Orionnnn/hub-stats huggingface dataset:
GuideX: Guided Synthetic Data Generation for Zero-Shot Information
  Extraction
Authors: name: Neil De La Fuente, name: Oscar Sainz, name: Iker García-Ferrero, name: Eneko Agirre
Published: 2025-05-31 17:36:18

Information Extraction (IE) systems are traditionally domain-specific,
requiring costly adaptation that involves expert schema design, data
annotation, and model training. While Large Language Models have shown promise
in zero-shot IE, performance degrades significantly in unseen domains where
label definitions differ. This paper introduces GUIDEX, a novel method that
automatically defines domain-specific schemas, infers guidelines, and generates
synthetically labeled instances, allowing for better out-of-domain
generalization. Fine-tuning Llama 3.1 with GUIDEX sets a new state-of-the-art
across seven zeroshot Named Entity Recognition benchmarks. Models trained with
GUIDEX gain up to 7 F1 points over previous methods without humanlabeled data,
and nearly 2 F1 points higher when combined with it. Models trained on GUIDEX
demonstrate enhanced comprehension of complex, domain-specific annotation
schemas. Code, models, and synthetic datasets are available at
neilus03.github.io/guidex.com
Is Extending Modality The Right Path Towards Omni-Modality?
Authors: name: Tinghui Zhu, name: Kai Zhang, name: Muhao Chen, name: Yu Su
Published: 2025-06-02 17:01:40

Omni-modal language models (OLMs) aim to integrate and reason over diverse
input modalities--such as text, images, video, and audio--while maintaining
strong language capabilities. Despite recent advancements, existing models,
especially open-source ones, remain far from true omni-modality, struggling to
generalize beyond the specific modality pairs they are trained on or to achieve
strong performance when processing multi-modal inputs. We study the effect of
extending modality, the dominant technique for training multimodal models,
where an off-the-shelf language model is fine-tuned on target-domain and
language data. Specifically, we investigate three key questions: (1) Does
modality extension compromise core language abilities? (2) Can model merging
effectively integrate independently fine-tuned modality-specific models to
achieve omni-modality? (3) Does omni-modality extension lead to better
knowledge sharing and generalization compared to sequential extension? Through
extensive experiments, we analyze these trade-offs and provide insights into
the feasibility of achieving true omni-modality using current approaches.
Will It Still Be True Tomorrow? Multilingual Evergreen Question
  Classification to Improve Trustworthy QA
Authors: name: Sergey Pletenev, name: Maria Marina, name: Nikolay Ivanov, name: Daria Galimzianova, name: Nikita Krayko, name: Mikhail Salnikov, name: Vasily Konovalov, name: Alexander Panchenko, name: Viktor Moskvoretskii
Published: 2025-05-27 12:35:13

Large Language Models (LLMs) often hallucinate in question answering (QA)
tasks. A key yet underexplored factor contributing to this is the temporality
of questions -- whether they are evergreen (answers remain stable over time) or
mutable (answers change). In this work, we introduce EverGreenQA, the first
multilingual QA dataset with evergreen labels, supporting both evaluation and
training. Using EverGreenQA, we benchmark 12 modern LLMs to assess whether they
encode question temporality explicitly (via verbalized judgments) or implicitly
(via uncertainty signals). We also train EG-E5, a lightweight multilingual
classifier that achieves SoTA performance on this task. Finally, we demonstrate
the practical utility of evergreen classification across three applications:
improving self-knowledge estimation, filtering QA datasets, and explaining
GPT-4o retrieval behavior.
Prefix Grouper: Efficient GRPO Training through Shared-Prefix Forward
Authors: name: Zikang Liu, name: Tongtian Yue, name: Yepeng Tang, name: Longteng Guo, name: Junxian Cai, name: Qingbin Liu, name: Xi Chen, name: Jing Liu
Published: 2025-06-05 09:13:37

Group Relative Policy Optimization (GRPO) enhances policy learning by
computing gradients from relative comparisons among candidate outputs that
share a common input prefix. Despite its effectiveness, GRPO introduces
substantial computational overhead when processing long shared prefixes, which
must be redundantly encoded for each group member. This inefficiency becomes a
major scalability bottleneck in long-context learning scenarios. We propose
Prefix Grouper, an efficient GRPO training algorithm that eliminates redundant
prefix computation via a Shared-Prefix Forward strategy. In particular, by
restructuring self-attention into two parts, our method enables the shared
prefix to be encoded only once, while preserving full differentiability and
compatibility with end-to-end training. We provide both theoretical and
empirical evidence that Prefix Grouper is training-equivalent to standard GRPO:
it yields identical forward outputs and backward gradients, ensuring that the
optimization dynamics and final policy performance remain unchanged.
Empirically, our experiments confirm that Prefix Grouper achieves consistent
results while significantly reducing the computational cost of training,
particularly in long-prefix scenarios. The proposed method is fully
plug-and-play: it is compatible with existing GRPO-based architectures and can
be seamlessly integrated into current training pipelines as a drop-in
replacement, requiring no structural modifications and only minimal changes to
input construction and attention computation. Prefix Grouper enables the use of
larger group sizes under the same computational budget, thereby improving the
scalability of GRPO to more complex tasks and larger models. Code is now
available at https://github.com/johncaged/PrefixGrouper
Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence
  with Egocentric-Exocentric Vision
Authors: name: Yuping He, name: Yifei Huang, name: Guo Chen, name: Lidong Lu, name: Baoqi Pei, name: Jilan Xu, name: Tong Lu, name: Yoichi Sato
Published: 2025-06-06 17:25:48

Perceiving the world from both egocentric (first-person) and exocentric
(third-person) perspectives is fundamental to human cognition, enabling rich
and complementary understanding of dynamic environments. In recent years,
allowing the machines to leverage the synergistic potential of these dual
perspectives has emerged as a compelling research direction in video
understanding. In this survey, we provide a comprehensive review of video
understanding from both exocentric and egocentric viewpoints. We begin by
highlighting the practical applications of integrating egocentric and
exocentric techniques, envisioning their potential collaboration across
domains. We then identify key research tasks to realize these applications.
Next, we systematically organize and review recent advancements into three main
research directions: (1) leveraging egocentric data to enhance exocentric
understanding, (2) utilizing exocentric data to improve egocentric analysis,
and (3) joint learning frameworks that unify both perspectives. For each
direction, we analyze a diverse set of tasks and relevant works. Additionally,
we discuss benchmark datasets that support research in both perspectives,
evaluating their scope, diversity, and applicability. Finally, we discuss
limitations in current works and propose promising future research directions.
By synthesizing insights from both perspectives, our goal is to inspire
advancements in video understanding and artificial intelligence, bringing
machines closer to perceiving the world in a human-like manner. A GitHub repo
of related works can be found at
https://github.com/ayiyayi/Awesome-Egocentric-and-Exocentric-Vision.
PartCrafter: Structured 3D Mesh Generation via Compositional Latent
  Diffusion Transformers
Authors: name: Yuchen Lin, name: Chenguo Lin, name: Panwang Pan, name: Honglei Yan, name: Yiqiang Feng, name: Yadong Mu, name: Katerina Fragkiadaki
Published: 2025-06-05 20:30:28

We introduce PartCrafter, the first structured 3D generative model that
jointly synthesizes multiple semantically meaningful and geometrically distinct
3D meshes from a single RGB image. Unlike existing methods that either produce
monolithic 3D shapes or follow two-stage pipelines, i.e., first segmenting an
image and then reconstructing each segment, PartCrafter adopts a unified,
compositional generation architecture that does not rely on pre-segmented
inputs. Conditioned on a single image, it simultaneously denoises multiple 3D
parts, enabling end-to-end part-aware generation of both individual objects and
complex multi-object scenes. PartCrafter builds upon a pretrained 3D mesh
diffusion transformer (DiT) trained on whole objects, inheriting the pretrained
weights, encoder, and decoder, and introduces two key innovations: (1) A
compositional latent space, where each 3D part is represented by a set of
disentangled latent tokens; (2) A hierarchical attention mechanism that enables
structured information flow both within individual parts and across all parts,
ensuring global coherence while preserving part-level detail during generation.
To support part-level supervision, we curate a new dataset by mining part-level
annotations from large-scale 3D object datasets. Experiments show that
PartCrafter outperforms existing approaches in generating decomposable 3D
meshes, including parts that are not directly visible in input images,
demonstrating the strength of part-aware generative priors for 3D understanding
and synthesis. Code and training data will be released.
STARFlow: Scaling Latent Normalizing Flows for High-resolution Image
  Synthesis
Authors: name: Jiatao Gu, name: Tianrong Chen, name: David Berthelot, name: Huangjie Zheng, name: Yuyang Wang, name: Ruixiang Zhang, name: Laurent Dinh, name: Miguel Angel Bautista, name: Josh Susskind, name: Shuangfei Zhai
Published: 2025-06-06 17:58:39

We present STARFlow, a scalable generative model based on normalizing flows
that achieves strong performance in high-resolution image synthesis. The core
of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the
expressive power of normalizing flows with the structured modeling capabilities
of Autoregressive Transformers. We first establish the theoretical universality
of TARFlow for modeling continuous distributions. Building on this foundation,
we introduce several key architectural and algorithmic innovations to
significantly enhance scalability: (1) a deep-shallow design, wherein a deep
Transformer block captures most of the model representational capacity,
complemented by a few shallow Transformer blocks that are computationally
efficient yet substantially beneficial; (2) modeling in the latent space of
pretrained autoencoders, which proves more effective than direct pixel-level
modeling; and (3) a novel guidance algorithm that significantly boosts sample
quality. Crucially, our model remains an end-to-end normalizing flow, enabling
exact maximum likelihood training in continuous spaces without discretization.
STARFlow achieves competitive performance in both class-conditional and
text-conditional image generation tasks, approaching state-of-the-art diffusion
models in sample quality. To our knowledge, this work is the first successful
demonstration of normalizing flows operating effectively at this scale and
resolution.
HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource
  Utilization
Authors: name: Kunal Pai, name: Parth Shah, name: Harshil Patel
Published: 2025-06-01 17:33:16

Rapid Large Language Model (LLM) advancements are fueling autonomous
Multi-Agent System (MAS) development. However, current frameworks often lack
flexibility, resource awareness, model diversity, and autonomous tool creation.
This paper introduces HASHIRU (Hierarchical Agent System for Hybrid Intelligent
Resource Utilization), a novel MAS framework enhancing flexibility, resource
efficiency, and adaptability. HASHIRU features a "CEO" agent dynamically
managing specialized "employee" agents, instantiated based on task needs and
resource constraints (cost, memory). Its hybrid intelligence prioritizes
smaller, local LLMs (via Ollama) while flexibly using external APIs and larger
models when necessary. An economic model with hiring/firing costs promotes team
stability and efficient resource allocation. The system also includes
autonomous API tool creation and a memory function. Evaluations on tasks like
academic paper review (58% success), safety assessments (100% on a
JailbreakBench subset), and complex reasoning (outperforming Gemini 2.0 Flash
on GSM8K: 96% vs. 61%; JEEBench: 80% vs. 68.3%; SVAMP: 92% vs. 84%) demonstrate
HASHIRU's capabilities. Case studies illustrate its self-improvement via
autonomous cost model generation, tool integration, and budget management.
HASHIRU offers a promising approach for more robust, efficient, and adaptable
MAS through dynamic hierarchical control, resource-aware hybrid intelligence,
and autonomous functional extension. Source code and benchmarks are available
at https://github.com/HASHIRU-AI/HASHIRU and
https://github.com/HASHIRU-AI/HASHIRUBench respectively, and a live demo is
available at https://hashiruagentx-hashiruai.hf.space upon request.
MORSE-500: A Programmatically Controllable Video Benchmark to
  Stress-Test Multimodal Reasoning
Authors: name: Zikui Cai, name: Andrew Wang, name: Anirudh Satheesh, name: Ankit Nakhawa, name: Hyunwoo Jae, name: Keenan Powell, name: Minghui Liu, name: Neel Jay, name: Sungbin Oh, name: Xiyao Wang, name: Yongyuan Liang, name: Tom Goldstein, name: Furong Huang
Published: 2025-06-05 19:12:45

Despite rapid advances in vision-language models (VLMs), current benchmarks
for multimodal reasoning fall short in three key dimensions. First, they
overwhelmingly rely on static images, failing to capture the temporal
complexity of real-world environments. Second, they narrowly focus on
mathematical problem-solving, neglecting the broader spectrum of reasoning
skills -- including abstract, physical, planning, spatial, and temporal
capabilities -- required for robust multimodal intelligence. Third, many
benchmarks quickly saturate, offering limited headroom for diagnosing failure
modes or measuring continued progress. We introduce MORSE-500 (Multimodal
Reasoning Stress-test Environment), a video benchmark composed of 500 fully
scripted clips with embedded questions spanning six complementary reasoning
categories. Each instance is programmatically generated using deterministic
Python scripts (via Manim, Matplotlib, MoviePy), generative video models, and
curated real footage. This script-driven design allows fine-grained control
over visual complexity, distractor density, and temporal dynamics -- enabling
difficulty to be scaled systematically as models improve. Unlike static
benchmarks that become obsolete once saturated, MORSE-500 is built to evolve:
its controllable generation pipeline supports the creation of arbitrarily
challenging new instances, making it ideally suited for stress-testing
next-generation models. Initial experiments with state-of-the-art systems --
including various Gemini 2.5 Pro and OpenAI o3 which represent the strongest
available at the time, alongside strong open-source models -- reveal
substantial performance gaps across all categories, with particularly large
deficits in abstract and planning tasks. We release the full dataset,
generation scripts, and evaluation harness to support transparent,
reproducible, and forward-looking multimodal reasoning research.
FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal
  Contextual Fusion
Authors: name: Shunian Chen, name: Xinyuan Xie, name: Zheshu Chen, name: Liyan Zhao, name: Owen Lee, name: Zhan Su, name: Qilin Sun, name: Benyou Wang
Published: 2025-06-01 18:29:17

High-quality, large-scale audio captioning is crucial for advancing audio
understanding, yet current automated methods often generate captions that lack
fine-grained detail and contextual accuracy, primarily due to their reliance on
limited unimodal or superficial multimodal information. Drawing inspiration
from human auditory perception, which adeptly integrates cross-modal cues and
performs sophisticated auditory scene analysis, we introduce a novel two-stage
automated pipeline. This pipeline first employs specialized pretrained models
to extract diverse contextual cues (e.g., speech, music, general sounds, and
visual information from associated video). A large language model (LLM) then
synthesizes these rich, multimodal inputs to generate detailed and
context-aware audio captions. Key contributions of this work include: (1) the
proposed scalable method for fine-grained audio caption generation; (2)
FusionAudio, a new large-scale dataset comprising 1.2 million such detailed
captions, combined with 6 million QA pairs; and (3) enhanced audio models
developed using FusionAudio, specifically a CLAP-based audio encoder with
superior audio-text alignment and instruction following. This paper paves the
way for more nuanced and accurate automated understanding of complex audio
environments. Code and data can be found in
https://github.com/satsuki2486441738/FusionAudio.
3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World
  Model
Authors: name: Hongyan Zhi, name: Peihao Chen, name: Siyuan Zhou, name: Yubo Dong, name: Quanxi Wu, name: Lei Han, name: Mingkui Tan
Published: 2025-06-06 16:00:31

Manipulation has long been a challenging task for robots, while humans can
effortlessly perform complex interactions with objects, such as hanging a cup
on the mug rack. A key reason is the lack of a large and uniform dataset for
teaching robots manipulation skills. Current robot datasets often record robot
action in different action spaces within a simple scene. This hinders the robot
to learn a unified and robust action representation for different robots within
diverse scenes. Observing how humans understand a manipulation task, we find
that understanding how the objects should move in the 3D space is a critical
clue for guiding actions. This clue is embodiment-agnostic and suitable for
both humans and different robots. Motivated by this, we aim to learn a 3D flow
world model from both human and robot manipulation data. This model predicts
the future movement of the interacting objects in 3D space, guiding action
planning for manipulation. Specifically, we synthesize a large-scale 3D optical
flow dataset, named ManiFlow-110k, through a moving object auto-detect
pipeline. A video diffusion-based world model then learns manipulation physics
from these data, generating 3D optical flow trajectories conditioned on
language instructions. With the generated 3D object optical flow, we propose a
flow-guided rendering mechanism, which renders the predicted final state and
leverages GPT-4o to assess whether the predicted flow aligns with the task
description. This equips the robot with a closed-loop planning ability.
Finally, we consider the predicted 3D optical flow as constraints for an
optimization policy to determine a chunk of robot actions for manipulation.
Extensive experiments demonstrate strong generalization across diverse robotic
manipulation tasks and reliable cross-embodiment adaptation without
hardware-specific training.
Leveraging Self-Attention for Input-Dependent Soft Prompting in LLMs
Authors: name: Ananth Muppidi, name: Abhilash Nandy, name: Sambaran Bandyopadhyay
Published: 2025-06-05 23:13:22

The performance of large language models in domain-specific tasks
necessitates fine-tuning, which is computationally expensive and technically
challenging. This paper focuses on parameter-efficient fine-tuning using soft
prompting, a promising approach that adapts pre-trained models to downstream
tasks by learning a small set of parameters. We propose a novel Input Dependent
Soft Prompting technique with a self-Attention Mechanism (ID-SPAM) that
generates soft prompts based on the input tokens and attends different tokens
with varying importance. Our method is simple and efficient, keeping the number
of trainable parameters small. We show the merits of the proposed approach
compared to state-of-the-art techniques on various tasks and show the improved
zero shot domain transfer capability.
Audio-Aware Large Language Models as Judges for Speaking Styles
Authors: name: Cheng-Han Chiang, name: Xiaofei Wang, name: Chung-Ching Lin, name: Kevin Lin, name: Linjie Li, name: Radu Kopetz, name: Yao Qian, name: Zhendong Wang, name: Zhengyuan Yang, name: Hung-yi Lee, name: Lijuan Wang
Published: 2025-06-06 11:05:48

Audio-aware large language models (ALLMs) can understand the textual and
non-textual information in the audio input. In this paper, we explore using
ALLMs as an automatic judge to assess the speaking styles of speeches. We use
ALLM judges to evaluate the speeches generated by SLMs on two tasks: voice
style instruction following and role-playing. The speaking style we consider
includes emotion, volume, speaking pace, word emphasis, pitch control, and
non-verbal elements. We use four spoken language models (SLMs) to complete the
two tasks and use humans and ALLMs to judge the SLMs' responses. We compare two
ALLM judges, GPT-4o-audio and Gemini-2.5-pro, with human evaluation results and
show that the agreement between Gemini and human judges is comparable to the
agreement between human evaluators. These promising results show that ALLMs can
be used as a judge to evaluate SLMs. Our results also reveal that current SLMs,
even GPT-4o-audio, still have room for improvement in controlling the speaking
style and generating natural dialogues.
Kinetics: Rethinking Test-Time Scaling Laws
Authors: name: Ranajoy Sadhukhan, name: Zhuoming Chen, name: Haizhong Zheng, name: Yang Zhou, name: Emma Strubell, name: Beidi Chen
Published: 2025-06-05 17:59:24

We rethink test-time scaling laws from a practical efficiency perspective,
revealing that the effectiveness of smaller models is significantly
overestimated. Prior work, grounded in compute-optimality, overlooks critical
memory access bottlenecks introduced by inference-time strategies (e.g.,
Best-of-N, long CoTs). Our holistic analysis, spanning models from 0.6B to
32B parameters, reveals a new Kinetics Scaling Law that better guides resource
allocation by incorporating both computation and memory access costs. Kinetics
Scaling Law suggests that test-time compute is more effective when used on
models above a threshold than smaller ones. A key reason is that in TTS,
attention, rather than parameter count, emerges as the dominant cost factor.
Motivated by this, we propose a new scaling paradigm centered on sparse
attention, which lowers per-token cost and enables longer generations and more
parallel samples within the same resource budget. Empirically, we show that
sparse attention models consistently outperform dense counterparts, achieving
over 60 points gains in low-cost regimes and over 5 points gains in high-cost
regimes for problem-solving accuracy on AIME, encompassing evaluations on
state-of-the-art MoEs. These results suggest that sparse attention is essential
for realizing the full potential of test-time scaling because, unlike training,
where parameter scaling saturates, test-time accuracy continues to improve
through increased generation. The code is available at
https://github.com/Infini-AI-Lab/Kinetics.