Orionnnn/hub-stats huggingface dataset:
Self-Rewarding Vision-Language Model via Reasoning Decomposition
Authors: name: Zongxia Li, name: Wenhao Yu, name: Chengsong Huang, name: Rui Liu, name: Zhenwen Liang, name: Fuxiao Liu, name: Jingxi Che, name: Dian Yu, name: Jordan Boyd-Graber, name: Haitao Mi, name: Dong Yu
Published: 2025-08-27 08:01:03

Vision-Language Models (VLMs) often suffer from visual hallucinations, saying
things that are not actually in the image, and language shortcuts, where they
skip the visual part and just rely on text priors. These issues arise because
most post-training methods for VLMs rely on simple verifiable answer matching
and supervise only final outputs, leaving intermediate visual reasoning without
explicit guidance. As a result, VLMs receive sparse visual signals and often
learn to prioritize language-based reasoning over visual perception. To
mitigate this, some existing methods add visual supervision using human
annotations or distilled labels from external large models. However, human
annotations are labor-intensive and costly, and because external signals cannot
adapt to the evolving policy, they cause distributional shifts that can lead to
reward hacking. In this paper, we introduce Vision-SR1, a self-rewarding method
that improves visual reasoning without relying on external visual supervisions
via reinforcement learning. Vision-SR1 decomposes VLM reasoning into two
stages: visual perception and language reasoning. The model is first prompted
to produce self-contained visual perceptions that are sufficient to answer the
question without referring back the input image. To validate this
self-containment, the same VLM model is then re-prompted to perform language
reasoning using only the generated perception as input to compute reward. This
self-reward is combined with supervision on final outputs, providing a balanced
training signal that strengthens both visual perception and language reasoning.
Our experiments demonstrate that Vision-SR1 improves visual reasoning,
mitigates visual hallucinations, and reduces reliance on language shortcuts
across diverse vision-language tasks.
Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H)
  Agent Unlocks Adversarial Skills
Authors: name: David Noever
Published: 2025-08-27 01:11:59

This paper identifies and analyzes a novel vulnerability class in Model
Context Protocol (MCP) based agent systems. The attack chain describes and
demonstrates how benign, individually authorized tasks can be orchestrated to
produce harmful emergent behaviors. Through systematic analysis using the MITRE
ATLAS framework, we demonstrate how 95 agents tested with access to multiple
services-including browser automation, financial analysis, location tracking,
and code deployment-can chain legitimate operations into sophisticated attack
sequences that extend beyond the security boundaries of any individual service.
These red team exercises survey whether current MCP architectures lack
cross-domain security measures necessary to detect or prevent a large category
of compositional attacks. We present empirical evidence of specific attack
chains that achieve targeted harm through service orchestration, including data
exfiltration, financial manipulation, and infrastructure compromise. These
findings reveal that the fundamental security assumption of service isolation
fails when agents can coordinate actions across multiple domains, creating an
exponential attack surface that grows with each additional capability. This
research provides a barebones experimental framework that evaluate not whether
agents can complete MCP benchmark tasks, but what happens when they complete
them too well and optimize across multiple services in ways that violate human
expectations and safety constraints. We propose three concrete experimental
directions using the existing MCP benchmark suite.
Select to Know: An Internal-External Knowledge Self-Selection Framework
  for Domain-Specific Question Answering
Authors: name: Bolei He, name: Xinran He, name: Run Shao, name: Shanfu Shu, name: Xianwei Xue, name: Mingquan Cheng, name: Haifeng Li, name: Zhenhua Ling
Published: 2025-08-21 03:53:35

Large Language Models (LLMs) perform well in general QA but often struggle in
domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces
external knowledge but suffers from hallucinations and latency due to noisy
retrievals. Continued pretraining internalizes domain knowledge but is costly
and lacks cross-domain flexibility. We attribute this challenge to the
long-tail distribution of domain knowledge, which leaves partial yet useful
internal knowledge underutilized. We further argue that knowledge acquisition
should be progressive, mirroring human learning: first understanding concepts,
then applying them to complex reasoning. To address this, we propose Selct2Know
(S2K), a cost-effective framework that internalizes domain knowledge through an
internal-external knowledge self-selection strategy and selective supervised
fine-tuning. We also introduce a structured reasoning data generation pipeline
and integrate GRPO to enhance reasoning ability. Experiments on medical, legal,
and financial QA benchmarks show that S2K consistently outperforms existing
methods and matches domain-pretrained LLMs with significantly lower cost.
Forecasting Probability Distributions of Financial Returns with Deep
  Neural Networks
Authors: name: Jakub Michańków
Published: 2025-08-26 10:48:16

This study evaluates deep neural networks for forecasting probability
distributions of financial returns. 1D convolutional neural networks (CNN) and
Long Short-Term Memory (LSTM) architectures are used to forecast parameters of
three probability distributions: Normal, Student's t, and skewed Student's t.
Using custom negative log-likelihood loss functions, distribution parameters
are optimized directly. The models are tested on six major equity indices (S\&P
500, BOVESPA, DAX, WIG, Nikkei 225, and KOSPI) using probabilistic evaluation
metrics including Log Predictive Score (LPS), Continuous Ranked Probability
Score (CRPS), and Probability Integral Transform (PIT). Results show that deep
learning models provide accurate distributional forecasts and perform
competitively with classical GARCH models for Value-at-Risk estimation. The
LSTM with skewed Student's t distribution performs best across multiple
evaluation criteria, capturing both heavy tails and asymmetry in financial
returns. This work shows that deep neural networks are viable alternatives to
traditional econometric models for financial risk assessment and portfolio
management.
DrugReasoner: Interpretable Drug Approval Prediction with a
  Reasoning-augmented Language Model
Authors: name: Mohammadreza Ghaffarzadeh-Esfahani, name: Ali Motahharynia, name: Nahid Yousefian, name: Navid Mazrouei, name: Jafar Ghaisari, name: Yousof Gheisari
Published: 2025-08-26 01:14:14

Drug discovery is a complex and resource-intensive process, making early
prediction of approval outcomes critical for optimizing research investments.
While classical machine learning and deep learning methods have shown promise
in drug approval prediction, their limited interpretability constraints their
impact. Here, we present DrugReasoner, a reasoning-based large language model
(LLM) built on the LLaMA architecture and fine-tuned with group relative policy
optimization (GRPO) to predict the likelihood of small-molecule approval.
DrugReasoner integrates molecular descriptors with comparative reasoning
against structurally similar approved and unapproved compounds, generating
predictions alongside step-by-step rationales and confidence scores.
DrugReasoner achieved robust performance with an AUC of 0.732 and an F1 score
of 0.729 on the validation set and 0.725 and 0.718 on the test set,
respectively. These results outperformed conventional baselines, including
logistic regression, support vector machine, and k-nearest neighbors and had
competitive performance relative to XGBoost. On an external independent
dataset, DrugReasoner outperformed both baseline and the recently developed
ChemAP model, achieving an AUC of 0.728 and an F1-score of 0.774, while
maintaining high precision and balanced sensitivity, demonstrating robustness
in real-world scenarios. These findings demonstrate that DrugReasoner not only
delivers competitive predictive accuracy but also enhances transparency through
its reasoning outputs, thereby addressing a key bottleneck in AI-assisted drug
discovery. This study highlights the potential of reasoning-augmented LLMs as
interpretable and effective tools for pharmaceutical decision-making.
Steering When Necessary: Flexible Steering Large Language Models with
  Backtracking
Authors: name: Jinwei Gan, name: Zifeng Cheng, name: Zhiwei Jiang, name: Cong Wang, name: Yafeng Yin, name: Xiang Luo, name: Yuchen Fu, name: Qing Gu
Published: 2025-08-25 03:01:30

Large language models (LLMs) have achieved remarkable performance across many
generation tasks. Nevertheless, effectively aligning them with desired
behaviors remains a significant challenge. Activation steering is an effective
and cost-efficient approach that directly modifies the activations of LLMs
during the inference stage, aligning their responses with the desired behaviors
and avoiding the high cost of fine-tuning. Existing methods typically
indiscriminately intervene to all generations or rely solely on the question to
determine intervention, which limits the accurate assessment of the
intervention strength. To this end, we propose the Flexible Activation Steering
with Backtracking (FASB) framework, which dynamically determines both the
necessity and strength of intervention by tracking the internal states of the
LLMs during generation, considering both the question and the generated
content. Since intervening after detecting a deviation from the desired
behavior is often too late, we further propose the backtracking mechanism to
correct the deviated tokens and steer the LLMs toward the desired behavior.
Extensive experiments on the TruthfulQA dataset and six multiple-choice
datasets demonstrate that our method outperforms baselines. Our code will be
released at https://github.com/gjw185/FASB.
ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation
Authors: name: Siying Zhou, name: Yiquan Wu, name: Hui Chen, name: Xavier Hu, name: Kun Kuang, name: Adam Jatowt, name: Ming Hu, name: Chunyan Zheng, name: Fei Wu
Published: 2025-08-24 07:19:25

Legal claims refer to the plaintiff's demands in a case and are essential to
guiding judicial reasoning and case resolution. While many works have focused
on improving the efficiency of legal professionals, the research on helping
non-professionals (e.g., plaintiffs) remains unexplored. This paper explores
the problem of legal claim generation based on the given case's facts. First,
we construct ClaimGen-CN, the first dataset for Chinese legal claim generation
task, from various real-world legal disputes. Additionally, we design an
evaluation metric tailored for assessing the generated claims, which
encompasses two essential dimensions: factuality and clarity. Building on this,
we conduct a comprehensive zero-shot evaluation of state-of-the-art general and
legal-domain large language models. Our findings highlight the limitations of
the current models in factual precision and expressive clarity, pointing to the
need for more targeted development in this domain. To encourage further
exploration of this important task, we will make the dataset publicly
available.
Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and
  Reasoning
Authors: name: Alan Li, name: Yixin Liu, name: Arpan Sarkar, name: Doug Downey, name: Arman Cohan
Published: 2025-08-26 17:04:23

Scientific problem solving poses unique challenges for LLMs, requiring both
deep domain knowledge and the ability to apply such knowledge through complex
reasoning. While automated scientific reasoners hold great promise for
assisting human scientists, there is currently no widely adopted holistic
benchmark for evaluating scientific reasoning, and few approaches
systematically disentangle the distinct roles of knowledge and reasoning in
these tasks. To address these gaps, we introduce SciReas, a diverse suite of
existing benchmarks for scientific reasoning tasks, and SciReas-Pro, a
selective subset that requires more complex reasoning. Our holistic evaluation
surfaces insights about scientific reasoning performance that remain hidden
when relying on individual benchmarks alone. We then propose KRUX, a probing
framework for studying the distinct roles of reasoning and knowledge in
scientific tasks. Combining the two, we conduct an in-depth analysis that
yields several key findings: (1) Retrieving task-relevant knowledge from model
parameters is a critical bottleneck for LLMs in scientific reasoning; (2)
Reasoning models consistently benefit from external knowledge added in-context
on top of the reasoning enhancement; (3) Enhancing verbalized reasoning
improves LLMs' ability to surface task-relevant knowledge. Finally, we conduct
a lightweight analysis, comparing our science-focused data composition with
concurrent efforts on long CoT SFT, and release SciLit01, a strong 8B baseline
for scientific reasoning.
ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks
Authors: name: Minghao Li, name: Ying Zeng, name: Zhihao Cheng, name: Cong Ma, name: Kai Jia
Published: 2025-08-14 03:33:43

The advent of Deep Research agents has substantially reduced the time
required for conducting extensive research tasks. However, these tasks
inherently demand rigorous standards of factual accuracy and comprehensiveness,
necessitating thorough evaluation before widespread adoption. In this paper, we
propose ReportBench, a systematic benchmark designed to evaluate the content
quality of research reports generated by large language models (LLMs). Our
evaluation focuses on two critical dimensions: (1) the quality and relevance of
cited literature, and (2) the faithfulness and veracity of the statements
within the generated reports. ReportBench leverages high-quality published
survey papers available on arXiv as gold-standard references, from which we
apply reverse prompt engineering to derive domain-specific prompts and
establish a comprehensive evaluation corpus. Furthermore, we develop an
agent-based automated framework within ReportBench that systematically analyzes
generated reports by extracting citations and statements, checking the
faithfulness of cited content against original sources, and validating
non-cited claims using web-based resources. Empirical evaluations demonstrate
that commercial Deep Research agents such as those developed by OpenAI and
Google consistently generate more comprehensive and reliable reports than
standalone LLMs augmented with search or browsing tools. However, there remains
substantial room for improvement in terms of the breadth and depth of research
coverage, as well as factual consistency. The complete code and data will be
released at the following link: https://github.com/ByteDance-BandAI/ReportBench
Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning
  Tasks
Authors: name: Taishi Nakamura, name: Satoki Ishikawa, name: Masaki Kawamura, name: Takumi Okamoto, name: Daisuke Nohara, name: Jun Suzuki, name: Rio Yokota
Published: 2025-08-26 04:31:28

Empirical scaling laws have driven the evolution of large language models
(LLMs), yet their coefficients shift whenever the model architecture or data
pipeline changes. Mixture-of-Experts (MoE) models, now standard in
state-of-the-art systems, introduce a new sparsity dimension that current
dense-model frontiers overlook. We investigate how MoE sparsity influences two
distinct capability regimes: memorization and reasoning. We train families of
MoE Transformers that systematically vary total parameters, active parameters,
and top-k routing while holding the compute budget fixed. For every model we
record pre-training loss, downstream task loss, and task accuracy, allowing us
to separate the train-test generalization gap from the loss-accuracy gap.
Memorization benchmarks improve monotonically with total parameters, mirroring
training loss. By contrast, reasoning performance saturates and can even
regress despite continued gains in both total parameters and training loss.
Altering top-k alone has little effect when active parameters are constant,
and classic hyperparameters such as learning rate and initialization modulate
the generalization gap in the same direction as sparsity. Neither post-training
reinforcement learning (GRPO) nor extra test-time compute rescues the reasoning
deficit of overly sparse models. Our model checkpoints, code and logs are
open-source at https://github.com/rioyokotalab/optimal-sparsity.
ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion
  Models
Authors: name: Haitang Feng, name: Jie Liu, name: Jie Tang, name: Gangshan Wu, name: Beiqi Chen, name: Jianhuang Lai, name: Guangcong Wang
Published: 2025-08-25 17:59:40

3D inpainting often relies on multi-view 2D image inpainting, where the
inherent inconsistencies across different inpainted views can result in blurred
textures, spatial discontinuities, and distracting visual artifacts. These
inconsistencies pose significant challenges when striving for accurate and
realistic 3D object completion, particularly in applications that demand high
fidelity and structural coherence. To overcome these limitations, we propose
ObjFiller-3D, a novel method designed for the completion and editing of
high-quality and consistent 3D objects. Instead of employing a conventional 2D
image inpainting model, our approach leverages a curated selection of
state-of-the-art video editing model to fill in the masked regions of 3D
objects. We analyze the representation gap between 3D and videos, and propose
an adaptation of a video inpainting model for 3D scene inpainting. In addition,
we introduce a reference-based 3D inpainting method to further enhance the
quality of reconstruction. Experiments across diverse datasets show that
compared to previous methods, ObjFiller-3D produces more faithful and
fine-grained reconstructions (PSNR of 26.6 vs. NeRFiller (15.9) and LPIPS of
0.19 vs. Instant3dit (0.25)). Moreover, it demonstrates strong potential for
practical deployment in real-world 3D editing applications. Project page:
https://objfiller3d.github.io/ Code:
https://github.com/objfiller3d/ObjFiller-3D .
MovieCORE: COgnitive REasoning in Movies
Authors: name: Gueter Josmy Faure, name: Min-Hung Chen, name: Jia-Fong Yeh, name: Ying Cheng, name: Hung-Ting Su, name: Yung-Hao Tang, name: Shang-Hong Lai, name: Winston H. Hsu
Published: 2025-08-26 13:43:45

This paper introduces MovieCORE, a novel video question answering (VQA)
dataset designed to probe deeper cognitive understanding of movie content.
Unlike existing datasets that focus on surface-level comprehension, MovieCORE
emphasizes questions that engage System-2 thinking while remaining specific to
the video material. We present an innovative agentic brainstorming approach,
utilizing multiple large language models (LLMs) as thought agents to generate
and refine high-quality question-answer pairs. To evaluate dataset quality, we
develop a set of cognitive tests assessing depth, thought-provocation
potential, and syntactic complexity. We also propose a comprehensive evaluation
scheme for assessing VQA model performance on deeper cognitive tasks. To
address the limitations of existing video-language models (VLMs), we introduce
an agentic enhancement module, Agentic Choice Enhancement (ACE), which improves
model reasoning capabilities post-training by up to 25%. Our work contributes
to advancing movie understanding in AI systems and provides valuable insights
into the capabilities and limitations of current VQA models when faced with
more challenging, nuanced questions about cinematic content. Our project page,
dataset and code can be found at
https://joslefaure.github.io/assets/html/moviecore.html.
Autoregressive Universal Video Segmentation Model
Authors: name: Miran Heo, name: Sukjun Hwang, name: Min-Hung Chen, name: Yu-Chiang Frank Wang, name: Albert Gu, name: Seon Joo Kim, name: Ryo Hachiuma
Published: 2025-08-26 17:59:13

Recent video foundation models such as SAM2 excel at prompted video
segmentation by treating masks as a general-purpose primitive. However, many
real-world settings require unprompted segmentation that aims to detect and
track all objects in a video without external cues, leaving today's landscape
fragmented across task-specific models and pipelines. We recast streaming video
segmentation as sequential mask prediction, analogous to language modeling, and
introduce the Autoregressive Universal Segmentation Model (AUSM), a single
architecture that unifies both prompted and unprompted video segmentation.
Built on recent state-space models, AUSM maintains a fixed-size spatial state
and scales to video streams of arbitrary length. Furthermore, all components of
AUSM are designed for parallel training across frames, yielding substantial
speedups over iterative training. On standard benchmarks (DAVIS17, YouTube-VOS
2018 & 2019, MOSE, YouTube-VIS 2019 & 2021, and OVIS) AUSM outperforms prior
universal streaming video segmentation methods and achieves up to 2.5x faster
training on 16-frame sequences.
QueryBandits for Hallucination Mitigation: Exploiting Semantic Features
  for No-Regret Rewriting
Authors: name: Nicole Cho, name: William Watson, name: Alec Koppel, name: Sumitra Ganesh, name: Manuela Veloso
Published: 2025-08-22 01:41:49

Advanced reasoning capabilities in Large Language Models (LLMs) have caused
higher hallucination prevalence; yet most mitigation work focuses on
after-the-fact filtering rather than shaping the queries that trigger them. We
introduce QueryBandits, a bandit framework that designs rewrite strategies to
maximize a reward model, that encapsulates hallucination propensity based upon
the sensitivities of 17 linguistic features of the input query-and therefore,
proactively steer LLMs away from generating hallucinations. Across 13 diverse
QA benchmarks and 1,050 lexically perturbed queries per dataset, our top
contextual QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a
no-rewrite baseline and also outperforms zero-shot static prompting
("paraphrase" or "expand") by 42.6% and 60.3% respectively. Therefore, we
empirically substantiate the effectiveness of QueryBandits in mitigating
hallucination via the intervention that takes the form of a query rewrite.
Interestingly, certain static prompting strategies, which constitute a
considerable number of current query rewriting literature, have a higher
cumulative regret than the no-rewrite baseline, signifying that static rewrites
can worsen hallucination. Moreover, we discover that the converged per-arm
regression feature weight vectors substantiate that there is no single rewrite
strategy optimal for all queries. In this context, guided rewriting via
exploiting semantic features with QueryBandits can induce significant shifts in
output behavior through forward-pass mechanisms, bypassing the need for
retraining or gradient-based adaptation.