Orionnnn/hub-stats huggingface dataset:
Improving large language models with concept-aware fine-tuning
Authors: name: Michael K. Chen, name: Xikun Zhang, name: Jiaxing Huang, name: Dacheng Tao
Published: 2025-06-09 14:55:00

Large language models (LLMs) have become the cornerstone of modern AI.
However, the existing paradigm of next-token prediction fundamentally limits
their ability to form coherent, high-level concepts, making it a critical
barrier to human-like understanding and reasoning. Take the phrase "ribonucleic
acid" as an example: an LLM will first decompose it into tokens, i.e.,
artificial text fragments ("rib", "on", ...), then learn each token
sequentially, rather than grasping the phrase as a unified, coherent semantic
entity. This fragmented representation hinders deeper conceptual understanding
and, ultimately, the development of truly intelligent systems. In response, we
introduce Concept-Aware Fine-Tuning (CAFT), a novel multi-token training method
that redefines how LLMs are fine-tuned. By enabling the learning of sequences
that span multiple tokens, this method fosters stronger concept-aware learning.
Our experiments demonstrate significant improvements compared to conventional
next-token finetuning methods across diverse tasks, including traditional
applications like text summarization and domain-specific ones like de novo
protein design. Multi-token prediction was previously only possible in the
prohibitively expensive pretraining phase; CAFT, to our knowledge, is the first
to bring the multi-token setting to the post-training phase, thus effectively
democratizing its benefits for the broader community of practitioners and
researchers. Finally, the unexpected effectiveness of our proposed method
suggests wider implications for the machine learning research community. All
code and data are available at https://github.com/michaelchen-lab/caft-llm
Bootstrapping World Models from Dynamics Models in Multimodal Foundation
  Models
Authors: name: Yifu Qiu, name: Yftah Ziser, name: Anna Korhonen, name: Shay B. Cohen, name: Edoardo M. Ponti
Published: 2025-06-06 11:50:18

To what extent do vision-and-language foundation models possess a realistic
world model (observation times action rightarrow observation) and a
dynamics model (observation times observation rightarrow action), when
actions are expressed through language? While open-source foundation models
struggle with both, we find that fine-tuning them to acquire a dynamics model
through supervision is significantly easier than acquiring a world model. In
turn, dynamics models can be used to bootstrap world models through two main
strategies: 1) weakly supervised learning from synthetic data and 2) inference
time verification. Firstly, the dynamics model can annotate actions for
unlabelled pairs of video frame observations to expand the training data. We
further propose a new objective, where image tokens in observation pairs are
weighted by their importance, as predicted by a recognition model. Secondly,
the dynamics models can assign rewards to multiple samples of the world model
to score them, effectively guiding search at inference time. We evaluate the
world models resulting from both strategies through the task of action-centric
image editing on Aurora-Bench. Our best model achieves a performance
competitive with state-of-the-art image editing models, improving on them by a
margin of 15% on real-world subsets according to GPT4o-as-judge, and
achieving the best average human evaluation across all subsets of Aurora-Bench.
Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal
  Learning
Authors: name: Sheng Chen, name: Peiyu He, name: Jiaxin Hu, name: Ziyang Liu, name: Yansheng Wang, name: Tao Xu, name: Chi Zhang, name: Chongchong Zhang, name: Chao An, name: Shiyu Cai, name: Duo Cao, name: Kangping Chen, name: Shuai Chu, name: Tianwei Chu, name: Mingdi Dan, name: Min Du, name: Weiwei Fang, name: Pengyou Fu, name: Junkai Hu, name: Xiaowei Jiang, name: Zhaodi Jiang, name: Fuxuan Li, name: Jun Li, name: Minghui Li, name: Mingyao Li, name: Yanchang Li, name: Zhibin Li, name: Guangming Liu, name: Kairui Liu, name: Lihao Liu, name: Weizhi Liu, name: Xiaoshun Liu, name: Yufei Liu, name: Yunfei Liu, name: Qiang Lu, name: Yuanfei Luo, name: Xiang Lv, name: Hongying Ma, name: Sai Ma, name: Lingxian Mi, name: Sha Sa, name: Hongxiang Shu, name: Lei Tian, name: Chengzhi Wang, name: Jiayu Wang, name: Kaijie Wang, name: Qingyi Wang, name: Renwen Wang, name: Tao Wang, name: Wei Wang, name: Xirui Wang, name: Chao Wei, name: Xuguang Wei, name: Zijun Xia, name: Zhaohao Xiao, name: Tingshuai Yan, name: Liyan Yang, name: Yifan Yang, name: Zhikai Yang, name: Zhong Yin, name: Li Yuan, name: Liuchun Yuan, name: Chi Zhang, name: Jinyang Zhang, name: Junhui Zhang, name: Linge Zhang, name: Zhenyi Zhang, name: Zheyu Zhang, name: Dongjie Zhu, name: Hang Li, name: Yangang Zhang
Published: 2025-06-06 16:08:47

Modern robot navigation systems encounter difficulties in diverse and complex
indoor environments. Traditional approaches rely on multiple modules with small
models or rule-based systems and thus lack adaptability to new environments. To
address this, we developed Astra, a comprehensive dual-model architecture,
Astra-Global and Astra-Local, for mobile robot navigation. Astra-Global, a
multimodal LLM, processes vision and language inputs to perform self and goal
localization using a hybrid topological-semantic graph as the global map, and
outperforms traditional visual place recognition methods. Astra-Local, a
multitask network, handles local path planning and odometry estimation. Its 4D
spatial-temporal encoder, trained through self-supervised learning, generates
robust 4D features for downstream tasks. The planning head utilizes flow
matching and a novel masked ESDF loss to minimize collision risks for
generating local trajectories, and the odometry head integrates multi-sensor
inputs via a transformer encoder to predict the relative pose of the robot.
Deployed on real in-house mobile robots, Astra achieves high end-to-end mission
success rate across diverse indoor environments.
Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models
Authors: name: Maciej Chrabąszcz, name: Katarzyna Lorenc, name: Karolina Seweryn
Published: 2025-06-09 11:09:39

Large language models (LLMs) have demonstrated impressive capabilities across
various natural language processing (NLP) tasks in recent years. However, their
susceptibility to jailbreaks and perturbations necessitates additional
evaluations. Many LLMs are multilingual, but safety-related training data
contains mainly high-resource languages like English. This can leave them
vulnerable to perturbations in low-resource languages such as Polish. We show
how surprisingly strong attacks can be cheaply created by altering just a few
characters and using a small proxy model for word importance calculation. We
find that these character and word-level attacks drastically alter the
predictions of different LLMs, suggesting a potential vulnerability that can be
used to circumvent their internal safety mechanisms. We validate our attack
construction methodology on Polish, a low-resource language, and find potential
vulnerabilities of LLMs in this language. Additionally, we show how it can be
extended to other languages. We release the created datasets and code for
further research.
Proactive Assistant Dialogue Generation from Streaming Egocentric Videos
Authors: name: Yichi Zhang, name: Xin Luna Dong, name: Zhaojiang Lin, name: Andrea Madotto, name: Anuj Kumar, name: Babak Damavandi, name: Joyce Chai, name: Seungwhan Moon
Published: 2025-06-06 09:23:29

Recent advances in conversational AI have been substantial, but developing
real-time systems for perceptual task guidance remains challenging. These
systems must provide interactive, proactive assistance based on streaming
visual inputs, yet their development is constrained by the costly and
labor-intensive process of data collection and system evaluation. To address
these limitations, we present a comprehensive framework with three key
contributions. First, we introduce a novel data curation pipeline that
synthesizes dialogues from annotated egocentric videos, resulting in \dataset,
a large-scale synthetic dialogue dataset spanning multiple domains. Second, we
develop a suite of automatic evaluation metrics, validated through extensive
human studies. Third, we propose an end-to-end model that processes streaming
video inputs to generate contextually appropriate responses, incorporating
novel techniques for handling data imbalance and long-duration videos. This
work lays the foundation for developing real-time, proactive AI assistants
capable of guiding users through diverse tasks. Project page:
https://pro-assist.github.io/
PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal
  Interaction and Enhancement
Authors: name: Teng Hu, name: Zhentao Yu, name: Zhengguang Zhou, name: Jiangning Zhang, name: Yuan Zhou, name: Qinglin Lu, name: Ran Yi
Published: 2025-06-09 15:11:09

Despite recent advances in video generation, existing models still lack
fine-grained controllability, especially for multi-subject customization with
consistent identity and interaction. In this paper, we propose PolyVivid, a
multi-subject video customization framework that enables flexible and
identity-consistent generation. To establish accurate correspondences between
subject images and textual entities, we design a VLLM-based text-image fusion
module that embeds visual identities into the textual space for precise
grounding. To further enhance identity preservation and subject interaction, we
propose a 3D-RoPE-based enhancement module that enables structured
bidirectional fusion between text and image embeddings. Moreover, we develop an
attention-inherited identity injection module to effectively inject fused
identity features into the video generation process, mitigating identity drift.
Finally, we construct an MLLM-based data pipeline that combines MLLM-based
grounding, segmentation, and a clique-based subject consolidation strategy to
produce high-quality multi-subject data, effectively enhancing subject
distinction and reducing ambiguity in downstream video generation. Extensive
experiments demonstrate that PolyVivid achieves superior performance in
identity fidelity, video realism, and subject alignment, outperforming existing
open-source and commercial baselines.
Learning What Reinforcement Learning Can't: Interleaved Online
  Fine-Tuning for Hardest Questions
Authors: name: Lu Ma, name: Hao Liang, name: Meiyi Qiang, name: Lexiang Tang, name: Xiaochen Ma, name: Zhen Hao Wong, name: Junbo Niu, name: Chengyu Shen, name: Runming He, name: Bin Cui, name: Wentao Zhang
Published: 2025-06-09 08:11:20

Recent advances in large language model (LLM) reasoning have shown that
sophisticated behaviors such as planning and self-reflection can emerge through
reinforcement learning (RL). However, despite these successes, RL in its
current form remains insufficient to induce capabilities that exceed the
limitations of the base model, as it is primarily optimized based on existing
knowledge of the model rather than facilitating the acquisition of new
information. To address this limitation, we employ supervised fine-tuning (SFT)
to learn what RL cannot, which enables the incorporation of new knowledge and
reasoning patterns by leveraging high-quality demonstration data. We analyze
the training dynamics of RL and SFT for LLM reasoning and find that RL excels
at maintaining and improving performance on questions within the model's
original capabilities, while SFT is more effective at enabling progress on
questions beyond the current scope of the model. Motivated by the complementary
strengths of RL and SFT, we introduce a novel training approach,
ReLIFT (Reinforcement Learning Interleaved
with Online Fine-Tuning). In ReLIFT, the model is primarily
trained using RL, but when it encounters challenging questions, high-quality
solutions are collected for fine-tuning, and the training process alternates
between RL and fine-tuning to enhance the model's reasoning abilities. ReLIFT
achieves an average improvement of over +5.2 points across five
competition-level benchmarks and one out-of-distribution benchmark compared to
other zero-RL models. Furthermore, we demonstrate that ReLIFT outperforms both
RL and SFT while using only 13\% of the detailed demonstration data,
highlighting its scalability. These results provide compelling evidence that
ReLIFT overcomes the fundamental limitations of RL and underscores the
significant potential.
Image Reconstruction as a Tool for Feature Analysis
Authors: name: Eduard Allakhverdov, name: Dmitrii Tarasov, name: Elizaveta Goncharova, name: Andrey Kuznetsov
Published: 2025-06-09 14:32:18

Vision encoders are increasingly used in modern applications, from
vision-only models to multimodal systems such as vision-language models.
Despite their remarkable success, it remains unclear how these architectures
represent features internally. Here, we propose a novel approach for
interpreting vision features via image reconstruction. We compare two related
model families, SigLIP and SigLIP2, which differ only in their training
objective, and show that encoders pre-trained on image-based tasks retain
significantly more image information than those trained on non-image tasks such
as contrastive learning. We further apply our method to a range of vision
encoders, ranking them by the informativeness of their feature representations.
Finally, we demonstrate that manipulating the feature space yields predictable
changes in reconstructed images, revealing that orthogonal rotations (rather
than spatial transformations) control color encoding. Our approach can be
applied to any vision encoder, shedding light on the inner structure of its
feature space. The code and model weights to reproduce the experiments are
available in GitHub.
Vision Transformers Don't Need Trained Registers
Authors: name: Nick Jiang, name: Amil Dravid, name: Alexei Efros, name: Yossi Gandelsman
Published: 2025-06-09 17:59:57

We investigate the mechanism underlying a previously identified phenomenon in
Vision Transformers -- the emergence of high-norm tokens that lead to noisy
attention maps. We observe that in multiple models (e.g., CLIP, DINOv2), a
sparse set of neurons is responsible for concentrating high-norm activations on
outlier tokens, leading to irregular attention patterns and degrading
downstream visual processing. While the existing solution for removing these
outliers involves retraining models from scratch with additional learned
register tokens, we use our findings to create a training-free approach to
mitigate these artifacts. By shifting the high-norm activations from our
discovered register neurons into an additional untrained token, we can mimic
the effect of register tokens on a model already trained without registers. We
demonstrate that our method produces cleaner attention and feature maps,
enhances performance over base models across multiple downstream visual tasks,
and achieves results comparable to models explicitly trained with register
tokens. We then extend test-time registers to off-the-shelf vision-language
models to improve their interpretability. Our results suggest that test-time
registers effectively take on the role of register tokens at test-time,
offering a training-free solution for any pre-trained model released without
them.
EVOREFUSE: Evolutionary Prompt Optimization for Evaluation and
  Mitigation of LLM Over-Refusal to Pseudo-Malicious Instructions
Authors: name: Xiaorui Wu, name: Xiaofeng Mao, name: Xin Zhang, name: Fei Li, name: Chong Teng, name: Yuxiang Peng, name: Li Zheng, name: Donghong Ji, name: Zhuang Li
Published: 2025-05-29 14:26:46

Large language models (LLMs) frequently refuse to respond to pseudo-malicious
instructions: semantically harmless input queries triggering unnecessary LLM
refusals due to conservative safety alignment, significantly impairing user
experience. Collecting such instructions is crucial for evaluating and
mitigating over-refusals, but existing instruction curation methods, like
manual creation or instruction rewriting, either lack scalability or fail to
produce sufficiently diverse and effective refusal-inducing prompts. To address
these limitations, we introduce EVOREFUSE, a prompt optimization approach that
generates diverse pseudo-malicious instructions consistently eliciting
confident refusals across LLMs. EVOREFUSE employs an evolutionary algorithm
exploring the instruction space in more diverse directions than existing
methods via mutation strategies and recombination, and iteratively evolves seed
instructions to maximize evidence lower bound on LLM refusal probability. Using
EVOREFUSE, we create two novel datasets: EVOREFUSE-TEST, a benchmark of 582
pseudo-malicious instructions that outperforms the next-best benchmark with
140.41% higher average refusal triggering rate across 9 LLMs, 34.86% greater
lexical diversity, and 40.03% improved LLM response confidence scores; and
EVOREFUSE-ALIGN, which provides 3,000 pseudo-malicious instructions with
responses for supervised and preference-based alignment training.
LLAMA3.1-8B-INSTRUCT supervisedly fine-tuned on EVOREFUSE-ALIGN achieves up to
14.31% fewer over-refusals than models trained on the second-best alignment
dataset, without compromising safety. Our analysis with EVOREFUSE-TEST reveals
models trigger over-refusals by overly focusing on sensitive keywords while
ignoring broader context.
MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character
  Recognition with over 97K Categories
Authors: name: Yuyi Zhang, name: Yongxin Shi, name: Peirong Zhang, name: Yixin Zhao, name: Zhenhua Yang, name: Lianwen Jin
Published: 2025-06-05 09:33:06

Foundational to the Chinese language and culture, Chinese characters
encompass extraordinarily extensive and ever-expanding categories, with the
latest Chinese GB18030-2022 standard containing 87,887 categories. The accurate
recognition of this vast number of characters, termed mega-category
recognition, presents a formidable yet crucial challenge for cultural heritage
preservation and digital applications. Despite significant advances in Optical
Character Recognition (OCR), mega-category recognition remains unexplored due
to the absence of comprehensive datasets, with the largest existing dataset
containing merely 16,151 categories. To bridge this critical gap, we introduce
MegaHan97K, a mega-category, large-scale dataset covering an unprecedented
97,455 categories of Chinese characters. Our work offers three major
contributions: (1) MegaHan97K is the first dataset to fully support the latest
GB18030-2022 standard, providing at least six times more categories than
existing datasets; (2) It effectively addresses the long-tail distribution
problem by providing balanced samples across all categories through its three
distinct subsets: handwritten, historical and synthetic subsets; (3)
Comprehensive benchmarking experiments reveal new challenges in mega-category
scenarios, including increased storage demands, morphologically similar
character recognition, and zero-shot learning difficulties, while also
unlocking substantial opportunities for future research. To the best of our
knowledge, the MetaHan97K is likely the dataset with the largest classes not
only in the field of OCR but may also in the broader domain of pattern
recognition. The dataset is available at
https://github.com/SCUT-DLVCLab/MegaHan97K.
GeometryZero: Improving Geometry Solving for LLM with Group Contrastive
  Policy Optimization
Authors: name: Yikun Wang, name: Yibin Wang, name: Dianyi Wang, name: Zimian Peng, name: Qipeng Guo, name: Dacheng Tao, name: Jiaqi Wang
Published: 2025-06-08 14:18:15

Recent advances in large language models (LLMs) have demonstrated remarkable
capabilities across diverse domains, particularly in mathematical reasoning,
amid which geometry problem solving remains a challenging area where auxiliary
construction plays a enssential role. Existing approaches either achieve
suboptimal performance or rely on massive LLMs (e.g., GPT-4o), incurring
massive computational costs. We posit that reinforcement learning with
verifiable reward (e.g., GRPO) offers a promising direction for training
smaller models that effectively combine auxiliary construction with robust
geometric reasoning. However, directly applying GRPO to geometric reasoning
presents fundamental limitations due to its dependence on unconditional
rewards, which leads to indiscriminate and counterproductive auxiliary
constructions. To address these challenges, we propose Group Contrastive Policy
Optimization (GCPO), a novel reinforcement learning framework featuring two key
innovations: (1) Group Contrastive Masking, which adaptively provides positive
or negative reward signals for auxiliary construction based on contextual
utility, and a (2) length reward that promotes longer reasoning chains.
Building on GCPO, we develop GeometryZero, a family of affordable-size
geometric reasoning models that judiciously determine when to employ auxiliary
construction. Our extensive empirical evaluation across popular geometric
benchmarks (Geometry3K, MathVista) demonstrates that GeometryZero models
consistently outperform baselines (e.g. GRPO), achieving an average improvement
of 4.29% across all benchmarks.
ConfQA: Answer Only If You Are Confident
Authors: name: Yin Huang, name: Yifan Ethan Xu, name: Kai Sun, name: Vera Yan, name: Alicia Sun, name: Haidar Khan, name: Jimmy Nguyen, name: Mohammad Kachuee, name: Zhaojiang Lin, name: Yue Liu, name: Aaron Colak, name: Anuj Kumar, name: Wen-tau Yih, name: Xin Luna Dong
Published: 2025-06-08 22:51:46

Can we teach Large Language Models (LLMs) to refrain from hallucinating
factual statements? In this paper we present a fine-tuning strategy that we
call ConfQA, which can reduce hallucination rate from 20-40% to under 5% across
multiple factuality benchmarks. The core idea is simple: when the LLM answers a
question correctly, it is trained to continue with the answer; otherwise, it is
trained to admit "I am unsure". But there are two key factors that make the
training highly effective. First, we introduce a dampening prompt "answer only
if you are confident" to explicitly guide the behavior, without which
hallucination remains high as 15%-25%. Second, we leverage simple factual
statements, specifically attribute values from knowledge graphs, to help LLMs
calibrate the confidence, resulting in robust generalization across domains and
question types. Building on this insight, we propose the Dual Neural Knowledge
framework, which seamlessly select between internally parameterized neural
knowledge and externally recorded symbolic knowledge based on ConfQA's
confidence. The framework enables potential accuracy gains to beyond 95%, while
reducing unnecessary external retrievals by over 30%.
Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path
  Lengths in LLMs
Authors: name: Roy Eisenstadt, name: Itamar Zimerman, name: Lior Wolf
Published: 2025-06-08 17:54:33

Recently, techniques such as explicit structured reasoning have demonstrated
strong test-time scaling behavior by enforcing a separation between the model's
internal "thinking" process and the final response. A key factor influencing
answer quality in this setting is the length of the thinking stage. When the
reasoning is too short, the model may fail to capture the complexity of the
task. Conversely, when it is too long, the model may overthink, leading to
unnecessary computation and degraded performance. This paper explores and
exploits the underlying mechanisms by which LLMs understand and regulate the
length of their reasoning during explicit thought processes. First, we show
that LLMs encode their progress through the reasoning process and introduce an
interactive progress bar visualization, which is then used to reveal insights
on the model's planning dynamics. Second, we manipulate the internal progress
encoding during inference to reduce unnecessary steps and generate a more
concise and decisive chain of thoughts. Our empirical results demonstrate that
this "overclocking" method mitigates overthinking, improves answer accuracy,
and reduces inference latency. Our code is publicly available.