novel ideas of occlusion-aware perception and planning-aware
prediction. We curate the V2V-GoT-QA dataset and develop the V2V-GoT model for
training and testing the cooperative driving graph-of-thoughts. Our
experimental results show that our method outperforms other baselines in
cooperative perception, prediction, and planning tasks.
Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with
  LLMs
Authors: name: Richard Cornelius Suwandi, name: Feng Yin, name: Juntao Wang, name: Renjie Li, name: Tsung-Hui Chang, name: Sergios Theodoridis
Published: 2025-09-22 16:39:12

The efficiency of Bayesian optimization (BO) relies heavily on the choice of
the Gaussian process (GP) kernel, which plays a central role in balancing
exploration and exploitation under limited evaluation budgets. Traditional BO
methods often rely on fixed or heuristic kernel selection strategies, which can
result in slow convergence or suboptimal solutions when the chosen kernel is
poorly suited to the underlying objective function. To address this limitation,
we propose a freshly-baked Context-Aware Kernel Evolution (CAKE) to enhance BO
with large language models (LLMs). Concretely, CAKE leverages LLMs as the
crossover and mutation operators to adaptively generate and refine GP kernels
based on the observed data throughout the optimization process. To maximize the
power of CAKE, we further propose BIC-Acquisition Kernel Ranking (BAKER) to
select the most effective kernel through balancing the model fit measured by
the Bayesian information criterion (BIC) with the expected improvement at each
iteration of BO. Extensive experiments demonstrate that our fresh CAKE-based BO
method consistently outperforms established baselines across a range of
real-world tasks, including hyperparameter optimization, controller tuning, and
photonic chip design. Our code is publicly available at
https://github.com/cake4bo/cake.
Strategic Dishonesty Can Undermine AI Safety Evaluations of Frontier LLM
Authors: name: Alexander Panfilov, name: Evgenii Kortukov, name: Kristina Nikolić, name: Matthias Bethge, name: Sebastian Lapuschkin, name: Wojciech Samek, name: Ameya Prabhu, name: Maksym Andriushchenko, name: Jonas Geiping
Published: 2025-09-22 17:30:56

Large language model (LLM) developers aim for their models to be honest,
helpful, and harmless. However, when faced with malicious requests, models are
trained to refuse, sacrificing helpfulness. We show that frontier LLMs can
develop a preference for dishonesty as a new strategy, even when other options
are available. Affected models respond to harmful requests with outputs that
sound harmful but are subtly incorrect or otherwise harmless in practice. This
behavior emerges with hard-to-predict variations even within models from the
same model family. We find no apparent cause for the propensity to deceive, but
we show that more capable models are better at executing this strategy.
Strategic dishonesty already has a practical impact on safety evaluations, as
we show that dishonest responses fool all output-based monitors used to detect
jailbreaks that we test, rendering benchmark scores unreliable. Further,
strategic dishonesty can act like a honeypot against malicious users, which
noticeably obfuscates prior jailbreak attacks. While output monitors fail, we
show that linear probes on internal activations can be used to reliably detect
strategic dishonesty. We validate probes on datasets with verifiable outcomes
and by using their features as steering vectors. Overall, we consider strategic
dishonesty as a concrete example of a broader concern that alignment of LLMs is
hard to control, especially when helpfulness and harmlessness conflict.