Orionnnn/hub-stats huggingface dataset:
MedSAMix: A Training-Free Model Merging Approach for Medical Image
  Segmentation
Authors: name: Yanwu Yang, name: Guinan Su, name: Jiesi Hu, name: Francesco Sammarco, name: Jonas Geiping, name: Thomas Wolfers
Published: 2025-08-14 19:35:57

Universal medical image segmentation models have emerged as a promising
paradigm due to their strong generalizability across diverse tasks, showing
great potential for a wide range of clinical applications. This potential has
been partly driven by the success of general-purpose vision models such as the
Segment Anything Model (SAM), which has inspired the development of various
fine-tuned variants for medical segmentation tasks. However, fine-tuned
variants like MedSAM are trained on comparatively limited medical imaging data
that often suffers from heterogeneity, scarce annotations, and distributional
shifts. These challenges limit their ability to generalize across a wide range
of medical segmentation tasks. In this regard, we propose MedSAMix, a
training-free model merging method that integrates the strengths of both
generalist models (e.g., SAM) and specialist models (e.g., MedSAM) for medical
image segmentation. In contrast to traditional model merging approaches that
rely on manual configuration and often result in suboptimal outcomes, we
propose a zero-order optimization method to automatically discover optimal
layer-wise merging solutions. Furthermore, for clinical applications, we
develop two regimes to meet the demand of domain-specificity and
generalizability in different scenarios by single-task optimization and
multi-objective optimization respectively. Extensive evaluations on 25 medical
segmentation tasks demonstrate that MedSAMix effectively mitigates model bias
and consistently improves performance in both domain-specific accuracy and
generalization, achieving improvements of 6.67% on specialized tasks and 4.37%
on multi-task evaluations.
Semantic IDs for Joint Generative Search and Recommendation
Authors: name: Gustavo Penha, name: Edoardo D'Amico, name: Marco De Nadai, name: Enrico Palumbo, name: Alexandre Tamborrino, name: Ali Vardasbi, name: Max Lefarov, name: Shawn Lin, name: Timothy Heath, name: Francesco Fabbri, name: Hugues Bouchard
Published: 2025-08-14 09:28:49

Generative models powered by Large Language Models (LLMs) are emerging as a
unified solution for powering both recommendation and search tasks. A key
design choice in these models is how to represent items, traditionally through
unique identifiers (IDs) and more recently with Semantic IDs composed of
discrete codes, obtained from embeddings. While task-specific embedding models
can improve performance for individual tasks, they may not generalize well in a
joint setting. In this paper, we explore how to construct Semantic IDs that
perform well both in search and recommendation when using a unified model. We
compare a range of strategies to construct Semantic IDs, looking into
task-specific and cross-tasks approaches, and also whether each task should
have its own semantic ID tokens in a joint search and recommendation generative
model. Our results show that using a bi-encoder model fine-tuned on both search
and recommendation tasks to obtain item embeddings, followed by the
construction of a unified Semantic ID space provides an effective trade-off,
enabling strong performance in both tasks. We hope these findings spark
follow-up work on generalisable, semantically grounded ID schemes and inform
the next wave of unified generative recommender architectures.
Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge
Authors: name: Francesco Fabbri, name: Gustavo Penha, name: Edoardo D'Amico, name: Alice Wang, name: Marco De Nadai, name: Jackie Doremus, name: Paul Gigioli, name: Andreas Damianou, name: Oskar Stal, name: Mounia Lalmas
Published: 2025-08-12 09:23:35

Evaluating personalized recommendations remains a central challenge,
especially in long-form audio domains like podcasts, where traditional offline
metrics suffer from exposure bias and online methods such as A/B testing are
costly and operationally constrained. In this paper, we propose a novel
framework that leverages Large Language Models (LLMs) as offline judges to
assess the quality of podcast recommendations in a scalable and interpretable
manner. Our two-stage profile-aware approach first constructs natural-language
user profiles distilled from 90 days of listening history. These profiles
summarize both topical interests and behavioral patterns, serving as compact,
interpretable representations of user preferences. Rather than prompting the
LLM with raw data, we use these profiles to provide high-level, semantically
rich context-enabling the LLM to reason more effectively about alignment
between a user's interests and recommended episodes. This reduces input
complexity and improves interpretability. The LLM is then prompted to deliver
fine-grained pointwise and pairwise judgments based on the profile-episode
match. In a controlled study with 47 participants, our profile-aware judge
matched human judgments with high fidelity and outperformed or matched a
variant using raw listening histories. The framework enables efficient,
profile-aware evaluation for iterative testing and model selection in
recommender systems.
Describe What You See with Multimodal Large Language Models to Enhance
  Video Recommendations
Authors: name: Marco De Nadai, name: Andreas Damianou, name: Mounia Lalmas
Published: 2025-08-13 13:19:31

Existing video recommender systems rely primarily on user-defined metadata or
on low-level visual and acoustic signals extracted by specialised encoders.
These low-level features describe what appears on the screen but miss deeper
semantics such as intent, humour, and world knowledge that make clips resonate
with viewers. For example, is a 30-second clip simply a singer on a rooftop, or
an ironic parody filmed amid the fairy chimneys of Cappadocia, Turkey? Such
distinctions are critical to personalised recommendations yet remain invisible
to traditional encoding pipelines. In this paper, we introduce a simple,
recommendation system-agnostic zero-finetuning framework that injects
high-level semantics into the recommendation pipeline by prompting an
off-the-shelf Multimodal Large Language Model (MLLM) to summarise each clip
into a rich natural-language description (e.g. "a superhero parody with
slapstick fights and orchestral stabs"), bridging the gap between raw content
and user intent. We use MLLM output with a state-of-the-art text encoder and
feed it into standard collaborative, content-based, and generative
recommenders. On the MicroLens-100K dataset, which emulates user interactions
with TikTok-style videos, our framework consistently surpasses conventional
video, audio, and metadata features in five representative models. Our findings
highlight the promise of leveraging MLLMs as on-the-fly knowledge extractors to
build more intent-aware video recommenders.
CorrSteer: Steering Improves Task Performance and Safety in LLMs through
  Correlation-based Sparse Autoencoder Feature Selection
Authors: name: Seonglae Cho, name: Zekun Wu, name: Adriano Koshiyama
Published: 2025-08-18 00:01:42

Sparse Autoencoders (SAEs) can extract interpretable features from large
language models (LLMs) without supervision. However, their effectiveness in
downstream steering tasks is limited by the requirement for contrastive
datasets or large activation storage. To address these limitations, we propose
CorrSteer, which selects features by correlating sample correctness with SAE
activations from generated tokens at inference time. This approach uses only
inference-time activations to extract more relevant features, thereby avoiding
spurious correlations. It also obtains steering coefficients from average
activations, automating the entire pipeline. Our method shows improved task
performance on QA, bias mitigation, jailbreaking prevention, and reasoning
benchmarks on Gemma 2 2B and LLaMA 3.1 8B, notably achieving a +4.1%
improvement in MMLU performance and a +22.9% improvement in HarmBench with only
4000 samples. Selected features demonstrate semantically meaningful patterns
aligned with each task's requirements, revealing the underlying capabilities
that drive performance. Our work establishes correlationbased selection as an
effective and scalable approach for automated SAE steering across language
model applications.
Radiance Fields in XR: A Survey on How Radiance Fields are Envisioned
  and Addressed for XR Research
Authors: name: Ke Li, name: Mana Masuda, name: Susanne Schmidt, name: Shohei Mori
Published: 2025-08-06 11:14:06

The development of radiance fields (RF), such as 3D Gaussian Splatting (3DGS)
and Neural Radiance Fields (NeRF), has revolutionized interactive
photorealistic view synthesis and presents enormous opportunities for XR
research and applications. However, despite the exponential growth of RF
research, RF-related contributions to the XR community remain sparse. To better
understand this research gap, we performed a systematic survey of current RF
literature to analyze (i) how RF is envisioned for XR applications, (ii) how
they have already been implemented, and (iii) the remaining research gaps. We
collected 365 RF contributions related to XR from computer vision, computer
graphics, robotics, multimedia, human-computer interaction, and XR communities,
seeking to answer the above research questions. Among the 365 papers, we
performed an analysis of 66 papers that already addressed a detailed aspect of
RF research for XR. With this survey, we extended and positioned XR-specific RF
research topics in the broader RF research field and provide a helpful resource
for the XR community to navigate within the rapid development of RF research.
Advances in Speech Separation: Techniques, Challenges, and Future Trends
Authors: name: Kai Li, name: Guo Chen, name: Wendi Sang, name: Yi Luo, name: Zhuo Chen, name: Shuai Wang, name: Shulin He, name: Zhong-Qiu Wang, name: Andong Li, name: Zhiyong Wu, name: Xiaolin Hu
Published: 2025-08-14 16:54:34

The field of speech separation, addressing the "cocktail party problem", has
seen revolutionary advances with DNNs. Speech separation enhances clarity in
complex acoustic environments and serves as crucial pre-processing for speech
recognition and speaker recognition. However, current literature focuses
narrowly on specific architectures or isolated approaches, creating fragmented
understanding. This survey addresses this gap by providing systematic
examination of DNN-based speech separation techniques. Our work differentiates
itself through: (I) Comprehensive perspective: We systematically investigate
learning paradigms, separation scenarios with known/unknown speakers,
comparative analysis of supervised/self-supervised/unsupervised frameworks, and
architectural components from encoders to estimation strategies. (II)
Timeliness: Coverage of cutting-edge developments ensures access to current
innovations and benchmarks. (III) Unique insights: Beyond summarization, we
evaluate technological trajectories, identify emerging patterns, and highlight
promising directions including domain-robust frameworks, efficient
architectures, multimodal integration, and novel self-supervised paradigms.
(IV) Fair evaluation: We provide quantitative evaluations on standard datasets,
revealing true capabilities and limitations of different methods. This
comprehensive survey serves as an accessible reference for experienced
researchers and newcomers navigating speech separation's complex landscape.
Copyright Protection for Large Language Models: A Survey of Methods,
  Challenges, and Trends
Authors: name: Zhenhua Xu, name: Xubin Yue, name: Zhebo Wang, name: Qichen Liu, name: Xixiang Zhao, name: Jingxuan Zhang, name: Wenjun Zeng, name: Wengpeng Xing, name: Dezhang Kong, name: Changting Lin, name: Meng Han
Published: 2025-08-15 15:50:20

Copyright protection for large language models is of critical importance,
given their substantial development costs, proprietary value, and potential for
misuse. Existing surveys have predominantly focused on techniques for tracing
LLM-generated content-namely, text watermarking-while a systematic exploration
of methods for protecting the models themselves (i.e., model watermarking and
model fingerprinting) remains absent. Moreover, the relationships and
distinctions among text watermarking, model watermarking, and model
fingerprinting have not been comprehensively clarified. This work presents a
comprehensive survey of the current state of LLM copyright protection
technologies, with a focus on model fingerprinting, covering the following
aspects: (1) clarifying the conceptual connection from text watermarking to
model watermarking and fingerprinting, and adopting a unified terminology that
incorporates model watermarking into the broader fingerprinting framework; (2)
providing an overview and comparison of diverse text watermarking techniques,
highlighting cases where such methods can function as model fingerprinting; (3)
systematically categorizing and comparing existing model fingerprinting
approaches for LLM copyright protection; (4) presenting, for the first time,
techniques for fingerprint transfer and fingerprint removal; (5) summarizing
evaluation metrics for model fingerprints, including effectiveness,
harmlessness, robustness, stealthiness, and reliability; and (6) discussing
open challenges and future research directions. This survey aims to offer
researchers a thorough understanding of both text watermarking and model
fingerprinting technologies in the era of LLMs, thereby fostering further
advances in protecting their intellectual property.
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval
  Driven LLM Agents
Authors: name: Zechen Li, name: Baiyu Chen, name: Hao Xue, name: Flora D. Salim
Published: 2025-08-06 02:57:57

Motion sensor time-series are central to human activity recognition (HAR),
with applications in health, sports, and smart devices. However, existing
methods are trained for fixed activity sets and require costly retraining when
new behaviours or sensor setups appear. Recent attempts to use large language
models (LLMs) for HAR, typically by converting signals into text or images,
suffer from limited accuracy and lack verifiable interpretability. We propose
ZARA, the first agent-based framework for zero-shot, explainable HAR directly
from raw motion time-series. ZARA integrates an automatically derived pair-wise
feature knowledge base that captures discriminative statistics for every
activity pair, a multi-sensor retrieval module that surfaces relevant evidence,
and a hierarchical agent pipeline that guides the LLM to iteratively select
features, draw on this evidence, and produce both activity predictions and
natural-language explanations. ZARA enables flexible and interpretable HAR
without any fine-tuning or task-specific classifiers. Extensive experiments on
8 HAR benchmarks show that ZARA achieves SOTA zero-shot performance, delivering
clear reasoning while exceeding the strongest baselines by 2.53x in macro F1.
Ablation studies further confirm the necessity of each module, marking ZARA as
a promising step toward trustworthy, plug-and-play motion time-series analysis.
Our codes are available at https://github.com/zechenli03/ZARA.
TempFlow-GRPO: When Timing Matters for GRPO in Flow Models
Authors: name: Xiaoxuan He, name: Siming Fu, name: Yuke Zhao, name: Wanli Li, name: Jian Yang, name: Dacheng Yin, name: Fengyun Rao, name: Bo Zhang
Published: 2025-08-06 11:10:39

Recent flow matching models for text-to-image generation have achieved
remarkable quality, yet their integration with reinforcement learning for human
preference alignment remains suboptimal, hindering fine-grained reward-based
optimization. We observe that the key impediment to effective GRPO training of
flow models is the temporal uniformity assumption in existing approaches:
sparse terminal rewards with uniform credit assignment fail to capture the
varying criticality of decisions across generation timesteps, resulting in
inefficient exploration and suboptimal convergence. To remedy this shortcoming,
we introduce TempFlow-GRPO (Temporal Flow GRPO), a principled GRPO
framework that captures and exploits the temporal structure inherent in
flow-based generation. TempFlow-GRPO introduces two key innovations: (i) a
trajectory branching mechanism that provides process rewards by concentrating
stochasticity at designated branching points, enabling precise credit
assignment without requiring specialized intermediate reward models; and (ii) a
noise-aware weighting scheme that modulates policy optimization according to
the intrinsic exploration potential of each timestep, prioritizing learning
during high-impact early stages while ensuring stable refinement in later
phases. These innovations endow the model with temporally-aware optimization
that respects the underlying generative dynamics, leading to state-of-the-art
performance in human preference alignment and standard text-to-image
benchmarks.
Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence
Authors: name: Ling-Hao Chen, name: Yuhong Zhang, name: Zixin Yin, name: Zhiyang Dou, name: Xin Chen, name: Jingbo Wang, name: Taku Komura, name: Lei Zhang
Published: 2025-08-18 17:50:31

This work studies the challenge of transfer animations between characters
whose skeletal topologies differ substantially. While many techniques have
advanced retargeting techniques in decades, transfer motions across diverse
topologies remains less-explored. The primary obstacle lies in the inherent
topological inconsistency between source and target skeletons, which restricts
the establishment of straightforward one-to-one bone correspondences. Besides,
the current lack of large-scale paired motion datasets spanning different
topological structures severely constrains the development of data-driven
approaches. To address these limitations, we introduce Motion2Motion, a novel,
training-free framework. Simply yet effectively, Motion2Motion works with only
one or a few example motions on the target skeleton, by accessing a sparse set
of bone correspondences between the source and target skeletons. Through
comprehensive qualitative and quantitative evaluations, we demonstrate that
Motion2Motion achieves efficient and reliable performance in both
similar-skeleton and cross-species skeleton transfer scenarios. The practical
utility of our approach is further evidenced by its successful integration in
downstream applications and user interfaces, highlighting its potential for
industrial applications. Code and data are available at
https://lhchen.top/Motion2Motion.
MultiRef: Controllable Image Generation with Multiple Visual References
Authors: name: Ruoxi Chen, name: Dongping Chen, name: Siyuan Wu, name: Sinan Wang, name: Shiyun Lang, name: Petr Sushko, name: Gaoyang Jiang, name: Yao Wan, name: Ranjay Krishna
Published: 2025-08-09 09:36:21

Visual designers naturally draw inspiration from multiple visual references,
combining diverse elements and aesthetic principles to create artwork. However,
current image generative frameworks predominantly rely on single-source inputs
-- either text prompts or individual reference images. In this paper, we focus
on the task of controllable image generation using multiple visual references.
We introduce MultiRef-bench, a rigorous evaluation framework comprising 990
synthetic and 1,000 real-world samples that require incorporating visual
content from multiple reference images. The synthetic samples are synthetically
generated through our data engine RefBlend, with 10 reference types and 33
reference combinations. Based on RefBlend, we further construct a dataset
MultiRef containing 38k high-quality images to facilitate further research. Our
experiments across three interleaved image-text models (i.e., OmniGen, ACE, and
Show-o) and six agentic frameworks (e.g., ChatDiT and LLM + SD) reveal that
even state-of-the-art systems struggle with multi-reference conditioning, with
the best model OmniGen achieving only 66.6% in synthetic samples and 79.0% in
real-world cases on average compared to the golden answer. These findings
provide valuable directions for developing more flexible and human-like
creative tools that can effectively integrate multiple sources of visual
inspiration. The dataset is publicly available at: https://multiref.github.io/.
Leveraging Large Language Models for Predictive Analysis of Human Misery
Authors: name: Bishanka Seal, name: Rahul Seetharaman, name: Aman Bansal, name: Abhilash Nandy
Published: 2025-08-18 07:02:59

This study investigates the use of Large Language Models (LLMs) for
predicting human-perceived misery scores from natural language descriptions of
real-world scenarios. The task is framed as a regression problem, where the
model assigns a scalar value from 0 to 100 to each input statement. We evaluate
multiple prompting strategies, including zero-shot, fixed-context few-shot, and
retrieval-based prompting using BERT sentence embeddings. Few-shot approaches
consistently outperform zero-shot baselines, underscoring the value of
contextual examples in affective prediction. To move beyond static evaluation,
we introduce the "Misery Game Show", a novel gamified framework inspired by a
television format. It tests LLMs through structured rounds involving ordinal
comparison, binary classification, scalar estimation, and feedback-driven
reasoning. This setup enables us to assess not only predictive accuracy but
also the model's ability to adapt based on corrective feedback. The gamified
evaluation highlights the broader potential of LLMs in dynamic emotional
reasoning tasks beyond standard regression. Code and data link:
https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub
LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
Authors: name: Chin-Yang Lin, name: Cheng Sun, name: Fu-En Yang, name: Min-Hung Chen, name: Yen-Yu Lin, name: Yu-Lun Liu
Published: 2025-08-19 17:59:56

LongSplat addresses critical challenges in novel view synthesis (NVS) from
casually captured long videos characterized by irregular camera motion, unknown
camera poses, and expansive scenes. Current methods often suffer from pose
drift, inaccurate geometry initialization, and severe memory limitations. To
address these issues, we introduce LongSplat, a robust unposed 3D Gaussian
Splatting framework featuring: (1) Incremental Joint Optimization that
concurrently optimizes camera poses and 3D Gaussians to avoid local minima and
ensure global consistency; (2) a robust Pose Estimation Module leveraging
learned 3D priors; and (3) an efficient Octree Anchor Formation mechanism that
converts dense point clouds into anchors based on spatial density. Extensive
experiments on challenging benchmarks demonstrate that LongSplat achieves
state-of-the-art results, substantially improving rendering quality, pose
accuracy, and computational efficiency compared to prior approaches. Project
page: https://linjohnss.github.io/longsplat/