Orionnnn/hub-stats huggingface dataset:
SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning
Authors: name: Yige Xu, name: Xu Guo, name: Zhiwei Zeng, name: Chunyan Miao
Published: 2025-05-16 17:47:50

Test-Time Scaling (TTS) refers to approaches that improve reasoning
performance by allocating extra computation during inference, without altering
the model's parameters. While existing TTS methods operate in a discrete token
space by generating more intermediate steps, recent studies in Coconut and
SoftCoT have demonstrated that thinking in the continuous latent space can
further enhance the reasoning performance. Such latent thoughts encode
informative thinking without the information loss associated with
autoregressive token generation, sparking increased interest in
continuous-space reasoning. Unlike discrete decoding, where repeated sampling
enables exploring diverse reasoning paths, latent representations in continuous
space are fixed for a given input, which limits diverse exploration, as all
decoded paths originate from the same latent thought. To overcome this
limitation, we introduce SoftCoT++ to extend SoftCoT to the Test-Time Scaling
paradigm by enabling diverse exploration of thinking paths. Specifically, we
perturb latent thoughts via multiple specialized initial tokens and apply
contrastive learning to promote diversity among soft thought representations.
Experiments across five reasoning benchmarks and two distinct LLM architectures
demonstrate that SoftCoT++ significantly boosts SoftCoT and also outperforms
SoftCoT with self-consistency scaling. Moreover, it shows strong compatibility
with conventional scaling techniques such as self-consistency. Source code is
available at https://github.com/xuyige/SoftCoT.
Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient
  in Latent Space
Authors: name: Hengli Li, name: Chenxi Li, name: Tong Wu, name: Xuekai Zhu, name: Yuxuan Wang, name: Zhaoxin Yu, name: Eric Hanchen Jiang, name: Song-Chun Zhu, name: Zixia Jia, name: Ying Nian Wu, name: Zilong Zheng
Published: 2025-05-19 16:26:02

Reasoning ability, a core component of human intelligence, continues to pose
a significant challenge for Large Language Models (LLMs) in the pursuit of AGI.
Although model performance has improved under the training scaling law,
significant challenges remain, particularly with respect to training
algorithms, such as catastrophic forgetting, and the limited availability of
novel training data. As an alternative, test-time scaling enhances reasoning
performance by increasing test-time computation without parameter updating.
Unlike prior methods in this paradigm focused on token space, we propose
leveraging latent space for more effective reasoning and better adherence to
the test-time scaling law. We introduce LatentSeek, a novel framework that
enhances LLM reasoning through Test-Time Instance-level Adaptation (TTIA)
within the model's latent space. Specifically, LatentSeek leverages policy
gradient to iteratively update latent representations, guided by self-generated
reward signals. LatentSeek is evaluated on a range of reasoning benchmarks,
including GSM8K, MATH-500, and AIME2024, across multiple LLM architectures.
Results show that LatentSeek consistently outperforms strong baselines, such as
Chain-of-Thought prompting and fine-tuning-based methods. Furthermore, our
analysis demonstrates that LatentSeek is highly efficient, typically converging
within a few iterations for problems of average complexity, while also
benefiting from additional iterations, thereby highlighting the potential of
test-time scaling in the latent space. These findings position LatentSeek as a
lightweight, scalable, and effective solution for enhancing the reasoning
capabilities of LLMs.
TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique
  Annotation in Cyber Threat Intelligence Text
Authors: name: Ahmed Lekssays, name: Utsav Shukla, name: Husrev Taha Sencar, name: Md Rizwan Parvez
Published: 2025-05-17 12:46:10

Accurately identifying adversarial techniques in security texts is critical
for effective cyber defense. However, existing methods face a fundamental
trade-off: they either rely on generic models with limited domain precision or
require resource-intensive pipelines that depend on large labeled datasets and
task-specific optimizations, such as custom hard-negative mining and denoising,
resources rarely available in specialized domains.
  We propose TechniqueRAG, a domain-specific retrieval-augmented generation
(RAG) framework that bridges this gap by integrating off-the-shelf retrievers,
instruction-tuned LLMs, and minimal text-technique pairs. Our approach
addresses data scarcity by fine-tuning only the generation component on limited
in-domain examples, circumventing the need for resource-intensive retrieval
training. While conventional RAG mitigates hallucination by coupling retrieval
and generation, its reliance on generic retrievers often introduces noisy
candidates, limiting domain-specific precision. To address this, we enhance
retrieval quality and domain specificity through zero-shot LLM re-ranking,
which explicitly aligns retrieved candidates with adversarial techniques.
  Experiments on multiple security benchmarks demonstrate that TechniqueRAG
achieves state-of-the-art performance without extensive task-specific
optimizations or labeled data, while comprehensive analysis provides further
insights.
Faster Video Diffusion with Trainable Sparse Attention
Authors: name: Peiyuan Zhang, name: Haofeng Huang, name: Yongqi Chen, name: Will Lin, name: Zhengzhong Liu, name: Ion Stoica, name: Eric P. Xing, name: Hao Zhang
Published: 2025-05-19 17:30:13

Scaling video diffusion transformers (DiTs) is limited by their quadratic 3D
attention, even though most of the attention mass concentrates on a small
subset of positions. We turn this observation into VSA, a trainable,
hardware-efficient sparse attention that replaces full attention at both
training and inference. In VSA, a lightweight coarse stage pools tokens into
tiles and identifies high-weight critical tokens; a fine stage computes
token-level attention only inside those tiles subjecting to block computing
layout to ensure hard efficiency. This leads to a single differentiable kernel
that trains end-to-end, requires no post-hoc profiling, and sustains 85\% of
FlashAttention3 MFU. We perform a large sweep of ablation studies and
scaling-law experiments by pretraining DiTs from 60M to 1.4B parameters. VSA
reaches a Pareto point that cuts training FLOPS by 2.53times with no drop in
diffusion loss. Retrofitting the open-source Wan-2.1 model speeds up attention
time by 6times and lowers end-to-end generation time from 31s to 18s with
comparable quality. These results establish trainable sparse attention as a
practical alternative to full attention and a key enabler for further scaling
of video diffusion models.
ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced
  Reinforcement Learning
Authors: name: Jiaan Wang, name: Fandong Meng, name: Jie Zhou
Published: 2025-05-19 11:34:47

In recent years, the emergence of large reasoning models (LRMs), such as
OpenAI-o1 and DeepSeek-R1, has shown impressive capabilities in complex
problems, e.g., mathematics and coding. Some pioneering studies attempt to
bring the success of LRMs in neural machine translation (MT). They try to build
LRMs with deep reasoning MT ability via reinforcement learning (RL). Despite
some progress that has been made, these attempts generally focus on several
high-resource languages, e.g., English and Chinese, leaving the performance on
other languages unclear. Besides, the reward modeling methods in previous work
do not fully unleash the potential of reinforcement learning in MT. In this
work, we first design a new reward modeling method that compares the
translation results of the policy MT model with a strong LRM (i.e.,
DeepSeek-R1-671B), and quantifies the comparisons to provide rewards.
Experimental results demonstrate the superiority of the reward modeling method.
Using Qwen2.5-7B-Instruct as the backbone, the trained model achieves the new
state-of-the-art performance in literary translation, and outperforms strong
LRMs including OpenAI-o1 and DeepSeeK-R1. Furthermore, we extend our method to
the multilingual settings with 11 languages. With a carefully designed
lightweight reward modeling in RL, we can simply transfer the strong MT ability
from a single direction into multiple (i.e., 90) translation directions and
achieve impressive multilingual MT performance.
When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification
  of Scientific Research
Authors: name: Guijin Son, name: Jiwoo Hong, name: Honglu Fan, name: Heejeong Nam, name: Hyunwoo Ko, name: Seungwon Lim, name: Jinyeop Song, name: Jinha Choi, name: Gonçalo Paulo, name: Youngjae Yu, name: Stella Biderman
Published: 2025-05-17 05:45:16

Recent advances in large language models (LLMs) have fueled the vision of
automated scientific discovery, often called AI Co-Scientists. To date, prior
work casts these systems as generative co-authors responsible for crafting
hypotheses, synthesizing code, or drafting manuscripts. In this work, we
explore a complementary application: using LLMs as verifiers to automate the
academic verification of scientific manuscripts. To that end, we
introduce SPOT, a dataset of 83 published papers paired with 91 errors
significant enough to prompt errata or retraction, cross-validated with actual
authors and human annotators. Evaluating state-of-the-art LLMs on SPOT, we find
that none surpasses 21.1\% recall or 6.1\% precision (o3 achieves the best
scores, with all others near zero). Furthermore, confidence estimates are
uniformly low, and across eight independent runs, models rarely rediscover the
same errors, undermining their reliability. Finally, qualitative analysis with
domain experts reveals that even the strongest models make mistakes resembling
student-level misconceptions derived from misunderstandings. These findings
highlight the substantial gap between current LLM capabilities and the
requirements for dependable AI-assisted academic verification.
Scaling Computer-Use Grounding via User Interface Decomposition and
  Synthesis
Authors: name: Tianbao Xie, name: Jiaqi Deng, name: Xiaochuan Li, name: Junlin Yang, name: Haoyuan Wu, name: Jixuan Chen, name: Wenjing Hu, name: Xinyuan Wang, name: Yuhui Xu, name: Zekun Wang, name: Yiheng Xu, name: Junli Wang, name: Doyen Sahoo, name: Tao Yu, name: Caiming Xiong
Published: 2025-05-19 15:09:23

Graphical user interface (GUI) grounding, the ability to map natural language
instructions to specific actions on graphical user interfaces, remains a
critical bottleneck in computer use agent development. Current benchmarks
oversimplify grounding tasks as short referring expressions, failing to capture
the complexity of real-world interactions that require software commonsense,
layout understanding, and fine-grained manipulation capabilities. To address
these limitations, we introduce OSWorld-G, a comprehensive benchmark comprising
564 finely annotated samples across diverse task types including text matching,
element recognition, layout understanding, and precise manipulation.
Additionally, we synthesize and release the largest computer use grounding
dataset Jedi, which contains 4 million examples through multi-perspective
decoupling of tasks. Our multi-scale models trained on Jedi demonstrate its
effectiveness by outperforming existing approaches on ScreenSpot-v2,
ScreenSpot-Pro, and our OSWorld-G. Furthermore, we demonstrate that improved
grounding with Jedi directly enhances agentic capabilities of general
foundation models on complex computer tasks, improving from 5% to 27% on
OSWorld. Through detailed ablation studies, we identify key factors
contributing to grounding performance and verify that combining specialized
data for different interface elements enables compositional generalization to
novel interfaces. All benchmark, data, checkpoints, and code are open-sourced
and available at https://osworld-grounding.github.io.
AI-Driven Scholarly Peer Review via Persistent Workflow Prompting,
  Meta-Prompting, and Meta-Reasoning
Authors: name: Evgeny Markhasin
Published: 2025-05-06 09:06:18

Critical peer review of scientific manuscripts presents a significant
challenge for Large Language Models (LLMs), partly due to data limitations and
the complexity of expert reasoning. This report introduces Persistent Workflow
Prompting (PWP), a potentially broadly applicable prompt engineering
methodology designed to bridge this gap using standard LLM chat interfaces
(zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical
analysis of experimental chemistry manuscripts, featuring a hierarchical,
modular architecture (structured via Markdown) that defines detailed analysis
workflows. We develop this PWP prompt through iterative application of
meta-prompting techniques and meta-reasoning aimed at systematically codifying
expert review workflows, including tacit knowledge. Submitted once at the start
of a session, this PWP prompt equips the LLM with persistent workflows
triggered by subsequent queries, guiding modern reasoning LLMs through
systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM
identifying major methodological flaws in a test case while mitigating LLM
input bias and performing complex tasks, including distinguishing claims from
evidence, integrating text/photo/figure analysis to infer parameters, executing
quantitative feasibility checks, comparing estimates against claims, and
assessing a priori plausibility. To ensure transparency and facilitate
replication, we provide full prompts, detailed demonstration analyses, and logs
of interactive chats as supplementary resources. Beyond the specific
application, this work offers insights into the meta-development process
itself, highlighting the potential of PWP, informed by detailed workflow
formalization, to enable sophisticated analysis using readily available LLMs
for complex scientific tasks.
Fractured Chain-of-Thought Reasoning
Authors: name: Baohao Liao, name: Hanze Dong, name: Yuhui Xu, name: Doyen Sahoo, name: Christof Monz, name: Junnan Li, name: Caiming Xiong
Published: 2025-05-19 11:30:41

Inference-time scaling techniques have significantly bolstered the reasoning
capabilities of large language models (LLMs) by harnessing additional
computational effort at inference without retraining. Similarly,
Chain-of-Thought (CoT) prompting and its extension, Long CoT, improve accuracy
by generating rich intermediate reasoning trajectories, but these approaches
incur substantial token costs that impede their deployment in latency-sensitive
settings. In this work, we first show that truncated CoT, which stops reasoning
before completion and directly generates the final answer, often matches full
CoT sampling while using dramatically fewer tokens. Building on this insight,
we introduce Fractured Sampling, a unified inference-time strategy that
interpolates between full CoT and solution-only sampling along three orthogonal
axes: (1) the number of reasoning trajectories, (2) the number of final
solutions per trajectory, and (3) the depth at which reasoning traces are
truncated. Through extensive experiments on five diverse reasoning benchmarks
and several model scales, we demonstrate that Fractured Sampling consistently
achieves superior accuracy-cost trade-offs, yielding steep log-linear scaling
gains in Pass@k versus token budget. Our analysis reveals how to allocate
computation across these dimensions to maximize performance, paving the way for
more efficient and scalable LLM reasoning.
LLM Context Conditioning and PWP Prompting for Multimodal Validation of
  Chemical Formulas
Authors: name: Evgeny Markhasin
Published: 2025-05-18 06:33:08

Identifying subtle technical errors within complex scientific and technical
documents, especially those requiring multimodal interpretation (e.g., formulas
in images), presents a significant hurdle for Large Language Models (LLMs)
whose inherent error-correction tendencies can mask inaccuracies. This
exploratory proof-of-concept (PoC) study investigates structured LLM context
conditioning, informed by Persistent Workflow Prompting (PWP) principles, as a
methodological strategy to modulate this LLM behavior at inference time. The
approach is designed to enhance the reliability of readily available,
general-purpose LLMs (specifically Gemini 2.5 Pro and ChatGPT Plus o3) for
precise validation tasks, crucially relying only on their standard chat
interfaces without API access or model modifications. To explore this
methodology, we focused on validating chemical formulas within a single,
complex test paper with known textual and image-based errors. Several prompting
strategies were evaluated: while basic prompts proved unreliable, an approach
adapting PWP structures to rigorously condition the LLM's analytical mindset
appeared to improve textual error identification with both models. Notably,
this method also guided Gemini 2.5 Pro to repeatedly identify a subtle
image-based formula error previously overlooked during manual review, a task
where ChatGPT Plus o3 failed in our tests. These preliminary findings highlight
specific LLM operational modes that impede detail-oriented validation and
suggest that PWP-informed context conditioning offers a promising and highly
accessible technique for developing more robust LLM-driven analytical
workflows, particularly for tasks requiring meticulous error detection in
scientific and technical documents. Extensive validation beyond this limited
PoC is necessary to ascertain broader applicability.
FedSVD: Adaptive Orthogonalization for Private Federated Learning with
  LoRA
Authors: name: Seanie Lee, name: Sangwoo Park, name: Dong Bok Lee, name: Dominik Wagner, name: Haebin Seong, name: Tobias Bocklet, name: Juho Lee, name: Sung Ju Hwang
Published: 2025-05-19 07:32:56

Low-Rank Adaptation (LoRA), which introduces a product of two trainable
low-rank matrices into frozen pre-trained weights, is widely used for efficient
fine-tuning of language models in federated learning (FL). However, when
combined with differentially private stochastic gradient descent (DP-SGD), LoRA
faces substantial noise amplification: DP-SGD perturbs per-sample gradients,
and the matrix multiplication of the LoRA update (BA) intensifies this
effect. Freezing one matrix (e.g., A) reduces the noise but restricts model
expressiveness, often resulting in suboptimal adaptation. To address this, we
propose FedSVD, a simple yet effective method that introduces a global
reparameterization based on singular value decomposition (SVD). In our
approach, each client optimizes only the B matrix and transmits it to the
server. The server aggregates the B matrices, computes the product BA using
the previous A, and refactorizes the result via SVD. This yields a new
adaptive A composed of the orthonormal right singular vectors of BA, and an
updated B containing the remaining SVD components. This reparameterization
avoids quadratic noise amplification, while allowing A to better capture the
principal directions of the aggregate updates. Moreover, the orthonormal
structure of A bounds the gradient norms of B and preserves more signal
under DP-SGD, as confirmed by our theoretical analysis. As a result, FedSVD
consistently improves stability and performance across a variety of privacy
settings and benchmarks, outperforming relevant baselines under both private
and non-private regimes.
Chain-of-Model Learning for Language Model
Authors: name: Kaitao Song, name: Xiaohua Wang, name: Xu Tan, name: Huiqiang Jiang, name: Chengruidong Zhang, name: Yongliang Shen, name: Cen LU, name: Zihao Li, name: Zifan Song, name: Caihua Shan, name: Yansen Wang, name: Kan Ren, name: Xiaoqing Zheng, name: Tao Qin, name: Yuqing Yang, name: Dongsheng Li, name: Lili Qiu
Published: 2025-05-17 04:06:12

In this paper, we propose a novel learning paradigm, termed Chain-of-Model
(CoM), which incorporates the causal relationship into the hidden states of
each layer as a chain style, thereby introducing great scaling efficiency in
model training and inference flexibility in deployment. We introduce the
concept of Chain-of-Representation (CoR), which formulates the hidden states at
each layer as a combination of multiple sub-representations (i.e., chains) at
the hidden dimension level. In each layer, each chain from the output
representations can only view all of its preceding chains in the input
representations. Consequently, the model built upon CoM framework can
progressively scale up the model size by increasing the chains based on the
previous models (i.e., chains), and offer multiple sub-models at varying sizes
for elastic inference by using different chain numbers. Based on this
principle, we devise Chain-of-Language-Model (CoLM), which incorporates the
idea of CoM into each layer of Transformer architecture. Based on CoLM, we
further introduce CoLM-Air by introducing a KV sharing mechanism, that computes
all keys and values within the first chain and then shares across all chains.
This design demonstrates additional extensibility, such as enabling seamless LM
switching, prefilling acceleration and so on. Experimental results demonstrate
our CoLM family can achieve comparable performance to the standard Transformer,
while simultaneously enabling greater flexiblity, such as progressive scaling
to improve training efficiency and offer multiple varying model sizes for
elastic inference, paving a a new way toward building language models. Our code
will be released in the future at: https://github.com/microsoft/CoLM.
ChartMuseum: Testing Visual Reasoning Capabilities of Large
  Vision-Language Models
Authors: name: Liyan Tang, name: Grace Kim, name: Xinyu Zhao, name: Thom Lake, name: Wenxuan Ding, name: Fangcong Yin, name: Prasann Singhal, name: Manya Wadhwa, name: Zeyu Leo Liu, name: Zayne Sprague, name: Ramya Namuduri, name: Bodun Hu, name: Juan Diego Rodriguez, name: Puyuan Peng, name: Greg Durrett
Published: 2025-05-19 17:59:27

Chart understanding presents a unique challenge for large vision-language
models (LVLMs), as it requires the integration of sophisticated textual and
visual reasoning capabilities. However, current LVLMs exhibit a notable
imbalance between these skills, falling short on visual reasoning that is
difficult to perform in text. We conduct a case study using a synthetic dataset
solvable only through visual reasoning and show that model performance degrades
significantly with increasing visual complexity, while human performance
remains robust. We then introduce ChartMuseum, a new Chart Question Answering
(QA) benchmark containing 1,162 expert-annotated questions spanning multiple
reasoning types, curated from real-world charts across 184 sources,
specifically built to evaluate complex visual and textual reasoning. Unlike
prior chart understanding benchmarks -- where frontier models perform similarly
and near saturation -- our benchmark exposes a substantial gap between model
and human performance, while effectively differentiating model capabilities:
although humans achieve 93% accuracy, the best-performing model Gemini-2.5-Pro
attains only 63.0%, and the leading open-source LVLM Qwen2.5-VL-72B-Instruct
achieves only 38.5%. Moreover, on questions requiring primarily visual
reasoning, all models experience a 35%-55% performance drop from
text-reasoning-heavy question performance. Lastly, our qualitative error
analysis reveals specific categories of visual reasoning that are challenging
for current LVLMs.