Orionnnn/hub-stats huggingface dataset:
T2R-bench: A Benchmark for Generating Article-Level Reports from Real
  World Industrial Tables
Authors: name: Jie Zhang, name: Changzai Pan, name: Kaiwen Wei, name: Sishi Xiong, name: Yu Zhao, name: Xiangyu Li, name: Jiaxin Peng, name: Xiaoyan Gu, name: Jian Yang, name: Wenhan Chang, name: Zhenhe Wu, name: Jiang Zhong, name: Shuangyong Song, name: Yongxiang Li, name: Xuelong Li
Published: 2025-08-27 11:55:40

Extensive research has been conducted to explore the capabilities of large
language models (LLMs) in table reasoning. However, the essential task of
transforming tables information into reports remains a significant challenge
for industrial applications. This task is plagued by two critical issues: 1)
the complexity and diversity of tables lead to suboptimal reasoning outcomes;
and 2) existing table benchmarks lack the capacity to adequately assess the
practical application of this task. To fill this gap, we propose the
table-to-report task and construct a bilingual benchmark named T2R-bench, where
the key information flow from the tables to the reports for this task. The
benchmark comprises 457 industrial tables, all derived from real-world
scenarios and encompassing 19 industry domains as well as 4 types of industrial
tables. Furthermore, we propose an evaluation criteria to fairly measure the
quality of report generation. The experiments on 25 widely-used LLMs reveal
that even state-of-the-art models like Deepseek-R1 only achieves performance
with 62.71 overall score, indicating that LLMs still have room for improvement
on T2R-bench. Source code and data will be available after acceptance.
UI-Level Evaluation of ALLaM 34B: Measuring an Arabic-Centric LLM via
  HUMAIN Chat
Authors: name: Omer Nacar
Published: 2025-08-24 14:32:15

Large language models (LLMs) trained primarily on English corpora often
struggle to capture the linguistic and cultural nuances of Arabic. To address
this gap, the Saudi Data and AI Authority (SDAIA) introduced the ALLaM family
of Arabic-focused models. The most capable of these available to the public,
ALLaM-34B, was subsequently adopted by HUMAIN, who developed and deployed
HUMAIN Chat, a closed conversational web service built on this model. This
paper presents an expanded and refined UI-level evaluation of ALLaM-34B.
Using a prompt pack spanning modern standard Arabic, five regional dialects,
code-switching, factual knowledge, arithmetic and temporal reasoning, creative
generation, and adversarial safety, we collected 115 outputs (23 prompts times
5 runs) and scored each with three frontier LLM judges (GPT-5, Gemini 2.5 Pro,
Claude Sonnet-4). We compute category-level means with 95\% confidence
intervals, analyze score distributions, and visualize dialect-wise metric heat
maps. The updated analysis reveals consistently high performance on generation
and code-switching tasks (both averaging 4.92/5), alongside strong results in
MSA handling (4.74/5), solid reasoning ability (4.64/5), and improved dialect
fidelity (4.21/5). Safety-related prompts show stable, reliable performance of
(4.54/5). Taken together, these results position ALLaM-34B as a robust and
culturally grounded Arabic LLM, demonstrating both technical strength and
practical readiness for real-world deployment.
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex
  Dynamic Environment? A Study on τ-bench
Authors: name: Venkatesh Mishra, name: Amir Saeidi, name: Satyam Raj, name: Mutsumi Nakamura, name: Jayanth Srinivasa, name: Gaowen Liu, name: Ali Payani, name: Chitta Baral
Published: 2025-08-28 15:57:33

Recent advances in reasoning and planning capabilities of large language
models (LLMs) have enabled their potential as autonomous agents capable of tool
use in dynamic environments. However, in multi-turn conversational environments
like tau-bench, these agents often struggle with consistent reasoning,
adherence to domain-specific policies, and extracting correct information over
a long horizon of tool-calls and conversation. To capture and mitigate these
failures, we conduct a comprehensive manual analysis of the common errors
occurring in the conversation trajectories. We then experiment with
reformulations of inputs to the tool-calling agent for improvement in agent
decision making. Finally, we propose the Input-Reformulation Multi-Agent (IRMA)
framework, which automatically reformulates user queries augmented with
relevant domain rules and tool suggestions for the tool-calling agent to focus
on. The results show that IRMA significantly outperforms ReAct, Function
Calling, and Self-Reflection by 16.1%, 12.7%, and 19.1%, respectively, in
overall pass^5 scores. These findings highlight the superior reliability and
consistency of IRMA compared to other methods in dynamic environments.
No Label Left Behind: A Unified Surface Defect Detection Model for all
  Supervision Regimes
Authors: name: Blaž Rolih, name: Matic Fučka, name: Danijel Skočaj
Published: 2025-08-26 14:20:21

Surface defect detection is a critical task across numerous industries, aimed
at efficiently identifying and localising imperfections or irregularities on
manufactured components. While numerous methods have been proposed, many fail
to meet industrial demands for high performance, efficiency, and adaptability.
Existing approaches are often constrained to specific supervision scenarios and
struggle to adapt to the diverse data annotations encountered in real-world
manufacturing processes, such as unsupervised, weakly supervised, mixed
supervision, and fully supervised settings. To address these challenges, we
propose SuperSimpleNet, a highly efficient and adaptable discriminative model
built on the foundation of SimpleNet. SuperSimpleNet incorporates a novel
synthetic anomaly generation process, an enhanced classification head, and an
improved learning procedure, enabling efficient training in all four
supervision scenarios, making it the first model capable of fully leveraging
all available data annotations. SuperSimpleNet sets a new standard for
performance across all scenarios, as demonstrated by its results on four
challenging benchmark datasets. Beyond accuracy, it is very fast, achieving an
inference time below 10 ms. With its ability to unify diverse supervision
paradigms while maintaining outstanding speed and reliability, SuperSimpleNet
represents a promising step forward in addressing real-world manufacturing
challenges and bridging the gap between academic research and industrial
applications. Code: https://github.com/blaz-r/SuperSimpleNet
PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic
  Reasoning
Authors: name: Wenfeng Feng, name: Penghong Zhao, name: Guochao Jiang, name: Chuzhan Hao, name: Yuewei Zhang, name: Hao Wang
Published: 2025-08-28 09:18:26

Critic-free reinforcement learning methods, particularly group policies, have
attracted considerable attention for their efficiency in complex tasks.
However, these methods rely heavily on multiple sampling and comparisons within
the policy to estimate advantage, which may cause the policy to fall into local
optimum and increase computational cost. To address these issues, we propose
PVPO, an efficient reinforcement learning method enhanced by an advantage
reference anchor and data pre-sampling. Specifically, we use the reference
model to rollout in advance and employ the calculated reward score as a
reference anchor. Our approach effectively corrects the cumulative bias
introduced by intra-group comparisons and significantly reduces reliance on the
number of rollouts during training. Meanwhile, the reference model can assess
sample difficulty during data pre-sampling, enabling effective selection of
high-gain data to improve training efficiency. Moreover, PVPO is orthogonal to
other advanced critic-free RL algorithms, making it compatible with and
complementary to these methods. Experiments conducted on nine datasets across
two domains demonstrate that PVPO achieves State-Of-The-Art (SOTA) performance.
Our approach not only demonstrates robust generalization across multiple tasks,
but also exhibits scalable performance across models of varying scales.
Deep Residual Echo State Networks: exploring residual orthogonal
  connections in untrained Recurrent Neural Networks
Authors: name: Matteo Pinna, name: Andrea Ceni, name: Claudio Gallicchio
Published: 2025-08-28 19:22:02

Echo State Networks (ESNs) are a particular type of untrained Recurrent
Neural Networks (RNNs) within the Reservoir Computing (RC) framework, popular
for their fast and efficient learning. However, traditional ESNs often struggle
with long-term information processing. In this paper, we introduce a novel
class of deep untrained RNNs based on temporal residual connections, called
Deep Residual Echo State Networks (DeepResESNs). We show that leveraging a
hierarchy of untrained residual recurrent layers significantly boosts memory
capacity and long-term temporal modeling. For the temporal residual
connections, we consider different orthogonal configurations, including
randomly generated and fixed-structure configurations, and we study their
effect on network dynamics. A thorough mathematical analysis outlines necessary
and sufficient conditions to ensure stable dynamics within DeepResESN. Our
experiments on a variety of time series tasks showcase the advantages of the
proposed approach over traditional shallow and deep RC.
EduRABSA: An Education Review Dataset for Aspect-based Sentiment
  Analysis Tasks
Authors: name: Yan Cathy Hua, name: Paul Denny, name: Jörg Wicker, name: Katerina Taskova
Published: 2025-08-23 12:38:40

Every year, most educational institutions seek and receive an enormous volume
of text feedback from students on courses, teaching, and overall experience.
Yet, turning this raw feedback into useful insights is far from
straightforward. It has been a long-standing challenge to adopt automatic
opinion mining solutions for such education review text data due to the content
complexity and low-granularity reporting requirements. Aspect-based Sentiment
Analysis (ABSA) offers a promising solution with its rich, sub-sentence-level
opinion mining capabilities. However, existing ABSA research and resources are
very heavily focused on the commercial domain. In education, they are scarce
and hard to develop due to limited public datasets and strict data protection.
A high-quality, annotated dataset is urgently needed to advance research in
this under-resourced area. In this work, we present EduRABSA (Education Review
ABSA), the first public, annotated ABSA education review dataset that covers
three review subject types (course, teaching staff, university) in the English
language and all main ABSA tasks, including the under-explored implicit aspect
and implicit opinion extraction. We also share ASQE-DPT (Data Processing Tool),
an offline, lightweight, installation-free manual data annotation tool that
generates labelled datasets for comprehensive ABSA tasks from a single-task
annotation. Together, these resources contribute to the ABSA community and
education domain by removing the dataset barrier, supporting research
transparency and reproducibility, and enabling the creation and sharing of
further resources. The dataset, annotation tool, and scripts and statistics for
dataset processing and sampling are available at
https://github.com/yhua219/edurabsa_dataset_and_annotation_tool.
Quantization Robustness to Input Degradations for Object Detection
Authors: name: Toghrul Karimov, name: Hassan Imani, name: Allan Kazakov
Published: 2025-08-27 06:20:38

Post-training quantization (PTQ) is crucial for deploying efficient object
detection models, like YOLO, on resource-constrained devices. However, the
impact of reduced precision on model robustness to real-world input
degradations such as noise, blur, and compression artifacts is a significant
concern. This paper presents a comprehensive empirical study evaluating the
robustness of YOLO models (nano to extra-large scales) across multiple
precision formats: FP32, FP16 (TensorRT), Dynamic UINT8 (ONNX), and Static INT8
(TensorRT). We introduce and evaluate a degradation-aware calibration strategy
for Static INT8 PTQ, where the TensorRT calibration process is exposed to a mix
of clean and synthetically degraded images. Models were benchmarked on the COCO
dataset under seven distinct degradation conditions (including various types
and levels of noise, blur, low contrast, and JPEG compression) and a
mixed-degradation scenario. Results indicate that while Static INT8 TensorRT
engines offer substantial speedups (~1.5-3.3x) with a moderate accuracy drop
(~3-7% mAP50-95) on clean data, the proposed degradation-aware calibration did
not yield consistent, broad improvements in robustness over standard clean-data
calibration across most models and degradations. A notable exception was
observed for larger model scales under specific noise conditions, suggesting
model capacity may influence the efficacy of this calibration approach. These
findings highlight the challenges in enhancing PTQ robustness and provide
insights for deploying quantized detectors in uncontrolled environments. All
code and evaluation tables are available at https://github.com/AllanK24/QRID.
Model-Task Alignment Drives Distinct RL Outcomes
Authors: name: Haoze Wu, name: Cheng Wang, name: Wenshuo Zhao, name: Junxian He
Published: 2025-08-28 20:02:10

Recent advances in applying reinforcement learning (RL) to large language
models (LLMs) have led to substantial progress. In particular, a series of
remarkable yet often counterintuitive phenomena have been reported in LLMs,
exhibiting patterns not typically observed in traditional RL settings. For
example, notable claims include that a single training example can match the
performance achieved with an entire dataset, that the reward signal does not
need to be very accurate, and that training solely with negative samples can
match or even surpass sophisticated reward-based methods. However, the precise
conditions under which these observations hold - and, critically, when they
fail - remain unclear. In this work, we identify a key factor that
differentiates RL observations: whether the pretrained model already exhibits
strong Model-Task Alignment, as measured by pass@k accuracy on the evaluated
task. Through a systematic and comprehensive examination of a series of
counterintuitive claims, supported by rigorous experimental validation across
different model architectures and task domains, our findings show that while
standard RL training remains consistently robust across settings, many of these
counterintuitive results arise only when the model and task already exhibit
strong model-task alignment. In contrast, these techniques fail to drive
substantial learning in more challenging regimes, where standard RL methods
remain effective.
Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula
  Discovery
Authors: name: Jiaqi Liu, name: Songning Lai, name: Pengze Li, name: Di Yu, name: Wenjie Zhou, name: Yiyang Zhou, name: Peng Xia, name: Zijun Wang, name: Xi Chen, name: Shixiang Tang, name: Lei Bai, name: Wanli Ouyang, name: Mingyu Ding, name: Huaxiu Yao, name: Aoran Wang
Published: 2025-08-24 14:34:21

Automated discovery of physical laws from observational data in the real
world is a grand challenge in AI. Current methods, relying on symbolic
regression or LLMs, are limited to uni-modal data and overlook the rich, visual
phenomenological representations of motion that are indispensable to
physicists. This "sensory deprivation" severely weakens their ability to
interpret the inherent spatio-temporal patterns within dynamic phenomena. To
address this gap, we propose VIPER-R1, a multimodal model that performs Visual
Induction for Physics-based Equation Reasoning to discover fundamental symbolic
formulas. It integrates visual perception, trajectory data, and symbolic
reasoning to emulate the scientific discovery process. The model is trained via
a curriculum of Motion Structure Induction (MSI), using supervised fine-tuning
to interpret kinematic phase portraits and to construct hypotheses guided by a
Causal Chain of Thought (C-CoT), followed by Reward-Guided Symbolic Calibration
(RGSC) to refine the formula structure with reinforcement learning. During
inference, the trained VIPER-R1 acts as an agent: it first posits a
high-confidence symbolic ansatz, then proactively invokes an external symbolic
regression tool to perform Symbolic Residual Realignment (SR^2). This final
step, analogous to a physicist's perturbation analysis, reconciles the
theoretical model with empirical data. To support this research, we introduce
PhysSymbol, a new 5,000-instance multimodal corpus. Experiments show that
VIPER-R1 consistently outperforms state-of-the-art VLM baselines in accuracy
and interpretability, enabling more precise discovery of physical laws. Project
page: https://jiaaqiliu.github.io/VIPER-R1/
Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation
Authors: name: Xiaochuan Li, name: Guoguang Du, name: Runze Zhang, name: Liang Jin, name: Qi Jia, name: Lihua Lu, name: Zhenhua Guo, name: Yaqian Zhao, name: Haiyang Liu, name: Tianqi Wang, name: Changsheng Li, name: Xiaoli Gong, name: Rengang Li, name: Baoyu Fan
Published: 2025-08-28 06:39:41

Scaling laws have validated the success and promise of large-data-trained
models in creative generation across text, image, and video domains. However,
this paradigm faces data scarcity in the 3D domain, as there is far less of it
available on the internet compared to the aforementioned modalities.
Fortunately, there exist adequate videos that inherently contain commonsense
priors, offering an alternative supervisory signal to mitigate the
generalization bottleneck caused by limited native 3D data. On the one hand,
videos capturing multiple views of an object or scene provide a spatial
consistency prior for 3D generation. On the other hand, the rich semantic
information contained within the videos enables the generated content to be
more faithful to the text prompts and semantically plausible. This paper
explores how to apply the video modality in 3D asset generation, spanning
datasets to models. We introduce Droplet3D-4M, the first large-scale video
dataset with multi-view level annotations, and train Droplet3D, a generative
model supporting both image and dense text input. Extensive experiments
validate the effectiveness of our approach, demonstrating its ability to
produce spatially consistent and semantically plausible content. Moreover, in
contrast to the prevailing 3D solutions, our approach exhibits the potential
for extension to scene-level applications. This indicates that the commonsense
priors from the videos significantly facilitate 3D creation. We have
open-sourced all resources including the dataset, code, technical framework,
and model weights: https://dropletx.github.io/.
CLIPSym: Delving into Symmetry Detection with CLIP
Authors: name: Tinghan Yang, name: Md Ashiqur Rahman, name: Raymond A. Yeh
Published: 2025-08-19 18:43:14

Symmetry is one of the most fundamental geometric cues in computer vision,
and detecting it has been an ongoing challenge. With the recent advances in
vision-language models,~i.e., CLIP, we investigate whether a pre-trained CLIP
model can aid symmetry detection by leveraging the additional symmetry cues
found in the natural image descriptions. We propose CLIPSym, which leverages
CLIP's image and language encoders and a rotation-equivariant decoder based on
a hybrid of Transformer and G-Convolution to detect rotation and reflection
symmetries. To fully utilize CLIP's language encoder, we have developed a novel
prompting technique called Semantic-Aware Prompt Grouping (SAPG), which
aggregates a diverse set of frequent object-based prompts to better integrate
the semantic cues for symmetry detection. Empirically, we show that CLIPSym
outperforms the current state-of-the-art on three standard symmetry detection
datasets (DENDI, SDRW, and LDRS). Finally, we conduct detailed ablations
verifying the benefits of CLIP's pre-training, the proposed equivariant
decoder, and the SAPG technique. The code is available at
https://github.com/timyoung2333/CLIPSym.
HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data
  for Mobile Dexterous Manipulation
Authors: name: Zhecheng Yuan, name: Tianming Wei, name: Langzhe Gu, name: Pu Hua, name: Tianhai Liang, name: Yuanpei Chen, name: Huazhe Xu
Published: 2025-08-27 17:53:46

Leveraging human motion data to impart robots with versatile manipulation
skills has emerged as a promising paradigm in robotic manipulation.
Nevertheless, translating multi-source human hand motions into feasible robot
behaviors remains challenging, particularly for robots equipped with
multi-fingered dexterous hands characterized by complex, high-dimensional
action spaces. Moreover, existing approaches often struggle to produce policies
capable of adapting to diverse environmental conditions. In this paper, we
introduce HERMES, a human-to-robot learning framework for mobile bimanual
dexterous manipulation. First, HERMES formulates a unified reinforcement
learning approach capable of seamlessly transforming heterogeneous human hand
motions from multiple sources into physically plausible robotic behaviors.
Subsequently, to mitigate the sim2real gap, we devise an end-to-end, depth
image-based sim2real transfer method for improved generalization to real-world
scenarios. Furthermore, to enable autonomous operation in varied and
unstructured environments, we augment the navigation foundation model with a
closed-loop Perspective-n-Point (PnP) localization mechanism, ensuring precise
alignment of visual goals and effectively bridging autonomous navigation and
dexterous manipulation. Extensive experimental results demonstrate that HERMES
consistently exhibits generalizable behaviors across diverse, in-the-wild
scenarios, successfully performing numerous complex mobile bimanual dexterous
manipulation tasks. Project Page:https://gemcollector.github.io/HERMES/.
Efficient Code Embeddings from Code Generation Models
Authors: name: Daria Kryvosheieva, name: Saba Sturua, name: Michael Günther, name: Scott Martens, name: Han Xiao
Published: 2025-08-29 01:18:15

jina-code-embeddings is a novel code embedding model suite designed to
retrieve code from natural language queries, perform technical
question-answering, and identify semantically similar code snippets across
programming languages. It makes innovative use of an autoregressive backbone
pre-trained on both text and code, generating embeddings via last-token
pooling. We outline the training recipe and demonstrate state-of-the-art
performance despite the relatively small size of the models, validating this
approach to code embedding model construction.