Orionnnn/hub-stats huggingface dataset:
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via
  Hierarchical Model Merging
Authors: name: Bowen Wang, name: Haiyuan Wan, name: Liwen Shi, name: Chen Yang, name: Peng He, name: Yue Ma, name: Haochen Han, name: Wenhao Li, name: Tiao Tan, name: Yongjian Li, name: Fangming Liu, name: Yifan Gong, name: Sheng Zhang
Published: 2025-10-23 12:17:37

We unveil that internal representations in large language models (LLMs) serve
as reliable proxies of learned knowledge, and propose RECALL, a novel
representation-aware model merging framework for continual learning without
access to historical data. RECALL computes inter-model similarity from
layer-wise hidden representations over clustered typical samples, and performs
adaptive, hierarchical parameter fusion to align knowledge across models. This
design enables the preservation of domain-general features in shallow layers
while allowing task-specific adaptation in deeper layers. Unlike prior methods
that require task labels or incur performance trade-offs, RECALL achieves
seamless multi-domain integration and strong resistance to catastrophic
forgetting. Extensive experiments across five NLP tasks and multiple continual
learning scenarios show that RECALL outperforms baselines in both knowledge
retention and generalization, providing a scalable and data-free solution for
evolving LLMs.
From Denoising to Refining: A Corrective Framework for Vision-Language
  Diffusion Model
Authors: name: Yatai Ji, name: Teng Wang, name: Yuying Ge, name: Zhiheng Liu, name: Sidi Yang, name: Ying Shan, name: Ping Luo
Published: 2025-10-22 06:58:55

Discrete diffusion models have emerged as a promising direction for
vision-language tasks, offering bidirectional context modeling and theoretical
parallelization. However, their practical application is severely hindered by a
train-inference discrepancy, which leads to catastrophic error cascades:
initial token errors during parallel decoding pollute the generation context,
triggering a chain reaction of compounding errors and leading to syntactic
errors and semantic hallucinations. To address this fundamental challenge, we
reframe the generation process from passive denoising to active refining. We
introduce ReDiff, a refining-enhanced diffusion framework that teaches the
model to identify and correct its own errors. Our approach features a two-stage
training process: first, we instill a foundational revision capability by
training the model to revise synthetic errors; second, we implement a novel
online self-correction loop where the model is explicitly trained to revise its
own flawed drafts by learning from an expert's corrections. This mistake-driven
learning endows the model with the crucial ability to revisit and refine its
already generated output, effectively breaking the error cascade. Extensive
experiments demonstrate that ReDiff significantly improves the coherence and
factual accuracy of generated content, enabling stable and efficient parallel
generation far superior to traditional denoising methods. Our codes and models
are available at https://rediff-hku.github.io/.
Document Understanding, Measurement, and Manipulation Using Category
  Theory
Authors: name: Jared Claypoole, name: Yunye Gong, name: Noson S. Yanofsky, name: Ajay Divakaran
Published: 2025-10-24 15:12:08

We apply category theory to extract multimodal document structure which leads
us to develop information theoretic measures, content summarization and
extension, and self-supervised improvement of large pretrained models. We first
develop a mathematical representation of a document as a category of
question-answer pairs. Second, we develop an orthogonalization procedure to
divide the information contained in one or more documents into non-overlapping
pieces. The structures extracted in the first and second steps lead us to
develop methods to measure and enumerate the information contained in a
document. We also build on those steps to develop new summarization techniques,
as well as to develop a solution to a new problem viz. exegesis resulting in an
extension of the original document. Our question-answer pair methodology
enables a novel rate distortion analysis of summarization techniques. We
implement our techniques using large pretrained models, and we propose a
multimodal extension of our overall mathematical framework. Finally, we develop
a novel self-supervised method using RLVR to improve large pretrained models
using consistency constraints such as composability and closure under certain
operations that stem naturally from our category theoretic framework.
Model Merging with Functional Dual Anchors
Authors: name: Kexuan Shi, name: Yandong Wen, name: Weiyang Liu
Published: 2025-10-24 07:54:06

Model merging is an efficient post-training strategy for integrating
knowledge from multiple finetuned checkpoints of a shared foundation model.
Existing methods operate in the parameter space, combining task vectors to
mitigate conflicts, but remain constrained by parameter inconsistencies. We
propose Functional Dual Anchors (FDAs), a framework that instead models the
input-representation space. FDAs are synthetic inputs whose induced gradients
align with task vectors, capturing task-specific functional shifts relative to
the pretrained model. This perspective bridges joint multi-task training and
post-hoc merging, offering both robustness and flexibility. We further
introduce a principled initialization scheme and show that FDAs are
complementary to parameter-space model merging. Comprehensive experiments
demonstrate the effectiveness of FDAs in model merging.
A Definition of AGI
Authors: name: Dan Hendrycks, name: Dawn Song, name: Christian Szegedy, name: Honglak Lee, name: Yarin Gal, name: Erik Brynjolfsson, name: Sharon Li, name: Andy Zou, name: Lionel Levine, name: Bo Han, name: Jie Fu, name: Ziwei Liu, name: Jinwoo Shin, name: Kimin Lee, name: Mantas Mazeika, name: Long Phan, name: George Ingebretsen, name: Adam Khoja, name: Cihang Xie, name: Olawale Salaudeen, name: Matthias Hein, name: Kevin Zhao, name: Alexander Pan, name: David Duvenaud, name: Bo Li, name: Steve Omohundro, name: Gabriel Alfour, name: Max Tegmark, name: Kevin McGrew, name: Gary Marcus, name: Jaan Tallinn, name: Eric Schmidt, name: Yoshua Bengio
Published: 2025-10-21 01:28:35

The lack of a concrete definition for Artificial General Intelligence (AGI)
obscures the gap between today's specialized AI and human-level cognition. This
paper introduces a quantifiable framework to address this, defining AGI as
matching the cognitive versatility and proficiency of a well-educated adult. To
operationalize this, we ground our methodology in Cattell-Horn-Carroll theory,
the most empirically validated model of human cognition. The framework dissects
general intelligence into ten core cognitive domains-including reasoning,
memory, and perception-and adapts established human psychometric batteries to
evaluate AI systems. Application of this framework reveals a highly "jagged"
cognitive profile in contemporary models. While proficient in
knowledge-intensive domains, current AI systems have critical deficits in
foundational cognitive machinery, particularly long-term memory storage. The
resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 58%) concretely quantify
both rapid progress and the substantial gap remaining before AGI.
DeepAgent: A General Reasoning Agent with Scalable Toolsets
Authors: name: Xiaoxi Li, name: Wenxiang Jiao, name: Jiarui Jin, name: Guanting Dong, name: Jiajie Jin, name: Yinuo Wang, name: Hao Wang, name: Yutao Zhu, name: Ji-Rong Wen, name: Yuan Lu, name: Zhicheng Dou
Published: 2025-10-24 16:24:01

Large reasoning models have demonstrated strong problem-solving abilities,
yet real-world tasks often require external tools and long-horizon
interactions. Existing agent frameworks typically follow predefined workflows,
which limit autonomous and global task completion. In this paper, we introduce
DeepAgent, an end-to-end deep reasoning agent that performs autonomous
thinking, tool discovery, and action execution within a single, coherent
reasoning process. To address the challenges of long-horizon interactions,
particularly the context length explosion from multiple tool calls and the
accumulation of interaction history, we introduce an autonomous memory folding
mechanism that compresses past interactions into structured episodic, working,
and tool memories, reducing error accumulation while preserving critical
information. To teach general-purpose tool use efficiently and stably, we
develop an end-to-end reinforcement learning strategy, namely ToolPO, that
leverages LLM-simulated APIs and applies tool-call advantage attribution to
assign fine-grained credit to the tool invocation tokens. Extensive experiments
on eight benchmarks, including general tool-use tasks (ToolBench, API-Bank,
TMDB, Spotify, ToolHop) and downstream applications (ALFWorld, WebShop, GAIA,
HLE), demonstrate that DeepAgent consistently outperforms baselines across both
labeled-tool and open-set tool retrieval scenarios. This work takes a step
toward more general and capable agents for real-world applications. The code
and demo are available at https://github.com/RUC-NLPIR/DeepAgent.
AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research
  Suite
Authors: name: Jonathan Bragg, name: Mike D'Arcy, name: Nishant Balepur, name: Dan Bareket, name: Bhavana Dalvi, name: Sergey Feldman, name: Dany Haddad, name: Jena D. Hwang, name: Peter Jansen, name: Varsha Kishore, name: Bodhisattwa Prasad Majumder, name: Aakanksha Naik, name: Sigal Rahamimov, name: Kyle Richardson, name: Amanpreet Singh, name: Harshit Surana, name: Aryeh Tiktinsky, name: Rosni Vasu, name: Guy Wiener, name: Chloe Anastasiades, name: Stefan Candra, name: Jason Dunkelberger, name: Dan Emery, name: Rob Evans, name: Malachi Hamada, name: Regan Huff, name: Rodney Kinney, name: Matt Latzke, name: Jaron Lochner, name: Ruben Lozano-Aguilera, name: Cecile Nguyen, name: Smita Rao, name: Amber Tanaka, name: Brooke Vlahos, name: Peter Clark, name: Doug Downey, name: Yoav Goldberg, name: Ashish Sabharwal, name: Daniel S. Weld
Published: 2025-10-24 17:10:26

AI agents hold the potential to revolutionize scientific productivity by
automating literature reviews, replicating experiments, analyzing data, and
even proposing new directions of inquiry; indeed, there are now many such
agents, ranging from general-purpose "deep research" systems to specialized
science-specific agents, such as AI Scientist and AIGS. Rigorous evaluation of
these agents is critical for progress. Yet existing benchmarks fall short on
several fronts: they (1) fail to provide holistic, product-informed measures of
real-world use cases such as science research; (2) lack reproducible agent
tools necessary for a controlled comparison of core agentic capabilities; (3)
do not account for confounding variables such as model cost and tool access;
(4) do not provide standardized interfaces for quick agent prototyping and
evaluation; and (5) lack comprehensive baseline agents necessary to identify
true advances. In response, we define principles and tooling for more
rigorously benchmarking agents. Using these, we present AstaBench, a suite that
provides the first holistic measure of agentic ability to perform scientific
research, comprising 2400+ problems spanning the entire scientific discovery
process and multiple scientific domains, and including many problems inspired
by actual user requests to deployed Asta agents. Our suite comes with the first
scientific research environment with production-grade search tools that enable
controlled, reproducible evaluation, better accounting for confounders.
Alongside, we provide a comprehensive suite of nine science-optimized classes
of Asta agents and numerous baselines. Our extensive evaluation of 57 agents
across 22 agent classes reveals several interesting findings, most importantly
that despite meaningful progress on certain individual aspects, AI remains far
from solving the challenge of science research assistance.
UI-Ins: Enhancing GUI Grounding with Multi-Perspective
  Instruction-as-Reasoning
Authors: name: Liangyu Chen, name: Hanzhang Zhou, name: Chenglin Cai, name: Jianan Zhang, name: Panrong Tong, name: Quyu Kong, name: Xu Zhang, name: Chen Liu, name: Yuqi Liu, name: Wenxuan Wang, name: Yue Wang, name: Qin Jin, name: Steven Hoi
Published: 2025-10-23 07:18:32

GUI grounding, which maps natural-language instructions to actionable UI
elements, is a core capability of GUI agents. Prior works largely treats
instructions as a static proxy for user intent, overlooking the impact of
instruction diversity and quality on grounding performance. Through a careful
investigation of existing grounding datasets, we find a 23.3% flaw rate in
their instructions and show that inference-time exploitation of instruction
diversity yields up to a substantial 76% relative performance improvement. In
this paper, we introduce the Instruction-as-Reasoning paradigm, treating
instructions as dynamic analytical pathways that offer distinct perspectives
and enabling the model to select the most effective pathway during reasoning.
To achieve this, we propose a two-stage training framework: supervised
fine-tuning (SFT) on synthesized, diverse instructions to instill
multi-perspective reasoning, followed by reinforcement learning (RL) to
optimize pathway selection and composition. Our resulting models, UI-Ins-7B and
UI-Ins-32B, achieve state-of-the-art results on five challenging grounding
benchmarks and exhibit emergent reasoning, selectively composing and
synthesizing novel instruction pathways at inference. In particular, UI-Ins-32B
attains the best grounding accuracy, scoring 87.3% on UI-I2E-Bench, 57.0% on
ScreenSpot-Pro, and 84.9% on MMBench-GUI L2. Furthermore, our model
demonstrates strong agentic potential, achieving a 74.1% success rate on
AndroidWorld using UI-Ins-7B as the executor. Our in-depth analysis reveals
additional insights such as how reasoning can be formulated to enhance rather
than hinder grounding performance, and how our method mitigates policy collapse
in the SFT+RL framework. All code and model checkpoints will be publicly
released in https://github.com/alibaba/UI-Ins.
Visual Diffusion Models are Geometric Solvers
Authors: name: Nir Goren, name: Shai Yehezkel, name: Omer Dahary, name: Andrey Voynov, name: Or Patashnik, name: Daniel Cohen-Or
Published: 2025-10-24 17:57:31

In this paper we show that visual diffusion models can serve as effective
geometric solvers: they can directly reason about geometric problems by working
in pixel space. We first demonstrate this on the Inscribed Square Problem, a
long-standing problem in geometry that asks whether every Jordan curve contains
four points forming a square. We then extend the approach to two other
well-known hard geometric problems: the Steiner Tree Problem and the Simple
Polygon Problem.
  Our method treats each problem instance as an image and trains a standard
visual diffusion model that transforms Gaussian noise into an image
representing a valid approximate solution that closely matches the exact one.
The model learns to transform noisy geometric structures into correct
configurations, effectively recasting geometric reasoning as image generation.
  Unlike prior work that necessitates specialized architectures and
domain-specific adaptations when applying diffusion to parametric geometric
representations, we employ a standard visual diffusion model that operates on
the visual representation of the problem. This simplicity highlights a
surprising bridge between generative modeling and geometric problem solving.
Beyond the specific problems studied here, our results point toward a broader
paradigm: operating in image space provides a general and practical framework
for approximating notoriously hard problems, and opens the door to tackling a
far wider class of challenging geometric tasks.
Video-As-Prompt: Unified Semantic Control for Video Generation
Authors: name: Yuxuan Bian, name: Xin Chen, name: Zenan Li, name: Tiancheng Zhi, name: Shen Sang, name: Linjie Luo, name: Qiang Xu
Published: 2025-10-23 17:59:52

Unified, generalizable semantic control in video generation remains a
critical open challenge. Existing methods either introduce artifacts by
enforcing inappropriate pixel-wise priors from structure-based controls, or
rely on non-generalizable, condition-specific finetuning or task-specific
architectures. We introduce Video-As-Prompt (VAP), a new paradigm that reframes
this problem as in-context generation. VAP leverages a reference video as a
direct semantic prompt, guiding a frozen Video Diffusion Transformer (DiT) via
a plug-and-play Mixture-of-Transformers (MoT) expert. This architecture
prevents catastrophic forgetting and is guided by a temporally biased position
embedding that eliminates spurious mapping priors for robust context retrieval.
To power this approach and catalyze future research, we built VAP-Data, the
largest dataset for semantic-controlled video generation with over 100K paired
videos across 100 semantic conditions. As a single unified model, VAP sets a
new state-of-the-art for open-source methods, achieving a 38.7% user preference
rate that rivals leading condition-specific commercial models. VAP's strong
zero-shot generalization and support for various downstream applications mark a
significant advance toward general-purpose, controllable video generation.
ARC-Encoder: learning compressed text representations for large language
  models
Authors: name: Hippolyte Pilchen, name: Edouard Grave, name: Patrick Pérez
Published: 2025-10-23 13:20:57

Recent techniques such as retrieval-augmented generation or chain-of-thought
reasoning have led to longer contexts and increased inference costs. Context
compression techniques can reduce these costs, but the most effective
approaches require fine-tuning the target model or even modifying its
architecture. This can degrade its general abilities when not used for this
specific purpose. Here we explore an alternative approach: an encoder that
compresses the context into continuous representations which replace token
embeddings in decoder LLMs. First, we perform a systematic study of training
strategies and architecture choices for the encoder. Our findings led to the
design of an Adaptable text Representations Compressor, named ARC-Encoder,
which outputs x-times fewer continuous representations (typically
x!in!{4,8}) than text tokens. We evaluate ARC-Encoder across a variety
of LLM usage scenarios, ranging from in-context learning to context window
extension, on both instruct and base decoders. Results show that ARC-Encoder
achieves state-of-the-art performance on several benchmarks while improving
computational efficiency at inference. Finally, we demonstrate that our models
can be adapted to multiple decoders simultaneously, allowing a single encoder
to generalize across different decoder LLMs. This makes ARC-Encoder a flexible
and efficient solution for portable encoders that work seamlessly with multiple
LLMs. We release a training code at https://github.com/kyutai-labs/ARC-Encoder
, fine-tuning dataset and pretrained models are available at
https://huggingface.co/collections/kyutai/arc-encoders-68ee18787301407d60a57047 .
PhysWorld: From Real Videos to World Models of Deformable Objects via
  Physics-Aware Demonstration Synthesis
Authors: name: Yu Yang, name: Zhilu Zhang, name: Xiang Zhang, name: Yihan Zeng, name: Hui Li, name: Wangmeng Zuo
Published: 2025-10-24 13:25:39

Interactive world models that simulate object dynamics are crucial for
robotics, VR, and AR. However, it remains a significant challenge to learn
physics-consistent dynamics models from limited real-world video data,
especially for deformable objects with spatially-varying physical properties.
To overcome the challenge of data scarcity, we propose PhysWorld, a novel
framework that utilizes a simulator to synthesize physically plausible and
diverse demonstrations to learn efficient world models. Specifically, we first
construct a physics-consistent digital twin within MPM simulator via
constitutive model selection and global-to-local optimization of physical
properties. Subsequently, we apply part-aware perturbations to the physical
properties and generate various motion patterns for the digital twin,
synthesizing extensive and diverse demonstrations. Finally, using these
demonstrations, we train a lightweight GNN-based world model that is embedded
with physical properties. The real video can be used to further refine the
physical properties. PhysWorld achieves accurate and fast future predictions
for various deformable objects, and also generalizes well to novel
interactions. Experiments show that PhysWorld has competitive performance while
enabling inference speeds 47 times faster than the recent state-of-the-art
method, i.e., PhysTwin.
WorldGrow: Generating Infinite 3D World
Authors: name: Sikuang Li, name: Chen Yang, name: Jiemin Fang, name: Taoran Yi, name: Jia Lu, name: Jiazhong Cen, name: Lingxi Xie, name: Wei Shen, name: Qi Tian
Published: 2025-10-24 17:39:52

We tackle the challenge of generating the infinitely extendable 3D world --
large, continuous environments with coherent geometry and realistic appearance.
Existing methods face key challenges: 2D-lifting approaches suffer from
geometric and appearance inconsistencies across views, 3D implicit
representations are hard to scale up, and current 3D foundation models are
mostly object-centric, limiting their applicability to scene-level generation.
Our key insight is leveraging strong generation priors from pre-trained 3D
models for structured scene block generation. To this end, we propose
WorldGrow, a hierarchical framework for unbounded 3D scene synthesis. Our
method features three core components: (1) a data curation pipeline that
extracts high-quality scene blocks for training, making the 3D structured
latent representations suitable for scene generation; (2) a 3D block inpainting
mechanism that enables context-aware scene extension; and (3) a coarse-to-fine
generation strategy that ensures both global layout plausibility and local
geometric/textural fidelity. Evaluated on the large-scale 3D-FRONT dataset,
WorldGrow achieves SOTA performance in geometry reconstruction, while uniquely
supporting infinite scene generation with photorealistic and structurally
consistent outputs. These results highlight its capability for constructing
large-scale virtual environments and potential for building future world
models.
MSC-Bench: A Rigorous Benchmark for Multi-Server Tool Orchestration
Authors: name: Jia-Kai Dong, name: I-Wei Huang, name: Chun-Tin Wu, name: Yi-Tien Tsai
Published: 2025-10-22 09:45:11

We introduce MSC-Bench, a large-scale benchmark for evaluating multi-hop,
end-to-end tool orchestration by LLM agents in a hierarchical Model-Context
Protocol (MCP) ecosystem. Existing benchmarks often evaluate tools in
isolation, ignoring challenges such as functional overlap and cross-server
orchestration, leading to overly optimistic assessments. MSC-Bench addresses
these gaps by constructing ground truth through 'equal function sets', allowing
objective metrics such as F1 score and reducing the dependency on
LLM-as-a-judge evaluation. Organized as a five-level curriculum, it
systematically tests agent capabilities from single-tool orchestration to
complex cross-server planning, and robustness to out-of-scope requests.
Experiments reveal that rigid hierarchies can hinder performance without
co-designed strategies, and even state-of-the-art agents exhibit systemic
weaknesses in robustness. MSC-Bench provides a diagnostic framework to expose
these limitations and guide the development of more capable and efficient
tool-using agents. The benchmark and resources are publicly available at
https://github.com/snooow1029/MSC_Bench.