papers.csv

arxiv_id,title,authors,abstract,categories,published_date,updated_date,abs_url
2506.02838v1,TaxAgent: How Large Language Model Designs Fiscal Policy,"Jizhou Wang, Xiaodan Fang, Lei Huang, Yongfeng Huang","Economic inequality is a global challenge, intensifying disparities in
education, healthcare, and social stability. Traditional systems like the U.S.
federal income tax reduce inequality but lack adaptability. Although models
like the Saez Optimal Taxation adjust dynamically, they fail to address
taxpayer heterogeneity and irrational behavior. This study introduces TaxAgent,
a novel integration of large language models (LLMs) with agent-based modeling
(ABM) to design adaptive tax policies. In our macroeconomic simulation,
heterogeneous H-Agents (households) simulate real-world taxpayer behaviors
while the TaxAgent (government) utilizes LLMs to iteratively optimize tax
rates, balancing equity and productivity. Benchmarked against Saez Optimal
Taxation, U.S. federal income taxes, and free markets, TaxAgent achieves
superior equity-efficiency trade-offs. This research offers a novel taxation
solution and a scalable, data-driven framework for fiscal policy evaluation.","cs.AI, econ.GN, q-fin.EC, I.2.11, I.6.5, J.4",2025-06-03T13:06:19+00:00,2025-06-03T13:06:19+00:00,http://arxiv.org/abs/2506.02838v1
2506.02634v1,"KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache
  at a Large Cloud Provider","Jiahao Wang, Jinbo Han, Xingda Wei, Sijie Shen, Dingyan Zhang, Chenguang Fang, Rong Chen, Wenyuan Yu, Haibo Chen","Serving large language models (LLMs) is important for cloud providers, and
caching intermediate results (KV\$) after processing each request substantially
improves serving throughput and latency. However, there is limited
understanding of how LLM serving benefits from KV\$ caching, where system
design decisions like cache eviction policies are highly workload-dependent. In
this paper, we present the first systematic characterization of the KV\$
workload patterns from one of the leading LLM service providers. We draw
observations that were not covered by previous studies focusing on synthetic
workloads, including: KV\$ reuses are skewed across requests, where reuses
between single-turn requests are equally important as multi-turn requests; the
reuse time and probability are diverse considering all requests, but for a
specific request category, the pattern tends to be predictable; and the overall
cache size required for an ideal cache hit ratio is moderate. Based on the
characterization, we further propose a workload-aware cache eviction policy
that improves the serving performance under real-world traces, especially with
limited cache capacity.","cs.DC, cs.AI",2025-06-03T08:51:38+00:00,2025-06-03T08:51:38+00:00,http://arxiv.org/abs/2506.02634v1
2506.00958v1,"Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning
  Nonverbal Cues from Video-Grounded Dialogues","Youngmin Kim, Jiwan Chung, Jisoo Kim, Sunghyun Lee, Sangkyu Lee, Junhyeok Kim, Cheoljong Yang, Youngjae Yu","Nonverbal communication is integral to human interaction, with gestures,
facial expressions, and body language conveying critical aspects of intent and
emotion. However, existing large language models (LLMs) fail to effectively
incorporate these nonverbal elements, limiting their capacity to create fully
immersive conversational experiences. We introduce MARS, a multimodal language
model designed to understand and generate nonverbal cues alongside text,
bridging this gap in conversational AI. Our key innovation is VENUS, a
large-scale dataset comprising annotated videos with time-aligned text, facial
expressions, and body language. Leveraging VENUS, we train MARS with a
next-token prediction objective, combining text with vector-quantized nonverbal
representations to achieve multimodal understanding and generation within a
unified framework. Based on various analyses of the VENUS datasets, we validate
its substantial scale and high effectiveness. Our quantitative and qualitative
results demonstrate that MARS successfully generates text and nonverbal
languages, corresponding to conversational input.","cs.AI, cs.CL, cs.CV",2025-06-01T11:07:25+00:00,2025-06-01T11:07:25+00:00,http://arxiv.org/abs/2506.00958v1
2506.00832v1,"Counterfactual Activation Editing for Post-hoc Prosody and
  Mispronunciation Correction in TTS Models","Kyowoon Lee, Artyom Stitsyuk, Gunu Jho, Inchul Hwang, Jaesik Choi","Recent advances in Text-to-Speech (TTS) have significantly improved speech
naturalness, increasing the demand for precise prosody control and
mispronunciation correction. Existing approaches for prosody manipulation often
depend on specialized modules or additional training, limiting their capacity
for post-hoc adjustments. Similarly, traditional mispronunciation correction
relies on grapheme-to-phoneme dictionaries, making it less practical in
low-resource settings. We introduce Counterfactual Activation Editing, a
model-agnostic method that manipulates internal representations in a
pre-trained TTS model to achieve post-hoc control of prosody and pronunciation.
Experimental results show that our method effectively adjusts prosodic features
and corrects mispronunciations while preserving synthesis quality. This opens
the door to inference-time refinement of TTS outputs without retraining,
bridging the gap between pre-trained TTS models and editable speech synthesis.","cs.SD, cs.AI, eess.AS",2025-06-01T04:33:37+00:00,2025-06-01T04:33:37+00:00,http://arxiv.org/abs/2506.00832v1
2506.00418v1,Dual Debiasing for Noisy In-Context Learning for Text Generation,"Siqi Liang, Sumyeong Ahn, Paramveer S. Dhillon, Jiayu Zhou","In context learning (ICL) relies heavily on high quality demonstrations drawn
from large annotated corpora. Existing approaches detect noisy annotations by
ranking local perplexities, presuming that noisy samples yield higher
perplexities than their clean counterparts. However, this assumption breaks
down when the noise ratio is high and many demonstrations are flawed. We
reexamine the perplexity based paradigm for text generation under noisy
annotations, highlighting two sources of bias in perplexity: the annotation
itself and the domain specific knowledge inherent in large language models
(LLMs). To overcome these biases, we introduce a dual debiasing framework that
uses synthesized neighbors to explicitly correct perplexity estimates, yielding
a robust Sample Cleanliness Score. This metric uncovers absolute sample
cleanliness regardless of the overall corpus noise level. Extensive experiments
demonstrate our method's superior noise detection capabilities and show that
its final ICL performance is comparable to that of a fully clean demonstration
corpus. Moreover, our approach remains robust even when noise ratios are
extremely high.","cs.CL, cs.AI, I.2.7",2025-05-31T06:44:48+00:00,2025-05-31T06:44:48+00:00,http://arxiv.org/abs/2506.00418v1
2505.24754v1,"Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
  based on Guided Space Transformation","Yingchaojie Feng, Yiqun Sun, Yandong Sun, Minfeng Zhu, Qiang Huang, Anthony K. H. Tung, Wei Chen","In this work, we investigate an important task named instruction-following
text embedding, which generates dynamic text embeddings that adapt to user
instructions, highlighting specific attributes of text. Despite recent
advancements, existing approaches suffer from significant computational
overhead, as they require re-encoding the entire corpus for each new
instruction. To address this challenge, we propose GSTransform, a novel
instruction-following text embedding framework based on Guided Space
Transformation. Our key observation is that instruction-relevant information is
inherently encoded in generic embeddings but remains underutilized. Instead of
repeatedly encoding the corpus for each instruction, GSTransform is a
lightweight transformation mechanism that adapts pre-computed embeddings in
real time to align with user instructions, guided by a small amount of text
data with instruction-focused label annotation. We conduct extensive
experiments on three instruction-awareness downstream tasks across nine
real-world datasets, demonstrating that GSTransform improves
instruction-following text embedding quality over state-of-the-art methods
while achieving dramatic speedups of 6~300x in real-time processing on
large-scale datasets. The source code is available at
https://github.com/YingchaojieFeng/GSTransform.","cs.CL, cs.AI, cs.IR",2025-05-30T16:16:22+00:00,2025-05-30T16:16:22+00:00,http://arxiv.org/abs/2505.24754v1
2505.24575v1,NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization,"Hyuntak Kim, Byung-Hak Kim","Summarizing long-form narratives--such as books, movies, and TV
scripts--requires capturing intricate plotlines, character interactions, and
thematic coherence, a task that remains challenging for existing LLMs. We
introduce NexusSum, a multi-agent LLM framework for narrative summarization
that processes long-form text through a structured, sequential
pipeline--without requiring fine-tuning. Our approach introduces two key
innovations: (1) Dialogue-to-Description Transformation: A narrative-specific
preprocessing method that standardizes character dialogue and descriptive text
into a unified format, improving coherence. (2) Hierarchical Multi-LLM
Summarization: A structured summarization pipeline that optimizes chunk
processing and controls output length for accurate, high-quality summaries. Our
method establishes a new state-of-the-art in narrative summarization, achieving
up to a 30.0% improvement in BERTScore (F1) across books, movies, and TV
scripts. These results demonstrate the effectiveness of multi-agent LLMs in
handling long-form content, offering a scalable approach for structured
summarization in diverse storytelling domains.","cs.CL, cs.AI",2025-05-30T13:26:23+00:00,2025-05-30T13:26:23+00:00,http://arxiv.org/abs/2505.24575v1
2506.00085v1,COSMIC: Generalized Refusal Direction Identification in LLM Activations,"Vincent Siu, Nicholas Crispino, Zihao Yu, Sam Pan, Zhun Wang, Yang Liu, Dawn Song, Chenguang Wang","Large Language Models (LLMs) encode behaviors such as refusal within their
activation space, yet identifying these behaviors remains a significant
challenge. Existing methods often rely on predefined refusal templates
detectable in output tokens or require manual analysis. We introduce
\textbf{COSMIC} (Cosine Similarity Metrics for Inversion of Concepts), an
automated framework for direction selection that identifies viable steering
directions and target layers using cosine similarity - entirely independent of
model outputs. COSMIC achieves steering performance comparable to prior methods
without requiring assumptions about a model's refusal behavior, such as the
presence of specific refusal tokens. It reliably identifies refusal directions
in adversarial settings and weakly aligned models, and is capable of steering
such models toward safer behavior with minimal increase in false refusals,
demonstrating robustness across a wide range of alignment conditions.","cs.CL, cs.AI",2025-05-30T04:54:18+00:00,2025-05-30T04:54:18+00:00,http://arxiv.org/abs/2506.00085v1
2505.23996v1,"Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
  LLMs","Yinong Oliver Wang, Nivedha Sivakumar, Falaah Arif Khan, Rin Metcalf Susa, Adam Golinski, Natalie Mackraz, Barry-John Theobald, Luca Zappella, Nicholas Apostoloff","The recent rapid adoption of large language models (LLMs) highlights the
critical need for benchmarking their fairness. Conventional fairness metrics,
which focus on discrete accuracy-based evaluations (i.e., prediction
correctness), fail to capture the implicit impact of model uncertainty (e.g.,
higher model confidence about one group over another despite similar accuracy).
To address this limitation, we propose an uncertainty-aware fairness metric,
UCerF, to enable a fine-grained evaluation of model fairness that is more
reflective of the internal bias in model decisions compared to conventional
fairness measures. Furthermore, observing data size, diversity, and clarity
issues in current datasets, we introduce a new gender-occupation fairness
evaluation dataset with 31,756 samples for co-reference resolution, offering a
more diverse and suitable dataset for evaluating modern LLMs. We establish a
benchmark, using our metric and dataset, and apply it to evaluate the behavior
of ten open-source LLMs. For example, Mistral-7B exhibits suboptimal fairness
due to high confidence in incorrect predictions, a detail overlooked by
Equalized Odds but captured by UCerF. Overall, our proposed LLM benchmark,
which evaluates fairness with uncertainty awareness, paves the way for
developing more transparent and accountable AI systems.","cs.CL, cs.AI, cs.LG",2025-05-29T20:45:18+00:00,2025-05-29T20:45:18+00:00,http://arxiv.org/abs/2505.23996v1
2505.23353v1,"Synthetic Generation and Latent Projection Denoising of Rim Lesions in
  Multiple Sclerosis","Alexandra G. Roberts, Ha M. Luu, Mert Şişman, Alexey V. Dimov, Ceren Tozlu, Ilhami Kovanlikaya, Susan A. Gauthier, Thanh D. Nguyen, Yi Wang","Quantitative susceptibility maps from magnetic resonance images can provide
both prognostic and diagnostic information in multiple sclerosis, a
neurodegenerative disease characterized by the formation of lesions in white
matter brain tissue. In particular, susceptibility maps provide adequate
contrast to distinguish between ""rim"" lesions, surrounded by deposited
paramagnetic iron, and ""non-rim"" lesion types. These paramagnetic rim lesions
(PRLs) are an emerging biomarker in multiple sclerosis. Much effort has been
devoted to both detection and segmentation of such lesions to monitor
longitudinal change. As paramagnetic rim lesions are rare, addressing this
problem requires confronting the class imbalance between rim and non-rim
lesions. We produce synthetic quantitative susceptibility maps of paramagnetic
rim lesions and show that inclusion of such synthetic data improves classifier
performance and provide a multi-channel extension to generate accompanying
contrasts and probabilistic segmentation maps. We exploit the projection
capability of our trained generative network to demonstrate a novel denoising
approach that allows us to train on ambiguous rim cases and substantially
increase the minority class. We show that both synthetic lesion synthesis and
our proposed rim lesion label denoising method best approximate the unseen rim
lesion distribution and improve detection in a clinically interpretable manner.
We release our code and generated data at https://github.com/agr78/PRLx-GAN
upon publication.","eess.IV, cs.AI, cs.CV",2025-05-29T11:22:48+00:00,2025-05-29T11:22:48+00:00,http://arxiv.org/abs/2505.23353v1
2505.22757v1,Pre-Training Curriculum for Multi-Token Prediction in Language Models,"Ansar Aynetdinov, Alan Akbik","Multi-token prediction (MTP) is a recently proposed pre-training objective
for language models. Rather than predicting only the next token (NTP), MTP
predicts the next $k$ tokens at each prediction step, using multiple prediction
heads. MTP has shown promise in improving downstream performance, inference
speed, and training efficiency, particularly for large models. However, prior
work has shown that smaller language models (SLMs) struggle with the MTP
objective. To address this, we propose a curriculum learning strategy for MTP
training, exploring two variants: a forward curriculum, which gradually
increases the complexity of the pre-training objective from NTP to MTP, and a
reverse curriculum, which does the opposite. Our experiments show that the
forward curriculum enables SLMs to better leverage the MTP objective during
pre-training, improving downstream NTP performance and generative output
quality, while retaining the benefits of self-speculative decoding. The reverse
curriculum achieves stronger NTP performance and output quality, but fails to
provide any self-speculative decoding benefits.","cs.CL, cs.AI",2025-05-28T18:19:18+00:00,2025-05-28T18:19:18+00:00,http://arxiv.org/abs/2505.22757v1
2506.02853v1,"Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
  Estimation","Mingjie Wei, Xuemei Xie, Yutong Zhong, Guangming Shi","Action coordination in human structure is indispensable for the spatial
constraints of 2D joints to recover 3D pose. Usually, action coordination is
represented as a long-range dependence among body parts. However, there are two
main challenges in modeling long-range dependencies. First, joints should not
only be constrained by other individual joints but also be modulated by the
body parts. Second, existing methods make networks deeper to learn dependencies
between non-linked parts. They introduce uncorrelated noise and increase the
model size. In this paper, we utilize a pyramid structure to better learn
potential long-range dependencies. It can capture the correlation across joints
and groups, which complements the context of the human sub-structure. In an
effective cross-scale way, it captures the pyramid-structured long-range
dependence. Specifically, we propose a novel Pyramid Graph Attention (PGA)
module to capture long-range cross-scale dependencies. It concatenates
information from various scales into a compact sequence, and then computes the
correlation between scales in parallel. Combining PGA with graph convolution
modules, we develop a Pyramid Graph Transformer (PGFormer) for 3D human pose
estimation, which is a lightweight multi-scale transformer architecture. It
encapsulates human sub-structures into self-attention by pooling. Extensive
experiments show that our approach achieves lower error and smaller model size
than state-of-the-art methods on Human3.6M and MPI-INF-3DHP datasets. The code
is available at https://github.com/MingjieWe/PGFormer.",cs.CV,2025-06-03T13:21:37+00:00,2025-06-03T13:21:37+00:00,http://arxiv.org/abs/2506.02853v1
2506.02547v1,Probabilistic Online Event Downsampling,"Andreu Girbau-Xalabarder, Jun Nagata, Shinichi Sumiyoshi","Event cameras capture scene changes asynchronously on a per-pixel basis,
enabling extremely high temporal resolution. However, this advantage comes at
the cost of high bandwidth, memory, and computational demands. To address this,
prior work has explored event downsampling, but most approaches rely on fixed
heuristics or threshold-based strategies, limiting their adaptability. Instead,
we propose a probabilistic framework, POLED, that models event importance
through an event-importance probability density function (ePDF), which can be
arbitrarily defined and adapted to different applications. Our approach
operates in a purely online setting, estimating event importance on-the-fly
from raw event streams, enabling scene-specific adaptation. Additionally, we
introduce zero-shot event downsampling, where downsampled events must remain
usable for models trained on the original event stream, without task-specific
adaptation. We design a contour-preserving ePDF that prioritizes structurally
important events and evaluate our method across four datasets and tasks--object
classification, image interpolation, surface normal estimation, and object
detection--demonstrating that intelligent sampling is crucial for maintaining
performance under event-budget constraints.","cs.CV, cs.ET",2025-06-03T07:33:11+00:00,2025-06-03T07:33:11+00:00,http://arxiv.org/abs/2506.02547v1
2506.01071v1,Aligned Contrastive Loss for Long-Tailed Recognition,"Jiali Ma, Jiequan Cui, Maeno Kazuki, Lakshmi Subramanian, Karlekar Jayashree, Sugiri Pranata, Hanwang Zhang","In this paper, we propose an Aligned Contrastive Learning (ACL) algorithm to
address the long-tailed recognition problem. Our findings indicate that while
multi-view training boosts the performance, contrastive learning does not
consistently enhance model generalization as the number of views increases.
Through theoretical gradient analysis of supervised contrastive learning (SCL),
we identify gradient conflicts, and imbalanced attraction and repulsion
gradients between positive and negative pairs as the underlying issues. Our ACL
algorithm is designed to eliminate these problems and demonstrates strong
performance across multiple benchmarks. We validate the effectiveness of ACL
through experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist
datasets. Results show that ACL achieves new state-of-the-art performance.",cs.CV,2025-06-01T16:19:30+00:00,2025-06-01T16:19:30+00:00,http://arxiv.org/abs/2506.01071v1
2506.01037v1,"Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world
  Video Super-resolution","Shijun Shi, Jing Xu, Lijing Lu, Zhihang Li, Kai Hu","Existing diffusion-based video super-resolution (VSR) methods are susceptible
to introducing complex degradations and noticeable artifacts into
high-resolution videos due to their inherent randomness. In this paper, we
propose a noise-robust real-world VSR framework by incorporating
self-supervised learning and Mamba into pre-trained latent diffusion models. To
ensure content consistency across adjacent frames, we enhance the diffusion
model with a global spatio-temporal attention mechanism using the Video
State-Space block with a 3D Selective Scan module, which reinforces coherence
at an affordable computational cost. To further reduce artifacts in generated
details, we introduce a self-supervised ControlNet that leverages HR features
as guidance and employs contrastive learning to extract degradation-insensitive
features from LR videos. Finally, a three-stage training strategy based on a
mixture of HR-LR videos is proposed to stabilize VSR training. The proposed
Self-supervised ControlNet with Spatio-Temporal Continuous Mamba based VSR
algorithm achieves superior perceptual quality than state-of-the-arts on
real-world VSR benchmark datasets, validating the effectiveness of the proposed
model design and training strategies.","cs.CV, I.4.4, I.2.6",2025-06-01T14:36:25+00:00,2025-06-01T14:36:25+00:00,http://arxiv.org/abs/2506.01037v1
2506.00434v1,"Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal
  Embedding","Tuan-Luc Huynh, Thanh-Danh Le, Tam V. Nguyen, Trung-Nghia Le, Minh-Triet Tran","In this paper, we address the crucial task of brain tumor segmentation in
medical imaging and propose innovative approaches to enhance its performance.
The current state-of-the-art nnU-Net has shown promising results but suffers
from extensive training requirements and underutilization of pre-trained
weights. To overcome these limitations, we integrate Axial-Coronal-Sagittal
convolutions and pre-trained weights from ImageNet into the nnU-Net framework,
resulting in reduced training epochs, reduced trainable parameters, and
improved efficiency. Two strategies for transferring 2D pre-trained weights to
the 3D domain are presented, ensuring the preservation of learned relationships
and feature representations critical for effective information propagation.
Furthermore, we explore a joint classification and segmentation model that
leverages pre-trained encoders from a brain glioma grade classification proxy
task, leading to enhanced segmentation performance, especially for challenging
tumor labels. Experimental results demonstrate that our proposed methods in the
fast training settings achieve comparable or even outperform the ensemble of
cross-validation models, a common practice in the brain tumor segmentation
literature.","eess.IV, cs.CV",2025-05-31T07:30:37+00:00,2025-05-31T07:30:37+00:00,http://arxiv.org/abs/2506.00434v1
2506.00333v1,Test-time Vocabulary Adaptation for Language-driven Object Detection,"Mingxuan Liu, Tyler L. Hayes, Massimiliano Mancini, Elisa Ricci, Riccardo Volpi, Gabriela Csurka","Open-vocabulary object detection models allow users to freely specify a class
vocabulary in natural language at test time, guiding the detection of desired
objects. However, vocabularies can be overly broad or even mis-specified,
hampering the overall performance of the detector. In this work, we propose a
plug-and-play Vocabulary Adapter (VocAda) to refine the user-defined
vocabulary, automatically tailoring it to categories that are relevant for a
given image. VocAda does not require any training, it operates at inference
time in three steps: i) it uses an image captionner to describe visible
objects, ii) it parses nouns from those captions, and iii) it selects relevant
classes from the user-defined vocabulary, discarding irrelevant ones.
Experiments on COCO and Objects365 with three state-of-the-art detectors show
that VocAda consistently improves performance, proving its versatility. The
code is open source.",cs.CV,2025-05-31T01:15:29+00:00,2025-05-31T01:15:29+00:00,http://arxiv.org/abs/2506.00333v1
2505.24443v1,"Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
  Learning with Outliers","Heejo Kong, Sung-Jin Kim, Gunho Jung, Seong-Whan Lee","Conventional semi-supervised learning (SSL) ideally assumes that labeled and
unlabeled data share an identical class distribution, however in practice, this
assumption is easily violated, as unlabeled data often includes unknown class
data, i.e., outliers. The outliers are treated as noise, considerably degrading
the performance of SSL models. To address this drawback, we propose a novel
framework, Diversify and Conquer (DAC), to enhance SSL robustness in the
context of open-set semi-supervised learning. In particular, we note that
existing open-set SSL methods rely on prediction discrepancies between inliers
and outliers from a single model trained on labeled data. This approach can be
easily failed when the labeled data is insufficient, leading to performance
degradation that is worse than naive SSL that do not account for outliers. In
contrast, our approach exploits prediction disagreements among multiple models
that are differently biased towards the unlabeled distribution. By leveraging
the discrepancies arising from training on unlabeled data, our method enables
robust outlier detection even when the labeled data is underspecified. Our key
contribution is constructing a collection of differently biased models through
a single training process. By encouraging divergent heads to be differently
biased towards outliers while making consistent predictions for inliers, we
exploit the disagreement among these heads as a measure to identify unknown
concepts. Our code is available at https://github.com/heejokong/DivCon.","cs.CV, cs.LG",2025-05-30T10:24:30+00:00,2025-05-30T10:24:30+00:00,http://arxiv.org/abs/2505.24443v1
2505.24334v1,"KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded
  Devices","Uzair Khan, Franco Fummi, Luigi Capogrosso","In the era of intelligent manufacturing, anomaly detection has become
essential for maintaining quality control on modern production lines. However,
while many existing models show promising performance, they are often too
large, computationally demanding, and impractical to deploy on
resource-constrained embedded devices that can be easily installed on the
production lines of Small and Medium Enterprises (SMEs). To bridge this gap, we
present KairosAD, a novel supervised approach that uses the power of the Mobile
Segment Anything Model (MobileSAM) for image-based anomaly detection. KairosAD
has been evaluated on the two well-known industrial anomaly detection datasets,
i.e., MVTec-AD and ViSA. The results show that KairosAD requires 78% fewer
parameters and boasts a 4x faster inference time compared to the leading
state-of-the-art model, while maintaining comparable AUROC performance. We
deployed KairosAD on two embedded devices, the NVIDIA Jetson NX, and the NVIDIA
Jetson AGX. Finally, KairosAD was successfully installed and tested on the real
production line of the Industrial Computer Engineering Laboratory (ICE Lab) at
the University of Verona. The code is available at
https://github.com/intelligolabs/KairosAD.",cs.CV,2025-05-30T08:18:49+00:00,2025-05-30T08:18:49+00:00,http://arxiv.org/abs/2505.24334v1
2505.23290v1,"Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven
  Facial Animation","Hao Li, Ju Dai, Xin Zhao, Feng Zhou, Junjun Pan, Lei Li","In 3D speech-driven facial animation generation, existing methods commonly
employ pre-trained self-supervised audio models as encoders. However, due to
the prevalence of phonetically similar syllables with distinct lip shapes in
language, these near-homophone syllables tend to exhibit significant coupling
in self-supervised audio feature spaces, leading to the averaging effect in
subsequent lip motion generation. To address this issue, this paper proposes a
plug-and-play semantic decorrelation module-Wav2Sem. This module extracts
semantic features corresponding to the entire audio sequence, leveraging the
added semantic information to decorrelate audio encodings within the feature
space, thereby achieving more expressive audio features. Extensive experiments
across multiple Speech-driven models indicate that the Wav2Sem module
effectively decouples audio features, significantly alleviating the averaging
effect of phonetically similar syllables in lip shape generation, thereby
enhancing the precision and naturalness of facial animations. Our source code
is available at https://github.com/wslh852/Wav2Sem.git.","cs.SD, cs.CV, eess.AS",2025-05-29T09:42:03+00:00,2025-05-29T09:42:03+00:00,http://arxiv.org/abs/2505.23290v1
2505.23180v1,"Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction
  Networks for Single-Pixel Imaging","Ping Wang, Lishun Wang, Gang Qu, Xiaodong Wang, Yulun Zhang, Xin Yuan","Deep-unrolling and plug-and-play (PnP) approaches have become the de-facto
standard solvers for single-pixel imaging (SPI) inverse problem. PnP
approaches, a class of iterative algorithms where regularization is implicitly
performed by an off-the-shelf deep denoiser, are flexible for varying
compression ratios (CRs) but are limited in reconstruction accuracy and speed.
Conversely, unrolling approaches, a class of multi-stage neural networks where
a truncated iterative optimization process is transformed into an end-to-end
trainable network, typically achieve better accuracy with faster inference but
require fine-tuning or even retraining when CR changes. In this paper, we
address the challenge of integrating the strengths of both classes of solvers.
To this end, we design an efficient deep image restorer (DIR) for the unrolling
of HQS (half quadratic splitting) and ADMM (alternating direction method of
multipliers). More importantly, a general proximal trajectory (PT) loss
function is proposed to train HQS/ADMM-unrolling networks such that learned DIR
approximates the proximal operator of an ideal explicit restoration
regularizer. Extensive experiments demonstrate that, the resulting proximal
unrolling networks can not only flexibly handle varying CRs with a single model
like PnP algorithms, but also outperform previous CR-specific unrolling
networks in both reconstruction accuracy and speed. Source codes and models are
available at https://github.com/pwangcs/ProxUnroll.","eess.IV, cs.CV",2025-05-29T07:16:57+00:00,2025-05-29T07:16:57+00:00,http://arxiv.org/abs/2505.23180v1
2505.22616v1,"PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and
  Optimization","Yezhi Shen, Qiuchen Zhai, Fengqing Zhu","Neural rendering methods have gained significant attention for their ability
to reconstruct 3D scenes from 2D images. The core idea is to take multiple
views as input and optimize the reconstructed scene by minimizing the
uncertainty in geometry and appearance across the views. However, the
reconstruction quality is limited by the number of input views. This limitation
is further pronounced in complex and dynamic scenes, where certain angles of
objects are never seen. In this paper, we propose to use video frame
interpolation as the data augmentation method for neural rendering.
Furthermore, we design a lightweight yet high-quality video frame interpolation
model, PS4PRO (Pixel-to-pixel Supervision for Photorealistic Rendering and
Optimization). PS4PRO is trained on diverse video datasets, implicitly modeling
camera movement as well as real-world 3D geometry. Our model performs as an
implicit world prior, enriching the photo supervision for 3D reconstruction. By
leveraging the proposed method, we effectively augment existing datasets for
neural rendering methods. Our experimental results indicate that our method
improves the reconstruction performance on both static and dynamic scenes.","cs.CV, eess.IV",2025-05-28T17:35:39+00:00,2025-05-28T17:35:39+00:00,http://arxiv.org/abs/2505.22616v1
2505.22458v1,Universal Domain Adaptation for Semantic Segmentation,"Seun-An Choe, Keon-Hee Park, Jinwoo Choi, Gyeong-Moon Park","Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to
transfer knowledge from labeled source data to unlabeled target data. However,
traditional UDA-SS methods assume that category settings between source and
target domains are known, which is unrealistic in real-world scenarios. This
leads to performance degradation if private classes exist. To address this
limitation, we propose Universal Domain Adaptation for Semantic Segmentation
(UniDA-SS), achieving robust adaptation even without prior knowledge of
category settings. We define the problem in the UniDA-SS scenario as low
confidence scores of common classes in the target domain, which leads to
confusion with private classes. To solve this problem, we propose UniMAP:
UniDA-SS with Image Matching and Prototype-based Distinction, a novel framework
composed of two key components. First, Domain-Specific Prototype-based
Distinction (DSPD) divides each class into two domain-specific prototypes,
enabling finer separation of domain-specific features and enhancing the
identification of common classes across domains. Second, Target-based Image
Matching (TIM) selects a source image containing the most common-class pixels
based on the target pseudo-label and pairs it in a batch to promote effective
learning of common classes. We also introduce a new UniDA-SS benchmark and
demonstrate through various experiments that UniMAP significantly outperforms
baselines. The code is available at
\href{https://github.com/KU-VGI/UniMAP}{this https URL}.",cs.CV,2025-05-28T15:14:11+00:00,2025-05-28T15:14:11+00:00,http://arxiv.org/abs/2505.22458v1
2505.22427v1,RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network,"Van-Tin Luu, Yon-Lin Cai, Vu-Hoang Tran, Wei-Chen Chiu, Yi-Ting Chen, Ching-Chun Huang","This paper presents a groundbreaking approach - the first online automatic
geometric calibration method for radar and camera systems. Given the
significant data sparsity and measurement uncertainty in radar height data,
achieving automatic calibration during system operation has long been a
challenge. To address the sparsity issue, we propose a Dual-Perspective
representation that gathers features from both frontal and bird's-eye views.
The frontal view contains rich but sensitive height information, whereas the
bird's-eye view provides robust features against height uncertainty. We thereby
propose a novel Selective Fusion Mechanism to identify and fuse reliable
features from both perspectives, reducing the effect of height uncertainty.
Moreover, for each view, we incorporate a Multi-Modal Cross-Attention Mechanism
to explicitly find location correspondences through cross-modal matching.
During the training phase, we also design a Noise-Resistant Matcher to provide
better supervision and enhance the robustness of the matching mechanism against
sparsity and height uncertainty. Our experimental results, tested on the
nuScenes dataset, demonstrate that our method significantly outperforms
previous radar-camera auto-calibration methods, as well as existing
state-of-the-art LiDAR-camera calibration techniques, establishing a new
benchmark for future research. The code is available at
https://github.com/nycu-acm/RC-AutoCalib.",cs.CV,2025-05-28T14:52:31+00:00,2025-05-28T14:52:31+00:00,http://arxiv.org/abs/2505.22427v1
2505.22167v1,"Q-VDiT: Towards Accurate Quantization and Distillation of
  Video-Generation Diffusion Transformers","Weilun Feng, Chuanguang Yang, Haotong Qin, Xiangqi Li, Yu Wang, Zhulin An, Libo Huang, Boyu Diao, Zixiang Zhao, Yongjun Xu, Michele Magno","Diffusion transformers (DiT) have demonstrated exceptional performance in
video generation. However, their large number of parameters and high
computational complexity limit their deployment on edge devices. Quantization
can reduce storage requirements and accelerate inference by lowering the
bit-width of model parameters. Yet, existing quantization methods for image
generation models do not generalize well to video generation tasks. We identify
two primary challenges: the loss of information during quantization and the
misalignment between optimization objectives and the unique requirements of
video generation. To address these challenges, we present Q-VDiT, a
quantization framework specifically designed for video DiT models. From the
quantization perspective, we propose the Token-aware Quantization Estimator
(TQE), which compensates for quantization errors in both the token and feature
dimensions. From the optimization perspective, we introduce Temporal
Maintenance Distillation (TMD), which preserves the spatiotemporal correlations
between frames and enables the optimization of each frame with respect to the
overall video context. Our W3A6 Q-VDiT achieves a scene consistency of 23.40,
setting a new benchmark and outperforming current state-of-the-art quantization
methods by 1.9$\times$. Code will be available at
https://github.com/cantbebetter2/Q-VDiT.",cs.CV,2025-05-28T09:33:52+00:00,2025-05-28T09:33:52+00:00,http://arxiv.org/abs/2505.22167v1
2505.22552v1,"ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
  with Lightweight Specialized LLM","Hoang Pham, Thanh-Do Nguyen, Khac-Hoai Nam Bui","Integrating knowledge graphs (KGs) to enhance the reasoning capabilities of
large language models (LLMs) is an emerging research challenge in claim
verification. While KGs provide structured, semantically rich representations
well-suited for reasoning, most existing verification methods rely on
unstructured text corpora, limiting their ability to effectively leverage KGs.
Additionally, despite possessing strong reasoning abilities, modern LLMs
struggle with multi-step modular pipelines and reasoning over KGs without
adaptation. To address these challenges, we propose ClaimPKG, an end-to-end
framework that seamlessly integrates LLM reasoning with structured knowledge
from KGs. Specifically, the main idea of ClaimPKG is to employ a lightweight,
specialized LLM to represent the input claim as pseudo-subgraphs, guiding a
dedicated subgraph retrieval module to identify relevant KG subgraphs. These
retrieved subgraphs are then processed by a general-purpose LLM to produce the
final verdict and justification. Extensive experiments on the FactKG dataset
demonstrate that ClaimPKG achieves state-of-the-art performance, outperforming
strong baselines in this research field by 9%-12% accuracy points across
multiple categories. Furthermore, ClaimPKG exhibits zero-shot generalizability
to unstructured datasets such as HoVer and FEVEROUS, effectively combining
structured knowledge from KGs with LLM reasoning across various LLM backbones.","cs.CL, cs.AI, cs.DB",2025-05-28T16:34:14+00:00,2025-05-28T16:34:14+00:00,http://arxiv.org/abs/2505.22552v1
2504.21752v1,"VDDP: Verifiable Distributed Differential Privacy under the
  Client-Server-Verifier Setup","Haochen Sun, Xi He","Despite differential privacy (DP) often being considered the de facto
standard for data privacy, its realization is vulnerable to unfaithful
execution of its mechanisms by servers, especially in distributed settings.
Specifically, servers may sample noise from incorrect distributions or generate
correlated noise while appearing to follow established protocols. This work
analyzes these malicious behaviors in a general differential privacy framework
within a distributed client-server-verifier setup. To address these adversarial
problems, we propose a novel definition called Verifiable Distributed
Differential Privacy (VDDP) by incorporating additional verification
mechanisms. We also explore the relationship between zero-knowledge proofs
(ZKP) and DP, demonstrating that while ZKPs are sufficient for achieving DP
under verifiability requirements, they are not necessary. Furthermore, we
develop two novel and efficient mechanisms that satisfy VDDP: (1) the
Verifiable Distributed Discrete Laplacian Mechanism (VDDLM), which offers up to
a $4 \times 10^5$x improvement in proof generation efficiency with only
0.1-0.2x error compared to the previous state-of-the-art verifiable
differentially private mechanism; (2) an improved solution to Verifiable
Randomized Response (VRR) under local DP, a special case of VDDP, achieving up
a reduction of up to 5000x in communication costs and the verifier's overhead.","cs.CR, cs.DB",2025-04-30T15:46:55+00:00,2025-04-30T15:46:55+00:00,http://arxiv.org/abs/2504.21752v1
2504.21282v1,"Birdie: Natural Language-Driven Table Discovery Using Differentiable
  Search Index","Yuxiang Guo, Zhonghao Hu, Yuren Mao, Baihua Zheng, Yunjun Gao, Mingwei Zhou","Natural language (NL)-driven table discovery identifies relevant tables from
large table repositories based on NL queries. While current deep-learning-based
methods using the traditional dense vector search pipeline, i.e.,
representation-index-search, achieve remarkable accuracy, they face several
limitations that impede further performance improvements: (i) the errors
accumulated during the table representation and indexing phases affect the
subsequent search accuracy; and (ii) insufficient query-table interaction
hinders effective semantic alignment, impeding accuracy improvements. In this
paper, we propose a novel framework Birdie, using a differentiable search
index. It unifies the indexing and search into a single encoder-decoder
language model, thus getting rid of error accumulations. Birdie first assigns
each table a prefix-aware identifier and leverages a large language model-based
query generator to create synthetic queries for each table. It then encodes the
mapping between synthetic queries/tables and their corresponding table
identifiers into the parameters of an encoder-decoder language model, enabling
deep query-table interactions. During search, the trained model directly
generates table identifiers for a given query. To accommodate the continual
indexing of dynamic tables, we introduce an index update strategy via parameter
isolation, which mitigates the issue of catastrophic forgetting. Extensive
experiments demonstrate that Birdie outperforms state-of-the-art dense methods
by 16.8% in accuracy, and reduces forgetting by over 90% compared to other
continual learning approaches.",cs.DB,2025-04-30T03:30:21+00:00,2025-04-30T03:30:21+00:00,http://arxiv.org/abs/2504.21282v1
2504.17448v1,"CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
  Active Learning","Jun Zhang, Jue Wang, Huan Li, Zhongle Xie, Ke Chen, Lidan Shou","Active learning (AL) reduces human annotation costs for machine learning
systems by strategically selecting the most informative unlabeled data for
annotation, but performing it individually may still be insufficient due to
restricted data diversity and annotation budget. Federated Active Learning
(FAL) addresses this by facilitating collaborative data selection and model
training, while preserving the confidentiality of raw data samples. Yet,
existing FAL methods fail to account for the heterogeneity of data distribution
across clients and the associated fluctuations in global and local model
parameters, adversely affecting model accuracy. To overcome these challenges,
we propose CHASe (Client Heterogeneity-Aware Data Selection), specifically
designed for FAL. CHASe focuses on identifying those unlabeled samples with
high epistemic variations (EVs), which notably oscillate around the decision
boundaries during training. To achieve both effectiveness and efficiency,
\model{} encompasses techniques for 1) tracking EVs by analyzing inference
inconsistencies across training epochs, 2) calibrating decision boundaries of
inaccurate models with a new alignment loss, and 3) enhancing data selection
efficiency via a data freeze and awaken mechanism with subset sampling.
Experiments show that CHASe surpasses various established baselines in terms of
effectiveness and efficiency, validated across diverse datasets, model
complexities, and heterogeneous federation settings.","cs.LG, cs.DB, cs.DC",2025-04-24T11:28:00+00:00,2025-04-24T11:28:00+00:00,http://arxiv.org/abs/2504.17448v1
2504.14861v1,"Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum
  Inner Product Search","Tingyang Chen, Cong Fu, Xiangyu Ke, Yunjun Gao, Yabo Ni, Anxiang Zeng","Maximum Inner Product Search (MIPS) is a fundamental challenge in machine
learning and information retrieval, particularly in high-dimensional data
applications. Existing approaches to MIPS either rely solely on Inner Product
(IP) similarity, which faces issues with local optima and redundant
computations, or reduce the MIPS problem to the Nearest Neighbor Search under
the Euclidean metric via space projection, leading to topology destruction and
information loss. Despite the divergence of the two paradigms, we argue that
there is no inherent binary opposition between IP and Euclidean metrics. By
stitching IP and Euclidean in the design of indexing and search algorithms, we
can significantly enhance MIPS performance. Specifically, this paper explores
the theoretical and empirical connections between these two metrics from the
MIPS perspective. Our investigation, grounded in graph-based search, reveals
that different indexing and search strategies offer distinct advantages for
MIPS, depending on the underlying data topology. Building on these insights, we
introduce a novel graph-based index called Metric-Amphibious Graph (MAG) and a
corresponding search algorithm, Adaptive Navigation with Metric Switch (ANMS).
To facilitate parameter tuning for optimal performance, we identify three
statistical indicators that capture essential data topology properties and
correlate strongly with parameter tuning. Extensive experiments on 12
real-world datasets demonstrate that MAG outperforms existing state-of-the-art
methods, achieving up to 4x search speedup while maintaining adaptability and
scalability.","cs.DB, cs.IR",2025-04-21T05:01:58+00:00,2025-04-21T05:01:58+00:00,http://arxiv.org/abs/2504.14861v1
2504.06975v1,AWDIT: An Optimal Weak Database Isolation Tester,"Lasse Møldrup, Andreas Pavlogiannis","In order to achieve low latency, high throughput, and partition tolerance,
modern databases forgo strong transaction isolation for weak isolation
guarantees. However, several production databases have been found to suffer
from isolation bugs, breaking their data-consistency contract. Black-box
testing is a prominent technique for detecting isolation bugs, by checking
whether histories of database transactions adhere to a prescribed isolation
level.
  Testing databases on realistic workloads of large size requires isolation
testers to be as efficient as possible, a requirement that has initiated a
study of the complexity of isolation testing. Although testing strong isolation
has been known to be NP-complete, weak isolation levels were recently shown to
be testable in polynomial time, which has propelled the scalability of testing
tools. However, existing testers have a large polynomial complexity,
restricting testing to workloads of only moderate size, which is not typical of
large-scale databases.
  In this work, we develop AWDIT, a highly-efficient and provably optimal
tester for weak database isolation. Given a history $H$ of size $n$ and $k$
sessions, AWDIT tests whether H satisfies the most common weak isolation levels
of Read Committed (RC), Read Atomic (RA), and Causal Consistency (CC) in time
$O(n^{3/2})$, $O(n^{3/2})$, and $O(n \cdot k)$, respectively, improving
significantly over the state of the art. Moreover, we prove that AWDIT is
essentially optimal, in the sense that there is a conditional lower bound of
$n^{3/2}$ for any weak isolation level between RC and CC. Our experiments show
that AWDIT is significantly faster than existing, highly optimized testers;
e.g., for the $\sim$20% largest histories, AWDIT obtains an average speedup of
$245\times$, $193\times$, and $62\times$ for RC, RA, and CC, respectively, over
the best baseline.","cs.PL, cs.DB, H.2.4, D.2.5, F.2.2",2025-04-09T15:30:09+00:00,2025-04-09T15:30:09+00:00,http://arxiv.org/abs/2504.06975v1
2506.01833v1,SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model,"Zhao Yang, Jiwei Zhu, Bing Su","Inspired by the success of unsupervised pre-training paradigms, researchers
have applied these approaches to DNA pre-training. However, we argue that these
approaches alone yield suboptimal results because pure DNA sequences lack
sufficient information, since their functions are regulated by genomic profiles
like chromatin accessibility. Here, we demonstrate that supervised training for
genomic profile prediction serves as a more effective alternative to pure
sequence pre-training. Furthermore, considering the multi-species and
multi-profile nature of genomic profile prediction, we introduce our
$\textbf{S}$pecies-$\textbf{P}$rofile $\textbf{A}$daptive
$\textbf{C}$ollaborative $\textbf{E}$xperts (SPACE) that leverages Mixture of
Experts (MoE) to better capture the relationships between DNA sequences across
different species and genomic profiles, thereby learning more effective DNA
representations. Through extensive experiments across various tasks, our model
achieves state-of-the-art performance, establishing that DNA models trained
with supervised genomic profiles serve as powerful DNA representation learners.
The code is available at https://github.com/ZhuJiwei111/SPACE.","cs.LG, q-bio.GN",2025-06-02T16:23:05+00:00,2025-06-02T16:23:05+00:00,http://arxiv.org/abs/2506.01833v1
2506.00382v1,"Spectral Insights into Data-Oblivious Critical Layers in Large Language
  Models","Xuyuan Liu, Lei Hsiung, Yaoqing Yang, Yujun Yan","Understanding how feature representations evolve across layers in large
language models (LLMs) is key to improving their interpretability and
robustness. While recent studies have identified critical layers linked to
specific functions or behaviors, these efforts typically rely on data-dependent
analyses of fine-tuned models, limiting their use to post-hoc settings. In
contrast, we introduce a data-oblivious approach to identify intrinsic critical
layers in pre-fine-tuned LLMs by analyzing representation dynamics via Centered
Kernel Alignment(CKA). We show that layers with significant shifts in
representation space are also those most affected during fine-tuning--a pattern
that holds consistently across tasks for a given model. Our spectral analysis
further reveals that these shifts are driven by changes in the top principal
components, which encode semantic transitions from rationales to conclusions.
We further apply these findings to two practical scenarios: efficient domain
adaptation, where fine-tuning critical layers leads to greater loss reduction
compared to non-critical layers; and backdoor defense, where freezing them
reduces attack success rates by up to 40%.","cs.LG, cs.CL",2025-05-31T04:21:39+00:00,2025-05-31T04:21:39+00:00,http://arxiv.org/abs/2506.00382v1
2506.00205v1,"Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
  Perspective","Junze Deng, Qinhang Wu, Peizhong Ju, Sen Lin, Yingbin Liang, Ness Shroff","Rehearsal-based methods have shown superior performance in addressing
catastrophic forgetting in continual learning (CL) by storing and training on a
subset of past data alongside new data in current task. While such a concurrent
rehearsal strategy is widely used, it remains unclear if this approach is
always optimal. Inspired by human learning, where sequentially revisiting tasks
helps mitigate forgetting, we explore whether sequential rehearsal can offer
greater benefits for CL compared to standard concurrent rehearsal. To address
this question, we conduct a theoretical analysis of rehearsal-based CL in
overparameterized linear models, comparing two strategies: 1) Concurrent
Rehearsal, where past and new data are trained together, and 2) Sequential
Rehearsal, where new data is trained first, followed by revisiting past data
sequentially. By explicitly characterizing forgetting and generalization error,
we show that sequential rehearsal performs better when tasks are less similar.
These insights further motivate a novel Hybrid Rehearsal method, which trains
similar tasks concurrently and revisits dissimilar tasks sequentially. We
characterize its forgetting and generalization performance, and our experiments
with deep neural networks further confirm that the hybrid approach outperforms
standard concurrent rehearsal. This work provides the first comprehensive
theoretical analysis of rehearsal-based CL.",cs.LG,2025-05-30T20:23:15+00:00,2025-05-30T20:23:15+00:00,http://arxiv.org/abs/2506.00205v1
2505.24835v1,"Timing is important: Risk-aware Fund Allocation based on Time-Series
  Forecasting","Fuyuan Lyu, Linfeng Du, Yunpeng Weng, Qiufang Ying, Zhiyan Xu, Wen Zou, Haolun Wu, Xiuqiang He, Xing Tang","Fund allocation has been an increasingly important problem in the financial
domain. In reality, we aim to allocate the funds to buy certain assets within a
certain future period. Naive solutions such as prediction-only or
Predict-then-Optimize approaches suffer from goal mismatch. Additionally, the
introduction of the SOTA time series forecasting model inevitably introduces
additional uncertainty in the predicted result. To solve both problems
mentioned above, we introduce a Risk-aware Time-Series Predict-and-Allocate
(RTS-PnO) framework, which holds no prior assumption on the forecasting models.
Such a framework contains three features: (i) end-to-end training with
objective alignment measurement, (ii) adaptive forecasting uncertainty
calibration, and (iii) agnostic towards forecasting models. The evaluation of
RTS-PnO is conducted over both online and offline experiments. For offline
experiments, eight datasets from three categories of financial applications are
used: Currency, Stock, and Cryptos. RTS-PnO consistently outperforms other
competitive baselines. The online experiment is conducted on the Cross-Border
Payment business at FiT, Tencent, and an 8.4\% decrease in regret is witnessed
when compared with the product-line approach. The code for the offline
experiment is available at https://github.com/fuyuanlyu/RTS-PnO.",cs.LG,2025-05-30T17:36:45+00:00,2025-05-30T17:36:45+00:00,http://arxiv.org/abs/2505.24835v1
2505.24203v1,Aligning Protein Conformation Ensemble Generation with Physical Feedback,"Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Aurélie Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang","Protein dynamics play a crucial role in protein biological functions and
properties, and their traditional study typically relies on time-consuming
molecular dynamics (MD) simulations conducted in silico. Recent advances in
generative modeling, particularly denoising diffusion models, have enabled
efficient accurate protein structure prediction and conformation sampling by
learning distributions over crystallographic structures. However, effectively
integrating physical supervision into these data-driven approaches remains
challenging, as standard energy-based objectives often lead to intractable
optimization. In this paper, we introduce Energy-based Alignment (EBA), a
method that aligns generative models with feedback from physical models,
efficiently calibrating them to appropriately balance conformational states
based on their energy differences. Experimental results on the MD ensemble
benchmark demonstrate that EBA achieves state-of-the-art performance in
generating high-quality protein ensembles. By improving the physical
plausibility of generated structures, our approach enhances model predictions
and holds promise for applications in structural biology and drug discovery.","q-bio.BM, cs.LG",2025-05-30T04:33:39+00:00,2025-05-30T04:33:39+00:00,http://arxiv.org/abs/2505.24203v1
2506.02847v1,"CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
  Edge","Chunlin Tian, Xinpeng Qin, Kahou Tam, Li Li, Zijian Wang, Yuanzhe Zhao, Minglei Zhang, Chengzhong Xu","Deploying large language models (LLMs) on edge devices is crucial for
delivering fast responses and ensuring data privacy. However, the limited
storage, weight, and power of edge devices make it difficult to deploy
LLM-powered applications. These devices must balance latency requirements with
energy consumption and model accuracy. In this paper, we first quantify the
challenges of deploying LLMs on off-the-shelf edge devices and then we present
CLONE, an in-depth algorithm-hardware co-design at both the model- and
system-level that intelligently integrates real-time, energy optimization while
maintaining robust generality. In order to maximize the synergistic benefits of
these algorithms in always-on and intermediate edge computing settings, we
specialize in a 28nm scalable hardware accelerator system. We implement and
extensively evaluate CLONE on two off-the-shelf edge platforms. Experiments
show that CLONE effectively accelerates the inference process up to 11.92x, and
saves energy up to 7.36x, while maintaining high-generation.","cs.AR, cs.SY, eess.SY",2025-06-03T13:16:00+00:00,2025-06-03T13:16:00+00:00,http://arxiv.org/abs/2506.02847v1
2505.22194v1,Refining Datapath for Microscaling ViTs,"Can Xiao, Jianyi Cheng, Aaron Zhao","Vision Transformers (ViTs) leverage the transformer architecture to
effectively capture global context, demonstrating strong performance in
computer vision tasks. A major challenge in ViT hardware acceleration is that
the model family contains complex arithmetic operations that are sensitive to
model accuracy, such as the Softmax and LayerNorm operations, which cannot be
mapped onto efficient hardware with low precision. Existing methods only
exploit parallelism in the matrix multiplication operations of the model on
hardware and keep these complex operations on the CPU. This results in
suboptimal performance due to the communication overhead between the CPU and
accelerator. Can new data formats solve this problem?
  In this work, we present the first ViT accelerator that maps all operations
of the ViT models onto FPGAs. We exploit a new arithmetic format named
Microscaling Integer (MXInt) for datapath designs and evaluate how different
design choices can be made to trade off accuracy, hardware performance, and
hardware utilization. Our contributions are twofold. First, we quantize ViTs
using the MXInt format, achieving both high area efficiency and accuracy.
Second, we propose MXInt-specific hardware optimization that map these complex
arithmetic operations into custom hardware. Within 1\% accuracy loss, our
method achieves at least 93$\times$ speedup compared to Float16 and at least
1.9$\times$ speedup compared to related work.",cs.AR,2025-05-28T10:15:37+00:00,2025-05-28T10:15:37+00:00,http://arxiv.org/abs/2505.22194v1
2505.11554v1,"Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for
  Multicore Real-Time Systems","Binqi Sun, Zhihang Wei, Andrea Bastoni, Debayan Roy, Mirco Theile, Tomasz Kloda, Rodolfo Pellizzoni, Marco Caccamo","Memory bandwidth regulation and cache partitioning are widely used techniques
for achieving predictable timing in real-time computing systems. Combined with
partitioned scheduling, these methods require careful co-allocation of tasks
and resources to cores, as task execution times strongly depend on available
allocated resources. To address this challenge, this paper presents a 0-1
linear program for task-resource co-allocation, along with a multi-objective
heuristic designed to minimize resource usage while guaranteeing schedulability
under a preemptive EDF scheduling policy. Our heuristic employs a multi-layer
framework, where an outer layer explores resource allocations using
Pareto-pruned search, and an inner layer optimizes task allocation by solving a
knapsack problem using dynamic programming. To evaluate the performance of the
proposed optimization algorithm, we profile real-world benchmarks on an
embedded AMD UltraScale+ ZCU102 platform, with fine-grained resource
partitioning enabled by the Jailhouse hypervisor, leveraging cache set
partitioning and MemGuard for memory bandwidth regulation. Experiments based on
the benchmarking results show that the proposed 0-1 linear program outperforms
existing mixed-integer programs by finding more optimal solutions within the
same time limit. Moreover, the proposed multi-objective multi-layer heuristic
performs consistently better than the state-of-the-art multi-resource-task
co-allocation algorithm in terms of schedulability, resource usage, number of
non-dominated solutions, and computational efficiency.","math.OC, cs.AR, cs.DC, cs.OS",2025-05-15T16:40:14+00:00,2025-05-15T16:40:14+00:00,http://arxiv.org/abs/2505.11554v1
2505.08071v1,"NMP-PaK: Near-Memory Processing Acceleration of Scalable De Novo Genome
  Assembly","Heewoo Kim, Sanjay Sri Vallabh Singapuram, Haojie Ye, Joseph Izraelevitz, Trevor Mudge, Ronald Dreslinski, Nishil Talati","De novo assembly enables investigations of unknown genomes, paving the way
for personalized medicine and disease management. However, it faces immense
computational challenges arising from the excessive data volumes and
algorithmic complexity.
  While state-of-the-art de novo assemblers utilize distributed systems for
extreme-scale genome assembly, they demand substantial computational and memory
resources. They also fail to address the inherent challenges of de novo
assembly, including a large memory footprint, memory-bound behavior, and
irregular data patterns stemming from complex, interdependent data structures.
Given these challenges, de novo assembly merits a custom hardware solution,
though existing approaches have not fully addressed the limitations.
  We propose NMP-PaK, a hardware-software co-design that accelerates scalable
de novo genome assembly through near-memory processing (NMP). Our channel-level
NMP architecture addresses memory bottlenecks while providing sufficient
scratchpad space for processing elements. Customized processing elements
maximize parallelism while efficiently handling large data structures that are
both dynamic and interdependent. Software optimizations include customized
batch processing to reduce the memory footprint and hybrid CPU-NMP processing
to address hardware underutilization caused by irregular data patterns.
  NMP-PaK conducts the same genome assembly while incurring a 14X smaller
memory footprint compared to the state-of-the-art de novo assembly. Moreover,
NMP-PaK delivers a 16X performance improvement over the CPU baseline, with a
2.4X reduction in memory operations. Consequently, NMP-PaK achieves 8.3X
greater throughput than state-of-the-art de novo assembly under the same
resource constraints, showcasing its superior computational efficiency.","cs.AR, cs.DC, q-bio.GN",2025-05-12T21:17:20+00:00,2025-05-12T21:17:20+00:00,http://arxiv.org/abs/2505.08071v1
2504.06211v1,Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge Proofs,"Alhad Daftardar, Jianqiao Mo, Joey Ah-kiow, Benedikt Bünz, Ramesh Karri, Siddharth Garg, Brandon Reagen","Zero-Knowledge Proofs (ZKPs) are rapidly gaining importance in
privacy-preserving and verifiable computing. ZKPs enable a proving party to
prove the truth of a statement to a verifying party without revealing anything
else. ZKPs have applications in blockchain technologies, verifiable machine
learning, and electronic voting, but have yet to see widespread adoption due to
the computational complexity of the proving process. Recent works have
accelerated the key primitives of state-of-the-art ZKP protocols on GPU and
ASIC. However, the protocols accelerated thus far face one of two challenges:
they either require a trusted setup for each application, or they generate
larger proof sizes with higher verification costs, limiting their applicability
in scenarios with numerous verifiers or strict verification time constraints.
This work presents an accelerator, zkSpeed, for HyperPlonk, a state-of-the-art
ZKP protocol that supports both one-time, universal setup and small proof sizes
for typical ZKP applications in publicly verifiable, consensus-based systems.
We accelerate the entire protocol, including two major primitives: SumCheck and
Multi-scalar Multiplications (MSMs). We develop a full-chip architecture using
366.46 mm$^2$ and 2 TB/s of bandwidth to accelerate the entire proof generation
process, achieving geometric mean speedups of 801$\times$ over CPU baselines.","cs.AR, cs.CR",2025-04-08T16:56:10+00:00,2025-04-08T16:56:10+00:00,http://arxiv.org/abs/2504.06211v1
2504.19283v1,"Efficient Serverless Cold Start: Reducing Library Loading Overhead by
  Profile-guided Optimization","Syed Salauddin Mohammad Tariq, Ali Al Zein, Soumya Sripad Vaidya, Arati Khanolkar, Zheng Song, Probir Roy","Serverless computing abstracts away server management, enabling automatic
scaling, efficient resource utilization, and cost-effective pricing models.
However, despite these advantages, it faces the significant challenge of
cold-start latency, adversely impacting end-to-end performance. Our study shows
that many serverless functions initialize libraries that are rarely or never
used under typical workloads, thus introducing unnecessary overhead. Although
existing static analysis techniques can identify unreachable libraries, they
fail to address workload-dependent inefficiencies, resulting in limited
performance improvements. To overcome these limitations, we present SLIMSTART,
a profile-guided optimization tool designed to identify and mitigate
inefficient library usage patterns in serverless applications. By leveraging
statistical sampling and call-path profiling, SLIMSTART collects runtime
library usage data, generates detailed optimization reports, and applies
automated code transformations to reduce cold-start overhead. Furthermore,
SLIMSTART integrates seamlessly into CI/CD pipelines, enabling adaptive
monitoring and continuous optimizations tailored to evolving workloads. Through
extensive evaluation across three benchmark suites and four real-world
serverless applications, SLIMSTART achieves up to a 2.30X speedup in
initialization latency, a 2.26X improvement in end-to-end latency, and a 1.51X
reduction in memory usage, demonstrating its effectiveness in addressing
cold-start inefficiencies and optimizing resource utilization.","cs.DC, cs.PF",2025-04-27T15:50:45+00:00,2025-04-27T15:50:45+00:00,http://arxiv.org/abs/2504.19283v1
2504.11007v1,"Kubernetes in the Cloud vs. Bare Metal: A Comparative Study of Network
  Costs","Rodrigo Mompo Redoli, Amjad Ullah","Modern cloud-native applications increasingly utilise managed cloud services
and containerisation technologies, such as Kubernetes, to achieve rapid
time-to-market and scalable deployments. Organisations must consider various
factors, including cost implications when deciding on a hosting platform for
containerised applications as the usage grows. An emerging discipline called
FinOps combines financial management and cloud operations to optimise costs in
cloud-based applications. While prior research has explored system-level
optimisation strategies for cost and resource efficiency in containerized
systems, analysing network costs in Kubernetes clusters remains underexplored.
This paper investigates the network usage and cost implications of
containerised applications running on Kubernetes clusters. Using a methodology
that combines measurement analysis, experimentation, and cost modelling, we aim
to provide organisations with actionable insights into network cost
optimisation. Our findings highlight key considerations for analysing network
expenditures and evaluating the potential cost benefits of deploying
applications on cloud providers. Overall, this paper contributes to the
emerging FinOps discipline by addressing the financial and operational aspects
of managing network costs in cloud-native environments.",cs.DC,2025-04-15T09:26:08+00:00,2025-04-15T09:26:08+00:00,http://arxiv.org/abs/2504.11007v1
2504.09307v1,"Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM
  Training","Mingyu Liang, Hiwot Tadese Kassa, Wenyin Fu, Brian Coutinho, Louis Feng, Christina Delimitrou","Training LLMs in distributed environments presents significant challenges due
to the complexity of model execution, deployment systems, and the vast space of
configurable strategies. Although various optimization techniques exist,
achieving high efficiency in practice remains difficult. Accurate performance
models that effectively characterize and predict a model's behavior are
essential for guiding optimization efforts and system-level studies. We propose
Lumos, a trace-driven performance modeling and estimation toolkit for
large-scale LLM training, designed to accurately capture and predict the
execution behaviors of modern LLMs. We evaluate Lumos on a production ML
cluster with up to 512 NVIDIA H100 GPUs using various GPT-3 variants,
demonstrating that it can replay execution time with an average error of just
3.3%, along with other runtime details, across different models and
configurations. Additionally, we validate its ability to estimate performance
for new setups from existing traces, facilitating efficient exploration of
model and deployment configurations.","cs.DC, cs.AI",2025-04-12T18:43:24+00:00,2025-04-12T18:43:24+00:00,http://arxiv.org/abs/2504.09307v1
2506.02750v1,"Learning Binarized Representations with Pseudo-positive Sample
  Enhancement for Efficient Graph Collaborative Filtering","Yankai Chen, Yue Que, Xinni Zhang, Chen Ma, Irwin King","Learning vectorized embeddings is fundamental to many recommender systems for
user-item matching. To enable efficient online inference, representation
binarization, which embeds latent features into compact binary sequences, has
recently shown significant promise in optimizing both memory usage and
computational overhead. However, existing approaches primarily focus on
numerical quantization, neglecting the associated information loss, which often
results in noticeable performance degradation. To address these issues, we
study the problem of graph representation binarization for efficient
collaborative filtering. Our findings indicate that explicitly mitigating
information loss at various stages of embedding binarization has a significant
positive impact on performance. Building on these insights, we propose an
enhanced framework, BiGeaR++, which specifically leverages supervisory signals
from pseudo-positive samples, incorporating both real item data and latent
embedding samples. Compared to its predecessor BiGeaR, BiGeaR++ introduces a
fine-grained inference distillation mechanism and an effective embedding sample
synthesis approach. Empirical evaluations across five real-world datasets
demonstrate that the new designs in BiGeaR++ work seamlessly well with other
modules, delivering substantial improvements of around 1%-10% over BiGeaR and
thus achieving state-of-the-art performance compared to the competing methods.
Our implementation is available at https://github.com/QueYork/BiGeaR-SS.",cs.IR,2025-06-03T11:11:43+00:00,2025-06-03T11:11:43+00:00,http://arxiv.org/abs/2506.02750v1
2505.23452v1,"What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile
  App Reviews","Quim Motger, Marc Oriol, Max Tiessler, Xavier Franch, Jordi Marco","Opinion mining plays a vital role in analysing user feedback and extracting
insights from textual data. While most research focuses on sentiment polarity
(e.g., positive, negative, neutral), fine-grained emotion classification in app
reviews remains underexplored. This paper addresses this gap by identifying and
addressing the challenges and limitations in fine-grained emotion analysis in
the context of app reviews. Our study adapts Plutchik's emotion taxonomy to app
reviews by developing a structured annotation framework and dataset. Through an
iterative human annotation process, we define clear annotation guidelines and
document key challenges in emotion classification. Additionally, we evaluate
the feasibility of automating emotion annotation using large language models,
assessing their cost-effectiveness and agreement with human-labelled data. Our
findings reveal that while large language models significantly reduce manual
effort and maintain substantial agreement with human annotators, full
automation remains challenging due to the complexity of emotional
interpretation. This work contributes to opinion mining by providing structured
guidelines, an annotated dataset, and insights for developing automated
pipelines to capture the complexity of emotions in app reviews.","cs.IR, cs.SE",2025-05-29T13:58:38+00:00,2025-05-29T13:58:38+00:00,http://arxiv.org/abs/2505.23452v1
2505.21811v1,Revisiting Self-attention for Cross-domain Sequential Recommendation,"Clark Mingxuan Ju, Leonardo Neves, Bhuvesh Kumar, Liam Collins, Tong Zhao, Yuwei Qiu, Qing Dou, Sohail Nizam, Sen Yang, Neil Shah","Sequential recommendation is a popular paradigm in modern recommender
systems. In particular, one challenging problem in this space is cross-domain
sequential recommendation (CDSR), which aims to predict future behaviors given
user interactions across multiple domains. Existing CDSR frameworks are mostly
built on the self-attention transformer and seek to improve by explicitly
injecting additional domain-specific components (e.g. domain-aware module
blocks). While these additional components help, we argue they overlook the
core self-attention module already present in the transformer, a naturally
powerful tool to learn correlations among behaviors. In this work, we aim to
improve the CDSR performance for simple models from a novel perspective of
enhancing the self-attention. Specifically, we introduce a Pareto-optimal
self-attention and formulate the cross-domain learning as a multi-objective
problem, where we optimize the recommendation task while dynamically minimizing
the cross-domain attention scores. Our approach automates knowledge transfer in
CDSR (dubbed as AutoCDSR) -- it not only mitigates negative transfer but also
encourages complementary knowledge exchange among auxiliary domains. Based on
the idea, we further introduce AutoCDSR+, a more performant variant with slight
additional cost. Our proposal is easy to implement and works as a plug-and-play
module that can be incorporated into existing transformer-based recommenders.
Besides flexibility, it is practical to deploy because it brings little extra
computational overheads without heavy hyper-parameter tuning. AutoCDSR on
average improves Recall@10 for SASRec and Bert4Rec by 9.8% and 16.0% and
NDCG@10 by 12.0% and 16.7%, respectively. Code is available at
https://github.com/snap-research/AutoCDSR.","cs.IR, cs.AI",2025-05-27T22:38:32+00:00,2025-05-27T22:38:32+00:00,http://arxiv.org/abs/2505.21811v1
2505.20227v1,"Measure Domain's Gap: A Similar Domain Selection Principle for
  Multi-Domain Recommendation","Yi Wen, Yue Liu, Derong Xu, Huishi Luo, Pengyue Jia, Yiqing Wu, Siwei Wang, Ke Liang, Maolin Wang, Yiqi Wang, Fuzhen Zhuang, Xiangyu Zhao","Multi-Domain Recommendation (MDR) achieves the desirable recommendation
performance by effectively utilizing the transfer information across different
domains. Despite the great success, most existing MDR methods adopt a single
structure to transfer complex domain-shared knowledge. However, the beneficial
transferring information should vary across different domains. When there is
knowledge conflict between domains or a domain is of poor quality,
unselectively leveraging information from all domains will lead to a serious
Negative Transfer Problem (NTP). Therefore, how to effectively model the
complex transfer relationships between domains to avoid NTP is still a
direction worth exploring. To address these issues, we propose a simple and
dynamic Similar Domain Selection Principle (SDSP) for multi-domain
recommendation in this paper. SDSP presents the initial exploration of
selecting suitable domain knowledge for each domain to alleviate NTP.
Specifically, we propose a novel prototype-based domain distance measure to
effectively model the complexity relationship between domains. Thereafter, the
proposed SDSP can dynamically find similar domains for each domain based on the
supervised signals of the domain metrics and the unsupervised distance measure
from the learned domain prototype. We emphasize that SDSP is a lightweight
method that can be incorporated with existing MDR methods for better
performance while not introducing excessive time overheads. To the best of our
knowledge, it is the first solution that can explicitly measure domain-level
gaps and dynamically select appropriate domains in the MDR field. Extensive
experiments on three datasets demonstrate the effectiveness of our proposed
method.",cs.IR,2025-05-26T17:07:31+00:00,2025-05-26T17:07:31+00:00,http://arxiv.org/abs/2505.20227v1
2505.19356v1,"Optimized Text Embedding Models and Benchmarks for Amharic Passage
  Retrieval","Kidist Amde Mekonnen, Yosef Worku Alemneh, Maarten de Rijke","Neural retrieval methods using transformer-based pre-trained language models
have advanced multilingual and cross-lingual retrieval. However, their
effectiveness for low-resource, morphologically rich languages such as Amharic
remains underexplored due to data scarcity and suboptimal tokenization. We
address this gap by introducing Amharic-specific dense retrieval models based
on pre-trained Amharic BERT and RoBERTa backbones. Our proposed
RoBERTa-Base-Amharic-Embed model (110M parameters) achieves a 17.6% relative
improvement in MRR@10 and a 9.86% gain in Recall@10 over the strongest
multilingual baseline, Arctic Embed 2.0 (568M parameters). More compact
variants, such as RoBERTa-Medium-Amharic-Embed (42M), remain competitive while
being over 13x smaller. Additionally, we train a ColBERT-based late interaction
retrieval model that achieves the highest MRR@10 score (0.843) among all
evaluated models. We benchmark our proposed models against both sparse and
dense retrieval baselines to systematically assess retrieval effectiveness in
Amharic. Our analysis highlights key challenges in low-resource settings and
underscores the importance of language-specific adaptation. To foster future
research in low-resource IR, we publicly release our dataset, codebase, and
trained models at https://github.com/kidist-amde/amharic-ir-benchmarks.","cs.IR, cs.AI, cs.CL, cs.LG, 68T50 (Primary), 68T05 (Secondary), H.3.3, H.3.1, I.2.7",2025-05-25T23:06:20+00:00,2025-05-25T23:06:20+00:00,http://arxiv.org/abs/2505.19356v1
2505.19307v1,"Aligning Web Query Generation with Ranking Objectives via Direct
  Preference Optimization","João Coelho, Bruno Martins, João Magalhães, Chenyan Xiong","Neural retrieval models excel in Web search, but their training requires
substantial amounts of labeled query-document pairs, which are costly to
obtain. With the widespread availability of Web document collections like
ClueWeb22, synthetic queries generated by large language models offer a
scalable alternative. Still, synthetic training queries often vary in quality,
which leads to suboptimal downstream retrieval performance. Existing methods
typically filter out noisy query-document pairs based on signals from an
external re-ranker. In contrast, we propose a framework that leverages Direct
Preference Optimization (DPO) to integrate ranking signals into the query
generation process, aiming to directly optimize the model towards generating
high-quality queries that maximize downstream retrieval effectiveness.
Experiments show higher ranker-assessed relevance between query-document pairs
after DPO, leading to stronger downstream performance on the MS~MARCO benchmark
when compared to baseline models trained with synthetic data.",cs.IR,2025-05-25T20:34:12+00:00,2025-05-25T20:34:12+00:00,http://arxiv.org/abs/2505.19307v1
2505.17507v1,"Benchmarking Recommendation, Classification, and Tracing Based on
  Hugging Face Knowledge Graph","Qiaosheng Chen, Kaijia Huang, Xiao Zhou, Weiqing Luo, Yuanning Cui, Gong Cheng","The rapid growth of open source machine learning (ML) resources, such as
models and datasets, has accelerated IR research. However, existing platforms
like Hugging Face do not explicitly utilize structured representations,
limiting advanced queries and analyses such as tracing model evolution and
recommending relevant datasets. To fill the gap, we construct HuggingKG, the
first large-scale knowledge graph built from the Hugging Face community for ML
resource management. With 2.6 million nodes and 6.2 million edges, HuggingKG
captures domain-specific relations and rich textual attributes. It enables us
to further present HuggingBench, a multi-task benchmark with three novel test
collections for IR tasks including resource recommendation, classification, and
tracing. Our experiments reveal unique characteristics of HuggingKG and the
derived tasks. Both resources are publicly available, expected to advance
research in open source resource sharing and management.",cs.IR,2025-05-23T06:00:20+00:00,2025-05-23T06:00:20+00:00,http://arxiv.org/abs/2505.17507v1
2505.12791v1,"Unlearning for Federated Online Learning to Rank: A Reproducibility
  Study","Yiling Tao, Shuyi Wang, Jiaxi Yang, Guido Zuccon","This paper reports on findings from a comparative study on the effectiveness
and efficiency of federated unlearning strategies within Federated Online
Learning to Rank (FOLTR), with specific attention to systematically analysing
the unlearning capabilities of methods in a verifiable manner.
  Federated approaches to ranking of search results have recently garnered
attention to address users privacy concerns. In FOLTR, privacy is safeguarded
by collaboratively training ranking models across decentralized data sources,
preserving individual user data while optimizing search results based on
implicit feedback, such as clicks.
  Recent legislation introduced across numerous countries is establishing the
so called ""the right to be forgotten"", according to which services based on
machine learning models like those in FOLTR should provide capabilities that
allow users to remove their own data from those used to train models. This has
sparked the development of unlearning methods, along with evaluation practices
to measure whether unlearning of a user data successfully occurred. Current
evaluation practices are however often controversial, necessitating the use of
multiple metrics for a more comprehensive assessment -- but previous proposals
of unlearning methods only used single evaluation metrics.
  This paper addresses this limitation: our study rigorously assesses the
effectiveness of unlearning strategies in managing both under-unlearning and
over-unlearning scenarios using adapted, and newly proposed evaluation metrics.
Thanks to our detailed analysis, we uncover the strengths and limitations of
five unlearning strategies, offering valuable insights into optimizing
federated unlearning to balance data privacy and system performance within
FOLTR. We publicly release our code and complete results at
https://github.com/Iris1026/Unlearning-for-FOLTR.git.","cs.IR, cs.LG",2025-05-19T07:23:46+00:00,2025-05-19T07:23:46+00:00,http://arxiv.org/abs/2505.12791v1
2505.07166v1,"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
  Knowledge Acquisition","Zheng Yao, Shuai Wang, Guido Zuccon","Dense retrievers utilize pre-trained backbone language models (e.g., BERT,
LLaMA) that are fine-tuned via contrastive learning to perform the task of
encoding text into sense representations that can be then compared via a
shallow similarity operation, e.g. inner product. Recent research has
questioned the role of fine-tuning vs. that of pre-training within dense
retrievers, specifically arguing that retrieval knowledge is primarily gained
during pre-training, meaning knowledge not acquired during pre-training cannot
be sub-sequentially acquired via fine-tuning. We revisit this idea here as the
claim was only studied in the context of a BERT-based encoder using DPR as
representative dense retriever. We extend the previous analysis by testing
other representation approaches (comparing the use of CLS tokens with that of
mean pooling), backbone architectures (encoder-only BERT vs. decoder-only
LLaMA), and additional datasets (MSMARCO in addition to Natural Questions). Our
study confirms that in DPR tuning, pre-trained knowledge underpins retrieval
performance, with fine-tuning primarily adjusting neuron activation rather than
reorganizing knowledge. However, this pattern does not hold universally, such
as in mean-pooled (Contriever) and decoder-based (LLaMA) models. We ensure full
reproducibility and make our implementation publicly available at
https://github.com/ielab/DenseRetriever-Knowledge-Acquisition.","cs.IR, cs.CL",2025-05-12T01:24:00+00:00,2025-05-12T01:24:00+00:00,http://arxiv.org/abs/2505.07166v1
2505.03484v1,"STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
  Sequential Recommendation","Maolin Wang, Sheng Zhang, Ruocheng Guo, Wanyu Wang, Xuetao Wei, Zitao Liu, Hongzhi Yin, Yi Chang, Xiangyu Zhao","Recent deep sequential recommendation models often struggle to effectively
model key characteristics of user behaviors, particularly in handling sequence
length variations and capturing diverse interaction patterns. We propose
STAR-Rec, a novel architecture that synergistically combines preference-aware
attention and state-space modeling through a sequence-level mixture-of-experts
framework. STAR-Rec addresses these challenges by: (1) employing
preference-aware attention to capture both inherently similar item
relationships and diverse preferences, (2) utilizing state-space modeling to
efficiently process variable-length sequences with linear complexity, and (3)
incorporating a mixture-of-experts component that adaptively routes different
behavioral patterns to specialized experts, handling both focused
category-specific browsing and diverse category exploration patterns. We
theoretically demonstrate how the state space model and attention mechanisms
can be naturally unified in recommendation scenarios, where SSM captures
temporal dynamics through state compression while attention models both similar
and diverse item relationships. Extensive experiments on four real-world
datasets demonstrate that STAR-Rec consistently outperforms state-of-the-art
sequential recommendation methods, particularly in scenarios involving diverse
user behaviors and varying sequence lengths.",cs.IR,2025-05-06T12:40:38+00:00,2025-05-06T12:40:38+00:00,http://arxiv.org/abs/2505.03484v1
2505.00552v1,Graph Spectral Filtering with Chebyshev Interpolation for Recommendation,"Chanwoo Kim, Jinkyu Sung, Yebonn Han, Joonseok Lee","Graph convolutional networks have recently gained prominence in collaborative
filtering (CF) for recommendations. However, we identify potential bottlenecks
in two foundational components. First, the embedding layer leads to a latent
space with limited capacity, overlooking locally observed but potentially
valuable preference patterns. Also, the widely-used neighborhood aggregation is
limited in its ability to leverage diverse preference patterns in a
fine-grained manner. Building on spectral graph theory, we reveal that these
limitations stem from graph filtering with a cut-off in the frequency spectrum
and a restricted linear form. To address these issues, we introduce ChebyCF, a
CF framework based on graph spectral filtering. Instead of a learned embedding,
it takes a user's raw interaction history to utilize the full spectrum of
signals contained in it. Also, it adopts Chebyshev interpolation to effectively
approximate a flexible non-linear graph filter, and further enhances it by
using an additional ideal pass filter and degree-based normalization. Through
extensive experiments, we verify that ChebyCF overcomes the aforementioned
bottlenecks and achieves state-of-the-art performance across multiple
benchmarks and reasonably fast inference. Our code is available at
https://github.com/chanwoo0806/ChebyCF.","cs.IR, cs.LG",2025-05-01T14:28:44+00:00,2025-05-01T14:28:44+00:00,http://arxiv.org/abs/2505.00552v1
2504.20458v1,"Search-Based Interaction For Conversation Recommendation via Generative
  Reward Model Based Simulated User","Xiaolei Wang, Chunxuan Xia, Junyi Li, Fanzhe Meng, Lei Huang, Jinpeng Wang, Wayne Xin Zhao, Ji-Rong Wen","Conversational recommendation systems (CRSs) use multi-turn interaction to
capture user preferences and provide personalized recommendations. A
fundamental challenge in CRSs lies in effectively understanding user
preferences from conversations. User preferences can be multifaceted and
complex, posing significant challenges for accurate recommendations even with
access to abundant external knowledge. While interaction with users can clarify
their true preferences, frequent user involvement can lead to a degraded user
experience.
  To address this problem, we propose a generative reward model based simulated
user, named GRSU, for automatic interaction with CRSs. The simulated user
provides feedback to the items recommended by CRSs, enabling them to better
capture intricate user preferences through multi-turn interaction. Inspired by
generative reward models, we design two types of feedback actions for the
simulated user: i.e., generative item scoring, which offers coarse-grained
feedback, and attribute-based item critique, which provides fine-grained
feedback. To ensure seamless integration, these feedback actions are unified
into an instruction-based format, allowing the development of a unified
simulated user via instruction tuning on synthesized data. With this simulated
user, automatic multi-turn interaction with CRSs can be effectively conducted.
Furthermore, to strike a balance between effectiveness and efficiency, we draw
inspiration from the paradigm of reward-guided search in complex reasoning
tasks and employ beam search for the interaction process. On top of this, we
propose an efficient candidate ranking method to improve the recommendation
results derived from interaction. Extensive experiments on public datasets
demonstrate the effectiveness, efficiency, and transferability of our approach.","cs.IR, cs.CL",2025-04-29T06:37:30+00:00,2025-04-29T06:37:30+00:00,http://arxiv.org/abs/2504.20458v1
2504.18383v1,"Bridge the Domains: Large Language Models Enhanced Cross-domain
  Sequential Recommendation","Qidong Liu, Xiangyu Zhao, Yejing Wang, Zijian Zhang, Howard Zhong, Chong Chen, Xiang Li, Wei Huang, Feng Tian","Cross-domain Sequential Recommendation (CDSR) aims to extract the preference
from the user's historical interactions across various domains. Despite some
progress in CDSR, two problems set the barrier for further advancements, i.e.,
overlap dilemma and transition complexity. The former means existing CDSR
methods severely rely on users who own interactions on all domains to learn
cross-domain item relationships, compromising the practicability. The latter
refers to the difficulties in learning the complex transition patterns from the
mixed behavior sequences. With powerful representation and reasoning abilities,
Large Language Models (LLMs) are promising to address these two problems by
bridging the items and capturing the user's preferences from a semantic view.
Therefore, we propose an LLMs Enhanced Cross-domain Sequential Recommendation
model (LLM4CDSR). To obtain the semantic item relationships, we first propose
an LLM-based unified representation module to represent items. Then, a
trainable adapter with contrastive regularization is designed to adapt the CDSR
task. Besides, a hierarchical LLMs profiling module is designed to summarize
user cross-domain preferences. Finally, these two modules are integrated into
the proposed tri-thread framework to derive recommendations. We have conducted
extensive experiments on three public cross-domain datasets, validating the
effectiveness of LLM4CDSR. We have released the code online.","cs.IR, cs.AI",2025-04-25T14:30:25+00:00,2025-04-25T14:30:25+00:00,http://arxiv.org/abs/2504.18383v1
2504.17519v1,Replication and Exploration of Generative Retrieval over Dynamic Corpora,"Zhen Zhang, Xinyu Ma, Weiwei Sun, Pengjie Ren, Zhumin Chen, Shuaiqiang Wang, Dawei Yin, Maarten de Rijke, Zhaochun Ren","Generative retrieval (GR) has emerged as a promising paradigm in information
retrieval (IR). However, most existing GR models are developed and evaluated
using a static document collection, and their performance in dynamic corpora
where document collections evolve continuously is rarely studied. In this
paper, we first reproduce and systematically evaluate various representative GR
approaches over dynamic corpora. Through extensive experiments, we reveal that
existing GR models with \textit{text-based} docids show superior generalization
to unseen documents. We observe that the more fine-grained the docid design in
the GR model, the better its performance over dynamic corpora, surpassing BM25
and even being comparable to dense retrieval methods. While GR models with
\textit{numeric-based} docids show high efficiency, their performance drops
significantly over dynamic corpora. Furthermore, our experiments find that the
underperformance of numeric-based docids is partly due to their excessive
tendency toward the initial document set, which likely results from overfitting
on the training set. We then conduct an in-depth analysis of the
best-performing GR methods. We identify three critical advantages of text-based
docids in dynamic corpora: (i) Semantic alignment with language models'
pretrained knowledge, (ii) Fine-grained docid design, and (iii) High lexical
diversity. Building on these insights, we finally propose a novel multi-docid
design that leverages both the efficiency of numeric-based docids and the
effectiveness of text-based docids, achieving improved performance in dynamic
corpus without requiring additional retraining. Our work offers empirical
evidence for advancing GR methods over dynamic corpora and paves the way for
developing more generalized yet efficient GR models in real-world search
engines.",cs.IR,2025-04-24T13:01:23+00:00,2025-04-24T13:01:23+00:00,http://arxiv.org/abs/2504.17519v1
2504.15849v1,"NLCTables: A Dataset for Marrying Natural Language Conditions with Table
  Discovery","Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, Gang Chen","With the growing abundance of repositories containing tabular data,
discovering relevant tables for in-depth analysis remains a challenging task.
Existing table discovery methods primarily retrieve desired tables based on a
query table or several vague keywords, leaving users to manually filter large
result sets. To address this limitation, we propose a new task: NL-conditional
table discovery (nlcTD), where users combine a query table with natural
language (NL) requirements to refine search results. To advance research in
this area, we present nlcTables, a comprehensive benchmark dataset comprising
627 diverse queries spanning NL-only, union, join, and fuzzy conditions, 22,080
candidate tables, and 21,200 relevance annotations. Our evaluation of six
state-of-the-art table discovery methods on nlcTables reveals substantial
performance gaps, highlighting the need for advanced techniques to tackle this
challenging nlcTD scenario. The dataset, construction framework, and baseline
implementations are publicly available at
https://github.com/SuDIS-ZJU/nlcTables to foster future research.","cs.IR, 68P20",2025-04-22T12:44:59+00:00,2025-04-22T12:44:59+00:00,http://arxiv.org/abs/2504.15849v1
2504.14991v1,"Understanding Accuracy-Fairness Trade-offs in Re-ranking through
  Elasticity in Economics","Chen Xu, Jujia Zhao, Wenjie Wang, Liang Pang, Jun Xu, Tat-Seng Chua, Maarten de Rijke","Fairness is an increasingly important factor in re-ranking tasks. Prior work
has identified a trade-off between ranking accuracy and item fairness. However,
the underlying mechanisms are still not fully understood. An analogy can be
drawn between re-ranking and the dynamics of economic transactions. The
accuracy-fairness trade-off parallels the coupling of the commodity tax
transfer process. Fairness considerations in re-ranking, similar to a commodity
tax on suppliers, ultimately translate into a cost passed on to consumers.
Analogously, item-side fairness constraints result in a decline in user-side
accuracy. In economics, the extent to which commodity tax on the supplier (item
fairness) transfers to commodity tax on users (accuracy loss) is formalized
using the notion of elasticity. The re-ranking fairness-accuracy trade-off is
similarly governed by the elasticity of utility between item groups. This
insight underscores the limitations of current fair re-ranking evaluations,
which often rely solely on a single fairness metric, hindering comprehensive
assessment of fair re-ranking algorithms. Centered around the concept of
elasticity, this work presents two significant contributions. We introduce the
Elastic Fairness Curve (EF-Curve) as an evaluation framework. This framework
enables a comparative analysis of algorithm performance across different
elasticity levels, facilitating the selection of the most suitable approach.
Furthermore, we propose ElasticRank, a fair re-ranking algorithm that employs
elasticity calculations to adjust inter-item distances within a curved space.
Experiments on three widely used ranking datasets demonstrate its effectiveness
and efficiency.",cs.IR,2025-04-21T09:41:08+00:00,2025-04-21T09:41:08+00:00,http://arxiv.org/abs/2504.14991v1
2504.14243v1,"Unconstrained Monotonic Calibration of Predictions in Deep Ranking
  Systems","Yimeng Bai, Shunyu Zhang, Yang Zhang, Hu Liu, Wentian Bao, Enyun Yu, Fuli Feng, Wenwu Ou","Ranking models primarily focus on modeling the relative order of predictions
while often neglecting the significance of the accuracy of their absolute
values. However, accurate absolute values are essential for certain downstream
tasks, necessitating the calibration of the original predictions. To address
this, existing calibration approaches typically employ predefined
transformation functions with order-preserving properties to adjust the
original predictions. Unfortunately, these functions often adhere to fixed
forms, such as piece-wise linear functions, which exhibit limited
expressiveness and flexibility, thereby constraining their effectiveness in
complex calibration scenarios. To mitigate this issue, we propose implementing
a calibrator using an Unconstrained Monotonic Neural Network (UMNN), which can
learn arbitrary monotonic functions with great modeling power. This approach
significantly relaxes the constraints on the calibrator, improving its
flexibility and expressiveness while avoiding excessively distorting the
original predictions by requiring monotonicity. Furthermore, to optimize this
highly flexible network for calibration, we introduce a novel additional loss
function termed Smooth Calibration Loss (SCLoss), which aims to fulfill a
necessary condition for achieving the ideal calibration state. Extensive
offline experiments confirm the effectiveness of our method in achieving
superior calibration performance. Moreover, deployment in Kuaishou's
large-scale online video ranking system demonstrates that the method's
calibration improvements translate into enhanced business metrics. The source
code is available at https://github.com/baiyimeng/UMC.","cs.IR, H.3.3, H.3.5",2025-04-19T09:35:11+00:00,2025-04-19T09:35:11+00:00,http://arxiv.org/abs/2504.14243v1
2504.12900v1,"FashionDPO:Fine-tune Fashion Outfit Generation Model using Direct
  Preference Optimization","Mingzhe Yu, Yunshan Ma, Lei Wu, Changshuo Wang, Xue Li, Lei Meng","Personalized outfit generation aims to construct a set of compatible and
personalized fashion items as an outfit. Recently, generative AI models have
received widespread attention, as they can generate fashion items for users to
complete an incomplete outfit or create a complete outfit. However, they have
limitations in terms of lacking diversity and relying on the supervised
learning paradigm. Recognizing this gap, we propose a novel framework
FashionDPO, which fine-tunes the fashion outfit generation model using direct
preference optimization. This framework aims to provide a general fine-tuning
approach to fashion generative models, refining a pre-trained fashion outfit
generation model using automatically generated feedback, without the need to
design a task-specific reward function. To make sure that the feedback is
comprehensive and objective, we design a multi-expert feedback generation
module which covers three evaluation perspectives, \ie quality, compatibility
and personalization. Experiments on two established datasets, \ie iFashion and
Polyvore-U, demonstrate the effectiveness of our framework in enhancing the
model's ability to align with users' personalized preferences while adhering to
fashion compatibility principles. Our code and model checkpoints are available
at https://github.com/Yzcreator/FashionDPO.","cs.MM, cs.IR",2025-04-17T12:41:41+00:00,2025-04-17T12:41:41+00:00,http://arxiv.org/abs/2504.12900v1
2504.09935v1,Constrained Auto-Regressive Decoding Constrains Generative Retrieval,"Shiguang Wu, Zhaochun Ren, Xin Xin, Jiyuan Yang, Mengqi Zhang, Zhumin Chen, Maarten de Rijke, Pengjie Ren","Generative retrieval seeks to replace traditional search index data
structures with a single large-scale neural network, offering the potential for
improved efficiency and seamless integration with generative large language
models. As an end-to-end paradigm, generative retrieval adopts a learned
differentiable search index to conduct retrieval by directly generating
document identifiers through corpus-specific constrained decoding. The
generalization capabilities of generative retrieval on out-of-distribution
corpora have gathered significant attention.
  In this paper, we examine the inherent limitations of constrained
auto-regressive generation from two essential perspectives: constraints and
beam search. We begin with the Bayes-optimal setting where the generative
retrieval model exactly captures the underlying relevance distribution of all
possible documents. Then we apply the model to specific corpora by simply
adding corpus-specific constraints. Our main findings are two-fold: (i) For the
effect of constraints, we derive a lower bound of the error, in terms of the KL
divergence between the ground-truth and the model-predicted step-wise marginal
distributions. (ii) For the beam search algorithm used during generation, we
reveal that the usage of marginal distributions may not be an ideal approach.
This paper aims to improve our theoretical understanding of the generalization
capabilities of the auto-regressive decoding retrieval paradigm, laying a
foundation for its limitations and inspiring future advancements toward more
robust and generalizable generative retrieval.",cs.IR,2025-04-14T06:54:49+00:00,2025-04-14T06:54:49+00:00,http://arxiv.org/abs/2504.09935v1