Orionnnn/hub-stats huggingface dataset:
R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs
  via Reinforcement Learning
Authors: name: Huatong Song, name: Jinhao Jiang, name: Wenqing Tian, name: Zhipeng Chen, name: Yuhuan Wu, name: Jiahao Zhao, name: Yingqian Min, name: Wayne Xin Zhao, name: Lei Fang, name: Ji-Rong Wen
Published: 2025-05-22 17:58:26

Large Language Models (LLMs) are powerful but prone to hallucinations due to
static knowledge. Retrieval-Augmented Generation (RAG) helps by injecting
external information, but current methods often are costly, generalize poorly,
or ignore the internal knowledge of the model. In this paper, we introduce
R1-Searcher++, a novel framework designed to train LLMs to adaptively leverage
both internal and external knowledge sources. R1-Searcher++ employs a two-stage
training strategy: an initial SFT Cold-start phase for preliminary format
learning, followed by RL for Dynamic Knowledge Acquisition. The RL stage uses
outcome-supervision to encourage exploration, incorporates a reward mechanism
for internal knowledge utilization, and integrates a memorization mechanism to
continuously assimilate retrieved information, thereby enriching the model's
internal knowledge. By leveraging internal knowledge and external search
engine, the model continuously improves its capabilities, enabling efficient
retrieval-augmented reasoning. Our experiments demonstrate that R1-Searcher++
outperforms previous RAG and reasoning methods and achieves efficient
retrieval. The code is available at
https://github.com/RUCAIBox/R1-Searcher-plus.
Exploring the Latent Capacity of LLMs for One-Step Text Generation
Authors: name: Gleb Mezentsev, name: Ivan Oseledets
Published: 2025-05-27 13:39:24

A recent study showed that large language models (LLMs) can reconstruct
surprisingly long texts - up to thousands of tokens - via autoregressive
generation from just one specially trained input embedding. In this work, we
explore whether such reconstruction is possible without autoregression. We show
that frozen LLMs can generate hundreds of accurate tokens in just one forward
pass, when provided with only two learned embeddings. This reveals a surprising
and underexplored capability of LLMs - multi-token generation without iterative
decoding. We investigate the behaviour of these embeddings and provide insight
into the type of information they encode. We also empirically show that
although these representations are not unique for a given text, they form
connected and local regions in embedding space - a property that suggests the
potential of learning a dedicated encoder into that space.
Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning
Authors: name: Mingyang Song, name: Mao Zheng
Published: 2025-05-27 13:29:51

As test-time scaling becomes a pivotal research frontier in Large Language
Models (LLMs) development, contemporary and advanced post-training
methodologies increasingly focus on extending the generation length of long
Chain-of-Thought (CoT) responses to enhance reasoning capabilities toward
DeepSeek R1-like performance. However, recent studies reveal a persistent
overthinking phenomenon in state-of-the-art reasoning models, manifesting as
excessive redundancy or repetitive thinking patterns in long CoT responses. To
address this issue, in this paper, we propose a simple yet effective two-stage
reinforcement learning framework for achieving concise reasoning in LLMs, named
ConciseR. Specifically, the first stage, using more training steps, aims to
incentivize the model's reasoning capabilities via Group Relative Policy
Optimization with clip-higher and dynamic sampling components (GRPO++), and the
second stage, using fewer training steps, explicitly enforces conciseness and
improves efficiency via Length-aware Group Relative Policy Optimization
(L-GRPO). Significantly, ConciseR only optimizes response length once all
rollouts of a sample are correct, following the "walk before you run"
principle. Extensive experimental results demonstrate that our ConciseR model,
which generates more concise CoT reasoning responses, outperforms recent
state-of-the-art reasoning models with zero RL paradigm across AIME 2024,
MATH-500, AMC 2023, Minerva, and Olympiad benchmarks.
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic
  Capabilities in LLM Compression
Authors: name: Peijie Dong, name: Zhenheng Tang, name: Xiang Liu, name: Lujun Li, name: Xiaowen Chu, name: Bo Li
Published: 2025-05-26 02:49:07

Post-training compression reduces the computational and memory costs of large
language models (LLMs), enabling resource-efficient deployment. However,
existing compression benchmarks only focus on language modeling (e.g.,
perplexity) and natural language understanding tasks (e.g., GLUE accuracy),
ignoring the agentic capabilities - workflow, tool use/function call,
long-context understanding and real-world application. We introduce the Agent
Compression Benchmark (ACBench), the first comprehensive benchmark for
evaluating how compression impacts LLMs' agentic abilities. ACBench spans (1)
12 tasks across 4 capabilities (e.g., WorfBench for workflow generation,
Needle-in-Haystack for long-context retrieval), (2) quantization (GPTQ, AWQ)
and pruning (Wanda, SparseGPT), and (3) 15 models, including small (Gemma-2B),
standard (Qwen2.5 7B-32B), and distilled reasoning LLMs (DeepSeek-R1-Distill).
Our experiments reveal compression tradeoffs: 4-bit quantization preserves
workflow generation and tool use (1%-3% drop) but degrades real-world
application accuracy by 10%-15%. We introduce ERank, Top-k Ranking Correlation
and Energy to systematize analysis. ACBench provides actionable insights for
optimizing LLM compression in agentic scenarios. The code can be found in
https://github.com/pprp/ACBench.
Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning
  of LLMs
Authors: name: Yaorui Shi, name: Sihang Li, name: Chang Wu, name: Zhiyuan Liu, name: Junfeng Fang, name: Hengxing Cai, name: An Zhang, name: Xiang Wang
Published: 2025-05-16 14:11:29

Large language models have demonstrated impressive reasoning capabilities but
are inherently limited by their knowledge reservoir. Retrieval-augmented
reasoning mitigates this limitation by allowing LLMs to query external
resources, but existing methods often retrieve irrelevant or noisy information,
hindering accurate reasoning. In this paper, we propose AutoRefine, a
reinforcement learning post-training framework that adopts a new
``search-and-refine-during-think'' paradigm. AutoRefine introduces explicit
knowledge refinement steps between successive search calls, enabling the model
to iteratively filter, distill, and organize evidence before generating an
answer. Furthermore, we incorporate tailored retrieval-specific rewards
alongside answer correctness rewards using group relative policy optimization.
Experiments on single-hop and multi-hop QA benchmarks demonstrate that
AutoRefine significantly outperforms existing approaches, particularly in
complex, multi-hop reasoning scenarios. Detailed analysis shows that AutoRefine
issues frequent, higher-quality searches and synthesizes evidence effectively.
ComfyMind: Toward General-Purpose Generation via Tree-Based Planning and
  Reactive Feedback
Authors: name: Litao Guo, name: Xinli Xu, name: Luozhou Wang, name: Jiantao Lin, name: Jinsong Zhou, name: Zixin Zhang, name: Bolan Su, name: Ying-Cong Chen
Published: 2025-05-23 13:53:03

With the rapid advancement of generative models, general-purpose generation
has gained increasing attention as a promising approach to unify diverse tasks
across modalities within a single system. Despite this progress, existing
open-source frameworks often remain fragile and struggle to support complex
real-world applications due to the lack of structured workflow planning and
execution-level feedback. To address these limitations, we present ComfyMind, a
collaborative AI system designed to enable robust and scalable general-purpose
generation, built on the ComfyUI platform. ComfyMind introduces two core
innovations: Semantic Workflow Interface (SWI) that abstracts low-level node
graphs into callable functional modules described in natural language, enabling
high-level composition and reducing structural errors; Search Tree Planning
mechanism with localized feedback execution, which models generation as a
hierarchical decision process and allows adaptive correction at each stage.
Together, these components improve the stability and flexibility of complex
generative workflows. We evaluate ComfyMind on three public benchmarks:
ComfyBench, GenEval, and Reason-Edit, which span generation, editing, and
reasoning tasks. Results show that ComfyMind consistently outperforms existing
open-source baselines and achieves performance comparable to GPT-Image-1.
ComfyMind paves a promising path for the development of open-source
general-purpose generative AI systems. Project page:
https://github.com/LitaoGuo/ComfyMind
HoliTom: Holistic Token Merging for Fast Video Large Language Models
Authors: name: Kele Shao, name: Keda Tao, name: Can Qin, name: Haoxuan You, name: Yang Sui, name: Huan Wang
Published: 2025-05-27 15:28:45

Video large language models (video LLMs) excel at video comprehension but
face significant computational inefficiency due to redundant video tokens.
Existing token pruning methods offer solutions. However, approaches operating
within the LLM (inner-LLM pruning), such as FastV, incur intrinsic
computational overhead in shallow layers. In contrast, methods performing token
pruning before the LLM (outer-LLM pruning) primarily address spatial redundancy
within individual frames or limited temporal windows, neglecting the crucial
global temporal dynamics and correlations across longer video sequences. This
leads to sub-optimal spatio-temporal reduction and does not leverage video
compressibility fully. Crucially, the synergistic potential and mutual
influence of combining these strategies remain unexplored. To further reduce
redundancy, we introduce HoliTom, a novel training-free holistic token merging
framework. HoliTom employs outer-LLM pruning through global redundancy-aware
temporal segmentation, followed by spatial-temporal merging to reduce visual
tokens by over 90%, significantly alleviating the LLM's computational burden.
Complementing this, we introduce a robust inner-LLM token similarity-based
merging approach, designed for superior performance and compatibility with
outer-LLM pruning. Evaluations demonstrate our method's promising
efficiency-performance trade-off on LLaVA-OneVision-7B, reducing computational
costs to 6.9% of FLOPs while maintaining 99.1% of the original performance.
Furthermore, we achieve a 2.28x reduction in Time-To-First-Token (TTFT) and a
1.32x acceleration in decoding throughput, highlighting the practical benefits
of our integrated pruning approach for efficient video LLMs inference.
Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM
  Reasoning
Authors: name: Shenao Zhang, name: Yaqing Wang, name: Yinxiao Liu, name: Tianqi Liu, name: Peter Grabowski, name: Eugene Ie, name: Zhaoran Wang, name: Yunxuan Li
Published: 2025-05-26 22:51:00

Large Language Models (LLMs) trained via Reinforcement Learning (RL) have
exhibited strong reasoning capabilities and emergent reflective behaviors, such
as backtracking and error correction. However, conventional Markovian RL
confines exploration to the training phase to learn an optimal deterministic
policy and depends on the history contexts only through the current state.
Therefore, it remains unclear whether reflective reasoning will emerge during
Markovian RL training, or why they are beneficial at test time. To remedy this,
we recast reflective exploration within the Bayes-Adaptive RL framework, which
explicitly optimizes the expected return under a posterior distribution over
Markov decision processes. This Bayesian formulation inherently incentivizes
both reward-maximizing exploitation and information-gathering exploration via
belief updates. Our resulting algorithm, BARL, instructs the LLM to stitch and
switch strategies based on the observed outcomes, offering principled guidance
on when and how the model should reflectively explore. Empirical results on
both synthetic and mathematical reasoning tasks demonstrate that BARL
outperforms standard Markovian RL approaches at test time, achieving superior
token efficiency with improved exploration effectiveness. Our code is available
at https://github.com/shenao-zhang/BARL.
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large
  Language Models via Share-GRPO
Authors: name: Huanjin Yao, name: Qixiang Yin, name: Jingyi Zhang, name: Min Yang, name: Yibo Wang, name: Wenhao Wu, name: Fei Su, name: Li Shen, name: Minghui Qiu, name: Dacheng Tao, name: Jiaxing Huang
Published: 2025-05-22 13:39:32

In this work, we aim to incentivize the reasoning ability of Multimodal Large
Language Models (MLLMs) via reinforcement learning (RL) and develop an
effective approach that mitigates the sparse reward and advantage vanishing
issues during RL. To this end, we propose Share-GRPO, a novel RL approach that
tackle these issues by exploring and sharing diverse reasoning trajectories
over expanded question space. Specifically, Share-GRPO first expands the
question space for a given question via data transformation techniques, and
then encourages MLLM to effectively explore diverse reasoning trajectories over
the expanded question space and shares the discovered reasoning trajectories
across the expanded questions during RL. In addition, Share-GRPO also shares
reward information during advantage computation, which estimates solution
advantages hierarchically across and within question variants, allowing more
accurate estimation of relative advantages and improving the stability of
policy training. Extensive evaluations over six widely-used reasoning
benchmarks showcase the superior performance of our method. Code will be
available at https://github.com/HJYao00/R1-ShareVL.
DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in
  Digital Forensics and Incident Response
Authors: name: Bilel Cherif, name: Tamas Bisztray, name: Richard A. Dubniczky, name: Aaesha Aldahmani, name: Saeed Alshehhi, name: Norbert Tihanyi
Published: 2025-05-26 13:35:37

Digital Forensics and Incident Response (DFIR) involves analyzing digital
evidence to support legal investigations. Large Language Models (LLMs) offer
new opportunities in DFIR tasks such as log analysis and memory forensics, but
their susceptibility to errors and hallucinations raises concerns in
high-stakes contexts. Despite growing interest, there is no comprehensive
benchmark to evaluate LLMs across both theoretical and practical DFIR domains.
To address this gap, we present DFIR-Metric, a benchmark with three components:
(1) Knowledge Assessment: a set of 700 expert-reviewed multiple-choice
questions sourced from industry-standard certifications and official
documentation; (2) Realistic Forensic Challenges: 150 CTF-style tasks testing
multi-step reasoning and evidence correlation; and (3) Practical Analysis: 500
disk and memory forensics cases from the NIST Computer Forensics Tool Testing
Program (CFTT). We evaluated 14 LLMs using DFIR-Metric, analyzing both their
accuracy and consistency across trials. We also introduce a new metric, the
Task Understanding Score (TUS), designed to more effectively evaluate models in
scenarios where they achieve near-zero accuracy. This benchmark offers a
rigorous, reproducible foundation for advancing AI in digital forensics. All
scripts, artifacts, and results are available on the project website at
https://github.com/DFIR-Metric.
How does Alignment Enhance LLMs' Multilingual Capabilities? A Language
  Neurons Perspective
Authors: name: Shimao Zhang, name: Zhejian Lai, name: Xiang Liu, name: Shuaijie She, name: Xiao Liu, name: Yeyun Gong, name: Shujian Huang, name: Jiajun Chen
Published: 2025-05-27 17:59:52

Multilingual Alignment is an effective and representative paradigm to enhance
LLMs' multilingual capabilities, which transfers the capabilities from the
high-resource languages to the low-resource languages. Meanwhile, some
researches on language-specific neurons reveal that there are language-specific
neurons that are selectively activated in LLMs when processing different
languages. This provides a new perspective to analyze and understand LLMs'
mechanisms more specifically in multilingual scenarios. In this work, we
propose a new finer-grained neuron identification algorithm, which detects
language neurons~(including language-specific neurons and language-related
neurons) and language-agnostic neurons. Furthermore, based on the
distributional characteristics of different types of neurons, we divide the
LLMs' internal process for multilingual inference into four parts: (1)
multilingual understanding, (2) shared semantic space reasoning, (3)
multilingual output space transformation, and (4) vocabulary space outputting.
Additionally, we systematically analyze the models before and after alignment
with a focus on different types of neurons. We also analyze the phenomenon of
''Spontaneous Multilingual Alignment''. Overall, our work conducts a
comprehensive investigation based on different types of neurons, providing
empirical results and valuable insights for better understanding multilingual
alignment and multilingual capabilities of LLMs.
Absolute Coordinates Make Motion Generation Easy
Authors: name: Zichong Meng, name: Zeyu Han, name: Xiaogang Peng, name: Yiming Xie, name: Huaizu Jiang
Published: 2025-05-26 00:36:00

State-of-the-art text-to-motion generation models rely on the
kinematic-aware, local-relative motion representation popularized by HumanML3D,
which encodes motion relative to the pelvis and to the previous frame with
built-in redundancy. While this design simplifies training for earlier
generation models, it introduces critical limitations for diffusion models and
hinders applicability to downstream tasks. In this work, we revisit the motion
representation and propose a radically simplified and long-abandoned
alternative for text-to-motion generation: absolute joint coordinates in global
space. Through systematic analysis of design choices, we show that this
formulation achieves significantly higher motion fidelity, improved text
alignment, and strong scalability, even with a simple Transformer backbone and
no auxiliary kinematic-aware losses. Moreover, our formulation naturally
supports downstream tasks such as text-driven motion control and
temporal/spatial editing without additional task-specific reengineering and
costly classifier guidance generation from control signals. Finally, we
demonstrate promising generalization to directly generate SMPL-H mesh vertices
in motion from text, laying a strong foundation for future research and
motion-related applications.
Code Graph Model (CGM): A Graph-Integrated Large Language Model for
  Repository-Level Software Engineering Tasks
Authors: name: Hongyuan Tao, name: Ying Zhang, name: Zhenhao Tang, name: Hongen Peng, name: Xukun Zhu, name: Bingchang Liu, name: Yingguang Yang, name: Ziyin Zhang, name: Zhaogui Xu, name: Haipeng Zhang, name: Linchao Zhu, name: Rui Wang, name: Hang Yu, name: Jianguo Li, name: Peng Di
Published: 2025-05-22 17:00:55

Recent advances in Large Language Models (LLMs) have shown promise in
function-level code generation, yet repository-level software engineering tasks
remain challenging. Current solutions predominantly rely on proprietary LLM
agents, which introduce unpredictability and limit accessibility, raising
concerns about data privacy and model customization. This paper investigates
whether open-source LLMs can effectively address repository-level tasks without
requiring agent-based approaches. We demonstrate this is possible by enabling
LLMs to comprehend functions and files within codebases through their semantic
information and structural dependencies. To this end, we introduce Code Graph
Models (CGMs), which integrate repository code graph structures into the LLM's
attention mechanism and map node attributes to the LLM's input space using a
specialized adapter. When combined with an agentless graph RAG framework, our
approach achieves a 43.00% resolution rate on the SWE-bench Lite benchmark
using the open-source Qwen2.5-72B model. This performance ranks first among
open weight models, second among methods with open-source systems, and eighth
overall, surpassing the previous best open-source model-based method by 12.33%.
UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based
  Mobile GUI Agents
Authors: name: Han Xiao, name: Guozhi Wang, name: Yuxiang Chai, name: Zimu Lu, name: Weifeng Lin, name: Hao He, name: Lue Fan, name: Liuyang Bian, name: Rui Hu, name: Liang Liu, name: Shuai Ren, name: Yafei Wen, name: Xiaoxin Chen, name: Aojun Zhou, name: Hongsheng Li
Published: 2025-05-27 17:58:06

In this paper, we introduce UI-Genie, a self-improving framework addressing
two key challenges in GUI agents: verification of trajectory outcome is
challenging and high-quality training data are not scalable. These challenges
are addressed by a reward model and a self-improving pipeline, respectively.
The reward model, UI-Genie-RM, features an image-text interleaved architecture
that efficiently pro- cesses historical context and unifies action-level and
task-level rewards. To sup- port the training of UI-Genie-RM, we develop
deliberately-designed data genera- tion strategies including rule-based
verification, controlled trajectory corruption, and hard negative mining. To
address the second challenge, a self-improvement pipeline progressively expands
solvable complex GUI tasks by enhancing both the agent and reward models
through reward-guided exploration and outcome verification in dynamic
environments. For training the model, we generate UI- Genie-RM-517k and
UI-Genie-Agent-16k, establishing the first reward-specific dataset for GUI
agents while demonstrating high-quality synthetic trajectory gen- eration
without manual annotation. Experimental results show that UI-Genie achieves
state-of-the-art performance across multiple GUI agent benchmarks with three
generations of data-model self-improvement. We open-source our complete
framework implementation and generated datasets to facilitate further research
in https://github.com/Euphoria16/UI-Genie.
ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in
  Vision-Language Models
Authors: name: Dingming Li, name: Hongxing Li, name: Zixuan Wang, name: Yuchen Yan, name: Hang Zhang, name: Siqi Chen, name: Guiyang Hou, name: Shengpei Jiang, name: Wenqi Zhang, name: Yongliang Shen, name: Weiming Lu, name: Yueting Zhuang
Published: 2025-05-27 17:59:26

Vision-language models (VLMs) have demonstrated remarkable capabilities in
understanding and reasoning about visual content, but significant challenges
persist in tasks requiring cross-viewpoint understanding and spatial reasoning.
We identify a critical limitation: current VLMs excel primarily at egocentric
spatial reasoning (from the camera's perspective) but fail to generalize to
allocentric viewpoints when required to adopt another entity's spatial frame of
reference. We introduce ViewSpatial-Bench, the first comprehensive benchmark
designed specifically for multi-viewpoint spatial localization recognition
evaluation across five distinct task types, supported by an automated 3D
annotation pipeline that generates precise directional labels. Comprehensive
evaluation of diverse VLMs on ViewSpatial-Bench reveals a significant
performance disparity: models demonstrate reasonable performance on
camera-perspective tasks but exhibit reduced accuracy when reasoning from a
human viewpoint. By fine-tuning VLMs on our multi-perspective spatial dataset,
we achieve an overall performance improvement of 46.24% across tasks,
highlighting the efficacy of our approach. Our work establishes a crucial
benchmark for spatial intelligence in embodied AI systems and provides
empirical evidence that modeling 3D spatial relationships enhances VLMs'
corresponding spatial comprehension capabilities.