Orionnnn/hub-stats huggingface dataset:
Enterprise Deep Research: Steerable Multi-Agent Deep Research for
  Enterprise Analytics
Authors: name: Akshara Prabhakar, name: Roshan Ram, name: Zixiang Chen, name: Silvio Savarese, name: Frank Wang, name: Caiming Xiong, name: Huan Wang, name: Weiran Yao
Published: 2025-10-20 17:55:11

As information grows exponentially, enterprises face increasing pressure to
transform unstructured data into coherent, actionable insights. While
autonomous agents show promise, they often struggle with domain-specific
nuances, intent alignment, and enterprise integration. We present Enterprise
Deep Research (EDR), a multi-agent system that integrates (1) a Master Planning
Agent for adaptive query decomposition, (2) four specialized search agents
(General, Academic, GitHub, LinkedIn), (3) an extensible MCP-based tool
ecosystem supporting NL2SQL, file analysis, and enterprise workflows, (4) a
Visualization Agent for data-driven insights, and (5) a reflection mechanism
that detects knowledge gaps and updates research direction with optional
human-in-the-loop steering guidance. These components enable automated report
generation, real-time streaming, and seamless enterprise deployment, as
validated on internal datasets. On open-ended benchmarks including DeepResearch
Bench and DeepConsult, EDR outperforms state-of-the-art agentic systems without
any human steering. We release the EDR framework and benchmark trajectories to
advance research on multi-agent reasoning applications.
  Code at https://github.com/SalesforceAIResearch/enterprise-deep-research and
Dataset at https://huggingface.co/datasets/Salesforce/EDR-200
Executable Knowledge Graphs for Replicating AI Research
Authors: name: Yujie Luo, name: Zhuoyun Yu, name: Xuehai Wang, name: Yuqi Zhu, name: Ningyu Zhang, name: Lanning Wei, name: Lun Du, name: Da Zheng, name: Huajun Chen
Published: 2025-10-20 17:53:23

Replicating AI research is a crucial yet challenging task for large language
model (LLM) agents. Existing approaches often struggle to generate executable
code, primarily due to insufficient background knowledge and the limitations of
retrieval-augmented generation (RAG) methods, which fail to capture latent
technical details hidden in referenced papers. Furthermore, previous approaches
tend to overlook valuable implementation-level code signals and lack structured
knowledge representations that support multi-granular retrieval and reuse. To
overcome these challenges, we propose Executable Knowledge Graphs (xKG), a
modular and pluggable knowledge base that automatically integrates technical
insights, code snippets, and domain-specific knowledge extracted from
scientific literature. When integrated into three agent frameworks with two
different LLMs, xKG shows substantial performance gains (10.9% with o3-mini) on
PaperBench, demonstrating its effectiveness as a general and extensible
solution for automated AI research replication. Code will released at
https://github.com/zjunlp/xKG.
Knowledge-based Visual Question Answer with Multimodal Processing,
  Retrieval and Filtering
Authors: name: Yuyang Hong, name: Jiaqi Gu, name: Qi Yang, name: Lubin Fan, name: Yue Wu, name: Ying Wang, name: Kun Ding, name: Shiming Xiang, name: Jieping Ye
Published: 2025-10-16 12:10:00

Knowledge-based visual question answering (KB-VQA) requires visual language
models (VLMs) to integrate visual understanding with external knowledge
retrieval. Although retrieval-augmented generation (RAG) achieves significant
advances in this task by combining knowledge-base querying, it still struggles
with the quality of multimodal queries and the relevance of retrieved results.
To overcome these challenges, we propose a novel three-stage method, termed
Wiki-PRF, including Processing, Retrieval and Filtering stages. The processing
stage dynamically invokes visual tools to extract precise multimodal
information for retrieval. The retrieval stage integrates visual and text
features to achieve multimodal knowledge retrieval. The filtering stage
performs relevance filtering and concentration on retrieval results. To this
end, we introduce a visual language model trained with answer accuracy and
format consistency as reward signals via a reinforcement learning manner. This
enhances the model's reasoning, tool invocation for accurate queries, and
filtering of irrelevant content. Experiments on benchmark datasets (E-VQA and
InfoSeek) show significant improvements~(36.0 and 42.8) in answer quality,
achieving state-of-the-art performance. Code is available at
https://github.com/cqu-student/Wiki-PRF
Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented
  Generation
Authors: name: Chenghao Zhang, name: Guanting Dong, name: Xinyu Yang, name: Zhicheng Dou
Published: 2025-10-20 09:56:43

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for
enhancing large language models (LLMs) by retrieving relevant documents from an
external corpus. However, existing RAG systems primarily focus on unimodal text
documents, and often fall short in real-world scenarios where both queries and
documents may contain mixed modalities (such as text and images). In this
paper, we address the challenge of Universal Retrieval-Augmented Generation
(URAG), which involves retrieving and reasoning over mixed-modal information to
improve vision-language generation. To this end, we propose Nyx, a unified
mixed-modal to mixed-modal retriever tailored for URAG scenarios. To mitigate
the scarcity of realistic mixed-modal data, we introduce a four-stage automated
pipeline for generation and filtering, leveraging web documents to construct
NyxQA, a dataset comprising diverse mixed-modal question-answer pairs that
better reflect real-world information needs. Building on this high-quality
dataset, we adopt a two-stage training framework for Nyx: we first perform
pre-training on NyxQA along with a variety of open-source retrieval datasets,
followed by supervised fine-tuning using feedback from downstream
vision-language models (VLMs) to align retrieval outputs with generative
preferences. Experimental results demonstrate that Nyx not only performs
competitively on standard text-only RAG benchmarks, but also excels in the more
general and realistic URAG setting, significantly improving generation quality
in vision-language tasks.
PICABench: How Far Are We from Physically Realistic Image Editing?
Authors: name: Yuandong Pu, name: Le Zhuo, name: Songhao Han, name: Jinbo Xing, name: Kaiwen Zhu, name: Shuo Cao, name: Bin Fu, name: Si Liu, name: Hongsheng Li, name: Yu Qiao, name: Wenlong Zhang, name: Xi Chen, name: Yihao Liu
Published: 2025-10-20 15:53:57

Image editing has achieved remarkable progress recently. Modern editing
models could already follow complex instructions to manipulate the original
content. However, beyond completing the editing instructions, the accompanying
physical effects are the key to the generation realism. For example, removing
an object should also remove its shadow, reflections, and interactions with
nearby objects. Unfortunately, existing models and benchmarks mainly focus on
instruction completion but overlook these physical effects. So, at this moment,
how far are we from physically realistic image editing? To answer this, we
introduce PICABench, which systematically evaluates physical realism across
eight sub-dimension (spanning optics, mechanics, and state transitions) for
most of the common editing operations (add, remove, attribute change, etc). We
further propose the PICAEval, a reliable evaluation protocol that uses
VLM-as-a-judge with per-case, region-level human annotations and questions.
Beyond benchmarking, we also explore effective solutions by learning physics
from videos and construct a training dataset PICA-100K. After evaluating most
of the mainstream models, we observe that physical realism remains a
challenging problem with large rooms to explore. We hope that our benchmark and
proposed solutions can serve as a foundation for future work moving from naive
content editing toward physically consistent realism.
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Authors: name: Shaolei Zhang, name: Ju Fan, name: Meihao Fan, name: Guoliang Li, name: Xiaoyong Du
Published: 2025-10-19 15:13:42

Autonomous data science, from raw data sources to analyst-grade deep research
reports, has been a long-standing challenge, and is now becoming feasible with
the emergence of powerful large language models (LLMs). Recent workflow-based
data agents have shown promising results on specific data tasks but remain
fundamentally limited in achieving fully autonomous data science due to their
reliance on predefined workflows. In this paper, we introduce DeepAnalyze-8B,
the first agentic LLM designed for autonomous data science, capable of
automatically completing the end-toend pipeline from data sources to
analyst-grade deep research reports. To tackle high-complexity data science
tasks, we propose a curriculum-based agentic training paradigm that emulates
the learning trajectory of human data scientists, enabling LLMs to
progressively acquire and integrate multiple capabilities in real-world
environments. We also introduce a data-grounded trajectory synthesis framework
that constructs high-quality training data. Through agentic training,
DeepAnalyze learns to perform a broad spectrum of data tasks, ranging from data
question answering and specialized analytical tasks to open-ended data
research. Experiments demonstrate that, with only 8B parameters, DeepAnalyze
outperforms previous workflow-based agents built on most advanced proprietary
LLMs. The model, code, and training data of DeepAnalyze are open-sourced,
paving the way toward autonomous data science.
ConsistEdit: Highly Consistent and Precise Training-free Visual Editing
Authors: name: Zixin Yin, name: Ling-Hao Chen, name: Lionel Ni, name: Xili Dai
Published: 2025-10-20 17:59:52

Recent advances in training-free attention control methods have enabled
flexible and efficient text-guided editing capabilities for existing generation
models. However, current approaches struggle to simultaneously deliver strong
editing strength while preserving consistency with the source. This limitation
becomes particularly critical in multi-round and video editing, where visual
errors can accumulate over time. Moreover, most existing methods enforce global
consistency, which limits their ability to modify individual attributes such as
texture while preserving others, thereby hindering fine-grained editing.
Recently, the architectural shift from U-Net to MM-DiT has brought significant
improvements in generative performance and introduced a novel mechanism for
integrating text and vision modalities. These advancements pave the way for
overcoming challenges that previous methods failed to resolve. Through an
in-depth analysis of MM-DiT, we identify three key insights into its attention
mechanisms. Building on these, we propose ConsistEdit, a novel attention
control method specifically tailored for MM-DiT. ConsistEdit incorporates
vision-only attention control, mask-guided pre-attention fusion, and
differentiated manipulation of the query, key, and value tokens to produce
consistent, prompt-aligned edits. Extensive experiments demonstrate that
ConsistEdit achieves state-of-the-art performance across a wide range of image
and video editing tasks, including both structure-consistent and
structure-inconsistent scenarios. Unlike prior methods, it is the first
approach to perform editing across all inference steps and attention layers
without handcraft, significantly enhancing reliability and consistency, which
enables robust multi-round and multi-region editing. Furthermore, it supports
progressive adjustment of structural consistency, enabling finer control.
Visual Autoregressive Models Beat Diffusion Models on Inference Time
  Scaling
Authors: name: Erik Riise, name: Mehmet Onurcan Kaya, name: Dim P. Papadopoulos
Published: 2025-10-19 08:28:06

While inference-time scaling through search has revolutionized Large Language
Models, translating these gains to image generation has proven difficult.
Recent attempts to apply search strategies to continuous diffusion models show
limited benefits, with simple random sampling often performing best. We
demonstrate that the discrete, sequential nature of visual autoregressive
models enables effective search for image generation. We show that beam search
substantially improves text-to-image generation, enabling a 2B parameter
autoregressive model to outperform a 12B parameter diffusion model across
benchmarks. Systematic ablations show that this advantage comes from the
discrete token space, which allows early pruning and computational reuse, and
our verifier analysis highlights trade-offs between speed and reasoning
capability. These findings suggest that model architecture, not just scale, is
critical for inference-time optimization in visual generation.
Balanced Multi-Task Attention for Satellite Image Classification: A
  Systematic Approach to Achieving 97.23% Accuracy on EuroSAT Without
  Pre-Training
Authors: name: Aditya Vir
Published: 2025-10-17 10:59:24

This work presents a systematic investigation of custom convolutional neural
network architectures for satellite land use classification, achieving 97.23%
test accuracy on the EuroSAT dataset without reliance on pre-trained models.
Through three progressive architectural iterations (baseline: 94.30%,
CBAM-enhanced: 95.98%, and balanced multi-task attention: 97.23%) we identify
and address specific failure modes in satellite imagery classification. Our
principal contribution is a novel balanced multi-task attention mechanism that
combines Coordinate Attention for spatial feature extraction with
Squeeze-Excitation blocks for spectral feature extraction, unified through a
learnable fusion parameter. Experimental results demonstrate that this
learnable parameter autonomously converges to alpha approximately 0.57,
indicating near-equal importance of spatial and spectral modalities for
satellite imagery. We employ progressive DropBlock regularization (5-20% by
network depth) and class-balanced loss weighting to address overfitting and
confusion pattern imbalance. The final 12-layer architecture achieves Cohen's
Kappa of 0.9692 with all classes exceeding 94.46% accuracy, demonstrating
confidence calibration with a 24.25% gap between correct and incorrect
predictions. Our approach achieves performance within 1.34% of fine-tuned
ResNet-50 (98.57%) while requiring no external data, validating the efficacy of
systematic architectural design for domain-specific applications. Complete
code, trained models, and evaluation scripts are publicly available.
Adapting Self-Supervised Representations as a Latent Space for Efficient
  Generation
Authors: name: Ming Gui, name: Johannes Schusterbauer, name: Timy Phan, name: Felix Krause, name: Josh Susskind, name: Miguel Angel Bautista, name: Björn Ommer
Published: 2025-10-16 12:43:03

We introduce Representation Tokenizer (RepTok), a generative modeling
framework that represents an image using a single continuous latent token
obtained from self-supervised vision transformers. Building on a pre-trained
SSL encoder, we fine-tune only the semantic token embedding and pair it with a
generative decoder trained jointly using a standard flow matching objective.
This adaptation enriches the token with low-level, reconstruction-relevant
details, enabling faithful image reconstruction. To preserve the favorable
geometry of the original SSL space, we add a cosine-similarity loss that
regularizes the adapted token, ensuring the latent space remains smooth and
suitable for generation. Our single-token formulation resolves spatial
redundancies of 2D latent spaces and significantly reduces training costs.
Despite its simplicity and efficiency, RepTok achieves competitive results on
class-conditional ImageNet generation and naturally extends to text-to-image
synthesis, reaching competitive zero-shot performance on MS-COCO under
extremely limited training budgets. Our findings highlight the potential of
fine-tuned SSL representations as compact and effective latent spaces for
efficient generative modeling.
Do LLMs "Feel"? Emotion Circuits Discovery and Control
Authors: name: Chenxi Wang, name: Yixuan Zhang, name: Ruiji Yu, name: Yufei Zheng, name: Lang Gao, name: Zirui Song, name: Zixiang Xu, name: Gus Xia, name: Huishuai Zhang, name: Dongyan Zhao, name: Xiuying Chen
Published: 2025-10-13 12:24:24

As the demand for emotional intelligence in large language models (LLMs)
grows, a key challenge lies in understanding the internal mechanisms that give
rise to emotional expression and in controlling emotions in generated text.
This study addresses three core questions: (1) Do LLMs contain context-agnostic
mechanisms shaping emotional expression? (2) What form do these mechanisms
take? (3) Can they be harnessed for universal emotion control? We first
construct a controlled dataset, SEV (Scenario-Event with Valence), to elicit
comparable internal states across emotions. Subsequently, we extract
context-agnostic emotion directions that reveal consistent, cross-context
encoding of emotion (Q1). We identify neurons and attention heads that locally
implement emotional computation through analytical decomposition and causal
analysis, and validate their causal roles via ablation and enhancement
interventions. Next, we quantify each sublayer's causal influence on the
model's final emotion representation and integrate the identified local
components into coherent global emotion circuits that drive emotional
expression (Q2). Directly modulating these circuits achieves 99.65%
emotion-expression accuracy on the test set, surpassing prompting- and
steering-based methods (Q3). To our knowledge, this is the first systematic
study to uncover and validate emotion circuits in LLMs, offering new insights
into interpretability and controllable emotional intelligence.
A Theoretical Study on Bridging Internal Probability and
  Self-Consistency for LLM Reasoning
Authors: name: Zhi Zhou, name: Yuhao Tan, name: Zenan Li, name: Yuan Yao, name: Lan-Zhe Guo, name: Yu-Feng Li, name: Xiaoxing Ma
Published: 2025-10-17 08:59:30

Test-time scaling seeks to improve the reasoning performance of large
language models (LLMs) by adding computational resources. A prevalent approach
within the field is sampling-based test-time scaling methods, which enhance
reasoning by generating multiple reasoning paths for a given input during
inference. However, despite its practical success, the theoretical foundations
remain underexplored. In this paper, we provide the first theoretical framework
for analyzing sampling-based test-time scaling methods, grounded in the
perspective of confidence estimation. Based on the framework, we analyze two
dominant paradigms: self-consistency and perplexity, and reveal key
limitations: self-consistency suffers from high estimation error while
perplexity exhibits substantial modeling error and possible degradation of the
estimation error convergence. To address these limitations, we introduce RPC, a
hybrid method that leverages our theoretical insights through two key
components: Perplexity Consistency and Reasoning Pruning. Perplexity
Consistency combines the strengths of self-consistency and perplexity, boosting
the convergence rate of estimation error from linear to exponential while
preserving model error. Reasoning Pruning prevents degradation by eliminating
low-probability reasoning paths. Both theoretical analysis and empirical
results across seven benchmark datasets demonstrate that RPC has a strong
potential for reducing reasoning error. Notably, RPC achieves reasoning
performance comparable to self-consistency while not only enhancing confidence
reliability but also reducing sampling costs by 50%. The code and resources are
available at https://wnjxyk.github.io/RPC.
DriveGen3D: Boosting Feed-Forward Driving Scene Generation with
  Efficient Video Diffusion
Authors: name: Weijie Wang, name: Jiagang Zhu, name: Zeyu Zhang, name: Xiaofeng Wang, name: Zheng Zhu, name: Guosheng Zhao, name: Chaojun Ni, name: Haoxiao Wang, name: Guan Huang, name: Xinze Chen, name: Yukun Zhou, name: Wenkang Qin, name: Duochao Shi, name: Haoyun Li, name: Guanghong Jia, name: Jiwen Lu
Published: 2025-10-17 03:00:08

We present DriveGen3D, a novel framework for generating high-quality and
highly controllable dynamic 3D driving scenes that addresses critical
limitations in existing methodologies. Current approaches to driving scene
synthesis either suffer from prohibitive computational demands for extended
temporal generation, focus exclusively on prolonged video synthesis without 3D
representation, or restrict themselves to static single-scene reconstruction.
Our work bridges this methodological gap by integrating accelerated long-term
video generation with large-scale dynamic scene reconstruction through
multimodal conditional control. DriveGen3D introduces a unified pipeline
consisting of two specialized components: FastDrive-DiT, an efficient video
diffusion transformer for high-resolution, temporally coherent video synthesis
under text and Bird's-Eye-View (BEV) layout guidance; and FastRecon3D, a
feed-forward reconstruction module that rapidly builds 3D Gaussian
representations across time, ensuring spatial-temporal consistency. Together,
these components enable real-time generation of extended driving videos (up to
424times800 at 12 FPS) and corresponding dynamic 3D scenes, achieving SSIM
of 0.811 and PSNR of 22.84 on novel view synthesis, all while maintaining
parameter efficiency.
Emergent Misalignment via In-Context Learning: Narrow in-context
  examples can produce broadly misaligned LLMs
Authors: name: Nikita Afonin, name: Nikita Andriyanov, name: Nikhil Bageshpura, name: Kyle Liu, name: Kevin Zhu, name: Sunishchal Dev, name: Ashwinee Panda, name: Alexander Panchenko, name: Oleg Rogov, name: Elena Tutubalina, name: Mikhail Seleznyov
Published: 2025-10-13 11:23:56

Recent work has shown that narrow finetuning can produce broadly misaligned
LLMs, a phenomenon termed emergent misalignment (EM). While concerning, these
findings were limited to finetuning and activation steering, leaving out
in-context learning (ICL). We therefore ask: does EM emerge in ICL? We find
that it does: across three datasets, three frontier models produce broadly
misaligned responses at rates between 2% and 17% given 64 narrow in-context
examples, and up to 58% with 256 examples. We also examine mechanisms of EM by
eliciting step-by-step reasoning (while leaving in-context examples unchanged).
Manual analysis of the resulting chain-of-thought shows that 67.5% of
misaligned traces explicitly rationalize harmful outputs by adopting a reckless
or dangerous ''persona'', echoing prior results on finetuning-induced EM.