Orionnnn/hub-stats huggingface dataset:
ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking
Authors: name: Baixuan Li, name: Dingchu Zhang, name: Jialong Wu, name: Wenbiao Yin, name: Zhengwei Tao, name: Yida Zhao, name: Liwen Zhang, name: Haiyang Shen, name: Runnan Fang, name: Pengjun Xie, name: Jingren Zhou, name: Yong Jiang
Published: 2025-10-28 17:51:50

Parallel thinking expands exploration breadth, complementing the deep
exploration of information-seeking (IS) agents to further enhance
problem-solving capability. However, conventional parallel thinking faces two
key challenges in this setting: inefficiency from repeatedly rolling out from
scratch, and difficulty in integrating long-horizon reasoning trajectories
during answer generation, as limited context capacity prevents full
consideration of the reasoning process. To address these issues, we propose
ParallelMuse, a two-stage paradigm designed for deep IS agents. The first
stage, Functionality-Specified Partial Rollout, partitions generated sequences
into functional regions and performs uncertainty-guided path reuse and
branching to enhance exploration efficiency. The second stage, Compressed
Reasoning Aggregation, exploits reasoning redundancy to losslessly compress
information relevant to answer derivation and synthesize a coherent final
answer. Experiments across multiple open-source agents and benchmarks
demonstrate up to 62% performance improvement with a 10--30% reduction in
exploratory token consumption.
AgentFold: Long-Horizon Web Agents with Proactive Context Management
Authors: name: Rui Ye, name: Zhongwang Zhang, name: Kuan Li, name: Huifeng Yin, name: Zhengwei Tao, name: Yida Zhao, name: Liangcai Su, name: Liwen Zhang, name: Zile Qiao, name: Xinyu Wang, name: Pengjun Xie, name: Fei Huang, name: Siheng Chen, name: Jingren Zhou, name: Yong Jiang
Published: 2025-10-28 17:51:50

LLM-based web agents show immense promise for information seeking, yet their
effectiveness on long-horizon tasks is hindered by a fundamental trade-off in
context management. Prevailing ReAct-based agents suffer from context
saturation as they accumulate noisy, raw histories, while methods that fixedly
summarize the full history at each step risk the irreversible loss of critical
details. Addressing these, we introduce AgentFold, a novel agent paradigm
centered on proactive context management, inspired by the human cognitive
process of retrospective consolidation. AgentFold treats its context as a
dynamic cognitive workspace to be actively sculpted, rather than a passive log
to be filled. At each step, it learns to execute a `folding' operation, which
manages its historical trajectory at multiple scales: it can perform granular
condensations to preserve vital, fine-grained details, or deep consolidations
to abstract away entire multi-step sub-tasks. The results on prominent
benchmarks are striking: with simple supervised fine-tuning (without continual
pre-training or RL), our AgentFold-30B-A3B agent achieves 36.2% on BrowseComp
and 47.3% on BrowseComp-ZH. Notably, this performance not only surpasses or
matches open-source models of a dramatically larger scale, such as the
DeepSeek-V3.1-671B-A37B, but also surpasses leading proprietary agents like
OpenAI's o4-mini.
Rethinking Visual Intelligence: Insights from Video Pretraining
Authors: name: Pablo Acuaviva, name: Aram Davtyan, name: Mariam Hassan, name: Sebastian Stapf, name: Ahmad Rahimi, name: Alexandre Alahi, name: Paolo Favaro
Published: 2025-10-28 14:12:11

Large language models (LLMs) have demonstrated that large-scale pretraining
enables systems to adapt rapidly to new problems with little supervision in the
language domain. This success, however, has not translated as effectively to
the visual domain, where models, including LLMs, continue to struggle with
compositional understanding, sample efficiency, and general-purpose
problem-solving. We investigate Video Diffusion Models (VDMs) as a promising
direction for bridging this gap. Pretraining on spatiotemporal data endows
these models with strong inductive biases for structure and dynamics, which we
hypothesize can support broad task adaptability. To test this, we design a
controlled evaluation in which both a pretrained LLM and a pretrained VDM are
equipped with lightweight adapters and presented with tasks in their natural
modalities. Across benchmarks including ARC-AGI, ConceptARC, visual games,
route planning, and cellular automata, VDMs demonstrate higher data efficiency
than their language counterparts. Taken together, our results indicate that
video pretraining offers inductive biases that support progress toward visual
foundation models.
Critique-RL: Training Language Models for Critiquing through Two-Stage
  Reinforcement Learning
Authors: name: Zhiheng Xi, name: Jixuan Huang, name: Xin Guo, name: Boyang Hong, name: Dingwen Yang, name: Xiaoran Fan, name: Shuo Li, name: Zehui Chen, name: Junjie Ye, name: Siyu Yuan, name: Zhengyin Du, name: Xuesong Yao, name: Yufei Xu, name: Jiecao Chen, name: Rui Zheng, name: Tao Gui, name: Qi Zhang, name: Xuanjing Huang
Published: 2025-10-28 11:37:01

Training critiquing language models to assess and provide feedback on model
outputs is a promising way to improve LLMs for complex reasoning tasks.
However, existing approaches typically rely on stronger supervisors for
annotating critique data. To address this, we propose Critique-RL, an online RL
approach for developing critiquing language models without stronger
supervision. Our approach operates on a two-player paradigm: the actor
generates a response, the critic provides feedback, and the actor refines the
response accordingly. We first reveal that relying solely on indirect reward
signals from the actor's outputs for RL optimization often leads to
unsatisfactory critics: while their helpfulness (i.e., providing constructive
feedback) improves, the discriminability (i.e., determining whether a response
is high-quality or not) remains poor, resulting in marginal performance gains.
To overcome this, Critique-RL adopts a two-stage optimization strategy. In
stage I, it reinforces the discriminability of the critic with direct
rule-based reward signals; in stage II, it introduces indirect rewards based on
actor refinement to improve the critic's helpfulness, while maintaining its
discriminability via appropriate regularization. Extensive experiments across
various tasks and models show that Critique-RL delivers substantial performance
improvements. For example, it achieves a 9.02% gain on in-domain tasks and a
5.70% gain on out-of-domain tasks for Qwen2.5-7B, highlighting its potential.
Generalization or Memorization: Dynamic Decoding for Mode Steering
Authors: name: Xuanming Zhang
Published: 2025-10-25 00:50:47

Large Language Models (LLMs) exhibit a troubling duality, capable of both
remarkable generalization and brittle, verbatim memorization of their training
data. This unpredictability undermines their reliability in high-stakes
applications. In this work, we propose a unified framework to understand,
identify, and control these distinct reasoning modes. First, we introduce a
theoretical model based on the Information Bottleneck (IB) principle,
formalizing generalization as the learning of a compressed, task-relevant
representation and memorization as a failure to compress. Building on this
theory, we develop Dynamic Mode Steering (DMS), a novel inference-time
algorithm which comprises two components: (1) a lightweight, causally-grounded
linear probe that identifies the model's instantaneous reliance on
memorization, and (2) a dynamic activation steering mechanism that nudges the
model's computation towards pre-identified generalization circuits. We frame
DMS as a form of adaptive, self-contrastive decoding. Experiments on reasoning
and faithfulness tasks demonstrate that DMS significantly improves logical
consistency and factual accuracy, thereby offering a principled approach to
enhancing LLM reliability.
VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a
  Unified Concept Set
Authors: name: Shufan Shen, name: Junshu Sun, name: Qingming Huang, name: Shuhui Wang
Published: 2025-10-24 10:29:31

The alignment of vision-language representations endows current
Vision-Language Models (VLMs) with strong multi-modal reasoning capabilities.
However, the interpretability of the alignment component remains uninvestigated
due to the difficulty in mapping the semantics of multi-modal representations
into a unified concept set. To address this problem, we propose VL-SAE, a
sparse autoencoder that encodes vision-language representations into its hidden
activations. Each neuron in its hidden layer correlates to a concept
represented by semantically similar images and texts, thereby interpreting
these representations with a unified concept set. To establish the
neuron-concept correlation, we encourage semantically similar representations
to exhibit consistent neuron activations during self-supervised training.
First, to measure the semantic similarity of multi-modal representations, we
perform their alignment in an explicit form based on cosine similarity. Second,
we construct the VL-SAE with a distance-based encoder and two modality-specific
decoders to ensure the activation consistency of semantically similar
representations. Experiments across multiple VLMs (e.g., CLIP, LLaVA)
demonstrate the superior capability of VL-SAE in interpreting and enhancing the
vision-language alignment. For interpretation, the alignment between vision and
language representations can be understood by comparing their semantics with
concepts. For enhancement, the alignment can be strengthened by aligning
vision-language representations at the concept level, contributing to
performance improvements in downstream tasks, including zero-shot image
classification and hallucination elimination. Codes are available at
https://github.com/ssfgunner/VL-SAE.
OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents
Authors: name: Hongrui Jia, name: Jitong Liao, name: Xi Zhang, name: Haiyang Xu, name: Tianbao Xie, name: Chaoya Jiang, name: Ming Yan, name: Si Liu, name: Wei Ye, name: Fei Huang
Published: 2025-10-28 15:56:36

With advances in decision-making and reasoning capabilities, multimodal
agents show strong potential in computer application scenarios. Past
evaluations have mainly assessed GUI interaction skills, while tool invocation
abilities, such as those enabled by the Model Context Protocol (MCP), have been
largely overlooked. Comparing agents with integrated tool invocation to those
evaluated only on GUI interaction is inherently unfair. We present OSWorld-MCP,
the first comprehensive and fair benchmark for assessing computer-use agents'
tool invocation, GUI operation, and decision-making abilities in a real-world
environment. We design a novel automated code-generation pipeline to create
tools and combine them with a curated selection from existing tools. Rigorous
manual validation yields 158 high-quality tools (covering 7 common
applications), each verified for correct functionality, practical
applicability, and versatility. Extensive evaluations of state-of-the-art
multimodal agents on OSWorld-MCP show that MCP tools generally improve task
success rates (e.g., from 8.3% to 20.4% for OpenAI o3 at 15 steps, from 40.1%
to 43.3% for Claude 4 Sonnet at 50 steps), underscoring the importance of
assessing tool invocation capabilities. However, even the strongest models have
relatively low tool invocation rates, Only 36.3%, indicating room for
improvement and highlighting the benchmark's challenge. By explicitly measuring
MCP tool usage skills, OSWorld-MCP deepens understanding of multimodal agents
and sets a new standard for evaluating performance in complex, tool-assisted
environments. Our code, environment, and data are publicly available at
https://osworld-mcp.github.io.
ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?
Authors: name: Christine Ye, name: Sihan Yuan, name: Suchetha Cooray, name: Steven Dillmann, name: Ian L. V. Roque, name: Dalya Baron, name: Philipp Frank, name: Sergio Martin-Alvarez, name: Nolan Koblischke, name: Frank J Qu, name: Diyi Yang, name: Risa Wechsler, name: Ioana Ciuca
Published: 2025-10-28 16:21:19

Frontier AI agents show increasing promise as scientific research assistants,
and may eventually be useful for extended, open-ended research workflows.
However, in order to use agents for novel research, we must first assess the
underlying faithfulness and correctness of their work. To evaluate agents as
research assistants, we introduce ReplicationBench, an evaluation framework
that tests whether agents can replicate entire research papers drawn from the
astrophysics literature. Astrophysics, where research relies heavily on
archival data and computational study while requiring little real-world
experimentation, is a particularly useful testbed for AI agents in scientific
research. We split each paper into tasks which require agents to replicate the
paper's core contributions, including the experimental setup, derivations, data
analysis, and codebase. Each task is co-developed with the original paper
authors and targets a key scientific result, enabling objective evaluation of
both faithfulness (adherence to original methods) and correctness (technical
accuracy of results). ReplicationBench is extremely challenging for current
frontier language models: even the best-performing language models score under
20%. We analyze ReplicationBench trajectories in collaboration with domain
experts and find a rich, diverse set of failure modes for agents in scientific
research. ReplicationBench establishes the first benchmark of paper-scale,
expert-validated astrophysics research tasks, reveals insights about agent
performance generalizable to other domains of data-driven science, and provides
a scalable framework for measuring AI agents' reliability in scientific
research.
Tongyi DeepResearch Technical Report
Authors: name: Tongyi DeepResearch Team, name: Baixuan Li, name: Bo Zhang, name: Dingchu Zhang, name: Fei Huang, name: Guangyu Li, name: Guoxin Chen, name: Huifeng Yin, name: Jialong Wu, name: Jingren Zhou, name: Kuan Li, name: Liangcai Su, name: Litu Ou, name: Liwen Zhang, name: Pengjun Xie, name: Rui Ye, name: Wenbiao Yin, name: Xinmiao Yu, name: Xinyu Wang, name: Xixi Wu, name: Xuanzhong Chen, name: Yida Zhao, name: Zhen Zhang, name: Zhengwei Tao, name: Zhongwang Zhang, name: Zile Qiao, name: Chenxi Wang, name: Donglei Yu, name: Gang Fu, name: Haiyang Shen, name: Jiayin Yang, name: Jun Lin, name: Junkai Zhang, name: Kui Zeng, name: Li Yang, name: Hailong Yin, name: Maojia Song, name: Ming Yan, name: Peng Xia, name: Qian Xiao, name: Rui Min, name: Ruixue Ding, name: Runnan Fang, name: Shaowei Chen, name: Shen Huang, name: Shihang Wang, name: Shihao Cai, name: Weizhou Shen, name: Xiaobin Wang, name: Xin Guan, name: Xinyu Geng, name: Yingcheng Shi, name: Yuning Wu, name: Zhuo Chen, name: Zijian Li, name: Yong Jiang
Published: 2025-10-28 17:53:02

We present Tongyi DeepResearch, an agentic large language model, which is
specifically designed for long-horizon, deep information-seeking research
tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is
developed through an end-to-end training framework that combines agentic
mid-training and agentic post-training, enabling scalable reasoning and
information seeking across complex tasks. We design a highly scalable data
synthesis pipeline that is fully automatic, without relying on costly human
annotation, and empowers all training stages. By constructing customized
environments for each stage, our system enables stable and consistent
interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total
parameters, with only 3.3 billion activated per token, achieves
state-of-the-art performance across a range of agentic deep research
benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH,
WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We
open-source the model, framework, and complete solutions to empower the
community.
Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion
  Transformers
Authors: name: Dogyun Park, name: Moayed Haji-Ali, name: Yanyu Li, name: Willi Menapace, name: Sergey Tulyakov, name: Hyunwoo J. Kim, name: Aliaksandr Siarohin, name: Anil Kag
Published: 2025-10-24 19:29:55

Diffusion Transformers (DiTs) deliver state-of-the-art generative performance
but their quadratic training cost with sequence length makes large-scale
pretraining prohibitively expensive. Token dropping can reduce training cost,
yet na\"ive strategies degrade representations, and existing methods are either
parameter-heavy or fail at high drop ratios. We present SPRINT, Sparse--Dense
Residual Fusion for Efficient Diffusion Transformers, a simple method that
enables aggressive token dropping (up to 75%) while preserving quality. SPRINT
leverages the complementary roles of shallow and deep layers: early layers
process all tokens to capture local detail, deeper layers operate on a sparse
subset to cut computation, and their outputs are fused through residual
connections. Training follows a two-stage schedule: long masked pre-training
for efficiency followed by short full-token fine-tuning to close the
train--inference gap. On ImageNet-1K 256x256, SPRINT achieves 9.8x training
savings with comparable FID/FDD, and at inference, its Path-Drop Guidance (PDG)
nearly halves FLOPs while improving quality. These results establish SPRINT as
a simple, effective, and general solution for efficient DiT training.
Multi-Agent Evolve: LLM Self-Improve through Co-evolution
Authors: name: Yixing Chen, name: Yiding Wang, name: Siqi Zhu, name: Haofei Yu, name: Tao Feng, name: Muhan Zhan, name: Mostofa Patwary, name: Jiaxuan You
Published: 2025-10-27 17:58:02

Reinforcement Learning (RL) has demonstrated significant potential in
enhancing the reasoning capabilities of large language models (LLMs). However,
the success of RL for LLMs heavily relies on human-curated datasets and
verifiable rewards, which limit their scalability and generality. Recent
Self-Play RL methods, inspired by the success of the paradigm in games and Go,
aim to enhance LLM reasoning capabilities without human-annotated data.
However, their methods primarily depend on a grounded environment for feedback
(e.g., a Python interpreter or a game engine); extending them to general
domains remains challenging. To address these challenges, we propose
Multi-Agent Evolve (MAE), a framework that enables LLMs to self-evolve in
solving diverse tasks, including mathematics, reasoning, and general knowledge
Q&A. The core design of MAE is based on a triplet of interacting agents
(Proposer, Solver, Judge) that are instantiated from a single LLM, and applies
reinforcement learning to optimize their behaviors. The Proposer generates
questions, the Solver attempts solutions, and the Judge evaluates both while
co-evolving. Experiments on Qwen2.5-3B-Instruct demonstrate that MAE achieves
an average improvement of 4.54% on multiple benchmarks. These results highlight
MAE as a scalable, data-efficient method for enhancing the general reasoning
abilities of LLMs with minimal reliance on human-curated supervision.
MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal
  Understanding
Authors: name: Xin Jin, name: Siyuan Li, name: Siyong Jian, name: Kai Yu, name: Huan Wang
Published: 2025-10-27 16:12:40

Vision-language alignment in multi-modal large language models (MLLMs)
typically relies on supervised fine-tuning (SFT) or reinforcement learning
(RL). SFT is stable and efficient but requires large-scale human annotations
and cannot capture subtle preferences, while RL brings in a reward signal for
training, but suffers from overhead and instability. These limitations
highlight a trade-off between scalability, robustness, and alignment quality.
To address this, we propose MergeMix, a training-time augmentation paradigm
that bridges SFT and RL. It first applies an attention-aware image mixing via
token merge with more cluster representation and spatial context, and then
presents a preference-driven training paradigm for MLLMs by building preference
pairs with mixed images and raw images, and optimizing via SimPO loss. As a
mixup augmentation, MergeMix enhances attention consistency and efficiency,
surpassing other heuristic-based methods in classification. Extensive
experiments demonstrate that MergeMix achieves competitive accuracy with
improved efficiency, providing a scalable approach to preference alignment in
classification and MLLMs.
Open Multimodal Retrieval-Augmented Factual Image Generation
Authors: name: Yang Tian, name: Fan Liu, name: Jingyuan Zhang, name: Wei Bi, name: Yupeng Hu, name: Liqiang Nie
Published: 2025-10-26 04:13:31

Large Multimodal Models (LMMs) have achieved remarkable progress in
generating photorealistic and prompt-aligned images, but they often produce
outputs that contradict verifiable knowledge, especially when prompts involve
fine-grained attributes or time-sensitive events. Conventional
retrieval-augmented approaches attempt to address this issue by introducing
external information, yet they are fundamentally incapable of grounding
generation in accurate and evolving knowledge due to their reliance on static
sources and shallow evidence integration. To bridge this gap, we introduce
ORIG, an agentic open multimodal retrieval-augmented framework for Factual
Image Generation (FIG), a new task that requires both visual realism and
factual grounding. ORIG iteratively retrieves and filters multimodal evidence
from the web and incrementally integrates the refined knowledge into enriched
prompts to guide generation. To support systematic evaluation, we build
FIG-Eval, a benchmark spanning ten categories across perceptual, compositional,
and temporal dimensions. Experiments demonstrate that ORIG substantially
improves factual consistency and overall image quality over strong baselines,
highlighting the potential of open multimodal retrieval for factual image
generation.
FlowOpt: Fast Optimization Through Whole Flow Processes for
  Training-Free Editing
Authors: name: Or Ronai, name: Vladimir Kulikov, name: Tomer Michaeli
Published: 2025-10-24 20:24:26

The remarkable success of diffusion and flow-matching models has ignited a
surge of works on adapting them at test time for controlled generation tasks.
Examples range from image editing to restoration, compression and
personalization. However, due to the iterative nature of the sampling process
in those models, it is computationally impractical to use gradient-based
optimization to directly control the image generated at the end of the process.
As a result, existing methods typically resort to manipulating each timestep
separately. Here we introduce FlowOpt - a zero-order (gradient-free)
optimization framework that treats the entire flow process as a black box,
enabling optimization through the whole sampling path without backpropagation
through the model. Our method is both highly efficient and allows users to
monitor the intermediate optimization results and perform early stopping if
desired. We prove a sufficient condition on FlowOpt's step-size, under which
convergence to the global optimum is guaranteed. We further show how to
empirically estimate this upper bound so as to choose an appropriate step-size.
We demonstrate how FlowOpt can be used for image editing, showcasing two
options: (i) inversion (determining the initial noise that generates a given
image), and (ii) directly steering the edited image to be similar to the source
image while conforming to a target text prompt. In both cases, FlowOpt achieves
state-of-the-art results while using roughly the same number of neural function
evaluations (NFEs) as existing methods. Code and examples are available on the
project's webpage.