Orionnnn/hub-stats huggingface dataset:
MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
Authors: name: Yuncong Yang, name: Jiageng Liu, name: Zheyuan Zhang, name: Siyuan Zhou, name: Reuben Tan, name: Jianwei Yang, name: Yilun Du, name: Chuang Gan
Published: 2025-07-16 17:59:36

Spatial reasoning in 3D space is central to human cognition and indispensable
for embodied tasks such as navigation and manipulation. However,
state-of-the-art vision-language models (VLMs) struggle frequently with tasks
as simple as anticipating how a scene will look after an egocentric motion:
they perceive 2D images but lack an internal model of 3D dynamics. We therefore
propose MindJourney, a test-time scaling framework that grants a VLM with this
missing capability by coupling it to a controllable world model based on video
diffusion. The VLM iteratively sketches a concise camera trajectory, while the
world model synthesizes the corresponding view at each step. The VLM then
reasons over this multi-view evidence gathered during the interactive
exploration. Without any fine-tuning, our MindJourney achieves over an average
8% performance boost on the representative spatial reasoning benchmark SAT,
showing that pairing VLMs with world models for test-time scaling offers a
simple, plug-and-play route to robust 3D reasoning. Meanwhile, our method also
improves upon the test-time inference VLMs trained through reinforcement
learning, which demonstrates the potential of our method that utilizes world
models for test-time scaling.
A Survey of Context Engineering for Large Language Models
Authors: name: Lingrui Mei, name: Jiayu Yao, name: Yuyao Ge, name: Yiwei Wang, name: Baolong Bi, name: Yujun Cai, name: Jiazhi Liu, name: Mingyu Li, name: Zhong-Zhi Li, name: Duzhen Zhang, name: Chenlin Zhou, name: Jiayi Mao, name: Tianze Xia, name: Jiafeng Guo, name: Shenghua Liu
Published: 2025-07-17 17:50:36

The performance of Large Language Models (LLMs) is fundamentally determined
by the contextual information provided during inference. This survey introduces
Context Engineering, a formal discipline that transcends simple prompt design
to encompass the systematic optimization of information payloads for LLMs. We
present a comprehensive taxonomy decomposing Context Engineering into its
foundational components and the sophisticated implementations that integrate
them into intelligent systems. We first examine the foundational components:
context retrieval and generation, context processing and context management. We
then explore how these components are architecturally integrated to create
sophisticated system implementations: retrieval-augmented generation (RAG),
memory systems and tool-integrated reasoning, and multi-agent systems. Through
this systematic analysis of over 1300 research papers, our survey not only
establishes a technical roadmap for the field but also reveals a critical
research gap: a fundamental asymmetry exists between model capabilities. While
current models, augmented by advanced context engineering, demonstrate
remarkable proficiency in understanding complex contexts, they exhibit
pronounced limitations in generating equally sophisticated, long-form outputs.
Addressing this gap is a defining priority for future research. Ultimately,
this survey provides a unified framework for both researchers and engineers
advancing context-aware AI.
FLEXITOKENS: Flexible Tokenization for Evolving Language Models
Authors: name: Abraham Toluase Owodunni, name: Orevaoghene Ahia, name: Sachin Kumar
Published: 2025-07-17 01:55:41

Language models (LMs) are challenging to adapt to new data distributions by
simple finetuning. This is due to the rigidity of their subword tokenizers,
which typically remain unchanged during adaptation. This inflexibility often
leads to inefficient tokenization, causing overfragmentation of
out-of-distribution domains, unseen languages, or scripts. In this work, we
develop byte-level LMs with learnable tokenizers to make tokenization adaptive.
Our models include a submodule that learns to predict boundaries between the
input byte sequence, encoding it into variable-length segments. Existing
tokenizer-free methods train this boundary predictor using an auxiliary loss
that enforces a fixed compression rate across the training corpus, introducing
a new kind of rigidity. We propose FLEXITOKENS, a simplified training objective
that enables significantly greater flexibility during adaptation. Evaluating
across multiple multilingual benchmarks, morphologically diverse tasks, and
domains, we demonstrate that FLEXITOKENS consistently reduces token
over-fragmentation and achieves up to 10\% improvements on downstream task
performance compared to subword and other gradient-based tokenizers. Code and
data for our experiments will be released at
https://github.com/owos/flexitokens
MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge
  Distillation
Authors: name: Hui Li, name: Pengfei Yang, name: Juanyang Chen, name: Le Dong, name: Yanxin Chen, name: Quan Wang
Published: 2025-07-09 16:45:28

Knowledge distillation as an efficient knowledge transfer technique, has
achieved remarkable success in unimodal scenarios. However, in cross-modal
settings, conventional distillation methods encounter significant challenges
due to data and statistical heterogeneities, failing to leverage the
complementary prior knowledge embedded in cross-modal teacher models. This
paper empirically reveals two critical issues in existing approaches:
distillation path selection and knowledge drift. To address these limitations,
we propose MST-Distill, a novel cross-modal knowledge distillation framework
featuring a mixture of specialized teachers. Our approach employs a diverse
ensemble of teacher models across both cross-modal and multimodal
configurations, integrated with an instance-level routing network that
facilitates adaptive and dynamic distillation. This architecture effectively
transcends the constraints of traditional methods that rely on monotonous and
static teacher models. Additionally, we introduce a plug-in masking module,
independently trained to suppress modality-specific discrepancies and
reconstruct teacher representations, thereby mitigating knowledge drift and
enhancing transfer effectiveness. Extensive experiments across five diverse
multimodal datasets, spanning visual, audio, and text, demonstrate that our
method significantly outperforms existing state-of-the-art knowledge
distillation methods in cross-modal distillation tasks. The source code is
available at https://github.com/Gray-OREO/MST-Distill.
(Almost) Free Modality Stitching of Foundation Models
Authors: name: Jaisidh Singh, name: Diganta Misra, name: Boris Knyazev, name: Antonio Orvieto
Published: 2025-07-14 07:51:01

Foundation multi-modal models are often designed by stitching of multiple
existing pretrained uni-modal models: for example, an image classifier with an
text model. This stitching process is performed by training a connector module
that aims to align the representation spaces of these uni-modal models towards
a multi-modal objective. However, given the complexity of training such
connectors on large scale web-based datasets coupled with the ever-increasing
number of available pretrained uni-modal models, the task of uni-modal models
selection and subsequent connector module training becomes computationally
demanding. To address this under-studied critical problem, we propose
Hypernetwork Model Alignment (Hyma), a novel all-in-one solution for optimal
uni-modal model selection and connector training by leveraging hypernetworks.
Specifically, our framework utilizes the parameter prediction capability of a
hypernetwork to obtain jointly trained connector modules for N times M
combinations of uni-modal models. In our experiments, Hyma reduces the cost of
searching for the best performing uni-modal model pair by 10times, while
matching the ranking and trained connector performance obtained via grid search
across a suite of diverse multi-modal benchmarks.
GitChameleon: Evaluating AI Code Generation Against Python Library
  Version Incompatibilities
Authors: name: Diganta Misra, name: Nizar Islah, name: Victor May, name: Brice Rauby, name: Zihan Wang, name: Justine Gehring, name: Antonio Orvieto, name: Muawiz Chaudhary, name: Eilif B. Muller, name: Irina Rish, name: Samira Ebrahimi Kahou, name: Massimo Caccia
Published: 2025-07-16 16:10:42

The rapid evolution of software libraries poses a considerable hurdle for
code generation, necessitating continuous adaptation to frequent version
updates while preserving backward compatibility. While existing code evolution
benchmarks provide valuable insights, they typically lack execution-based
evaluation for generating code compliant with specific library versions. To
address this, we introduce GitChameleon, a novel, meticulously curated dataset
comprising 328 Python code completion problems, each conditioned on specific
library versions and accompanied by executable unit tests. GitChameleon
rigorously evaluates the capacity of contemporary large language models (LLMs),
LLM-powered agents, code assistants, and RAG systems to perform
version-conditioned code generation that demonstrates functional accuracy
through execution. Our extensive evaluations indicate that state-of-the-art
systems encounter significant challenges with this task; enterprise models
achieving baseline success rates in the 48-51\% range, underscoring the
intricacy of the problem. By offering an execution-based benchmark emphasizing
the dynamic nature of code libraries, GitChameleon enables a clearer
understanding of this challenge and helps guide the development of more
adaptable and dependable AI code generation methods. We make the dataset and
evaluation code publicly available at
https://github.com/mrcabbage972/GitChameleonBenchmark.
Seq vs Seq: An Open Suite of Paired Encoders and Decoders
Authors: name: Orion Weller, name: Kathryn Ricci, name: Marc Marone, name: Antoine Chaffin, name: Dawn Lawrie, name: Benjamin Van Durme
Published: 2025-07-15 15:31:51

The large language model (LLM) community focuses almost exclusively on
decoder-only language models, since they are easier to use for text generation.
However, a large subset of the community still uses encoder-only models for
tasks such as classification or retrieval. Previous work has attempted to
compare these architectures, but is forced to make comparisons with models that
have different numbers of parameters, training techniques, and datasets. We
introduce the SOTA open-data Ettin suite of models: paired encoder-only and
decoder-only models ranging from 17 million parameters to 1 billion, trained on
up to 2 trillion tokens. Using the same recipe for both encoder-only and
decoder-only models produces SOTA recipes in both categories for their
respective sizes, beating ModernBERT as an encoder and Llama 3.2 and SmolLM2 as
decoders. Like previous work, we find that encoder-only models excel at
classification and retrieval tasks while decoders excel at generative tasks.
However, we show that adapting a decoder model to encoder tasks (and vice
versa) through continued training is subpar compared to using only the reverse
objective (i.e. a 400M encoder outperforms a 1B decoder on MNLI, and vice versa
for generative tasks). We open-source all artifacts of this study including
training data, training order segmented by checkpoint, and 200+ checkpoints to
allow future work to analyze or extend all aspects of training.
AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings
  with Sentiment for Subjectivity Detection in News Articles
Authors: name: Matteo Fasulo, name: Luca Babboni, name: Luca Tedeschini
Published: 2025-07-15 22:10:20

This paper presents AI Wizards' participation in the CLEF 2025 CheckThat! Lab
Task 1: Subjectivity Detection in News Articles, classifying sentences as
subjective/objective in monolingual, multilingual, and zero-shot settings.
Training/development datasets were provided for Arabic, German, English,
Italian, and Bulgarian; final evaluation included additional unseen languages
(e.g., Greek, Romanian, Polish, Ukrainian) to assess generalization. Our
primary strategy enhanced transformer-based classifiers by integrating
sentiment scores, derived from an auxiliary model, with sentence
representations, aiming to improve upon standard fine-tuning. We explored this
sentiment-augmented architecture with mDeBERTaV3-base, ModernBERT-base
(English), and Llama3.2-1B. To address class imbalance, prevalent across
languages, we employed decision threshold calibration optimized on the
development set. Our experiments show sentiment feature integration
significantly boosts performance, especially subjective F1 score. This
framework led to high rankings, notably 1st for Greek (Macro F1 = 0.51).
RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning
Authors: name: Hongzhi Zhang, name: Jia Fu, name: Jingyuan Zhang, name: Kai Fu, name: Qi Wang, name: Fuzheng Zhang, name: Guorui Zhou
Published: 2025-07-10 05:58:55

Reinforcement learning (RL) for large language models is an energy-intensive
endeavor: training can be unstable, and the policy may gradually drift away
from its pretrained weights. We present RLEP\, -- \,Reinforcement
Learning with Experience rePlay\, -- \,a two-phase framework that first
collects verified trajectories and then replays them during subsequent
training. At every update step, the policy is optimized on mini-batches that
blend newly generated rollouts with these replayed successes. By replaying
high-quality examples, RLEP steers the model away from fruitless exploration,
focuses learning on promising reasoning paths, and delivers both faster
convergence and stronger final performance. On the Qwen2.5-Math-7B base model,
RLEP reaches baseline peak accuracy with substantially fewer updates and
ultimately surpasses it, improving accuracy on AIME-2024 from 38.2% to 39.9%,
on AIME-2025 from 19.8% to 22.3%, and on AMC-2023 from 77.0% to 82.2%. Our
code, datasets, and checkpoints are publicly available at
https://github.com/Kwai-Klear/RLEP to facilitate reproducibility and further
research.
SpatialTrackerV2: 3D Point Tracking Made Easy
Authors: name: Yuxi Xiao, name: Jianyuan Wang, name: Nan Xue, name: Nikita Karaev, name: Yuri Makarov, name: Bingyi Kang, name: Xing Zhu, name: Hujun Bao, name: Yujun Shen, name: Xiaowei Zhou
Published: 2025-07-16 17:59:03

We present SpatialTrackerV2, a feed-forward 3D point tracking method for
monocular videos. Going beyond modular pipelines built on off-the-shelf
components for 3D tracking, our approach unifies the intrinsic connections
between point tracking, monocular depth, and camera pose estimation into a
high-performing and feedforward 3D point tracker. It decomposes world-space 3D
motion into scene geometry, camera ego-motion, and pixel-wise object motion,
with a fully differentiable and end-to-end architecture, allowing scalable
training across a wide range of datasets, including synthetic sequences, posed
RGB-D videos, and unlabeled in-the-wild footage. By learning geometry and
motion jointly from such heterogeneous data, SpatialTrackerV2 outperforms
existing 3D tracking methods by 30%, and matches the accuracy of leading
dynamic 3D reconstruction approaches while running 50times faster.
Replacing thinking with tool usage enables reasoning in small language
  models
Authors: name: Corrado Rainone, name: Tim Bakker, name: Roland Memisevic
Published: 2025-07-07 14:49:18

Recent advances have established a new machine learning paradigm based on
scaling up compute at inference time as well as at training time. In that line
of work, a combination of Supervised Fine-Tuning (SFT) on synthetic
demonstrations and Reinforcement Learning with Verifiable Rewards (RLVR) is
used for training Large Language Models to expend extra compute during
inference in the form of "thoughts" expressed in natural language. In this
paper, we propose to instead format these tokens as a multi-turn interaction
trace with a stateful tool. At each turn, the new state of the tool is appended
to the context of the model, whose job is to generate the tokens necessary to
control the tool via a custom DSL. We benchmark this approach on the problem of
repairing malfunctioning Python code, and show that this constrained setup
allows for faster sampling of experience and a denser reward signal, allowing
even models of size up to 3B parameters to learn how to proficiently expend
additional compute on the task.
AnyI2V: Animating Any Conditional Image with Motion Control
Authors: name: Ziye Li, name: Hao Luo, name: Xincheng Shuai, name: Henghui Ding
Published: 2025-07-03 17:59:02

Recent advancements in video generation, particularly in diffusion models,
have driven notable progress in text-to-video (T2V) and image-to-video (I2V)
synthesis. However, challenges remain in effectively integrating dynamic motion
signals and flexible spatial constraints. Existing T2V methods typically rely
on text prompts, which inherently lack precise control over the spatial layout
of generated content. In contrast, I2V methods are limited by their dependence
on real images, which restricts the editability of the synthesized content.
Although some methods incorporate ControlNet to introduce image-based
conditioning, they often lack explicit motion control and require
computationally expensive training. To address these limitations, we propose
AnyI2V, a training-free framework that animates any conditional images with
user-defined motion trajectories. AnyI2V supports a broader range of modalities
as the conditional image, including data types such as meshes and point clouds
that are not supported by ControlNet, enabling more flexible and versatile
video generation. Additionally, it supports mixed conditional inputs and
enables style transfer and editing via LoRA and text prompts. Extensive
experiments demonstrate that the proposed AnyI2V achieves superior performance
and provides a new perspective in spatial- and motion-controlled video
generation. Code is available at https://henghuiding.com/AnyI2V/.
SWE-Perf: Can Language Models Optimize Code Performance on Real-World
  Repositories?
Authors: name: Xinyi He, name: Qian Liu, name: Mingzhe Du, name: Lin Yan, name: Zhijie Fan, name: Yiming Huang, name: Zejian Yuan, name: Zejun Ma
Published: 2025-07-16 17:05:17

Code performance optimization is paramount in real-world software engineering
and critical for production-level systems. While Large Language Models (LLMs)
have demonstrated impressive capabilities in code generation and bug fixing,
their proficiency in enhancing code performance at the repository level remains
largely unexplored. To address this gap, we introduce SWE-Perf, the first
benchmark specifically designed to systematically evaluate LLMs on code
performance optimization tasks within authentic repository contexts. SWE-Perf
comprises 140 carefully curated instances, each derived from
performance-improving pull requests from popular GitHub repositories. Each
benchmark instance includes the relevant codebase, target functions,
performance-related tests, expert-authored patches, and executable
environments. Through a comprehensive evaluation of representative methods that
span file-level and repo-level approaches (e.g., Agentless and OpenHands), we
reveal a substantial capability gap between existing LLMs and expert-level
optimization performance, highlighting critical research opportunities in this
emerging field.
MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior
  Understanding
Authors: name: Renjie Li, name: Ruijie Ye, name: Mingyang Wu, name: Hao Frank Yang, name: Zhiwen Fan, name: Hezhen Hu, name: Zhengzhong Tu
Published: 2025-07-16 17:59:30

Humans are integral components of the transportation ecosystem, and
understanding their behaviors is crucial to facilitating the development of
safe driving systems. Although recent progress has explored various aspects of
human behaviorx2014such as motion, trajectories, and
intentionx2014a comprehensive benchmark for evaluating human
behavior understanding in autonomous driving remains unavailable. In this work,
we propose MMHU, a large-scale benchmark for human behavior analysis
featuring rich annotations, such as human motion and trajectories, text
description for human motions, human intention, and critical behavior labels
relevant to driving safety. Our dataset encompasses 57k human motion clips and
1.73M frames gathered from diverse sources, including established driving
datasets such as Waymo, in-the-wild videos from YouTube, and self-collected
data. A human-in-the-loop annotation pipeline is developed to generate rich
behavior captions. We provide a thorough dataset analysis and benchmark
multiple tasksx2014ranging from motion prediction to motion
generation and human behavior question answeringx2014thereby
offering a broad evaluation suite. Project page :
https://MMHU-Benchmark.github.io.
MOSPA: Human Motion Generation Driven by Spatial Audio
Authors: name: Shuyang Xu, name: Zhiyang Dou, name: Mingyi Shi, name: Liang Pan, name: Leo Ho, name: Jingbo Wang, name: Yuan Liu, name: Cheng Lin, name: Yuexin Ma, name: Wenping Wang, name: Taku Komura
Published: 2025-07-16 06:33:11

Enabling virtual humans to dynamically and realistically respond to diverse
auditory stimuli remains a key challenge in character animation, demanding the
integration of perceptual modeling and motion synthesis. Despite its
significance, this task remains largely unexplored. Most previous works have
primarily focused on mapping modalities like speech, audio, and music to
generate human motion. As of yet, these models typically overlook the impact of
spatial features encoded in spatial audio signals on human motion. To bridge
this gap and enable high-quality modeling of human movements in response to
spatial audio, we introduce the first comprehensive Spatial Audio-Driven Human
Motion (SAM) dataset, which contains diverse and high-quality spatial audio and
motion data. For benchmarking, we develop a simple yet effective
diffusion-based generative framework for human MOtion generation driven by
SPatial Audio, termed MOSPA, which faithfully captures the relationship between
body motion and spatial audio through an effective fusion mechanism. Once
trained, MOSPA could generate diverse realistic human motions conditioned on
varying spatial audio inputs. We perform a thorough investigation of the
proposed dataset and conduct extensive experiments for benchmarking, where our
method achieves state-of-the-art performance on this task. Our model and
dataset will be open-sourced upon acceptance. Please refer to our supplementary
video for more details.
Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning
  Systems in LLMs
Authors: name: Yangning Li, name: Weizhi Zhang, name: Yuyao Yang, name: Wei-Chieh Huang, name: Yaozu Wu, name: Junyu Luo, name: Yuanchen Bei, name: Henry Peng Zou, name: Xiao Luo, name: Yusheng Zhao, name: Chunkit Chan, name: Yankai Chen, name: Zhongfen Deng, name: Yinghui Li, name: Hai-Tao Zheng, name: Dongyuan Li, name: Renhe Jiang, name: Ming Zhang, name: Yangqiu Song, name: Philip S. Yu
Published: 2025-07-13 03:29:41

Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language
Models (LLMs) by injecting external knowledge, yet it falls short on problems
that demand multi-step inference; conversely, purely reasoning-oriented
approaches often hallucinate or mis-ground facts. This survey synthesizes both
strands under a unified reasoning-retrieval perspective. We first map how
advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then,
we show how retrieved knowledge of different type supply missing premises and
expand context for complex inference (RAG-Enhanced Reasoning). Finally, we
spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs
iteratively interleave search and reasoning to achieve state-of-the-art
performance across knowledge-intensive benchmarks. We categorize methods,
datasets, and open challenges, and outline research avenues toward deeper
RAG-Reasoning systems that are more effective, multimodally-adaptive,
trustworthy, and human-centric. The collection is available at
https://github.com/DavidZWZ/Awesome-RAG-Reasoning.