Orionnnn/hub-stats huggingface dataset:
MMSearch-R1: Incentivizing LMMs to Search
Authors: name: Jinming Wu, name: Zihao Deng, name: Wei Li, name: Yiding Liu, name: Bo You, name: Bo Li, name: Zejun Ma, name: Ziwei Liu
Published: 2025-06-25 17:59:42

Robust deployment of large multimodal models (LMMs) in real-world scenarios
requires access to external knowledge sources, given the complexity and dynamic
nature of real-world information. Existing approaches such as
retrieval-augmented generation (RAG) and prompt engineered search agents rely
on rigid pipelines, often leading to inefficient or excessive search behaviors.
We present MMSearch-R1, the first end-to-end reinforcement learning framework
that enables LMMs to perform on-demand, multi-turn search in real-world
Internet environments. Our framework integrates both image and text search
tools, allowing the model to reason about when and how to invoke them guided by
an outcome-based reward with a search penalty. To support training, We collect
a multimodal search VQA dataset through a semi-automated pipeline that covers
diverse visual and textual knowledge needs and curate a search-balanced subset
with both search-required and search-free samples, which proves essential for
shaping efficient and on-demand search behavior. Extensive experiments on
knowledge-intensive and info-seeking VQA tasks show that our model not only
outperforms RAG-based baselines of the same model size, but also matches the
performance of a larger RAG-based model while reducing search calls by over
30%. We further analyze key empirical findings to offer actionable insights for
advancing research in multimodal search.
Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge
Authors: name: Boyu Gou, name: Zanming Huang, name: Yuting Ning, name: Yu Gu, name: Michael Lin, name: Weijian Qi, name: Andrei Kopanev, name: Botao Yu, name: Bernal Jiménez Gutiérrez, name: Yiheng Shu, name: Chan Hee Song, name: Jiaman Wu, name: Shijie Chen, name: Hanane Nour Moussa, name: Tianshu Zhang, name: Jian Xie, name: Yifei Li, name: Tianci Xue, name: Zeyi Liao, name: Kai Zhang, name: Boyuan Zheng, name: Zhaowei Cai, name: Viktor Rozgic, name: Morteza Ziyadi, name: Huan Sun, name: Yu Su
Published: 2025-06-26 17:32:50

Agentic search such as Deep Research systems, where large language models
autonomously browse the web, synthesize information, and return comprehensive
citation-backed answers, represents a major shift in how users interact with
web-scale information. While promising greater efficiency and cognitive
offloading, the growing complexity and open-endedness of agentic search have
outpaced existing evaluation benchmarks and methodologies, which largely assume
short search horizons and static answers. In this paper, we introduce Mind2Web
2, a benchmark of 130 realistic, high-quality, and long-horizon tasks that
require real-time web browsing and extensive information synthesis, constructed
with over 1,000 hours of human labor. To address the challenge of evaluating
time-varying and complex answers, we propose a novel Agent-as-a-Judge
framework. Our method constructs task-specific judge agents based on a
tree-structured rubric design to automatically assess both answer correctness
and source attribution. We conduct a comprehensive evaluation of nine frontier
agentic search systems and human performance, along with a detailed error
analysis to draw insights for future development. The best-performing system,
OpenAI Deep Research, can already achieve 50-70% of human performance while
spending half the time, showing a great potential. Altogether, Mind2Web 2
provides a rigorous foundation for developing and benchmarking the next
generation of agentic search systems.
FilMaster: Bridging Cinematic Principles and Generative AI for Automated
  Film Generation
Authors: name: Kaiyi Huang, name: Yukun Huang, name: Xintao Wang, name: Zinan Lin, name: Xuefei Ning, name: Pengfei Wan, name: Di Zhang, name: Yu Wang, name: Xihui Liu
Published: 2025-06-23 17:59:16

AI-driven content creation has shown potential in film production. However,
existing film generation systems struggle to implement cinematic principles and
thus fail to generate professional-quality films, particularly lacking diverse
camera language and cinematic rhythm. This results in templated visuals and
unengaging narratives. To address this, we introduce FilMaster, an end-to-end
AI system that integrates real-world cinematic principles for
professional-grade film generation, yielding editable, industry-standard
outputs. FilMaster is built on two key principles: (1) learning cinematography
from extensive real-world film data and (2) emulating professional,
audience-centric post-production workflows. Inspired by these principles,
FilMaster incorporates two stages: a Reference-Guided Generation Stage which
transforms user input to video clips, and a Generative Post-Production Stage
which transforms raw footage into audiovisual outputs by orchestrating visual
and auditory elements for cinematic rhythm. Our generation stage highlights a
Multi-shot Synergized RAG Camera Language Design module to guide the AI in
generating professional camera language by retrieving reference clips from a
vast corpus of 440,000 film clips. Our post-production stage emulates
professional workflows by designing an Audience-Centric Cinematic Rhythm
Control module, including Rough Cut and Fine Cut processes informed by
simulated audience feedback, for effective integration of audiovisual elements
to achieve engaging content. The system is empowered by generative AI models
like (M)LLMs and video generation models. Furthermore, we introduce FilmEval, a
comprehensive benchmark for evaluating AI-generated films. Extensive
experiments show FilMaster's superior performance in camera language design and
cinematic rhythm control, advancing generative AI in professional filmmaking.
FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data
  Processing to Every Language
Authors: name: Guilherme Penedo, name: Hynek Kydlíček, name: Vinko Sabolčec, name: Bettina Messmer, name: Negar Foroutan, name: Amir Hossein Kargaran, name: Colin Raffel, name: Martin Jaggi, name: Leandro Von Werra, name: Thomas Wolf
Published: 2025-06-26 01:01:47

Pre-training state-of-the-art large language models (LLMs) requires vast
amounts of clean and diverse text data. While the open development of large
high-quality English pre-training datasets has seen substantial recent
progress, training performant multilingual LLMs remains a challenge, in large
part due to the inherent difficulty of tailoring filtering and deduplication
pipelines to a large number of languages. In this work, we introduce a new
pre-training dataset curation pipeline based on FineWeb that can be
automatically adapted to support any language. We extensively ablate our
pipeline design choices on a set of nine diverse languages, guided by a set of
meaningful and informative evaluation tasks that were chosen through a novel
selection process based on measurable criteria. Ultimately, we show that our
pipeline can be used to create non-English corpora that produce more performant
models than prior datasets. We additionally introduce a straightforward and
principled approach to rebalance datasets that takes into consideration both
duplication count and quality, providing an additional performance uplift.
Finally, we scale our pipeline to over 1000 languages using almost 100 Common
Crawl snapshots to produce FineWeb2, a new 20 terabyte (5 billion document)
multilingual dataset which we release along with our pipeline, training, and
evaluation codebases.
Thought Anchors: Which LLM Reasoning Steps Matter?
Authors: name: Paul C. Bogdan, name: Uzay Macar, name: Neel Nanda, name: Arthur Conmy
Published: 2025-06-23 21:28:45

Reasoning large language models have recently achieved state-of-the-art
performance in many fields. However, their long-form chain-of-thought reasoning
creates interpretability challenges as each generated token depends on all
previous ones, making the computation harder to decompose. We argue that
analyzing reasoning traces at the sentence level is a promising approach to
understanding reasoning processes. We present three complementary attribution
methods: (1) a black-box method measuring each sentence's counterfactual
importance by comparing final answers across 100 rollouts conditioned on the
model generating that sentence or one with a different meaning; (2) a white-box
method of aggregating attention patterns between pairs of sentences, which
identified ``broadcasting'' sentences that receive disproportionate attention
from all future sentences via ``receiver'' attention heads; (3) a causal
attribution method measuring logical connections between sentences by
suppressing attention toward one sentence and measuring the effect on each
future sentence's tokens. Each method provides evidence for the existence of
thought anchors, reasoning steps that have outsized importance and that
disproportionately influence the subsequent reasoning process. These thought
anchors are typically planning or backtracking sentences. We provide an
open-source tool (www.thought-anchors.com) for visualizing the outputs of our
methods, and present a case study showing converging patterns across methods
that map how a model performs multi-step reasoning. The consistency across
methods demonstrates the potential of sentence-level analysis for a deeper
understanding of reasoning models.
GPTailor: Large Language Model Pruning Through Layer Cutting and
  Stitching
Authors: name: Guinan Su, name: Li Shen, name: Lu Yin, name: Shiwei Liu, name: Yanwu Yang, name: Jonas Geiping
Published: 2025-06-25 14:24:59

Large language models (LLMs) have shown remarkable capabilities in language
understanding and generation. However, such impressive capability typically
comes with a substantial model size, which presents significant challenges in
deployment and inference. While structured pruning of model parameters offers a
promising way to reduce computational costs at deployment time, current methods
primarily focus on single model pruning. In this work, we develop a novel
strategy to compress models by strategically combining or merging layers from
finetuned model variants, which preserves the original model's abilities by
aggregating capabilities accentuated in different finetunes. We pose the
optimal tailoring of these LLMs as a zero-order optimization problem, adopting
a search space that supports three different operations: (1) Layer removal, (2)
Layer selection from different candidate models, and (3) Layer merging. Our
experiments demonstrate that this approach leads to competitive model pruning,
for example, for the Llama2-13B model families, our compressed models maintain
approximately 97.3\% of the original performance while removing sim25% of
parameters, significantly outperforming previous state-of-the-art methods. The
code is available at https://github.com/Guinan-Su/auto-merge-llm.
Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency
  Models
Authors: name: Ilia Beletskii, name: Andrey Kuznetsov, name: Aibek Alanov
Published: 2025-06-23 20:34:43

Recent advances in image editing with diffusion models have achieved
impressive results, offering fine-grained control over the generation process.
However, these methods are computationally intensive because of their iterative
nature. While distilled diffusion models enable faster inference, their editing
capabilities remain limited, primarily because of poor inversion quality.
High-fidelity inversion and reconstruction are essential for precise image
editing, as they preserve the structural and semantic integrity of the source
image. In this work, we propose a novel framework that enhances image inversion
using consistency models, enabling high-quality editing in just four steps. Our
method introduces a cycle-consistency optimization strategy that significantly
improves reconstruction accuracy and enables a controllable trade-off between
editability and content preservation. We achieve state-of-the-art performance
across various image editing tasks and datasets, demonstrating that our method
matches or surpasses full-step diffusion models while being substantially more
efficient. The code of our method is available on GitHub at
https://github.com/ControlGenAI/Inverse-and-Edit.
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining
  and Extracting Rare and Hidden Content
Authors: name: Rian Touchent, name: Nathan Godey, name: Eric de la Clergerie
Published: 2025-06-25 11:30:25

We introduce Biomed-Enriched, a biomedical text dataset constructed from
PubMed via a two-stage annotation process. In the first stage, a large language
model annotates 400K paragraphs from PubMed scientific articles, assigning
scores for their type (review, study, clinical case, other), domain (clinical,
biomedical, other), and educational quality. The educational quality score
(rated 1 to 5) estimates how useful a paragraph is for college-level learning.
These annotations are then used to fine-tune a small language model, which
propagates the labels across the full PMC-OA corpus. The resulting metadata
allows us to extract refined subsets, including 2M clinical case paragraphs
with over 450K high-quality ones from articles with commercial-use licenses,
and to construct several variants via quality filtering and domain upsampling.
Clinical text is typically difficult to access due to privacy constraints, as
hospital records cannot be publicly shared. Hence, our dataset provides an
alternative large-scale, openly available collection of clinical cases from
PubMed, making it a valuable resource for biomedical and clinical NLP.
Preliminary continual-pretraining experiments with OLMo2 suggest these curated
subsets enable targeted improvements, with clinical upsampling boosting
performance by ~5% on MMLU ProfMed and educational quality filtering improving
MedQA and MedMCQA by ~1%. Combinations of these techniques led to faster
convergence, reaching same performance with a third of training tokens,
indicating potential for more efficient and effective biomedical pretraining
strategies.
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
Authors: name: Zengzhi Wang, name: Fan Zhou, name: Xuefeng Li, name: Pengfei Liu
Published: 2025-06-25 14:58:13

Different base language model families, such as Llama and Qwen, exhibit
divergent behaviors during post-training with reinforcement learning (RL),
especially on reasoning-intensive tasks. What makes a base language model
suitable for reinforcement learning? Gaining deeper insight into this question
is essential for developing RL-scalable foundation models of the next
generation. In this work, we investigate how mid-training strategies shape RL
dynamics, focusing on two representative model families: Qwen and Llama. Our
study reveals that (1) high-quality mathematical corpora, such as
MegaMath-Web-Pro, significantly improve both base model and RL performance,
while existing alternatives (e.g., FineMath-4plus) fail to do so; (2) further
adding QA-style data, particularly long chain-of-thought (CoT) reasoning
examples, enhances RL outcomes, and instruction data further unlocks this
effect; (3) while long-CoT improves reasoning depth, it can also induce
verbosity of model responses and unstability of RL training, underscoring the
importance of data formatting; (4) scaling mid-training consistently leads to
stronger downstream RL performance. Building on these insights, we introduce a
two-stage mid-training strategy, Stable-then-Decay, in which base models are
first trained on 200B tokens with a constant learning rate, followed by 20B
tokens across three CoT-focused branches with learning rate decay. This yields
OctoThinker, a family of models demonstrating strong RL compatibility and
closing the performance gap with more RL-friendly model families, i.e., Qwen.
We hope our work will help shape pre-training strategies for foundation models
in the RL era. To support further research, we release our open-source models
along with a curated math reasoning-intensive corpus of over 70 billion tokens
(i.e., MegaMath-Web-Pro-Max).
ReCode: Updating Code API Knowledge with Reinforcement Learning
Authors: name: Haoze Wu, name: Yunzhi Yao, name: Wenhao Yu, name: Huajun Chen, name: Ningyu Zhang
Published: 2025-06-25 14:41:13

Large Language Models (LLMs) exhibit remarkable code generation capabilities
but falter when adapting to frequent updates in external library APIs. This
critical limitation, stemming from reliance on outdated API knowledge from
their training data, even with access to current documentation, impedes
reliable code generation in dynamic environments. To tackle this issue, we
propose ReCode (rule-based Reinforcement learning for Code Update), a novel
framework that mimics human programmer adaptation to API changes. Specifically,
we construct a dataset of approximately 2,000 data entries to train the LLMs to
perform version migration based on updated information. Then, we introduce a
modified string similarity metric for code evaluation as the reward for
reinforcement learning. Our experiments demonstrate that ReCode substantially
boosts LLMs' code generation performance in dynamic API scenarios, especially
on the unseen CodeUpdateArena task. Crucially, compared to supervised
fine-tuning, ReCode has less impact on LLMs' general code generation abilities.
We apply ReCode on various LLMs and reinforcement learning algorithms (GRPO and
DAPO), all achieving consistent improvements. Notably, after training,
Qwen2.5-Coder-7B outperforms that of the 32B parameter code instruction-tuned
model and the reasoning model with the same architecture. Code is available at
https://github.com/zjunlp/ReCode.
HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based
  Diffusion Sampling
Authors: name: Tobias Vontobel, name: Seyedmorteza Sadat, name: Farnood Salehi, name: Romann M. Weber
Published: 2025-06-25 13:58:37

Diffusion models have emerged as the leading approach for image synthesis,
demonstrating exceptional photorealism and diversity. However, training
diffusion models at high resolutions remains computationally prohibitive, and
existing zero-shot generation techniques for synthesizing images beyond
training resolutions often produce artifacts, including object duplication and
spatial incoherence. In this paper, we introduce HiWave, a training-free,
zero-shot approach that substantially enhances visual fidelity and structural
coherence in ultra-high-resolution image synthesis using pretrained diffusion
models. Our method employs a two-stage pipeline: generating a base image from
the pretrained model followed by a patch-wise DDIM inversion step and a novel
wavelet-based detail enhancer module. Specifically, we first utilize inversion
methods to derive initial noise vectors that preserve global coherence from the
base image. Subsequently, during sampling, our wavelet-domain detail enhancer
retains low-frequency components from the base image to ensure structural
consistency, while selectively guiding high-frequency components to enrich fine
details and textures. Extensive evaluations using Stable Diffusion XL
demonstrate that HiWave effectively mitigates common visual artifacts seen in
prior methods, achieving superior perceptual quality. A user study confirmed
HiWave's performance, where it was preferred over the state-of-the-art
alternative in more than 80% of comparisons, highlighting its effectiveness for
high-quality, ultra-high-resolution image synthesis without requiring
retraining or architectural modifications.
Use Property-Based Testing to Bridge LLM Code Generation and Validation
Authors: name: Lehan He, name: Zeren Chen, name: Zhe Zhang, name: Jing Shao, name: Xiang Gao, name: Lu Sheng
Published: 2025-06-23 06:01:12

Large Language Models (LLMs) excel at code generation, but ensuring their
outputs to be functionally correct, especially in complex programming tasks, is
a persistent challenge. While traditional Test-Driven Development (TDD) offers
a path for code refinement, its efficacy with LLMs is often undermined by the
scarcity of high-quality test cases or the pitfalls of automated test
generation, including biased tests or inaccurate output predictions that can
misdirect the correction process. This paper introduces Property-Generated
Solver, a novel framework that leverages Property-Based Testing (PBT) to
validate high-level program properties or invariants, instead of relying on
specific input-output examples. These properties are often simpler to define
and verify than directly predicting exhaustive test oracles, breaking the
"cycle of self-deception" where tests might share flaws with the code they are
meant to validate. Property-Generated Solver employs two collaborative
LLM-based agents: a Generator dedicated to code generation and iterative
refinement, and a Tester that manages the PBT life-cycle and formulate
semantically rich feedback from property violations. The resulting
comprehensive and actionable feedback then guides the Generator in its
refinement efforts. By establishing PBT as the core validation engine within
this iterative, closed-loop paradigm, Property-Generated Solver provides a
robust mechanism for steering LLMs towards more correct and generalizable code.
Extensive experimental results on multiple code generation benchmarks
demonstrate that Property-Generated Solver achieves substantial pass@1
improvements, ranging from 23.1% to 37.3% relative gains over established TDD
methods.
RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain
  Randomization for Robust Bimanual Robotic Manipulation
Authors: name: Tianxing Chen, name: Zanxin Chen, name: Baijun Chen, name: Zijian Cai, name: Yibin Liu, name: Qiwei Liang, name: Zixuan Li, name: Xianliang Lin, name: Yiheng Ge, name: Zhenyu Gu, name: Weiliang Deng, name: Yubin Guo, name: Tian Nian, name: Xuanbing Xie, name: Qiangyu Chen, name: Kailun Su, name: Tianling Xu, name: Guodong Liu, name: Mengkang Hu, name: Huan-ang Gao, name: Kaixuan Wang, name: Zhixuan Liang, name: Yusen Qin, name: Xiaokang Yang, name: Ping Luo, name: Yao Mu
Published: 2025-06-22 16:26:53

Simulation-based data synthesis has emerged as a powerful paradigm for
enhancing real-world robotic manipulation. However, existing synthetic datasets
remain insufficient for robust bimanual manipulation due to two challenges: (1)
the lack of an efficient, scalable data generation method for novel tasks, and
(2) oversimplified simulation environments that fail to capture real-world
complexity. We present RoboTwin 2.0, a scalable simulation framework that
enables automated, large-scale generation of diverse and realistic data, along
with unified evaluation protocols for dual-arm manipulation. We first construct
RoboTwin-OD, a large-scale object library comprising 731 instances across 147
categories, each annotated with semantic and manipulation-relevant labels.
Building on this foundation, we develop an expert data synthesis pipeline that
combines multimodal large language models (MLLMs) with simulation-in-the-loop
refinement to generate task-level execution code automatically. To improve
sim-to-real transfer, RoboTwin 2.0 incorporates structured domain randomization
along five axes: clutter, lighting, background, tabletop height and language
instructions, thereby enhancing data diversity and policy robustness. We
instantiate this framework across 50 dual-arm tasks spanning five robot
embodiments, and pre-collect over 100,000 domain-randomized expert
trajectories. Empirical results show a 10.9% gain in code generation success
and improved generalization to novel real-world scenarios. A VLA model
fine-tuned on our dataset achieves a 367% relative improvement (42.0% vs. 9.0%)
on unseen scene real-world tasks, while zero-shot models trained solely on our
synthetic data achieve a 228% relative gain, highlighting strong generalization
without real-world supervision. We release the data generator, benchmark,
dataset, and code to support scalable research in robust bimanual manipulation.