Orionnnn/hub-stats huggingface dataset:
NORA: A Small Open-Sourced Generalist Vision Language Action Model for
  Embodied Tasks
Authors: name: Chia-Yu Hung, name: Qi Sun, name: Pengfei Hong, name: Amir Zadeh, name: Chuan Li, name: U-Xuan Tan, name: Navonil Majumder, name: Soujanya Poria
Published: 2025-04-28 14:47:34

Existing Visual-Language-Action (VLA) models have shown promising performance
in zero-shot scenarios, demonstrating impressive task execution and reasoning
capabilities. However, a significant challenge arises from the limitations of
visual encoding, which can result in failures during tasks such as object
grasping. Moreover, these models typically suffer from high computational
overhead due to their large sizes, often exceeding 7B parameters. While these
models excel in reasoning and task planning, the substantial computational
overhead they incur makes them impractical for real-time robotic environments,
where speed and efficiency are paramount. To address the limitations of
existing VLA models, we propose NORA, a 3B-parameter model designed to reduce
computational overhead while maintaining strong task performance. NORA adopts
the Qwen-2.5-VL-3B multimodal model as its backbone, leveraging its superior
visual-semantic understanding to enhance visual reasoning and action grounding.
Additionally, our  is trained on 970k real-world robot demonstrations
and equipped with the FAST+ tokenizer for efficient action sequence generation.
Experimental results demonstrate that NORA outperforms existing large-scale VLA
models, achieving better task performance with significantly reduced
computational overhead, making it a more practical solution for real-time
robotic autonomy.
Versatile Framework for Song Generation with Prompt-based Control
Authors: name: Yu Zhang, name: Wenxiang Guo, name: Changhao Pan, name: Zhiyuan Zhu, name: Ruiqi Li, name: Jingyu Lu, name: Rongjie Huang, name: Ruiyuan Zhang, name: Zhiqing Hong, name: Ziyue Jiang, name: Zhou Zhao
Published: 2025-04-27 01:00:06

Song generation focuses on producing controllable high-quality songs based on
various prompts. However, existing methods struggle to generate vocals and
accompaniments with prompt-based control and proper alignment. Additionally,
they fall short in supporting various tasks. To address these challenges, we
introduce VersBand, a multi-task song generation framework for synthesizing
high-quality, aligned songs with prompt-based control. VersBand comprises these
primary models: 1) VocalBand, a decoupled model, leverages the flow-matching
method for generating singing styles, pitches, and mel-spectrograms, allowing
fast, high-quality vocal generation with style control. 2) AccompBand, a
flow-based transformer model, incorporates the Band-MOE, selecting suitable
experts for enhanced quality, alignment, and control. This model allows for
generating controllable, high-quality accompaniments aligned with vocals. 3)
Two generation models, LyricBand for lyrics and MelodyBand for melodies,
contribute to the comprehensive multi-task song generation system, allowing for
extensive control based on multiple prompts. Experimental results demonstrate
that VersBand performs better over baseline models across multiple song
generation tasks using objective and subjective metrics. Audio samples are
available at https://VersBand.github.io.
ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile
  Hardware Development
Authors: name: Bowei Wang, name: Jiaran Gao, name: Yelai Feng, name: Renzhi Chen, name: Shanshan Li, name: Lei Wang
Published: 2025-04-27 07:56:49

The growing demand for Domain-Specific Architecture (DSA) has driven the
development of Agile Hardware Development Methodology (AHDM). Hardware
Construction Language (HCL) like Chisel offers high-level abstraction features,
making it an ideal language for HCL-Based AHDM. While Large Language Models
(LLMs) excel in code generation tasks, they still face challenges with Chisel
generation, particularly regarding syntax correctness and design variability.
Recent reasoning models have significantly enhanced code generation
capabilities through test-time scaling techniques. However, we found that
reasoning models without domain adaptation cannot bring substantial benefits to
Chisel code generation tasks. This paper presents ChiseLLM, a solution
comprising data processing and transformation, prompt-guided reasoning trace
synthesis, and domain-adapted model training. We constructed high-quality
datasets from public RTL code resources and guided the model to adopt
structured thinking patterns through prompt enhancement methods. Experiments
demonstrate that our ChiseLLM-7B and ChiseLLM-32B models improved syntax
correctness by 18.85% and 26.32% respectively over base models, while
increasing variability design ability by 47.58% compared to baseline reasoning
models. Our datasets and models are publicly available, providing
high-performance, cost-effective models for HCL-Based AHDM, and offering an
effective baseline for future research. Github repository:
https://github.com/observerw/ChiseLLM
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual
  Dependency
Authors: name: Zhikai Wang, name: Jiashuo Sun, name: Wenqi Zhang, name: Zhiqiang Hu, name: Xin Li, name: Fan Wang, name: Deli Zhao
Published: 2025-04-24 06:16:38

Recent advancements in Large Vision-Language Models (LVLMs) have
significantly enhanced their ability to integrate visual and linguistic
information, achieving near-human proficiency in tasks like object recognition,
captioning, and visual question answering. However, current benchmarks
typically focus on knowledge-centric evaluations that assess domain-specific
expertise, often neglecting the core ability to reason about fundamental
mathematical elements and visual concepts. We identify a gap in evaluating
elementary-level math problems, which rely on explicit visual
dependencies-requiring models to discern, integrate, and reason across multiple
images while incorporating commonsense knowledge, all of which are crucial for
advancing toward broader AGI capabilities. To address this gap, we introduce
VCBENCH, a comprehensive benchmark for multimodal mathematical reasoning with
explicit visual dependencies. VCBENCH includes 1,720 problems across six
cognitive domains, featuring 6,697 images (averaging 3.9 per question) to
ensure multi-image reasoning. We evaluate 26 state-of-the-art LVLMs on VCBENCH,
revealing substantial performance disparities, with even the top models unable
to exceed 50% accuracy. Our findings highlight the ongoing challenges in
visual-mathematical integration and suggest avenues for future LVLM
advancements.
SPC: Evolving Self-Play Critic via Adversarial Games for LLM Reasoning
Authors: name: Jiaqi Chen, name: Bang Zhang, name: Ruotian Ma, name: Peisong Wang, name: Xiaodan Liang, name: Zhaopeng Tu, name: Xiaolong Li, name: Kwan-Yee K. Wong
Published: 2025-04-27 08:45:06

Evaluating the step-by-step reliability of large language model (LLM)
reasoning, such as Chain-of-Thought, remains challenging due to the difficulty
and cost of obtaining high-quality step-level supervision. In this paper, we
introduce Self-Play Critic (SPC), a novel approach where a critic model evolves
its ability to assess reasoning steps through adversarial self-play games,
eliminating the need for manual step-level annotation. SPC involves fine-tuning
two copies of a base model to play two roles, namely a "sneaky generator" that
deliberately produces erroneous steps designed to be difficult to detect, and a
"critic" that analyzes the correctness of reasoning steps. These two models
engage in an adversarial game in which the generator aims to fool the critic,
while the critic model seeks to identify the generator's errors. Using
reinforcement learning based on the game outcomes, the models iteratively
improve; the winner of each confrontation receives a positive reward and the
loser receives a negative reward, driving continuous self-evolution.
Experiments on three reasoning process benchmarks (ProcessBench, PRM800K,
DeltaBench) demonstrate that our SPC progressively enhances its error detection
capabilities (e.g., accuracy increases from 70.8% to 77.7% on ProcessBench) and
surpasses strong baselines, including distilled R1 model. Furthermore, applying
SPC to guide the test-time search of diverse LLMs significantly improves their
mathematical reasoning performance on MATH500 and AIME2024, outperforming
state-of-the-art process reward models.
ICL CIPHERS: Quantifying "Learning'' in In-Context Learning via
  Substitution Ciphers
Authors: name: Zhouxiang Fang, name: Aayush Mishra, name: Muhan Gao, name: Anqi Liu, name: Daniel Khashabi
Published: 2025-04-28 00:05:29

Recent works have suggested that In-Context Learning (ICL) operates in dual
modes, i.e. task retrieval (remember learned patterns from pre-training) and
task learning (inference-time ``learning'' from demonstrations). However,
disentangling these the two modes remains a challenging goal. We introduce ICL
CIPHERS, a class of task reformulations based on substitution ciphers borrowed
from classic cryptography. In this approach, a subset of tokens in the
in-context inputs are substituted with other (irrelevant) tokens, rendering
English sentences less comprehensible to human eye. However, by design, there
is a latent, fixed pattern to this substitution, making it reversible. This
bijective (reversible) cipher ensures that the task remains a well-defined task
in some abstract sense, despite the transformations. It is a curious question
if LLMs can solve ICL CIPHERS with a BIJECTIVE mapping, which requires
deciphering the latent cipher. We show that LLMs are better at solving ICL
CIPHERS with BIJECTIVE mappings than the NON-BIJECTIVE (irreversible) baseline,
providing a novel approach to quantify ``learning'' in ICL. While this gap is
small, it is consistent across the board on four datasets and six models.
Finally, we examine LLMs' internal representations and identify evidence in
their ability to decode the ciphered inputs.
CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through
  Cryptography Challenges
Authors: name: Yu Li, name: Qizhi Pei, name: Mengyuan Sun, name: Honglin Lin, name: Chenlin Ming, name: Xin Gao, name: Jiang Wu, name: Conghui He, name: Lijun Wu
Published: 2025-04-27 03:41:17

Large language models (LLMs) have demonstrated remarkable capabilities,
especially the recent advancements in reasoning, such as o1 and o3, pushing the
boundaries of AI. Despite these impressive achievements in mathematics and
coding, the reasoning abilities of LLMs in domains requiring cryptographic
expertise remain underexplored. In this paper, we introduce CipherBank, a
comprehensive benchmark designed to evaluate the reasoning capabilities of LLMs
in cryptographic decryption tasks. CipherBank comprises 2,358 meticulously
crafted problems, covering 262 unique plaintexts across 5 domains and 14
subdomains, with a focus on privacy-sensitive and real-world scenarios that
necessitate encryption. From a cryptographic perspective, CipherBank
incorporates 3 major categories of encryption methods, spanning 9 distinct
algorithms, ranging from classical ciphers to custom cryptographic techniques.
We evaluate state-of-the-art LLMs on CipherBank, e.g., GPT-4o, DeepSeek-V3, and
cutting-edge reasoning-focused models such as o1 and DeepSeek-R1. Our results
reveal significant gaps in reasoning abilities not only between general-purpose
chat LLMs and reasoning-focused LLMs but also in the performance of current
reasoning-focused models when applied to classical cryptographic decryption
tasks, highlighting the challenges these models face in understanding and
manipulating encrypted data. Through detailed analysis and error
investigations, we provide several key observations that shed light on the
limitations and potential improvement areas for LLMs in cryptographic
reasoning. These findings underscore the need for continuous advancements in
LLM reasoning capabilities.
MMInference: Accelerating Pre-filling for Long-Context VLMs via
  Modality-Aware Permutation Sparse Attention
Authors: name: Yucheng Li, name: Huiqiang Jiang, name: Chengruidong Zhang, name: Qianhui Wu, name: Xufang Luo, name: Surin Ahn, name: Amir H. Abdi, name: Dongsheng Li, name: Jianfeng Gao, name: Yuqing Yang, name: Lili Qiu
Published: 2025-04-22 17:59:51

The integration of long-context capabilities with visual understanding
unlocks unprecedented potential for Vision Language Models (VLMs). However, the
quadratic attention complexity during the pre-filling phase remains a
significant obstacle to real-world deployment. To overcome this limitation, we
introduce MMInference (Multimodality Million tokens Inference), a dynamic
sparse attention method that accelerates the prefilling stage for long-context
multi-modal inputs. First, our analysis reveals that the temporal and spatial
locality of video input leads to a unique sparse pattern, the Grid pattern.
Simultaneously, VLMs exhibit markedly different sparse distributions across
different modalities. We introduce a permutation-based method to leverage the
unique Grid pattern and handle modality boundary issues. By offline search the
optimal sparse patterns for each head, MMInference constructs the sparse
distribution dynamically based on the input. We also provide optimized GPU
kernels for efficient sparse computations. Notably, MMInference integrates
seamlessly into existing VLM pipelines without any model modifications or
fine-tuning. Experiments on multi-modal benchmarks-including Video QA,
Captioning, VisionNIAH, and Mixed-Modality NIAH-with state-of-the-art
long-context VLMs (LongVila, LlavaVideo, VideoChat-Flash, Qwen2.5-VL) show that
MMInference accelerates the pre-filling stage by up to 8.3x at 1M tokens while
maintaining accuracy. Our code is available at https://aka.ms/MMInference.
Group Downsampling with Equivariant Anti-aliasing
Authors: name: Md Ashiqur Rahman, name: Raymond A. Yeh
Published: 2025-04-24 05:29:51

Downsampling layers are crucial building blocks in CNN architectures, which
help to increase the receptive field for learning high-level features and
reduce the amount of memory/computation in the model. In this work, we study
the generalization of the uniform downsampling layer for group equivariant
architectures, e.g., G-CNNs. That is, we aim to downsample signals (feature
maps) on general finite groups with anti-aliasing. This involves the following:
(a) Given a finite group and a downsampling rate, we present an algorithm to
form a suitable choice of subgroup. (b) Given a group and a subgroup, we study
the notion of bandlimited-ness and propose how to perform anti-aliasing.
Notably, our method generalizes the notion of downsampling based on classical
sampling theory. When the signal is on a cyclic group, i.e., periodic, our
method recovers the standard downsampling of an ideal low-pass filter followed
by a subsampling operation. Finally, we conducted experiments on image
classification tasks demonstrating that the proposed downsampling operation
improves accuracy, better preserves equivariance, and reduces model size when
incorporated into G-equivariant networks
TrustGeoGen: Scalable and Formal-Verified Data Engine for Trustworthy
  Multi-modal Geometric Problem Solving
Authors: name: Daocheng Fu, name: Zijun Chen, name: Renqiu Xia, name: Qi Liu, name: Yuan Feng, name: Hongbin Zhou, name: Renrui Zhang, name: Shiyang Feng, name: Peng Gao, name: Junchi Yan, name: Botian Shi, name: Bo Zhang, name: Yu Qiao
Published: 2025-04-22 10:45:23

Mathematical geometric problem solving (GPS) often requires effective
integration of multimodal information and verifiable logical coherence. Despite
the fast development of large language models in general problem solving, it
remains unresolved regarding with both methodology and benchmarks, especially
given the fact that exiting synthetic GPS benchmarks are often not
self-verified and contain noise and self-contradicted information due to the
illusion of LLMs. In this paper, we propose a scalable data engine called
TrustGeoGen for problem generation, with formal verification to provide a
principled benchmark, which we believe lays the foundation for the further
development of methods for GPS. The engine synthesizes geometric data through
four key innovations: 1) multimodal-aligned generation of diagrams, textual
descriptions, and stepwise solutions; 2) formal verification ensuring
rule-compliant reasoning paths; 3) a bootstrapping mechanism enabling
complexity escalation via recursive state generation and 4) our devised
GeoExplore series algorithms simultaneously produce multi-solution variants and
self-reflective backtracking traces. By formal logical verification,
TrustGeoGen produces GeoTrust-200K dataset with guaranteed modality integrity,
along with GeoTrust-test testset. Experiments reveal the state-of-the-art
models achieve only 49.17\% accuracy on GeoTrust-test, demonstrating its
evaluation stringency. Crucially, models trained on GeoTrust achieve OOD
generalization on GeoQA, significantly reducing logical inconsistencies
relative to pseudo-label annotated by OpenAI-o1. Our code is available at
https://github.com/Alpha-Innovator/TrustGeoGen
RepText: Rendering Visual Text via Replicating
Authors: name: Haofan Wang, name: Yujia Xu, name: Yimeng Li, name: Junchen Li, name: Chaowei Zhang, name: Jing Wang, name: Kejia Yang, name: Zhibo Chen
Published: 2025-04-28 12:19:53

Although contemporary text-to-image generation models have achieved
remarkable breakthroughs in producing visually appealing images, their capacity
to generate precise and flexible typographic elements, especially non-Latin
alphabets, remains constrained. To address these limitations, we start from an
naive assumption that text understanding is only a sufficient condition for
text rendering, but not a necessary condition. Based on this, we present
RepText, which aims to empower pre-trained monolingual text-to-image generation
models with the ability to accurately render, or more precisely, replicate,
multilingual visual text in user-specified fonts, without the need to really
understand them. Specifically, we adopt the setting from ControlNet and
additionally integrate language agnostic glyph and position of rendered text to
enable generating harmonized visual text, allowing users to customize text
content, font and position on their needs. To improve accuracy, a text
perceptual loss is employed along with the diffusion loss. Furthermore, to
stabilize rendering process, at the inference phase, we directly initialize
with noisy glyph latent instead of random initialization, and adopt region
masks to restrict the feature injection to only the text region to avoid
distortion of the background. We conducted extensive experiments to verify the
effectiveness of our RepText relative to existing works, our approach
outperforms existing open-source methods and achieves comparable results to
native multi-language closed-source models. To be more fair, we also
exhaustively discuss its limitations in the end.
Clinical knowledge in LLMs does not translate to human interactions
Authors: name: Andrew M. Bean, name: Rebecca Payne, name: Guy Parsons, name: Hannah Rose Kirk, name: Juan Ciro, name: Rafael Mosquera, name: Sara Hincapié Monsalve, name: Aruna S. Ekanayaka, name: Lionel Tarassenko, name: Luc Rocher, name: Adam Mahdi
Published: 2025-04-26 13:32:49

Global healthcare providers are exploring use of large language models (LLMs)
to provide medical advice to the public. LLMs now achieve nearly perfect scores
on medical licensing exams, but this does not necessarily translate to accurate
performance in real-world settings. We tested if LLMs can assist members of the
public in identifying underlying conditions and choosing a course of action
(disposition) in ten medical scenarios in a controlled study with 1,298
participants. Participants were randomly assigned to receive assistance from an
LLM (GPT-4o, Llama 3, Command R+) or a source of their choice (control). Tested
alone, LLMs complete the scenarios accurately, correctly identifying conditions
in 94.9% of cases and disposition in 56.3% on average. However, participants
using the same LLMs identified relevant conditions in less than 34.5% of cases
and disposition in less than 44.2%, both no better than the control group. We
identify user interactions as a challenge to the deployment of LLMs for medical
advice. Standard benchmarks for medical knowledge and simulated patient
interactions do not predict the failures we find with human participants.
Moving forward, we recommend systematic human user testing to evaluate
interactive capabilities prior to public deployments in healthcare.
LLM-Powered GUI Agents in Phone Automation: Surveying Progress and
  Prospects
Authors: name: Guangyi Liu, name: Pengxiang Zhao, name: Liang Liu, name: Yaxuan Guo, name: Han Xiao, name: Weifeng Lin, name: Yuxiang Chai, name: Yue Han, name: Shuai Ren, name: Hao Wang, name: Xiaoyu Liang, name: Wenhao Wang, name: Tianze Wu, name: Linghao Li, name: Hao Wang, name: Guanjing Xiong, name: Yong Liu, name: Hongsheng Li
Published: 2025-04-28 14:39:25

With the rapid rise of large language models (LLMs), phone automation has
undergone transformative changes. This paper systematically reviews LLM-driven
phone GUI agents, highlighting their evolution from script-based automation to
intelligent, adaptive systems. We first contextualize key challenges, (i)
limited generality, (ii) high maintenance overhead, and (iii) weak intent
comprehension, and show how LLMs address these issues through advanced language
understanding, multimodal perception, and robust decision-making. We then
propose a taxonomy covering fundamental agent frameworks (single-agent,
multi-agent, plan-then-act), modeling approaches (prompt engineering,
training-based), and essential datasets and benchmarks. Furthermore, we detail
task-specific architectures, supervised fine-tuning, and reinforcement learning
strategies that bridge user intent and GUI operations. Finally, we discuss open
challenges such as dataset diversity, on-device deployment efficiency,
user-centric adaptation, and security concerns, offering forward-looking
insights into this rapidly evolving field. By providing a structured overview
and identifying pressing research gaps, this paper serves as a definitive
reference for researchers and practitioners seeking to harness LLMs in
designing scalable, user-friendly phone GUI agents.
Even Small Reasoners Should Quote Their Sources: Introducing the
  Pleias-RAG Model Family
Authors: name: Pierre-Carl Langlais, name: Pavel Chizhov, name: Mattia Nee, name: Carlos Rosas Hinostroza, name: Matthieu Delsart, name: Irène Girard, name: Othman Hicheur, name: Anastasia Stasenko, name: Ivan P. Yamshchikov
Published: 2025-04-25 10:17:04

We introduce a new generation of small reasoning models for RAG, search, and
source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a
large synthetic dataset emulating the retrieval of a wide variety of
multilingual open sources from the Common Corpus. They provide native support
for citation and grounding with literal quotes and reintegrate multiple
features associated with RAG workflows, such as query routing, query
reformulation, and source reranking. Pleias-RAG-350m and Pleias-RAG-1B
outperform SLMs below 4 billion parameters on standardized RAG benchmarks
(HotPotQA, 2wiki) and are competitive with popular larger models, including
Qwen-2.5-7B, Llama-3.1-8B, and Gemma-3-4B. They are the only SLMs to date
maintaining consistent RAG performance across leading European languages and
ensuring systematic reference grounding for statements. Due to their size and
ease of deployment on constrained infrastructure and higher factuality by
design, the models unlock a range of new use cases for generative AI.