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2502.14372
|
Discovering highly efficient low-weight quantum error-correcting codes
with reinforcement learning
|
quant-ph cs.AI cs.IT cs.LG math.IT
|
The realization of scalable fault-tolerant quantum computing is expected to
hinge on quantum error-correcting codes. In the quest for more efficient
quantum fault tolerance, a critical code parameter is the weight of
measurements that extract information about errors to enable error correction:
as higher measurement weights require higher implementation costs and introduce
more errors, it is important in code design to optimize measurement weight.
This underlies the surging interest in quantum low-density parity-check (qLDPC)
codes, the study of which has primarily focused on the asymptotic
(large-code-limit) properties. In this work, we introduce a versatile and
computationally efficient approach to stabilizer code weight reduction based on
reinforcement learning (RL), which produces new low-weight codes that
substantially outperform the state of the art in practically relevant parameter
regimes, extending significantly beyond previously accessible small distances.
For example, our approach demonstrates savings in physical qubit overhead
compared to existing results by 1 to 2 orders of magnitude for weight 6 codes
and brings the overhead into a feasible range for near-future experiments. We
also investigate the interplay between code parameters using our RL framework,
offering new insights into the potential efficiency and power of practically
viable coding strategies. Overall, our results demonstrate how RL can
effectively advance the crucial yet challenging problem of quantum code
discovery and thereby facilitate a faster path to the practical implementation
of fault-tolerant quantum technologies.
|
2502.14373
|
CrossVTON: Mimicking the Logic Reasoning on Cross-category Virtual
Try-on guided by Tri-zone Priors
|
cs.CV
|
Despite remarkable progress in image-based virtual try-on systems, generating
realistic and robust fitting images for cross-category virtual try-on remains a
challenging task. The primary difficulty arises from the absence of human-like
reasoning, which involves addressing size mismatches between garments and
models while recognizing and leveraging the distinct functionalities of various
regions within the model images. To address this issue, we draw inspiration
from human cognitive processes and disentangle the complex reasoning required
for cross-category try-on into a structured framework. This framework
systematically decomposes the model image into three distinct regions: try-on,
reconstruction, and imagination zones. Each zone plays a specific role in
accommodating the garment and facilitating realistic synthesis. To endow the
model with robust reasoning capabilities for cross-category scenarios, we
propose an iterative data constructor. This constructor encompasses diverse
scenarios, including intra-category try-on, any-to-dress transformations
(replacing any garment category with a dress), and dress-to-any transformations
(replacing a dress with another garment category). Utilizing the generated
dataset, we introduce a tri-zone priors generator that intelligently predicts
the try-on, reconstruction, and imagination zones by analyzing how the input
garment is expected to align with the model image. Guided by these tri-zone
priors, our proposed method, CrossVTON, achieves state-of-the-art performance,
surpassing existing baselines in both qualitative and quantitative evaluations.
Notably, it demonstrates superior capability in handling cross-category virtual
try-on, meeting the complex demands of real-world applications.
|
2502.14375
|
VFL-RPS: Relevant Participant Selection in Vertical Federated Learning
|
cs.LG
|
Federated Learning (FL) allows collaboration between different parties, while
ensuring that the data across these parties is not shared. However, not every
collaboration is helpful in terms of the resulting model performance.
Therefore, it is an important challenge to select the correct participants in a
collaboration. As it currently stands, most of the efforts in participant
selection in the literature have focused on Horizontal Federated Learning
(HFL), which assumes that all features are the same across all participants,
disregarding the possibility of different features across participants which is
captured in Vertical Federated Learning (VFL). To close this gap in the
literature, we propose a novel method VFL-RPS for participant selection in VFL,
as a pre-training step. We have tested our method on several data sets
performing both regression and classification tasks, showing that our method
leads to comparable results as using all data by only selecting a few
participants. In addition, we show that our method outperforms existing methods
for participant selection in VFL.
|
2502.14376
|
A Similarity Paradigm Through Textual Regularization Without Forgetting
|
cs.CL cs.CV
|
Prompt learning has emerged as a promising method for adapting pre-trained
visual-language models (VLMs) to a range of downstream tasks. While optimizing
the context can be effective for improving performance on specific tasks, it
can often lead to poor generalization performance on unseen classes or datasets
sampled from different distributions. It may be attributed to the fact that
textual prompts tend to overfit downstream data distributions, leading to the
forgetting of generalized knowledge derived from hand-crafted prompts. In this
paper, we propose a novel method called Similarity Paradigm with Textual
Regularization (SPTR) for prompt learning without forgetting. SPTR is a
two-pronged design based on hand-crafted prompts that is an inseparable
framework. 1) To avoid forgetting general textual knowledge, we introduce the
optimal transport as a textual regularization to finely ensure approximation
with hand-crafted features and tuning textual features. 2) In order to
continuously unleash the general ability of multiple hand-crafted prompts, we
propose a similarity paradigm for natural alignment score and adversarial
alignment score to improve model robustness for generalization. Both modules
share a common objective in addressing generalization issues, aiming to
maximize the generalization capability derived from multiple hand-crafted
prompts. Four representative tasks (i.e., non-generalization few-shot learning,
base-to-novel generalization, cross-dataset generalization, domain
generalization) across 11 datasets demonstrate that SPTR outperforms existing
prompt learning methods.
|
2502.14377
|
RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers
|
cs.CV
|
The Diffusion Transformer plays a pivotal role in advancing text-to-image and
text-to-video generation, owing primarily to its inherent scalability. However,
existing controlled diffusion transformer methods incur significant parameter
and computational overheads and suffer from inefficient resource allocation due
to their failure to account for the varying relevance of control information
across different transformer layers. To address this, we propose the
Relevance-Guided Efficient Controllable Generation framework, RelaCtrl,
enabling efficient and resource-optimized integration of control signals into
the Diffusion Transformer. First, we evaluate the relevance of each layer in
the Diffusion Transformer to the control information by assessing the
"ControlNet Relevance Score"-i.e., the impact of skipping each control layer on
both the quality of generation and the control effectiveness during inference.
Based on the strength of the relevance, we then tailor the positioning,
parameter scale, and modeling capacity of the control layers to reduce
unnecessary parameters and redundant computations. Additionally, to further
improve efficiency, we replace the self-attention and FFN in the commonly used
copy block with the carefully designed Two-Dimensional Shuffle Mixer (TDSM),
enabling efficient implementation of both the token mixer and channel mixer.
Both qualitative and quantitative experimental results demonstrate that our
approach achieves superior performance with only 15% of the parameters and
computational complexity compared to PixArt-delta. More examples are available
at https://relactrl.github.io/RelaCtrl/.
|
2502.14378
|
Extremal Self-Dual Codes and Linear Complementary Dual Codes from Double
Circulant Codes
|
cs.IT math.IT
|
This paper explores extremal self-dual double circulant (DC) codes and linear
complementary dual (LCD) codes of arbitrary length over the Galois field
$\mathbb F_2$. We establish the sufficient and necessary conditions for DC
codes and bordered DC codes to be self-dual and identify the conditions for
self-dual DC codes of length up to 44 to be extremal or non-extremal.
Additionally, The self-duality and extremality between DC codes and bordered DC
codes are also examined. Finally, sufficient conditions for bordered DC codes
to be LCD codes over $\mathbb F_2$ under Euclidean inner product are presented.
|
2502.14379
|
Achieving adaptivity and optimality for multi-armed bandits using
Exponential-Kullback Leiblier Maillard Sampling
|
cs.LG cs.DS
|
We study the problem of Multi-Armed Bandits (MAB) with reward distributions
belonging to a One-Parameter Exponential Distribution (OPED) family. In the
literature, several criteria have been proposed to evaluate the performance of
such algorithms, including Asymptotic Optimality (A.O.), Minimax Optimality
(M.O.), Sub-UCB, and variance-adaptive worst-case regret bound. Thompson
Sampling (TS)-based and Upper Confidence Bound (UCB)-based algorithms have been
employed to achieve some of these criteria. However, none of these algorithms
simultaneously satisfy all the aforementioned criteria.
In this paper, we design an algorithm, Exponential Kullback-Leibler Maillard
Sampling (abbrev. \expklms), that can achieve multiple optimality criteria
simultaneously, including A.O., M.O. with a logarithmic factor, Sub-UCB, and
variance-adaptive worst-case regret bound.
|
2502.14380
|
Affinity and Diversity: A Unified Metric for Demonstration Selection via
Internal Representations
|
cs.CL cs.AI cs.LG
|
The performance of In-Context Learning (ICL) is highly sensitive to the
selected demonstrations. Existing approaches to demonstration selection
optimize different objectives, yielding inconsistent results. To address this,
we propose a unified metric--affinity and diversity--that leverages ICL model's
internal representations. Our experiments show that both affinity and diversity
strongly correlate with test accuracies, indicating their effectiveness for
demonstration selection. Moreover, we show that our proposed metrics align well
with various previous works to unify the inconsistency.
|
2502.14381
|
dtaianomaly: A Python library for time series anomaly detection
|
cs.LG cs.DB
|
dtaianomaly is an open-source Python library for time series anomaly
detection, designed to bridge the gap between academic research and real-world
applications. Our goal is to (1) accelerate the development of novel
state-of-the-art anomaly detection techniques through simple extensibility; (2)
offer functionality for large-scale experimental validation; and thereby (3)
bring cutting-edge research to business and industry through a standardized
API, similar to scikit-learn to lower the entry barrier for both new and
experienced users. Besides these key features, dtaianomaly offers (1) a broad
range of built-in anomaly detectors, (2) support for time series preprocessing,
(3) tools for visual analysis, (4) confidence prediction of anomaly scores, (5)
runtime and memory profiling, (6) comprehensive documentation, and (7)
cross-platform unit testing.
The source code of dtaianomaly, documentation, code examples and installation
guides are publicly available at https://github.com/ML-KULeuven/dtaianomaly.
|
2502.14382
|
S*: Test Time Scaling for Code Generation
|
cs.LG cs.AI
|
Increasing test-time compute for LLMs shows promise across domains but
remains underexplored in code generation, despite extensive study in math. In
this paper, we propose S*, the first hybrid test-time scaling framework that
substantially improves the coverage and selection accuracy of generated code.
S* extends the existing parallel scaling paradigm with sequential scaling to
push performance boundaries. It further leverages a novel selection mechanism
that adaptively generates distinguishing inputs for pairwise comparison,
combined with execution-grounded information to robustly identify correct
solutions. We evaluate across 12 Large Language Models and Large Reasoning
Model and show: (1) S* consistently improves performance across model families
and sizes, enabling a 3B model to outperform GPT-4o-mini; (2) S* enables
non-reasoning models to surpass reasoning models - GPT-4o-mini with S*
outperforms o1-preview by 3.7% on LiveCodeBench; (3) S* further boosts
state-of-the-art reasoning models - DeepSeek-R1-Distill-Qwen-32B with S*
achieves 85.7% on LiveCodeBench, approaching o1 (high) at 88.5%. Code will be
available under https://github.com/NovaSky-AI/SkyThought.
|
2502.14383
|
Rumor Detection by Multi-task Suffix Learning based on Time-series Dual
Sentiments
|
cs.CL
|
The widespread dissemination of rumors on social media has a significant
impact on people's lives, potentially leading to public panic and fear. Rumors
often evoke specific sentiments, resonating with readers and prompting sharing.
To effectively detect and track rumors, it is essential to observe the
fine-grained sentiments of both source and response message pairs as the rumor
evolves over time. However, current rumor detection methods fail to account for
this aspect. In this paper, we propose MSuf, the first multi-task suffix
learning framework for rumor detection and tracking using time series dual
(coupled) sentiments. MSuf includes three modules: (1) an LLM to extract
sentiment intensity features and sort them chronologically; (2) a module that
fuses the sorted sentiment features with their source text word embeddings to
obtain an aligned embedding; (3) two hard prompts are combined with the aligned
vector to perform rumor detection and sentiment analysis using one frozen LLM.
MSuf effectively enhances the performance of LLMs for rumor detection with only
minimal parameter fine-tuning. Evaluating MSuf on four rumor detection
benchmarks, we find significant improvements compared to other emotion-based
methods.
|
2502.14385
|
Tradutor: Building a Variety Specific Translation Model
|
cs.CL
|
Language models have become foundational to many widely used systems.
However, these seemingly advantageous models are double-edged swords. While
they excel in tasks related to resource-rich languages like English, they often
lose the fine nuances of language forms, dialects, and varieties that are
inherent to languages spoken in multiple regions of the world. Languages like
European Portuguese are neglected in favor of their more popular counterpart,
Brazilian Portuguese, leading to suboptimal performance in various linguistic
tasks. To address this gap, we introduce the first open-source translation
model specifically tailored for European Portuguese, along with a novel dataset
specifically designed for this task. Results from automatic evaluations on two
benchmark datasets demonstrate that our best model surpasses existing
open-source translation systems for Portuguese and approaches the performance
of industry-leading closed-source systems for European Portuguese. By making
our dataset, models, and code publicly available, we aim to support and
encourage further research, fostering advancements in the representation of
underrepresented language varieties.
|
2502.14387
|
MPPI-DBaS: Safe Trajectory Optimization with Adaptive Exploration
|
eess.SY cs.SY
|
In trajectory optimization, Model Predictive Path Integral (MPPI) control is
a sampling-based Model Predictive Control (MPC) framework that generates
optimal inputs by efficiently simulating numerous trajectories. In practice,
however, MPPI often struggles to guarantee safety assurance and balance
efficient sampling in open spaces with the need for more extensive exploration
under tight constraints. To address this challenge, we incorporate discrete
barrier states (DBaS) into MPPI and propose a novel MPPI-DBaS algorithm that
ensures system safety and enables adaptive exploration across diverse
scenarios. We evaluate our method in simulation experiments where the vehicle
navigates through closely placed obstacles. The results demonstrate that the
proposed algorithm significantly outperforms standard MPPI, achieving a higher
success rate and lower tracking errors.
|
2502.14389
|
Leveraging Small LLMs for Argument Mining in Education: Argument
Component Identification, Classification, and Assessment
|
cs.CL cs.HC
|
Argument mining algorithms analyze the argumentative structure of essays,
making them a valuable tool for enhancing education by providing targeted
feedback on the students' argumentation skills. While current methods often use
encoder or encoder-decoder deep learning architectures, decoder-only models
remain largely unexplored, offering a promising research direction.
This paper proposes leveraging open-source, small Large Language Models
(LLMs) for argument mining through few-shot prompting and fine-tuning. These
models' small size and open-source nature ensure accessibility, privacy, and
computational efficiency, enabling schools and educators to adopt and deploy
them locally. Specifically, we perform three tasks: segmentation of student
essays into arguments, classification of the arguments by type, and assessment
of their quality. We empirically evaluate the models on the Feedback Prize -
Predicting Effective Arguments dataset of grade 6-12 students essays and
demonstrate how fine-tuned small LLMs outperform baseline methods in segmenting
the essays and determining the argument types while few-shot prompting yields
comparable performance to that of the baselines in assessing quality. This work
highlights the educational potential of small, open-source LLMs to provide
real-time, personalized feedback, enhancing independent learning and writing
skills while ensuring low computational cost and privacy.
|
2502.14394
|
Enhancing Portuguese Variety Identification with Cross-Domain Approaches
|
cs.CL
|
Recent advances in natural language processing have raised expectations for
generative models to produce coherent text across diverse language varieties.
In the particular case of the Portuguese language, the predominance of
Brazilian Portuguese corpora online introduces linguistic biases in these
models, limiting their applicability outside of Brazil. To address this gap and
promote the creation of European Portuguese resources, we developed a
cross-domain language variety identifier (LVI) to discriminate between European
and Brazilian Portuguese. Motivated by the findings of our literature review,
we compiled the PtBrVarId corpus, a cross-domain LVI dataset, and study the
effectiveness of transformer-based LVI classifiers for cross-domain scenarios.
Although this research focuses on two Portuguese varieties, our contribution
can be extended to other varieties and languages. We open source the code,
corpus, and models to foster further research in this task.
|
2502.14397
|
PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
|
cs.CV
|
We introduce PhotoDoodle, a novel image editing framework designed to
facilitate photo doodling by enabling artists to overlay decorative elements
onto photographs. Photo doodling is challenging because the inserted elements
must appear seamlessly integrated with the background, requiring realistic
blending, perspective alignment, and contextual coherence. Additionally, the
background must be preserved without distortion, and the artist's unique style
must be captured efficiently from limited training data. These requirements are
not addressed by previous methods that primarily focus on global style transfer
or regional inpainting. The proposed method, PhotoDoodle, employs a two-stage
training strategy. Initially, we train a general-purpose image editing model,
OmniEditor, using large-scale data. Subsequently, we fine-tune this model with
EditLoRA using a small, artist-curated dataset of before-and-after image pairs
to capture distinct editing styles and techniques. To enhance consistency in
the generated results, we introduce a positional encoding reuse mechanism.
Additionally, we release a PhotoDoodle dataset featuring six high-quality
styles. Extensive experiments demonstrate the advanced performance and
robustness of our method in customized image editing, opening new possibilities
for artistic creation.
|
2502.14400
|
HPS: Hard Preference Sampling for Human Preference Alignment
|
cs.AI
|
Aligning Large Language Model (LLM) responses with human preferences is vital
for building safe and controllable AI systems. While preference optimization
methods based on Plackett-Luce (PL) and Bradley-Terry (BT) models have shown
promise, they face challenges such as poor handling of harmful content,
inefficient use of dispreferred responses, and, specifically for PL, high
computational costs. To address these issues, we propose Hard Preference
Sampling (HPS), a novel framework for robust and efficient human preference
alignment. HPS introduces a training loss that prioritizes the most preferred
response while rejecting all dispreferred and harmful ones. It emphasizes
"hard" dispreferred responses--those closely resembling preferred ones--to
enhance the model's rejection capabilities. By leveraging a single-sample Monte
Carlo sampling strategy, HPS reduces computational overhead while maintaining
alignment quality. Theoretically, HPS improves sample efficiency over existing
PL methods and maximizes the reward margin between preferred and dispreferred
responses, ensuring clearer distinctions. Experiments on HH-RLHF and PKU-Safety
datasets validate HPS's effectiveness, achieving comparable BLEU and reward
scores while greatly improving reward margins and thus reducing harmful content
generation.
|
2502.14401
|
MedFuncta: Modality-Agnostic Representations Based on Efficient Neural
Fields
|
eess.IV cs.CV
|
Recent research in medical image analysis with deep learning almost
exclusively focuses on grid- or voxel-based data representations. We challenge
this common choice by introducing MedFuncta, a modality-agnostic continuous
data representation based on neural fields. We demonstrate how to scale neural
fields from single instances to large datasets by exploiting redundancy in
medical signals and by applying an efficient meta-learning approach with a
context reduction scheme. We further address the spectral bias in commonly used
SIREN activations, by introducing an $\omega_0$-schedule, improving
reconstruction quality and convergence speed. We validate our proposed approach
on a large variety of medical signals of different dimensions and modalities
(1D: ECG; 2D: Chest X-ray, Retinal OCT, Fundus Camera, Dermatoscope, Colon
Histopathology, Cell Microscopy; 3D: Brain MRI, Lung CT) and successfully
demonstrate that we can solve relevant downstream tasks on these
representations. We additionally release a large-scale dataset of > 550k
annotated neural fields to promote research in this direction.
|
2502.14403
|
A Macro- and Micro-Hierarchical Transfer Learning Framework for
Cross-Domain Fake News Detection
|
cs.SI cs.CL cs.LG
|
Cross-domain fake news detection aims to mitigate domain shift and improve
detection performance by transferring knowledge across domains. Existing
approaches transfer knowledge based on news content and user engagements from a
source domain to a target domain. However, these approaches face two main
limitations, hindering effective knowledge transfer and optimal fake news
detection performance. Firstly, from a micro perspective, they neglect the
negative impact of veracity-irrelevant features in news content when
transferring domain-shared features across domains. Secondly, from a macro
perspective, existing approaches ignore the relationship between user
engagement and news content, which reveals shared behaviors of common users
across domains and can facilitate more effective knowledge transfer. To address
these limitations, we propose a novel macro- and micro- hierarchical transfer
learning framework (MMHT) for cross-domain fake news detection. Firstly, we
propose a micro-hierarchical disentangling module to disentangle
veracity-relevant and veracity-irrelevant features from news content in the
source domain for improving fake news detection performance in the target
domain. Secondly, we propose a macro-hierarchical transfer learning module to
generate engagement features based on common users' shared behaviors in
different domains for improving effectiveness of knowledge transfer. Extensive
experiments on real-world datasets demonstrate that our framework significantly
outperforms the state-of-the-art baselines.
|
2502.14409
|
Unstructured Evidence Attribution for Long Context Query Focused
Summarization
|
cs.CL cs.IR
|
Large language models (LLMs) are capable of generating coherent summaries
from very long contexts given a user query. Extracting and properly citing
evidence spans could help improve the transparency and reliability of these
summaries. At the same time, LLMs suffer from positional biases in terms of
which information they understand and attend to, which could affect evidence
citation. Whereas previous work has focused on evidence citation with
predefined levels of granularity (e.g. sentence, paragraph, document, etc.), we
propose the task of long-context query focused summarization with unstructured
evidence citation. We show how existing systems struggle to generate and
properly cite unstructured evidence from their context, and that evidence tends
to be "lost-in-the-middle". To help mitigate this, we create the Summaries with
Unstructured Evidence Text dataset (SUnsET), a synthetic dataset generated
using a novel domain-agnostic pipeline which can be used as supervision to
adapt LLMs to this task. We demonstrate across 5 LLMs of different sizes and 4
datasets with varying document types and lengths that LLMs adapted with SUnsET
data generate more relevant and factually consistent evidence than their base
models, extract evidence from more diverse locations in their context, and can
generate more relevant and consistent summaries.
|
2502.14412
|
Evaluating Precise Geolocation Inference Capabilities of Vision Language
Models
|
cs.CV cs.CR cs.LG
|
The prevalence of Vision-Language Models (VLMs) raises important questions
about privacy in an era where visual information is increasingly available.
While foundation VLMs demonstrate broad knowledge and learned capabilities, we
specifically investigate their ability to infer geographic location from
previously unseen image data. This paper introduces a benchmark dataset
collected from Google Street View that represents its global distribution of
coverage. Foundation models are evaluated on single-image geolocation
inference, with many achieving median distance errors of <300 km. We further
evaluate VLM "agents" with access to supplemental tools, observing up to a
30.6% decrease in distance error. Our findings establish that modern foundation
VLMs can act as powerful image geolocation tools, without being specifically
trained for this task. When coupled with increasing accessibility of these
models, our findings have greater implications for online privacy. We discuss
these risks, as well as future work in this area.
|
2502.14413
|
Towards Efficient Automatic Self-Pruning of Large Language Models
|
cs.LG
|
Despite exceptional capabilities, Large Language Models (LLMs) still face
deployment challenges due to their enormous size. Post-training structured
pruning is a promising solution that prunes LLMs without the need for
retraining, reducing computational overhead, and it is hardware-deployment
friendly. However, the training-free nature of post-training structured pruning
leads to significant performance degradation. We argue that the key to
mitigating this issue lies in accurately determining the pruning rate for each
layer. Meanwhile, we find that LLMs may have prior knowledge about their own
redundancy. Based on this insight, we introduce $\textbf{Self-Pruner}$ an
end-to-end automatic self-pruning framework for LLMs, which efficiently search
layer-wise pruning rates. Specifically, $\textbf{Self-Pruner}$ leverages LLMs
to autonomously execute the entire evolutionary search process to search for
pruning rate configurations. In this process, LLMs are used to generate
populations, select parent solutions from the current population, and perform
crossover and mutation operations to produce offspring solutions. In this way,
LLMs automatically generate and evaluate a large number of candidate solutions,
effectively converging to find the pruning rate configurations with minimal
human intervention. Extensive experiments demonstrate $\textbf{Self-Pruner}$'s
better performance compared to existing state-of-the-art methods. Notably,
$\textbf{Self-Pruner}$ prunes LLaMA-2-70B to 49B level with only 0.80$\%$ drop
in accuracy across seven commonsense reasoning tasks, achieving a 1.39$\times$
speedup on NVIDIA A100 80GB GPU. Further pruning to 35B level resulted in only
a 3.80$\%$ decrease in accuracy while obtaining a 1.70$\times$ speedup.
|
2502.14416
|
Reliable Explainability of Deep Learning Spatial-Spectral Classifiers
for Improved Semantic Segmentation in Autonomous Driving
|
eess.IV cs.AI cs.LG
|
Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can
strengthen the accuracy of intelligent vision systems by combining spectral and
spatial information, which is useful for tasks like semantic segmentation in
autonomous driving. To advance research in such safety-critical systems,
determining the precise contribution of spectral information to complex DNNs'
output is needed. To address this, several saliency methods, such as class
activation maps (CAM), have been proposed primarily for image classification.
However, recent studies have raised concerns regarding their reliability. In
this paper, we address their limitations and propose an alternative approach by
leveraging the data provided by activations and weights from relevant DNN
layers to better capture the relationship between input features and
predictions. The study aims to assess the superior performance of HSI compared
to 3-channel and single-channel DNNs. We also address the influence of spectral
signature normalization for enhancing DNN robustness in real-world driving
conditions.
|
2502.14418
|
Role of the Pretraining and the Adaptation data sizes for low-resource
real-time MRI video segmentation
|
eess.AS cs.CV eess.SP
|
Real-time Magnetic Resonance Imaging (rtMRI) is frequently used in speech
production studies as it provides a complete view of the vocal tract during
articulation. This study investigates the effectiveness of rtMRI in analyzing
vocal tract movements by employing the SegNet and UNet models for Air-Tissue
Boundary (ATB)segmentation tasks. We conducted pretraining of a few base models
using increasing numbers of subjects and videos, to assess performance on two
datasets. First, consisting of unseen subjects with unseen videos from the same
data source, achieving 0.33% and 0.91% (Pixel-wise Classification Accuracy
(PCA) and Dice Coefficient respectively) better than its matched condition.
Second, comprising unseen videos from a new data source, where we obtained an
accuracy of 99.63% and 98.09% (PCA and Dice Coefficient respectively) of its
matched condition performance. Here, matched condition performance refers to
the performance of a model trained only on the test subjects which was set as a
benchmark for the other models. Our findings highlight the significance of
fine-tuning and adapting models with limited data. Notably, we demonstrated
that effective model adaptation can be achieved with as few as 15 rtMRI frames
from any new dataset.
|
2502.14420
|
ChatVLA: Unified Multimodal Understanding and Robot Control with
Vision-Language-Action Model
|
cs.RO cs.CV cs.LG
|
Humans possess a unified cognitive ability to perceive, comprehend, and
interact with the physical world. Why can't large language models replicate
this holistic understanding? Through a systematic analysis of existing training
paradigms in vision-language-action models (VLA), we identify two key
challenges: spurious forgetting, where robot training overwrites crucial
visual-text alignments, and task interference, where competing control and
understanding tasks degrade performance when trained jointly. To overcome these
limitations, we propose ChatVLA, a novel framework featuring Phased Alignment
Training, which incrementally integrates multimodal data after initial control
mastery, and a Mixture-of-Experts architecture to minimize task interference.
ChatVLA demonstrates competitive performance on visual question-answering
datasets and significantly surpasses state-of-the-art vision-language-action
(VLA) methods on multimodal understanding benchmarks. Notably, it achieves a
six times higher performance on MMMU and scores 47.2% on MMStar with a more
parameter-efficient design than ECoT. Furthermore, ChatVLA demonstrates
superior performance on 25 real-world robot manipulation tasks compared to
existing VLA methods like OpenVLA. Our findings highlight the potential of our
unified framework for achieving both robust multimodal understanding and
effective robot control.
|
2502.14422
|
Towards Routing and Edge Computing in Satellite-Terrestrial Networks: A
Column Generation Approach
|
eess.SY cs.SY
|
Edge computing that enables satellites to process raw data locally is
expected to bring further timeliness and flexibility to satellite-terrestrial
networks (STNs). In this letter, In this letter, we propose a three-layer edge
computing protocol, where raw data collected by satellites can be processed
locally, or transmitted to other satellites or the ground station via multi-hop
routing for further processing. The overall computing capacity of the proposed
framework is maximized by determining the offloading strategy and route
formation, subject to channel capacity and hop constraints. Given that the
problem scale grows exponentially with the number of satellites and
maximum-allowed hops, the column generation approach is employed to obtain the
global optimal solution by activating only a subset of variables. Numerical
investigations reveal that the proposed three-layer computing protocol improves
the computing capacity by 40\%, compared to the single-layer configuration.
|
2502.14424
|
Distribution Matching for Self-Supervised Transfer Learning
|
stat.ML cs.AI cs.LG stat.ME
|
In this paper, we propose a novel self-supervised transfer learning method
called Distribution Matching (DM), which drives the representation distribution
toward a predefined reference distribution while preserving augmentation
invariance. The design of DM results in a learned representation space that is
intuitively structured and offers easily interpretable hyperparameters.
Experimental results across multiple real-world datasets and evaluation metrics
demonstrate that DM performs competitively on target classification tasks
compared to existing self-supervised transfer learning methods. Additionally,
we provide robust theoretical guarantees for DM, including a population theorem
and an end-to-end sample theorem. The population theorem bridges the gap
between the self-supervised learning task and target classification accuracy,
while the sample theorem shows that, even with a limited number of samples from
the target domain, DM can deliver exceptional classification performance,
provided the unlabeled sample size is sufficiently large.
|
2502.14425
|
A Survey on Data Contamination for Large Language Models
|
cs.CL
|
Recent advancements in Large Language Models (LLMs) have demonstrated
significant progress in various areas, such as text generation and code
synthesis. However, the reliability of performance evaluation has come under
scrutiny due to data contamination-the unintended overlap between training and
test datasets. This overlap has the potential to artificially inflate model
performance, as LLMs are typically trained on extensive datasets scraped from
publicly available sources. These datasets often inadvertently overlap with the
benchmarks used for evaluation, leading to an overestimation of the models'
true generalization capabilities. In this paper, we first examine the
definition and impacts of data contamination. Secondly, we review methods for
contamination-free evaluation, focusing on three strategies: data
updating-based methods, data rewriting-based methods, and prevention-based
methods. Specifically, we highlight dynamic benchmarks and LLM-driven
evaluation methods. Finally, we categorize contamination detecting methods
based on model information dependency: white-Box, gray-Box, and black-Box
detection approaches. Our survey highlights the requirements for more rigorous
evaluation protocols and proposes future directions for addressing data
contamination challenges.
|
2502.14427
|
Token-Level Density-Based Uncertainty Quantification Methods for
Eliciting Truthfulness of Large Language Models
|
cs.CL
|
Uncertainty quantification (UQ) is a prominent approach for eliciting
truthful answers from large language models (LLMs). To date, information-based
and consistency-based UQ have been the dominant UQ methods for text generation
via LLMs. Density-based methods, despite being very effective for UQ in text
classification with encoder-based models, have not been very successful with
generative LLMs. In this work, we adapt Mahalanobis Distance (MD) - a
well-established UQ technique in classification tasks - for text generation and
introduce a new supervised UQ method. Our method extracts token embeddings from
multiple layers of LLMs, computes MD scores for each token, and uses linear
regression trained on these features to provide robust uncertainty scores.
Through extensive experiments on eleven datasets, we demonstrate that our
approach substantially improves over existing UQ methods, providing accurate
and computationally efficient uncertainty scores for both sequence-level
selective generation and claim-level fact-checking tasks. Our method also
exhibits strong generalization to out-of-domain data, making it suitable for a
wide range of LLM-based applications.
|
2502.14429
|
Early-Exit and Instant Confidence Translation Quality Estimation
|
cs.CL
|
Quality estimation is omnipresent in machine translation, for both evaluation
and generation. Unfortunately, quality estimation models are often opaque and
computationally expensive, making them impractical to be part of large-scale
pipelines. In this work, we tackle two connected challenges: (1) reducing the
cost of quality estimation at scale, and (2) developing an inexpensive
uncertainty estimation method for quality estimation. To address the latter, we
introduce Instant Confidence COMET, an uncertainty-aware quality estimation
model that matches the performance of previous approaches at a fraction of
their costs. We extend this to Early-Exit COMET, a quality estimation model
that can compute quality scores and associated confidences already at early
model layers, allowing us to early-exit computations and reduce evaluation
costs. We also apply our model to machine translation reranking. We combine
Early-Exit COMET with an upper confidence bound bandit algorithm to find the
best candidate from a large pool without having to run the full evaluation
model on all candidates. In both cases (evaluation and reranking) our methods
reduce the required compute by 50% with very little degradation in performance.
|
2502.14430
|
Cardiac Evidence Backtracking for Eating Behavior Monitoring using
Collocative Electrocardiogram Imagining
|
cs.LG cs.CE
|
Eating monitoring has remained an open challenge in medical research for
years due to the lack of non-invasive sensors for continuous monitoring and the
reliable methods for automatic behavior detection. In this paper, we present a
pilot study using the wearable 24-hour ECG for sensing and tailoring the
sophisticated deep learning for ad-hoc and interpretable detection. This is
accomplished using a collocative learning framework in which 1) we construct
collocative tensors as pseudo-images from 1D ECG signals to improve the
feasibility of 2D image-based deep models; 2) we formulate the cardiac logic of
analyzing the ECG data in a comparative way as periodic attention regulators so
as to guide the deep inference to collect evidence in a human comprehensible
manner; and 3) we improve the interpretability of the framework by enabling the
backtracking of evidence with a set of methods designed for Class Activation
Mapping (CAM) decoding and decision tree/forest generation. The effectiveness
of the proposed framework has been validated on the largest ECG dataset of
eating behavior with superior performance over conventional models, and its
capacity of cardiac evidence mining has also been verified through the
consistency of the evidence it backtracked and that of the previous medical
studies.
|
2502.14432
|
Port-Hamiltonian Neural Networks with Output Error Noise Models
|
cs.LG
|
Hamiltonian neural networks (HNNs) represent a promising class of
physics-informed deep learning methods that utilize Hamiltonian theory as
foundational knowledge within neural networks. However, their direct
application to engineering systems is often challenged by practical issues,
including the presence of external inputs, dissipation, and noisy measurements.
This paper introduces a novel framework that enhances the capabilities of HNNs
to address these real-life factors. We integrate port-Hamiltonian theory into
the neural network structure, allowing for the inclusion of external inputs and
dissipation, while mitigating the impact of measurement noise through an
output-error (OE) model structure. The resulting output error port-Hamiltonian
neural networks (OE-pHNNs) can be adapted to tackle modeling complex
engineering systems with noisy measurements. Furthermore, we propose the
identification of OE-pHNNs based on the subspace encoder approach (SUBNET),
which efficiently approximates the complete simulation loss using subsections
of the data and uses an encoder function to predict initial states. By
integrating SUBNET with OE-pHNNs, we achieve consistent models of complex
engineering systems under noisy measurements. In addition, we perform a
consistency analysis to ensure the reliability of the proposed data-driven
model learning method. We demonstrate the effectiveness of our approach on
system identification benchmarks, showing its potential as a powerful tool for
modeling dynamic systems in real-world applications.
|
2502.14433
|
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track
Areas Using Deep Ensemble Learning With Uncertainty Quantification
|
cs.CV
|
Many real-world applications rely on land surface temperature (LST) data at
high spatiotemporal resolution. In complex urban areas, LST exhibits
significant variations, fluctuating dramatically within and across city blocks.
Landsat provides high spatial resolution data at 100 meters but is limited by
long revisit time, with cloud cover further disrupting data collection. Here,
we propose DELAG, a deep ensemble learning method that integrates annual
temperature cycles and Gaussian processes, to reconstruct Landsat LST in
complex urban areas. Leveraging the cross-track characteristics and
dual-satellite operation of Landsat since 2021, we further enhance data
availability to 4 scenes every 16 days. We select New York City, London and
Hong Kong from three different continents as study areas. Experiments show that
DELAG successfully reconstructed LST in the three cities under clear-sky (RMSE
= 0.73-0.96 K) and heavily-cloudy (RMSE = 0.84-1.62 K) situations, superior to
existing methods. Additionally, DELAG can quantify uncertainty that enhances
LST reconstruction reliability. We further tested the reconstructed LST to
estimate near-surface air temperature, achieving results (RMSE = 1.48-2.11 K)
comparable to those derived from clear-sky LST (RMSE = 1.63-2.02 K). The
results demonstrate the successful reconstruction through DELAG and highlight
the broader applications of LST reconstruction for estimating accurate air
temperature. Our study thus provides a novel and practical method for Landsat
LST reconstruction, particularly suited for complex urban areas within Landsat
cross-track areas, taking one step toward addressing complex climate events at
high spatiotemporal resolution.
|
2502.14437
|
Natural Language Generation
|
cs.CL
|
This book provides a broad overview of Natural Language Generation (NLG),
including technology, user requirements, evaluation, and real-world
applications. The focus is on concepts and insights which hopefully will remain
relevant for many years, not on the latest LLM innovations. It draws on decades
of work by the author and others on NLG.
The book has the following chapters: Introduction to NLG; Rule-Based NLG;
Machine Learning and Neural NLG; Requirements; Evaluation; Safety, Maintenance,
and Testing; and Applications. All chapters include examples and anecdotes from
the author's personal experiences, and end with a Further Reading section.
The book should be especially useful to people working on applied NLG,
including NLG researchers, people in other fields who want to use NLG, and
commercial developers. It will not however be useful to people who want to
understand the latest LLM technology.
There is a companion site with more information at
https://ehudreiter.com/book/
|
2502.14442
|
Stochastic Resonance Improves the Detection of Low Contrast Images in
Deep Learning Models
|
cs.CV cs.AI
|
Stochastic resonance describes the utility of noise in improving the
detectability of weak signals in certain types of systems. It has been observed
widely in natural and engineered settings, but its utility in image
classification with rate-based neural networks has not been studied
extensively. In this analysis a simple LSTM recurrent neural network is trained
for digit recognition and classification. During the test phase, image contrast
is reduced to a point where the model fails to recognize the presence of a
stimulus. Controlled noise is added to partially recover classification
performance. The results indicate the presence of stochastic resonance in
rate-based recurrent neural networks.
|
2502.14444
|
An Enhancement of Jiang, Z., et al.s Compression-Based Classification
Algorithm Applied to News Article Categorization
|
cs.CL
|
This study enhances Jiang et al.'s compression-based classification algorithm
by addressing its limitations in detecting semantic similarities between text
documents. The proposed improvements focus on unigram extraction and optimized
concatenation, eliminating reliance on entire document compression. By
compressing extracted unigrams, the algorithm mitigates sliding window
limitations inherent to gzip, improving compression efficiency and similarity
detection. The optimized concatenation strategy replaces direct concatenation
with the union of unigrams, reducing redundancy and enhancing the accuracy of
Normalized Compression Distance (NCD) calculations. Experimental results across
datasets of varying sizes and complexities demonstrate an average accuracy
improvement of 5.73%, with gains of up to 11% on datasets containing longer
documents. Notably, these improvements are more pronounced in datasets with
high-label diversity and complex text structures. The methodology achieves
these results while maintaining computational efficiency, making it suitable
for resource-constrained environments. This study provides a robust, scalable
solution for text classification, emphasizing lightweight preprocessing
techniques to achieve efficient compression, which in turn enables more
accurate classification.
|
2502.14445
|
PredictaBoard: Benchmarking LLM Score Predictability
|
cs.CL cs.AI stat.ML
|
Despite possessing impressive skills, Large Language Models (LLMs) often fail
unpredictably, demonstrating inconsistent success in even basic common sense
reasoning tasks. This unpredictability poses a significant challenge to
ensuring their safe deployment, as identifying and operating within a reliable
"safe zone" is essential for mitigating risks. To address this, we present
PredictaBoard, a novel collaborative benchmarking framework designed to
evaluate the ability of score predictors (referred to as assessors) to
anticipate LLM errors on specific task instances (i.e., prompts) from existing
datasets. PredictaBoard evaluates pairs of LLMs and assessors by considering
the rejection rate at different tolerance errors. As such, PredictaBoard
stimulates research into developing better assessors and making LLMs more
predictable, not only with a higher average performance. We conduct
illustrative experiments using baseline assessors and state-of-the-art LLMs.
PredictaBoard highlights the critical need to evaluate predictability alongside
performance, paving the way for safer AI systems where errors are not only
minimised but also anticipated and effectively mitigated. Code for our
benchmark can be found at
https://github.com/Kinds-of-Intelligence-CFI/PredictaBoard
|
2502.14451
|
Optimal word order for non-causal text generation with Large Language
Models: the Spanish case
|
cs.CL
|
Natural Language Generation (NLG) popularity has increased owing to the
progress in Large Language Models (LLMs), with zero-shot inference
capabilities. However, most neural systems utilize decoder-only causal
(unidirectional) transformer models, which are effective for English but may
reduce the richness of languages with less strict word order, subject omission,
or different relative clause attachment preferences. This is the first work
that analytically addresses optimal text generation order for non-causal
language models. We present a novel Viterbi algorithm-based methodology for
maximum likelihood word order estimation. We analyze the non-causal
most-likelihood order probability for NLG in Spanish and, then, the probability
of generating the same phrases with Spanish causal NLG. This comparative
analysis reveals that causal NLG prefers English-like SVO structures. We also
analyze the relationship between optimal generation order and causal
left-to-right generation order using Spearman's rank correlation. Our results
demonstrate that the ideal order predicted by the maximum likelihood estimator
is not closely related to the causal order and may be influenced by the
syntactic structure of the target sentence.
|
2502.14454
|
Exploiting Deblurring Networks for Radiance Fields
|
cs.CV
|
In this paper, we propose DeepDeblurRF, a novel radiance field deblurring
approach that can synthesize high-quality novel views from blurred training
views with significantly reduced training time. DeepDeblurRF leverages deep
neural network (DNN)-based deblurring modules to enjoy their deblurring
performance and computational efficiency. To effectively combine DNN-based
deblurring and radiance field construction, we propose a novel radiance field
(RF)-guided deblurring and an iterative framework that performs RF-guided
deblurring and radiance field construction in an alternating manner. Moreover,
DeepDeblurRF is compatible with various scene representations, such as voxel
grids and 3D Gaussians, expanding its applicability. We also present
BlurRF-Synth, the first large-scale synthetic dataset for training radiance
field deblurring frameworks. We conduct extensive experiments on both camera
motion blur and defocus blur, demonstrating that DeepDeblurRF achieves
state-of-the-art novel-view synthesis quality with significantly reduced
training time.
|
2502.14455
|
An Efficient Ground-aerial Transportation System for Pest Control
Enabled by AI-based Autonomous Nano-UAVs
|
cs.RO cs.AI
|
Efficient crop production requires early detection of pest outbreaks and
timely treatments; we consider a solution based on a fleet of multiple
autonomous miniaturized unmanned aerial vehicles (nano-UAVs) to visually detect
pests and a single slower heavy vehicle that visits the detected outbreaks to
deliver treatments. To cope with the extreme limitations aboard nano-UAVs,
e.g., low-resolution sensors and sub-100 mW computational power budget, we
design, fine-tune, and optimize a tiny image-based convolutional neural network
(CNN) for pest detection. Despite the small size of our CNN (i.e., 0.58
GOps/inference), on our dataset, it scores a mean average precision (mAP) of
0.79 in detecting harmful bugs, i.e., 14% lower mAP but 32x fewer operations
than the best-performing CNN in the literature. Our CNN runs in real-time at
6.8 frame/s, requiring 33 mW on a GWT GAP9 System-on-Chip aboard a Crazyflie
nano-UAV. Then, to cope with in-field unexpected obstacles, we leverage a
global+local path planner based on the A* algorithm. The global path planner
determines the best route for the nano-UAV to sweep the entire area, while the
local one runs up to 50 Hz aboard our nano-UAV and prevents collision by
adjusting the short-distance path. Finally, we demonstrate with in-simulator
experiments that once a 25 nano-UAVs fleet has combed a 200x200 m vineyard,
collected information can be used to plan the best path for the tractor,
visiting all and only required hotspots. In this scenario, our efficient
transportation system, compared to a traditional single-ground vehicle
performing both inspection and treatment, can save up to 20 h working time.
|
2502.14456
|
Narrative-Driven Travel Planning: Geoculturally-Grounded Script
Generation with Evolutionary Itinerary Optimization
|
cs.AI
|
To enhance tourists' experiences and immersion, this paper proposes a
narrative-driven travel planning framework called NarrativeGuide, which
generates a geoculturally-grounded narrative script for travelers, offering a
novel, role-playing experience for their journey. In the initial stage,
NarrativeGuide constructs a knowledge graph for attractions within a city, then
configures the worldview, character setting, and exposition based on the
knowledge graph. Using this foundation, the knowledge graph is combined to
generate an independent scene unit for each attraction. During the itinerary
planning stage, NarrativeGuide models narrative-driven travel planning as an
optimization problem, utilizing a genetic algorithm (GA) to refine the
itinerary. Before evaluating the candidate itinerary, transition scripts are
generated for each pair of adjacent attractions, which, along with the scene
units, form a complete script. The weighted sum of script coherence, travel
time, and attraction scores is then used as the fitness value to update the
candidate solution set. Experimental results across four cities, i.e., Nanjing
and Yangzhou in China, Paris in France, and Berlin in Germany, demonstrate
significant improvements in narrative coherence and cultural fit, alongside a
notable reduction in travel time and an increase in the quality of visited
attractions. Our study highlights that incorporating external evolutionary
optimization effectively addresses the limitations of large language models in
travel planning.Our codes are available at
https://github.com/Evan01225/Narrative-Driven-Travel-Planning.
|
2502.14457
|
Watch Less, Feel More: Sim-to-Real RL for Generalizable Articulated
Object Manipulation via Motion Adaptation and Impedance Control
|
cs.RO cs.AI cs.LG
|
Articulated object manipulation poses a unique challenge compared to rigid
object manipulation as the object itself represents a dynamic environment. In
this work, we present a novel RL-based pipeline equipped with variable
impedance control and motion adaptation leveraging observation history for
generalizable articulated object manipulation, focusing on smooth and dexterous
motion during zero-shot sim-to-real transfer. To mitigate the sim-to-real gap,
our pipeline diminishes reliance on vision by not leveraging the vision data
feature (RGBD/pointcloud) directly as policy input but rather extracting useful
low-dimensional data first via off-the-shelf modules. Additionally, we
experience less sim-to-real gap by inferring object motion and its intrinsic
properties via observation history as well as utilizing impedance control both
in the simulation and in the real world. Furthermore, we develop a
well-designed training setting with great randomization and a specialized
reward system (task-aware and motion-aware) that enables multi-staged,
end-to-end manipulation without heuristic motion planning. To the best of our
knowledge, our policy is the first to report 84\% success rate in the real
world via extensive experiments with various unseen objects.
|
2502.14458
|
Llamba: Scaling Distilled Recurrent Models for Efficient Language
Processing
|
cs.LG cs.AI
|
We introduce Llamba, a family of efficient recurrent language models
distilled from Llama-3.x into the Mamba architecture. The series includes
Llamba-1B, Llamba-3B, and Llamba-8B, which achieve higher inference throughput
and handle significantly larger batch sizes than Transformer-based models while
maintaining comparable benchmark performance. Furthermore, Llamba demonstrates
the effectiveness of cross-architecture distillation using MOHAWK (Bick et al.,
2024), achieving these results with less than 0.1% of the training data
typically used for models of similar size. To take full advantage of their
efficiency, we provide an optimized implementation of Llamba for
resource-constrained devices such as smartphones and edge platforms, offering a
practical and memory-efficient alternative to Transformers. Overall, Llamba
improves the tradeoff between speed, memory efficiency, and performance, making
high-quality language models more accessible.
|
2502.14462
|
Single-image Reflectance and Transmittance Estimation from Any Flatbed
Scanner
|
cs.GR cs.AI cs.CV cs.LG
|
Flatbed scanners have emerged as promising devices for high-resolution,
single-image material capture. However, existing approaches assume very
specific conditions, such as uniform diffuse illumination, which are only
available in certain high-end devices, hindering their scalability and cost. In
contrast, in this work, we introduce a method inspired by intrinsic image
decomposition, which accurately removes both shading and specularity,
effectively allowing captures with any flatbed scanner. Further, we extend
previous work on single-image material reflectance capture with the estimation
of opacity and transmittance, critical components of full material appearance
(SVBSDF), improving the results for any material captured with a flatbed
scanner, at a very high resolution and accuracy
|
2502.14467
|
Provable Quantum Algorithm Advantage for Gaussian Process Quadrature
|
stat.CO cs.LG quant-ph
|
The aim of this paper is to develop novel quantum algorithms for Gaussian
process quadrature methods. Gaussian process quadratures are numerical
integration methods where Gaussian processes are used as functional priors for
the integrands to capture the uncertainty arising from the sparse function
evaluations. Quantum computers have emerged as potential replacements for
classical computers, offering exponential reductions in the computational
complexity of machine learning tasks. In this paper, we combine Gaussian
process quadratures and quantum computing by proposing a quantum low-rank
Gaussian process quadrature method based on a Hilbert space approximation of
the Gaussian process kernel and enhancing the quadrature using a quantum
circuit. The method combines the quantum phase estimation algorithm with the
quantum principal component analysis technique to extract information up to a
desired rank. Then, Hadamard and SWAP tests are implemented to find the
expected value and variance that determines the quadrature. We use numerical
simulations of a quantum computer to demonstrate the effectiveness of the
method. Furthermore, we provide a theoretical complexity analysis that shows a
polynomial advantage over classical Gaussian process quadrature methods. The
code is available at https://github.com/cagalvisf/Quantum_HSGPQ.
|
2502.14469
|
Enhancing Smart Environments with Context-Aware Chatbots using Large
Language Models
|
cs.CL cs.AI cs.SI
|
This work presents a novel architecture for context-aware interactions within
smart environments, leveraging Large Language Models (LLMs) to enhance user
experiences. Our system integrates user location data obtained through UWB tags
and sensor-equipped smart homes with real-time human activity recognition (HAR)
to provide a comprehensive understanding of user context. This contextual
information is then fed to an LLM-powered chatbot, enabling it to generate
personalised interactions and recommendations based on the user's current
activity and environment. This approach moves beyond traditional static chatbot
interactions by dynamically adapting to the user's real-time situation. A case
study conducted from a real-world dataset demonstrates the feasibility and
effectiveness of our proposed architecture, showcasing its potential to create
more intuitive and helpful interactions within smart homes. The results
highlight the significant benefits of integrating LLM with real-time activity
and location data to deliver personalised and contextually relevant user
experiences.
|
2502.14471
|
Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
|
cs.CV
|
Camouflaged Object Segmentation (COS) remains a challenging problem due to
the subtle visual differences between camouflaged objects and backgrounds.
Owing to the exceedingly limited visual cues available from visible spectrum,
previous RGB single-modality approaches often struggle to achieve satisfactory
results, prompting the exploration of multimodal data to enhance detection
accuracy. In this work, we present UniCOS, a novel framework that effectively
leverages diverse data modalities to improve segmentation performance. UniCOS
comprises two key components: a multimodal segmentor, UniSEG, and a cross-modal
knowledge learning module, UniLearner. UniSEG employs a state space fusion
mechanism to integrate cross-modal features within a unified state space,
enhancing contextual understanding and improving robustness to integration of
heterogeneous data. Additionally, it includes a fusion-feedback mechanism that
facilitate feature extraction. UniLearner exploits multimodal data unrelated to
the COS task to improve the segmentation ability of the COS models by
generating pseudo-modal content and cross-modal semantic associations.
Extensive experiments demonstrate that UniSEG outperforms existing Multimodal
COS (MCOS) segmentors, regardless of whether real or pseudo-multimodal COS data
is available. Moreover, in scenarios where multimodal COS data is unavailable
but multimodal non-COS data is accessible, UniLearner effectively exploits
these data to enhance segmentation performance. Our code will be made publicly
available on \href{https://github.com/cnyvfang/UniCOS}{GitHub}.
|
2502.14476
|
Argument-Based Comparative Question Answering Evaluation Benchmark
|
cs.CL
|
In this paper, we aim to solve the problems standing in the way of automatic
comparative question answering. To this end, we propose an evaluation framework
to assess the quality of comparative question answering summaries. We formulate
15 criteria for assessing comparative answers created using manual annotation
and annotation from 6 large language models and two comparative question
asnwering datasets. We perform our tests using several LLMs and manual
annotation under different settings and demonstrate the constituency of both
evaluations. Our results demonstrate that the Llama-3 70B Instruct model
demonstrates the best results for summary evaluation, while GPT-4 is the best
for answering comparative questions. All used data, code, and evaluation
results are publicly
available\footnote{\url{https://anonymous.4open.science/r/cqa-evaluation-benchmark-4561/README.md}}.
|
2502.14477
|
Unshackling Context Length: An Efficient Selective Attention Approach
through Query-Key Compression
|
cs.CL
|
Handling long-context sequences efficiently remains a significant challenge
in large language models (LLMs). Existing methods for token selection in
sequence extrapolation either employ a permanent eviction strategy or select
tokens by chunk, which may lead to the loss of critical information. We propose
Efficient Selective Attention (ESA), a novel approach that extends context
length by efficiently selecting the most critical tokens at the token level to
compute attention. ESA reduces the computational complexity of token selection
by compressing query and key vectors into lower-dimensional representations. We
evaluate ESA on long sequence benchmarks with maximum lengths up to 256k using
open-source LLMs with context lengths of 8k and 32k. ESA outperforms other
selective attention methods, especially in tasks requiring the retrieval of
multiple pieces of information, achieving comparable performance to
full-attention extrapolation methods across various tasks, with superior
results in certain tasks.
|
2502.14482
|
NLoRA: Nystr\"om-Initiated Low-Rank Adaptation for Large Language Models
|
cs.CL
|
Parameter-efficient fine-tuning (PEFT) is essential for adapting large
language models (LLMs), with low-rank adaptation (LoRA) being the most popular
approach. However, LoRA suffers from slow convergence, and some recent LoRA
variants, such as PiSSA, primarily rely on Singular Value Decomposition (SVD)
for initialization, leading to expensive computation. To mitigate these
problems, we use the Nystr\"om method, which follows a three-matrix
manipulation. We first introduce StructuredLoRA (SLoRA), which investigates
adding a small intermediate matrix between the low-rank matrices A and B.
Secondly, we propose Nystr\"omLoRA (NLoRA), which leverages Nystr\"om-based
initialization for SLoRA to improve its effectiveness and efficiency. Finally,
we propose IntermediateTune (IntTune), which explores fine-tuning exclusively
on the intermediate matrix of NLoRA to further boost LLM efficiency. We
evaluate our methods on five natural language generation (NLG) tasks and eight
natural language understanding (NLU) tasks. On GSM8K, SLoRA and NLoRA achieve
accuracies of 56.48% and 57.70%, surpassing LoRA by 33.52% and 36.41%, with
only 3.67 million additional trainable parameters. IntTune improves average NLG
performance over LoRA by 7.45% while using only 1.25% of its parameters. These
results demonstrate the efficiency and effectiveness of our approach in
enhancing model performance with minimal parameter overhead.
|
2502.14486
|
How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation
|
cs.CR cs.AI cs.CL
|
Jailbreak attacks, where harmful prompts bypass generative models' built-in
safety, raise serious concerns about model vulnerability. While many defense
methods have been proposed, the trade-offs between safety and helpfulness, and
their application to Large Vision-Language Models (LVLMs), are not well
understood. This paper systematically examines jailbreak defenses by reframing
the standard generation task as a binary classification problem to assess model
refusal tendencies for both harmful and benign queries. We identify two key
defense mechanisms: safety shift, which increases refusal rates across all
queries, and harmfulness discrimination, which improves the model's ability to
distinguish between harmful and benign inputs. Using these mechanisms, we
develop two ensemble defense strategies-inter-mechanism ensembles and
intra-mechanism ensembles-to balance safety and helpfulness. Experiments on the
MM-SafetyBench and MOSSBench datasets with LLaVA-1.5 models show that these
strategies effectively improve model safety or optimize the trade-off between
safety and helpfulness.
|
2502.14487
|
Temporal Misalignment and Probabilistic Neurons
|
cs.LG cs.AI cs.CV
|
Spiking Neural Networks (SNNs) offer a more energy-efficient alternative to
Artificial Neural Networks (ANNs) by mimicking biological neural principles,
establishing them as a promising approach to mitigate the increasing energy
demands of large-scale neural models. However, fully harnessing the
capabilities of SNNs remains challenging due to their discrete signal
processing and temporal dynamics. ANN-SNN conversion has emerged as a practical
approach, enabling SNNs to achieve competitive performance on complex machine
learning tasks. In this work, we identify a phenomenon in the ANN-SNN
conversion framework, termed temporal misalignment, in which random spike
rearrangement across SNN layers leads to performance improvements. Based on
this observation, we introduce biologically plausible two-phase probabilistic
(TPP) spiking neurons, further enhancing the conversion process. We demonstrate
the advantages of our proposed method both theoretically and empirically
through comprehensive experiments on CIFAR-10/100, CIFAR10-DVS, and ImageNet
across a variety of architectures, achieving state-of-the-art results.
|
2502.14491
|
Statistical Scenario Modelling and Lookalike Distributions for
Multi-Variate AI Risk
|
cs.AI
|
Evaluating AI safety requires statistically rigorous methods and risk metrics
for understanding how the use of AI affects aggregated risk. However, much AI
safety literature focuses upon risks arising from AI models in isolation,
lacking consideration of how modular use of AI affects risk distribution of
workflow components or overall risk metrics. There is also a lack of
statistical grounding enabling sensitisation of risk models in the presence of
absence of AI to estimate causal contributions of AI. This is in part due to
the dearth of AI impact data upon which to fit distributions. In this work, we
address these gaps in two ways. First, we demonstrate how scenario modelling
(grounded in established statistical techniques such as Markov chains, copulas
and Monte Carlo simulation) can be used to model AI risk holistically. Second,
we show how lookalike distributions from phenomena analogous to AI can be used
to estimate AI impacts in the absence of directly observable data. We
demonstrate the utility of our methods for benchmarking cumulative AI risk via
risk analysis of a logistic scenario simulations.
|
2502.14493
|
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor
Top-K Vision Alignment and Beyond
|
cs.CV cs.LG
|
Infrared and visible image fusion (IVIF) is increasingly applied in critical
fields such as video surveillance and autonomous driving systems. Significant
progress has been made in deep learning-based fusion methods. However, these
models frequently encounter out-of-distribution (OOD) scenes in real-world
applications, which severely impact their performance and reliability.
Therefore, addressing the challenge of OOD data is crucial for the safe
deployment of these models in open-world environments. Unlike existing
research, our focus is on the challenges posed by OOD data in real-world
applications and on enhancing the robustness and generalization of models. In
this paper, we propose an infrared-visible fusion framework based on Multi-View
Augmentation. For external data augmentation, Top-k Selective Vision Alignment
is employed to mitigate distribution shifts between datasets by performing
RGB-wise transformations on visible images. This strategy effectively
introduces augmented samples, enhancing the adaptability of the model to
complex real-world scenarios. Additionally, for internal data augmentation,
self-supervised learning is established using Weak-Aggressive Augmentation.
This enables the model to learn more robust and general feature representations
during the fusion process, thereby improving robustness and generalization.
Extensive experiments demonstrate that the proposed method exhibits superior
performance and robustness across various conditions and environments. Our
approach significantly enhances the reliability and stability of IVIF tasks in
practical applications.
|
2502.14494
|
StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction
Following
|
cs.CL
|
Multi-turn instruction following capability constitutes a core competency of
large language models (LLMs) in real-world applications. Existing evaluation
benchmarks predominantly focus on fine-grained constraint satisfaction and
domain-specific capability assessment, yet overlook the crucial structural
dependency between dialogue turns that distinguishes multi-turn from
single-turn interactions. This structural dependency not only reflects user
intent but also establishes a second dimension for instruction following
evaluation beyond constraint satisfaction. To address this gap, we propose
StructFlowBench, a multi-turn instruction following benchmark with structural
flow modeling. The benchmark innovatively defines a structural flow framework
comprising six fundamental inter-turn relationships, which not only introduces
novel structural constraints for model evaluation but also serves as generation
parameters for creating customized dialogue flows tailored to specific
scenarios. Adopting established LLM-based automatic evaluation methodologies,
we conduct systematic evaluations of 13 leading open-source and closed-source
LLMs. Experimental results reveal significant deficiencies in current models'
comprehension of multi-turn dialogue structures. The code is available at
\url{https://github.com/MLGroupJLU/StructFlowBench}.
|
2502.14495
|
Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral
Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and
Benchmark Dataset
|
cs.CV
|
UAV-borne hyperspectral remote sensing has emerged as a promising approach
for underwater target detection (UTD). However, its effectiveness is hindered
by spectral distortions in nearshore environments, which compromise the
accuracy of traditional hyperspectral UTD (HUTD) methods that rely on
bathymetric model. These distortions lead to significant uncertainty in target
and background spectra, challenging the detection process. To address this, we
propose the Hyperspectral Underwater Contrastive Learning Network (HUCLNet), a
novel framework that integrates contrastive learning with a self-paced learning
paradigm for robust HUTD in nearshore regions. HUCLNet extracts discriminative
features from distorted hyperspectral data through contrastive learning, while
the self-paced learning strategy selectively prioritizes the most informative
samples. Additionally, a reliability-guided clustering strategy enhances the
robustness of learned representations.To evaluate the method effectiveness, we
conduct a novel nearshore HUTD benchmark dataset, ATR2-HUTD, covering three
diverse scenarios with varying water types and turbidity, and target types.
Extensive experiments demonstrate that HUCLNet significantly outperforms
state-of-the-art methods. The dataset and code will be publicly available at:
https://github.com/qjh1996/HUTD
|
2502.14496
|
Enhancing Language Multi-Agent Learning with Multi-Agent Credit
Re-Assignment for Interactive Environment Generalization
|
cs.CL
|
LLM-based agents have made significant advancements in interactive
environments, such as mobile operations and web browsing, and other domains
beyond computer using. Current multi-agent systems universally excel in
performance, compared to single agents, but struggle with generalization across
environments due to predefined roles and inadequate strategies for generalizing
language agents. The challenge of achieving both strong performance and good
generalization has hindered the progress of multi-agent systems for interactive
environments. To address these issues, we propose CollabUIAgents, a multi-agent
reinforcement learning framework with a novel multi-agent credit re-assignment
(CR) strategy, assigning process rewards with LLMs rather than
environment-specific rewards and learning with synthesized preference data, in
order to foster generalizable, collaborative behaviors among the role-free
agents' policies. Empirical results show that our framework improves both
performance and cross-environment generalizability of multi-agent systems.
Moreover, our 7B-parameter system achieves results on par with or exceed strong
closed-source models, and the LLM that guides the CR. We also provide insights
in using granular CR rewards effectively for environment generalization, and
accommodating trained LLMs in multi-agent systems.
|
2502.14497
|
Stories that (are) Move(d by) Markets: A Causal Exploration of Market
Shocks and Semantic Shifts across Different Partisan Groups
|
cs.CL cs.CE econ.GN q-fin.EC
|
Macroeconomic fluctuations and the narratives that shape them form a mutually
reinforcing cycle: public discourse can spur behavioural changes leading to
economic shifts, which then result in changes in the stories that propagate. We
show that shifts in semantic embedding space can be causally linked to
financial market shocks -- deviations from the expected market behaviour.
Furthermore, we show how partisanship can influence the predictive power of
text for market fluctuations and shape reactions to those same shocks. We also
provide some evidence that text-based signals are particularly salient during
unexpected events such as COVID-19, highlighting the value of language data as
an exogenous variable in economic forecasting. Our findings underscore the
bidirectional relationship between news outlets and market shocks, offering a
novel empirical approach to studying their effect on each other.
|
2502.14499
|
MLGym: A New Framework and Benchmark for Advancing AI Research Agents
|
cs.CL cs.AI cs.LG
|
We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for
evaluating and developing LLM agents on AI research tasks. This is the first
Gym environment for machine learning (ML) tasks, enabling research on
reinforcement learning (RL) algorithms for training such agents. MLGym-bench
consists of 13 diverse and open-ended AI research tasks from diverse domains
such as computer vision, natural language processing, reinforcement learning,
and game theory. Solving these tasks requires real-world AI research skills
such as generating new ideas and hypotheses, creating and processing data,
implementing ML methods, training models, running experiments, analyzing the
results, and iterating through this process to improve on a given task. We
evaluate a number of frontier large language models (LLMs) on our benchmarks
such as Claude-3.5-Sonnet, Llama-3.1 405B, GPT-4o, o1-preview, and Gemini-1.5
Pro. Our MLGym framework makes it easy to add new tasks, integrate and evaluate
models or agents, generate synthetic data at scale, as well as develop new
learning algorithms for training agents on AI research tasks. We find that
current frontier models can improve on the given baselines, usually by finding
better hyperparameters, but do not generate novel hypotheses, algorithms,
architectures, or substantial improvements. We open-source our framework and
benchmark to facilitate future research in advancing the AI research
capabilities of LLM agents.
|
2502.14501
|
Towards a Perspectivist Turn in Argument Quality Assessment
|
cs.CL
|
The assessment of argument quality depends on well-established logical,
rhetorical, and dialectical properties that are unavoidably subjective:
multiple valid assessments may exist, there is no unequivocal ground truth.
This aligns with recent paths in machine learning, which embrace the
co-existence of different perspectives. However, this potential remains largely
unexplored in NLP research on argument quality. One crucial reason seems to be
the yet unexplored availability of suitable datasets. We fill this gap by
conducting a systematic review of argument quality datasets. We assign them to
a multi-layered categorization targeting two aspects: (a) What has been
annotated: we collect the quality dimensions covered in datasets and
consolidate them in an overarching taxonomy, increasing dataset comparability
and interoperability. (b) Who annotated: we survey what information is given
about annotators, enabling perspectivist research and grounding our
recommendations for future actions. To this end, we discuss datasets suitable
for developing perspectivist models (i.e., those containing individual,
non-aggregated annotations), and we showcase the importance of a controlled
selection of annotators in a pilot study.
|
2502.14502
|
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
|
cs.CL
|
The performance of Large Language Models (LLMs) on many tasks is greatly
limited by the knowledge learned during pre-training and stored in the model's
parameters. Low-rank adaptation (LoRA) is a popular and efficient training
technique for updating or domain-specific adaptation of LLMs. In this study, we
investigate how new facts can be incorporated into the LLM using LoRA without
compromising the previously learned knowledge. We fine-tuned
Llama-3.1-8B-instruct using LoRA with varying amounts of new knowledge. Our
experiments have shown that the best results are obtained when the training
data contains a mixture of known and new facts. However, this approach is still
potentially harmful because the model's performance on external
question-answering benchmarks declines after such fine-tuning. When the
training data is biased towards certain entities, the model tends to regress to
few overrepresented answers. In addition, we found that the model becomes more
confident and refuses to provide an answer in only few cases. These findings
highlight the potential pitfalls of LoRA-based LLM updates and underscore the
importance of training data composition and tuning parameters to balance new
knowledge integration and general model capabilities.
|
2502.14503
|
LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of
4D Radar and Camera
|
cs.CV
|
As the previous state-of-the-art 4D radar-camera fusion-based 3D object
detection method, LXL utilizes the predicted image depth distribution maps and
radar 3D occupancy grids to assist the sampling-based image view
transformation. However, the depth prediction lacks accuracy and consistency,
and the concatenation-based fusion in LXL impedes the model robustness. In this
work, we propose LXLv2, where modifications are made to overcome the
limitations and improve the performance. Specifically, considering the position
error in radar measurements, we devise a one-to-many depth supervision strategy
via radar points, where the radar cross section (RCS) value is further
exploited to adjust the supervision area for object-level depth consistency.
Additionally, a channel and spatial attention-based fusion module named
CSAFusion is introduced to improve feature adaptiveness. Experimental results
on the View-of-Delft and TJ4DRadSet datasets show that the proposed LXLv2 can
outperform LXL in detection accuracy, inference speed and robustness,
demonstrating the effectiveness of the model.
|
2502.14504
|
PLPHP: Per-Layer Per-Head Vision Token Pruning for Efficient Large
Vision-Language Models
|
cs.CV cs.AI
|
Large Vision-Language Models (LVLMs) have demonstrated remarkable
capabilities across a range of multimodal tasks. However, their inference
efficiency is constrained by the large number of visual tokens processed during
decoding. To address this challenge, we propose Per-Layer Per-Head Vision Token
Pruning (PLPHP), a two-level fine-grained pruning method including Layer-Level
Retention Rate Allocation and Head-Level Vision Token Pruning. Motivated by the
Vision Token Re-attention phenomenon across decoder layers, we dynamically
adjust token retention rates layer by layer. Layers that exhibit stronger
attention to visual information preserve more vision tokens, while layers with
lower vision attention are aggressively pruned. Furthermore, PLPHP applies
pruning at the attention head level, enabling different heads within the same
layer to independently retain critical context. Experiments on multiple
benchmarks demonstrate that PLPHP delivers an 18% faster decoding speed and
reduces the Key-Value Cache (KV Cache) size by over 50%, all at the cost of
0.46% average performance drop, while also achieving notable performance
improvements in multi-image tasks. These results highlight the effectiveness of
fine-grained token pruning and contribute to advancing the efficiency and
scalability of LVLMs. Our source code will be made publicly available.
|
2502.14507
|
Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis
of L1-Dependent Biases
|
cs.CL
|
This study evaluates Large Language Models' (LLMs) ability to simulate
non-native-like English use observed in human second language (L2) learners
interfered with by their native first language (L1). In dialogue-based
interviews, we prompt LLMs to mimic L2 English learners with specific L1s
(e.g., Japanese, Thai, Urdu) across seven languages, comparing their outputs to
real L2 learner data. Our analysis examines L1-driven linguistic biases, such
as reference word usage and avoidance behaviors, using information-theoretic
and distributional density measures. Results show that modern LLMs (e.g.,
Qwen2.5, LLAMA3.3, DeepseekV3, GPT-4o) replicate L1-dependent patterns observed
in human L2 data, with distinct influences from various languages (e.g.,
Japanese, Korean, and Mandarin significantly affect tense agreement, and Urdu
influences noun-verb collocations). Our results reveal the potential of LLMs
for L2 dialogue generation and evaluation for future educational applications.
|
2502.14509
|
MultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of
Multilinguality
|
cs.CL
|
Does multilingual Neural Machine Translation (NMT) lead to The Curse of the
Multlinguality or provides the Cross-lingual Knowledge Transfer within a
language family? In this study, we explore multiple approaches for extending
the available data-regime in NMT and we prove cross-lingual benefits even in
0-shot translation regime for low-resource languages. With this paper, we
provide state-of-the-art open-source NMT models for translating between
selected Slavic languages. We released our models on the HuggingFace Hub
(https://hf.co/collections/allegro/multislav-6793d6b6419e5963e759a683) under
the CC BY 4.0 license. Slavic language family comprises morphologically rich
Central and Eastern European languages. Although counting hundreds of millions
of native speakers, Slavic Neural Machine Translation is under-studied in our
opinion. Recently, most NMT research focuses either on: high-resource languages
like English, Spanish, and German - in WMT23 General Translation Task 7 out of
8 task directions are from or to English; massively multilingual models
covering multiple language groups; or evaluation techniques.
|
2502.14514
|
A Mobile Robotic Approach to Autonomous Surface Scanning in Legal
Medicine
|
cs.RO cs.CV cs.SY eess.SY
|
Purpose: Comprehensive legal medicine documentation includes both an internal
but also an external examination of the corpse. Typically, this documentation
is conducted manually during conventional autopsy. A systematic digital
documentation would be desirable, especially for the external examination of
wounds, which is becoming more relevant for legal medicine analysis. For this
purpose, RGB surface scanning has been introduced. While a manual full surface
scan using a handheld camera is timeconsuming and operator dependent, floor or
ceiling mounted robotic systems require substantial space and a dedicated room.
Hence, we consider whether a mobile robotic system can be used for external
documentation. Methods: We develop a mobile robotic system that enables
full-body RGB-D surface scanning. Our work includes a detailed configuration
space analysis to identify the environmental parameters that need to be
considered to successfully perform a surface scan. We validate our findings
through an experimental study in the lab and demonstrate the system's
application in a legal medicine environment. Results: Our configuration space
analysis shows that a good trade-off between coverage and time is reached with
three robot base positions, leading to a coverage of 94.96 %. Experiments
validate the effectiveness of the system in accurately capturing body surface
geometry with an average surface coverage of 96.90 +- 3.16 % and 92.45 +- 1.43
% for a body phantom and actual corpses, respectively. Conclusion: This work
demonstrates the potential of a mobile robotic system to automate RGB-D surface
scanning in legal medicine, complementing the use of post-mortem CT scans for
inner documentation. Our results indicate that the proposed system can
contribute to more efficient and autonomous legal medicine documentation,
reducing the need for manual intervention.
|
2502.14520
|
Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance
|
cs.CV
|
3D Semantic Scene Completion (SSC) provides comprehensive scene geometry and
semantics for autonomous driving perception, which is crucial for enabling
accurate and reliable decision-making. However, existing SSC methods are
limited to capturing sparse information from the current frame or naively
stacking multi-frame temporal features, thereby failing to acquire effective
scene context. These approaches ignore critical motion dynamics and struggle to
achieve temporal consistency. To address the above challenges, we propose a
novel temporal SSC method FlowScene: Learning Temporal 3D Semantic Scene
Completion via Optical Flow Guidance. By leveraging optical flow, FlowScene can
integrate motion, different viewpoints, occlusions, and other contextual cues,
thereby significantly improving the accuracy of 3D scene completion.
Specifically, our framework introduces two key components: (1) a Flow-Guided
Temporal Aggregation module that aligns and aggregates temporal features using
optical flow, capturing motion-aware context and deformable structures; and (2)
an Occlusion-Guided Voxel Refinement module that injects occlusion masks and
temporally aggregated features into 3D voxel space, adaptively refining voxel
representations for explicit geometric modeling. Experimental results
demonstrate that FlowScene achieves state-of-the-art performance on the
SemanticKITTI and SSCBench-KITTI-360 benchmarks.
|
2502.14522
|
Investigating the Generalizability of ECG Noise Detection Across Diverse
Data Sources and Noise Types
|
cs.LG
|
Electrocardiograms (ECGs) are essential for monitoring cardiac health,
allowing clinicians to analyze heart rate variability (HRV), detect abnormal
rhythms, and diagnose cardiovascular diseases. However, ECG signals, especially
those from wearable devices, are often affected by noise artifacts caused by
motion, muscle activity, or device-related interference. These artifacts
distort R-peaks and the characteristic QRS complex, making HRV analysis
unreliable and increasing the risk of misdiagnosis.
Despite this, the few existing studies on ECG noise detection have primarily
focused on a single dataset, limiting the understanding of how well noise
detection models generalize across different datasets. In this paper, we
investigate the generalizability of noise detection in ECG using a novel
HRV-based approach through cross-dataset experiments on four datasets. Our
results show that machine learning achieves an average accuracy of over 90\%
and an AUPRC of more than 0.9. These findings suggest that regardless of the
ECG data source or the type of noise, the proposed method maintains high
accuracy even on unseen datasets, demonstrating the feasibility of
generalizability.
|
2502.14523
|
Generative adversarial networks vs large language models: a comparative
study on synthetic tabular data generation
|
cs.LG cs.CL
|
We propose a new framework for zero-shot generation of synthetic tabular
data. Using the large language model (LLM) GPT-4o and plain-language prompting,
we demonstrate the ability to generate high-fidelity tabular data without
task-specific fine-tuning or access to real-world data (RWD) for pre-training.
To benchmark GPT-4o, we compared the fidelity and privacy of LLM-generated
synthetic data against data generated with the conditional tabular generative
adversarial network (CTGAN), across three open-access datasets: Iris, Fish
Measurements, and Real Estate Valuation. Despite the zero-shot approach, GPT-4o
outperformed CTGAN in preserving means, 95% confidence intervals, bivariate
correlations, and data privacy of RWD, even at amplified sample sizes. Notably,
correlations between parameters were consistently preserved with appropriate
direction and strength. However, refinement is necessary to better retain
distributional characteristics. These findings highlight the potential of LLMs
in tabular data synthesis, offering an accessible alternative to generative
adversarial networks and variational autoencoders.
|
2502.14525
|
Small Graph Is All You Need: DeepStateGNN for Scalable Traffic
Forecasting
|
cs.LG cs.AI
|
We propose a novel Graph Neural Network (GNN) model, named DeepStateGNN, for
analyzing traffic data, demonstrating its efficacy in two critical tasks:
forecasting and reconstruction. Unlike typical GNN methods that treat each
traffic sensor as an individual graph node, DeepStateGNN clusters sensors into
higher-level graph nodes, dubbed Deep State Nodes, based on various similarity
criteria, resulting in a fixed number of nodes in a Deep State graph. The term
"Deep State" nodes is a play on words, referencing hidden networks of power
that, like these nodes, secretly govern traffic independently of visible
sensors. These Deep State Nodes are defined by several similarity factors,
including spatial proximity (e.g., sensors located nearby in the road network),
functional similarity (e.g., sensors on similar types of freeways), and
behavioral similarity under specific conditions (e.g., traffic behavior during
rain). This clustering approach allows for dynamic and adaptive node grouping,
as sensors can belong to multiple clusters and clusters may evolve over time.
Our experimental results show that DeepStateGNN offers superior scalability and
faster training, while also delivering more accurate results than competitors.
It effectively handles large-scale sensor networks, outperforming other methods
in both traffic forecasting and reconstruction accuracy.
|
2502.14527
|
Inter-turbine Modelling of Wind-Farm Power using Multi-task Learning
|
cs.LG
|
Because of the global need to increase power production from renewable energy
resources, developments in the online monitoring of the associated
infrastructure is of interest to reduce operation and maintenance costs.
However, challenges exist for data-driven approaches to this problem, such as
incomplete or limited histories of labelled damage-state data, operational and
environmental variability, or the desire for the quantification of uncertainty
to support risk management.
This work first introduces a probabilistic regression model for predicting
wind-turbine power, which adjusts for wake effects learnt from data. Spatial
correlations in the learned model parameters for different tasks (turbines) are
then leveraged in a hierarchical Bayesian model (an approach to multi-task
learning) to develop a "metamodel", which can be used to make power-predictions
which adjust for turbine location - including on previously unobserved turbines
not included in the training data. The results show that the metamodel is able
to outperform a series of benchmark models, and demonstrates a novel strategy
for making efficient use of data for inference in populations of structures, in
particular where correlations exist in the variable(s) of interest (such as
those from wind-turbine wake-effects).
|
2502.14529
|
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems
Based on Large Language Models
|
cs.CL cs.AI
|
Large Language Model-based Multi-Agent Systems (LLM-MASs) have demonstrated
remarkable real-world capabilities, effectively collaborating to complete
complex tasks. While these systems are designed with safety mechanisms, such as
rejecting harmful instructions through alignment, their security remains
largely unexplored. This gap leaves LLM-MASs vulnerable to targeted
disruptions. In this paper, we introduce Contagious Recursive Blocking Attacks
(Corba), a novel and simple yet highly effective attack that disrupts
interactions between agents within an LLM-MAS. Corba leverages two key
properties: its contagious nature allows it to propagate across arbitrary
network topologies, while its recursive property enables sustained depletion of
computational resources. Notably, these blocking attacks often involve
seemingly benign instructions, making them particularly challenging to mitigate
using conventional alignment methods. We evaluate Corba on two widely-used
LLM-MASs, namely, AutoGen and Camel across various topologies and commercial
models. Additionally, we conduct more extensive experiments in open-ended
interactive LLM-MASs, demonstrating the effectiveness of Corba in complex
topology structures and open-source models. Our code is available at:
https://github.com/zhrli324/Corba.
|
2502.14536
|
Preordering: A hybrid of correlation clustering and partial ordering
|
cs.LG
|
We discuss the preordering problem, a joint relaxation of the correlation
clustering problem and the partial ordering problem. We show that preordering
remains NP-hard even for values in $\{-1,0,1\}$. We introduce a linear-time
$4$-approximation algorithm and a local search technique. For an integer linear
program formulation, we establish a class of non-canonical facets of the
associated preorder polytope. By solving a non-canonical linear program
relaxation, we obtain non-trivial upper bounds on the objective value. We
provide implementations of the algorithms we define, apply these to published
social networks and compare the output and efficiency qualitatively and
quantitatively.
|
2502.14538
|
LoRA-GGPO: Mitigating Double Descent in LoRA Fine-Tuning via
Gradient-Guided Perturbation Optimization
|
cs.CL
|
Large Language Models (LLMs) have achieved remarkable success in natural
language processing, but their full fine-tuning remains resource-intensive.
Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation
(LoRA), have emerged as a practical solution by approximating parameter updates
with low-rank matrices. However, LoRA often exhibits a "double descent"
phenomenon during fine-tuning, where model performance degrades due to
overfitting and limited expressiveness caused by low-rank constraints. To
address this issue, we propose LoRA-GGPO (Gradient-Guided Perturbation
Optimization), a novel method that leverages gradient and weight norms to
generate targeted perturbations. By optimizing the sharpness of the loss
landscape, LoRA-GGPO guides the model toward flatter minima, mitigating the
double descent problem and improving generalization. Extensive experiments on
natural language understanding (NLU) and generation (NLG) tasks demonstrate
that LoRA-GGPO outperforms LoRA and its state-of-the-art variants. Furthermore,
extended experiments specifically designed to analyze the double descent
phenomenon confirm that LoRA-GGPO effectively alleviates this issue, producing
more robust and generalizable models. Our work provides a robust and efficient
solution for fine-tuning LLMs, with broad applicability in real-world
scenarios. The code is available at https://github.com/llm172/LoRA-GGPO.
|
2502.14541
|
LLM-based User Profile Management for Recommender System
|
cs.CL
|
The rapid advancement of Large Language Models (LLMs) has opened new
opportunities in recommender systems by enabling zero-shot recommendation
without conventional training. Despite their potential, most existing works
rely solely on users' purchase histories, leaving significant room for
improvement by incorporating user-generated textual data, such as reviews and
product descriptions. Addressing this gap, we propose PURE, a novel LLM-based
recommendation framework that builds and maintains evolving user profiles by
systematically extracting and summarizing key information from user reviews.
PURE consists of three core components: a Review Extractor for identifying user
preferences and key product features, a Profile Updater for refining and
updating user profiles, and a Recommender for generating personalized
recommendations using the most current profile. To evaluate PURE, we introduce
a continuous sequential recommendation task that reflects real-world scenarios
by adding reviews over time and updating predictions incrementally. Our
experimental results on Amazon datasets demonstrate that PURE outperforms
existing LLM-based methods, effectively leveraging long-term user information
while managing token limitations.
|
2502.14544
|
Generalization Error of $f$-Divergence Stabilized Algorithms via Duality
|
stat.ML cs.LG
|
The solution to empirical risk minimization with $f$-divergence
regularization (ERM-$f$DR) is extended to constrained optimization problems,
establishing conditions for equivalence between the solution and constraints. A
dual formulation of ERM-$f$DR is introduced, providing a computationally
efficient method to derive the normalization function of the ERM-$f$DR
solution. This dual approach leverages the Legendre-Fenchel transform and the
implicit function theorem, enabling explicit characterizations of the
generalization error for general algorithms under mild conditions, and another
for ERM-$f$DR solutions.
|
2502.14545
|
An Entropic Metric for Measuring Calibration of Machine Learning Models
|
cs.LG
|
Understanding the confidence with which a machine learning model classifies
an input datum is an important, and perhaps under-investigated, concept. In
this paper, we propose a new calibration metric, the Entropic Calibration
Difference (ECD). Based on existing research in the field of state estimation,
specifically target tracking (TT), we show how ECD may be applied to binary
classification machine learning models. We describe the relative importance of
under- and over-confidence and how they are not conflated in the TT literature.
Indeed, our metric distinguishes under- from over-confidence. We consider this
important given that algorithms that are under-confident are likely to be
'safer' than algorithms that are over-confident, albeit at the expense of also
being over-cautious and so statistically inefficient. We demonstrate how this
new metric performs on real and simulated data and compare with other metrics
for machine learning model probability calibration, including the Expected
Calibration Error (ECE) and its signed counterpart, the Expected Signed
Calibration Error (ESCE).
|
2502.14546
|
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
|
cs.LG cs.AI cs.NE
|
While machine learning on graphs has demonstrated promise in drug design and
molecular property prediction, significant benchmarking challenges hinder its
further progress and relevance. Current benchmarking practices often lack focus
on transformative, real-world applications, favoring narrow domains like
two-dimensional molecular graphs over broader, impactful areas such as
combinatorial optimization, relational databases, or chip design. Additionally,
many benchmark datasets poorly represent the underlying data, leading to
inadequate abstractions and misaligned use cases. Fragmented evaluations and an
excessive focus on accuracy further exacerbate these issues, incentivizing
overfitting rather than fostering generalizable insights. These limitations
have prevented the development of truly useful graph foundation models. This
position paper calls for a paradigm shift toward more meaningful benchmarks,
rigorous evaluation protocols, and stronger collaboration with domain experts
to drive impactful and reliable advances in graph learning research, unlocking
the potential of graph learning.
|
2502.14553
|
Multiscale Byte Language Models -- A Hierarchical Architecture for
Causal Million-Length Sequence Modeling
|
cs.CL cs.AI cs.LG
|
Bytes form the basis of the digital world and thus are a promising building
block for multimodal foundation models. Recently, Byte Language Models (BLMs)
have emerged to overcome tokenization, yet the excessive length of bytestreams
requires new architectural paradigms. Therefore, we present the Multiscale Byte
Language Model (MBLM), a model-agnostic hierarchical decoder stack that allows
training with context windows of $5$M bytes on single GPU in full model
precision. We thoroughly examine MBLM's performance with Transformer and Mamba
blocks on both unimodal and multimodal tasks. Our experiments demonstrate that
hybrid architectures are efficient in handling extremely long byte sequences
during training while achieving near-linear generational efficiency. To the
best of our knowledge, we present the first evaluation of BLMs on visual Q\&A
tasks and find that, despite serializing images and the absence of an encoder,
a MBLM with pure next token prediction can match custom CNN-LSTM architectures
with designated classification heads. We show that MBLMs exhibit strong
adaptability in integrating diverse data representations, including pixel and
image filestream bytes, underlining their potential toward omnimodal foundation
models. Source code is publicly available at:
https://github.com/ai4sd/multiscale-byte-lm
|
2502.14558
|
FUIA: Model Inversion Attack against Federated Unlearning
|
cs.CR cs.AI
|
With the introduction of regulations related to the ``right to be forgotten",
federated learning (FL) is facing new privacy compliance challenges. To address
these challenges, researchers have proposed federated unlearning (FU). However,
existing FU research has primarily focused on improving the efficiency of
unlearning, with less attention paid to the potential privacy vulnerabilities
inherent in these methods. To address this gap, we draw inspiration from
gradient inversion attacks in FL and propose the federated unlearning inversion
attack (FUIA). The FUIA is specifically designed for the three types of FU
(sample unlearning, client unlearning, and class unlearning), aiming to provide
a comprehensive analysis of the privacy leakage risks associated with FU. In
FUIA, the server acts as an honest-but-curious attacker, recording and
exploiting the model differences before and after unlearning to expose the
features and labels of forgotten data. FUIA significantly leaks the privacy of
forgotten data and can target all types of FU. This attack contradicts the goal
of FU to eliminate specific data influence, instead exploiting its
vulnerabilities to recover forgotten data and expose its privacy flaws.
Extensive experimental results show that FUIA can effectively reveal the
private information of forgotten data. To mitigate this privacy leakage, we
also explore two potential defense methods, although these come at the cost of
reduced unlearning effectiveness and the usability of the unlearned model.
|
2502.14560
|
Less is More: Improving LLM Alignment via Preference Data Selection
|
cs.LG cs.AI cs.CL
|
Direct Preference Optimization (DPO) has emerged as a promising approach for
aligning large language models with human preferences. While prior work mainly
extends DPO from the aspect of the objective function, we instead improve DPO
from the largely overlooked but critical aspect of data selection.
Specifically, we address the issue of parameter shrinkage caused by noisy data
by proposing a novel margin-maximization principle for dataset curation in DPO
training. To accurately estimate margins for data selection, we propose a
dual-margin guided approach that considers both external reward margins and
implicit DPO reward margins. Extensive experiments demonstrate that our method
reduces computational cost dramatically while improving performance.
Remarkably, by using just 10\% of the Ultrafeedback dataset, our approach
achieves 3\% to 8\% improvements across various Llama and Mistral series models
on the AlpacaEval 2.0 benchmark. Furthermore, our approach seamlessly extends
to iterative DPO, yielding a roughly 3\% improvement with 25\% online data,
while further reducing training time. These results highlight the potential of
data selection strategies for advancing preference optimization.
|
2502.14561
|
Can LLMs Predict Citation Intent? An Experimental Analysis of In-context
Learning and Fine-tuning on Open LLMs
|
cs.CL cs.DL
|
This work investigates the ability of open Large Language Models (LLMs) to
predict citation intent through in-context learning and fine-tuning. Unlike
traditional approaches that rely on pre-trained models like SciBERT, which
require extensive domain-specific pretraining and specialized architectures, we
demonstrate that general-purpose LLMs can be adapted to this task with minimal
task-specific data. We evaluate twelve model variations across five prominent
open LLM families using zero, one, few, and many-shot prompting to assess
performance across scenarios. Our experimental study identifies the
top-performing model through extensive experimentation of in-context
learning-related parameters, which we fine-tune to further enhance task
performance. The results highlight the strengths and limitations of LLMs in
recognizing citation intents, providing valuable insights for model selection
and prompt engineering. Additionally, we make our end-to-end evaluation
framework and models openly available for future use.
|
2502.14563
|
Plan-over-Graph: Towards Parallelable LLM Agent Schedule
|
cs.AI
|
Large Language Models (LLMs) have demonstrated exceptional abilities in
reasoning for task planning. However, challenges remain under-explored for
parallel schedules. This paper introduces a novel paradigm, plan-over-graph, in
which the model first decomposes a real-life textual task into executable
subtasks and constructs an abstract task graph. The model then understands this
task graph as input and generates a plan for parallel execution. To enhance the
planning capability of complex, scalable graphs, we design an automated and
controllable pipeline to generate synthetic graphs and propose a two-stage
training scheme. Experimental results show that our plan-over-graph method
significantly improves task performance on both API-based LLMs and trainable
open-sourced LLMs. By normalizing complex tasks as graphs, our method naturally
supports parallel execution, demonstrating global efficiency. The code and data
are available at https://github.com/zsq259/Plan-over-Graph.
|
2502.14565
|
ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification
|
cs.LG cs.CL
|
Self-awareness, i.e., the ability to assess and correct one's own generation,
is a fundamental aspect of human intelligence, making its replication in large
language models (LLMs) an important yet challenging task. Previous works tackle
this by employing extensive reinforcement learning or rather relying on large
external verifiers. In this work, we propose Refine via Intrinsic
Self-Verification (ReVISE), an efficient and effective framework that enables
LLMs to self-correct their outputs through self-verification. The core idea of
ReVISE is to enable LLMs to verify their reasoning processes and continually
rethink reasoning trajectories based on its verification. We introduce a
structured curriculum based upon online preference learning to implement this
efficiently. Specifically, as ReVISE involves two challenging tasks (i.e.,
self-verification and reasoning correction), we tackle each task sequentially
using curriculum learning, collecting both failed and successful reasoning
paths to construct preference pairs for efficient training. During inference,
our approach enjoys natural test-time scaling by integrating self-verification
and correction capabilities, further enhanced by our proposed confidence-aware
decoding mechanism. Our experiments on various reasoning tasks demonstrate that
ReVISE achieves efficient self-correction and significantly improves reasoning
performance.
|
2502.14571
|
Predicting Filter Medium Performances in Chamber Filter Presses with
Digital Twins Using Neural Network Technologies
|
cs.LG cs.CE
|
Efficient solid-liquid separation is crucial in industries like mining, but
traditional chamber filter presses depend heavily on manual monitoring, leading
to inefficiencies, downtime, and resource wastage. This paper introduces a
machine learning-powered digital twin framework to improve operational
flexibility and predictive control. A key challenge addressed is the
degradation of the filter medium due to repeated cycles and clogging, which
reduces filtration efficiency. To solve this, a neural network-based predictive
model was developed to forecast operational parameters, such as pressure and
flow rates, under various conditions. This predictive capability allows for
optimized filtration cycles, reduced downtime, and improved process efficiency.
Additionally, the model predicts the filter mediums lifespan, aiding in
maintenance planning and resource sustainability. The digital twin framework
enables seamless data exchange between filter press sensors and the predictive
model, ensuring continuous updates to the training data and enhancing accuracy
over time. Two neural network architectures, feedforward and recurrent, were
evaluated. The recurrent neural network outperformed the feedforward model,
demonstrating superior generalization. It achieved a relative $L^2$-norm error
of $5\%$ for pressure and $9.3\%$ for flow rate prediction on partially known
data. For completely unknown data, the relative errors were $18.4\%$ and
$15.4\%$, respectively. Qualitative analysis showed strong alignment between
predicted and measured data, with deviations within a confidence band of
$8.2\%$ for pressure and $4.8\%$ for flow rate predictions. This work
contributes an accurate predictive model, a new approach to predicting filter
medium cycle impacts, and a real-time interface for model updates, ensuring
adaptability to changing operational conditions.
|
2502.14572
|
Factor Graph-based Interpretable Neural Networks
|
cs.LG cs.AI
|
Comprehensible neural network explanations are foundations for a better
understanding of decisions, especially when the input data are infused with
malicious perturbations. Existing solutions generally mitigate the impact of
perturbations through adversarial training, yet they fail to generate
comprehensible explanations under unknown perturbations. To address this
challenge, we propose AGAIN, a fActor GrAph-based Interpretable neural Network,
which is capable of generating comprehensible explanations under unknown
perturbations. Instead of retraining like previous solutions, the proposed
AGAIN directly integrates logical rules by which logical errors in explanations
are identified and rectified during inference. Specifically, we construct the
factor graph to express logical rules between explanations and categories. By
treating logical rules as exogenous knowledge, AGAIN can identify
incomprehensible explanations that violate real-world logic. Furthermore, we
propose an interactive intervention switch strategy rectifying explanations
based on the logical guidance from the factor graph without learning
perturbations, which overcomes the inherent limitation of adversarial
training-based methods in defending only against known perturbations.
Additionally, we theoretically demonstrate the effectiveness of employing
factor graph by proving that the comprehensibility of explanations is strongly
correlated with factor graph. Extensive experiments are conducted on three
datasets and experimental results illustrate the superior performance of AGAIN
compared to state-of-the-art baselines.
|
2502.14573
|
Self-supervised Monocular Depth Estimation Robust to Reflective Surface
Leveraged by Triplet Mining
|
cs.CV cs.LG
|
Self-supervised monocular depth estimation (SSMDE) aims to predict the dense
depth map of a monocular image, by learning depth from RGB image sequences,
eliminating the need for ground-truth depth labels. Although this approach
simplifies data acquisition compared to supervised methods, it struggles with
reflective surfaces, as they violate the assumptions of Lambertian reflectance,
leading to inaccurate training on such surfaces. To tackle this problem, we
propose a novel training strategy for an SSMDE by leveraging triplet mining to
pinpoint reflective regions at the pixel level, guided by the camera geometry
between different viewpoints. The proposed reflection-aware triplet mining loss
specifically penalizes the inappropriate photometric error minimization on the
localized reflective regions while preserving depth accuracy in non-reflective
areas. We also incorporate a reflection-aware knowledge distillation method
that enables a student model to selectively learn the pixel-level knowledge
from reflective and non-reflective regions. This results in robust depth
estimation across areas. Evaluation results on multiple datasets demonstrate
that our method effectively enhances depth quality on reflective surfaces and
outperforms state-of-the-art SSMDE baselines.
|
2502.14574
|
Real-world Troublemaker: A Novel Track Testing Framework for Automated
Driving Systems in Safety-critical Interaction Scenarios
|
cs.RO cs.ET
|
Track testing plays a critical role in the safety evaluation of autonomous
driving systems (ADS), as it provides real-world object targets and a
safety-controllable interaction environment. However, existing track testing
scenarios are often pre-fixed and limited, primarily due to the inflexibility
of object target control methods and the lack of intelligent interactive
behaviors. To overcome this limitation, we propose a novel track testing
framework, Real-world Troublemaker, which can generate adversarial object
target motion trajectories and facilitate intelligent interactions with the
vehicle under test (VUT), creating a more realistic and dynamic testing
environment. To enable flexible motion trajectories, cloud-controlled
technology is utilized to remotely and dynamically control object targets to
create a realistic traffic environment. To achieve intelligent interactions, an
interactive concrete scenario generation method is introduced within a
game-theoretic structure. The proposed framework has been successfully
implemented at the Tongji University Intelligent Connected Vehicle Evaluation
Base. Field test results demonstrate that Troublemaker can perform dynamic
interactive testing of ADS accurately and effectively. Compared to traditional
track testing methods, Troublemaker improves scenario reproduction accuracy by
65.2\%, increases the diversity of target vehicle interaction strategies by
approximately 9.2 times, and enhances exposure frequency of safety-critical
scenarios by 3.5 times in unprotected left-turn scenarios.
|
2502.14581
|
A Statistical Case Against Empirical Human-AI Alignment
|
cs.AI cs.CL cs.LG stat.OT
|
Empirical human-AI alignment aims to make AI systems act in line with
observed human behavior. While noble in its goals, we argue that empirical
alignment can inadvertently introduce statistical biases that warrant caution.
This position paper thus advocates against naive empirical alignment, offering
prescriptive alignment and a posteriori empirical alignment as alternatives. We
substantiate our principled argument by tangible examples like human-centric
decoding of language models.
|
2502.14583
|
A Theory for Conditional Generative Modeling on Multiple Data Sources
|
cs.LG cs.AI
|
The success of large generative models has driven a paradigm shift,
leveraging massive multi-source data to enhance model capabilities. However,
the interaction among these sources remains theoretically underexplored. This
paper takes the first step toward a rigorous analysis of multi-source training
in conditional generative modeling, where each condition represents a distinct
data source. Specifically, we establish a general distribution estimation error
bound in average total variation distance for conditional maximum likelihood
estimation based on the bracketing number. Our result shows that when source
distributions share certain similarities and the model is expressive enough,
multi-source training guarantees a sharper bound than single-source training.
We further instantiate the general theory on conditional Gaussian estimation
and deep generative models including autoregressive and flexible energy-based
models, by characterizing their bracketing numbers. The results highlight that
the number of sources and similarity among source distributions improve the
advantage of multi-source training. Simulations and real-world experiments
validate our theory. Code is available at:
\url{https://github.com/ML-GSAI/Multi-Source-GM}.
|
2502.14584
|
Vision Foundation Models in Medical Image Analysis: Advances and
Challenges
|
eess.IV cs.CV
|
The rapid development of Vision Foundation Models (VFMs), particularly Vision
Transformers (ViT) and Segment Anything Model (SAM), has sparked significant
advances in the field of medical image analysis. These models have demonstrated
exceptional capabilities in capturing long-range dependencies and achieving
high generalization in segmentation tasks. However, adapting these large models
to medical image analysis presents several challenges, including domain
differences between medical and natural images, the need for efficient model
adaptation strategies, and the limitations of small-scale medical datasets.
This paper reviews the state-of-the-art research on the adaptation of VFMs to
medical image segmentation, focusing on the challenges of domain adaptation,
model compression, and federated learning. We discuss the latest developments
in adapter-based improvements, knowledge distillation techniques, and
multi-scale contextual feature modeling, and propose future directions to
overcome these bottlenecks. Our analysis highlights the potential of VFMs,
along with emerging methodologies such as federated learning and model
compression, to revolutionize medical image analysis and enhance clinical
applications. The goal of this work is to provide a comprehensive overview of
current approaches and suggest key areas for future research that can drive the
next wave of innovation in medical image segmentation.
|
2502.14585
|
A Stackelberg Game Approach for Signal Temporal Logic Control Synthesis
with Uncontrollable Agents
|
eess.SY cs.SY
|
In this paper, we investigate the control synthesis problem for Signal
Temporal Logic (STL) specifications in the presence of uncontrollable agents.
Existing works mainly address this problem in a robust control setting by
assuming the uncontrollable agents are adversarial and accounting for the
worst-case scenario. While this approach ensures safety, it can be overly
conservative in scenarios where uncontrollable agents have their own objectives
that are not entirely opposed to the system's goals. Motivated by this
limitation, we propose a new framework for STL control synthesis within the
Stackelberg game setting. Specifically, we assume that the system controller,
acting as the leader, first commits to a plan, after which the uncontrollable
agents, acting as followers, take a best response based on the committed plan
and their own objectives. Our goal is to synthesize a control sequence for the
leader such that, for any rational followers producing a best response, the
leader's STL task is guaranteed to be satisfied. We present an effective
solution to this problem by transforming it into a single-stage optimization
problem and leveraging counter-example guided synthesis techniques. We
demonstrate that the proposed approach is sound and identify conditions under
which it is optimal. Simulation results are also provided to illustrate the
effectiveness of the proposed framework.
|
2502.14586
|
Moshi Moshi? A Model Selection Hijacking Adversarial Attack
|
cs.LG cs.CR
|
Model selection is a fundamental task in Machine Learning~(ML), focusing on
selecting the most suitable model from a pool of candidates by evaluating their
performance on specific metrics. This process ensures optimal performance,
computational efficiency, and adaptability to diverse tasks and environments.
Despite its critical role, its security from the perspective of adversarial ML
remains unexplored. This risk is heightened in the
Machine-Learning-as-a-Service model, where users delegate the training phase
and the model selection process to third-party providers, supplying data and
training strategies. Therefore, attacks on model selection could harm both the
user and the provider, undermining model performance and driving up operational
costs.
In this work, we present MOSHI (MOdel Selection HIjacking adversarial
attack), the first adversarial attack specifically targeting model selection.
Our novel approach manipulates model selection data to favor the adversary,
even without prior knowledge of the system. Utilizing a framework based on
Variational Auto Encoders, we provide evidence that an attacker can induce
inefficiencies in ML deployment. We test our attack on diverse computer vision
and speech recognition benchmark tasks and different settings, obtaining an
average attack success rate of 75.42%. In particular, our attack causes an
average 88.30% decrease in generalization capabilities, an 83.33% increase in
latency, and an increase of up to 105.85% in energy consumption. These results
highlight the significant vulnerabilities in model selection processes and
their potential impact on real-world applications.
|
2502.14589
|
Explicit adaptive time stepping for the Cahn-Hilliard equation by
exponential Krylov subspace and Chebyshev polynomial methods
|
math.NA cs.CE cs.NA physics.comp-ph
|
The Cahn-Hilliard equation has been widely employed within various
mathematical models in physics, chemistry and engineering. Explicit stabilized
time stepping methods can be attractive for time integration of the
Cahn-Hilliard equation, especially on parallel and hybrid supercomputers. In
this paper, we propose an exponential time integration method for the
Cahn-Hilliard equation and describe its efficient Krylov subspace based
implementation. We compare the method to a Chebyshev polynomial local iteration
modified (LIM) time stepping scheme. Both methods are explicit (i.e., do not
involve linear system solution) and tested with both constant and adaptively
chosen time steps.
|
2502.14591
|
Data-driven Control of T-Product-based Dynamical Systems
|
eess.SY cs.SY
|
Data-driven control is a powerful tool that enables the design and
implementation of control strategies directly from data without explicitly
identifying the underlying system dynamics. While various data-driven control
techniques, such as stabilization, linear quadratic regulation, and model
predictive control, have been extensively developed, these methods are not
inherently suited for multi-linear dynamical systems, where the states are
represented as higher-order tensors. In this article, we propose a novel
framework for data-driven control of T-product-based dynamical systems (TPDSs),
where the system evolution is governed by the T-product between a third-order
dynamic tensor and a third-order state tensor. In particular, we offer
necessary and sufficient conditions to determine the data informativity for
system identification, stabilization by state feedback, and T-product quadratic
regulation of TPDSs with detailed complexity analyses. Finally, we validate our
framework through numerical examples.
|
2502.14597
|
Multi-Class Imbalanced Learning with Support Vector Machines via
Differential Evolution
|
cs.LG cs.NE
|
Support vector machine (SVM) is a powerful machine learning algorithm to
handle classification tasks. However, the classical SVM is developed for binary
problems with the assumption of balanced datasets. Obviously, the multi-class
imbalanced classification problems are more complex. In this paper, we propose
an improved SVM via Differential Evolution (i-SVM-DE) method to deal with it.
An improved SVM (i-SVM) model is proposed to handle the data imbalance by
combining cost sensitive technique and separation margin modification in the
constraints, which formalize a parameter optimization problem. By using
one-versus-one (OVO) scheme, a multi-class problem is decomposed into a number
of binary subproblems. A large optimization problem is formalized through
concatenating the parameters in the binary subproblems. To find the optimal
model effectively and learn the support vectors for each class simultaneously,
an improved differential evolution (DE) algorithm is applied to solve this
large optimization problem. Instead of the validation set, we propose the
fitness functions to evaluate the learned model and obtain the optimal
parameters in the search process of DE. A series of experiments are carried out
to verify the benefits of our proposed method. The results indicate that
i-SVM-DE is statistically superior by comparing with the other baseline
methods.
|
2502.14604
|
Noisy Test-Time Adaptation in Vision-Language Models
|
cs.LG
|
Test-time adaptation (TTA) aims to address distribution shifts between source
and target data by relying solely on target data during testing. In open-world
scenarios, models often encounter noisy samples, i.e., samples outside the
in-distribution (ID) label space. Leveraging the zero-shot capability of
pre-trained vision-language models (VLMs), this paper introduces Zero-Shot
Noisy TTA (ZS-NTTA), focusing on adapting the model to target data with noisy
samples during test-time in a zero-shot manner. We find existing TTA methods
underperform under ZS-NTTA, often lagging behind even the frozen model. We
conduct comprehensive experiments to analyze this phenomenon, revealing that
the negative impact of unfiltered noisy data outweighs the benefits of clean
data during model updating. Also, adapting a classifier for ID classification
and noise detection hampers both sub-tasks. Built on this, we propose a
framework that decouples the classifier and detector, focusing on developing an
individual detector while keeping the classifier frozen. Technically, we
introduce the Adaptive Noise Detector (AdaND), which utilizes the frozen
model's outputs as pseudo-labels to train a noise detector. To handle clean
data streams, we further inject Gaussian noise during adaptation, preventing
the detector from misclassifying clean samples as noisy. Beyond the ZS-NTTA,
AdaND can also improve the zero-shot out-of-distribution (ZS-OOD) detection
ability of VLMs. Experiments show that AdaND outperforms in both ZS-NTTA and
ZS-OOD detection. On ImageNet, AdaND achieves a notable improvement of $8.32\%$
in harmonic mean accuracy ($\text{Acc}_\text{H}$) for ZS-NTTA and $9.40\%$ in
FPR95 for ZS-OOD detection, compared to SOTA methods. Importantly, AdaND is
computationally efficient and comparable to the model-frozen method. The code
is publicly available at: https://github.com/tmlr-group/ZS-NTTA.
|
2502.14613
|
Behavioral Analysis of Information Salience in Large Language Models
|
cs.CL
|
Large Language Models (LLMs) excel at text summarization, a task that
requires models to select content based on its importance. However, the exact
notion of salience that LLMs have internalized remains unclear. To bridge this
gap, we introduce an explainable framework to systematically derive and
investigate information salience in LLMs through their summarization behavior.
Using length-controlled summarization as a behavioral probe into the content
selection process, and tracing the answerability of Questions Under Discussion
throughout, we derive a proxy for how models prioritize information. Our
experiments on 13 models across four datasets reveal that LLMs have a nuanced,
hierarchical notion of salience, generally consistent across model families and
sizes. While models show highly consistent behavior and hence salience
patterns, this notion of salience cannot be accessed through introspection, and
only weakly correlates with human perceptions of information salience.
|
2502.14614
|
FIND: Fine-grained Information Density Guided Adaptive
Retrieval-Augmented Generation for Disease Diagnosis
|
cs.CL
|
Retrieval-Augmented Large Language Models (LLMs), which integrate external
knowledge into LLMs, have shown remarkable performance in various medical
domains, including clinical diagnosis. However, existing RAG methods struggle
to effectively assess task difficulty to make retrieval decisions, thereby
failing to meet the clinical requirements for balancing efficiency and
accuracy. So in this paper, we propose FIND (\textbf{F}ine-grained
\textbf{In}formation \textbf{D}ensity Guided Adaptive RAG), a novel framework
that improves the reliability of RAG in disease diagnosis scenarios. FIND
incorporates a fine-grained adaptive control module to determine whether
retrieval is necessary based on the information density of the input. By
optimizing the retrieval process and implementing a knowledge filtering module,
FIND ensures that the retrieval is better suited to clinical scenarios.
Experiments on three Chinese electronic medical record datasets demonstrate
that FIND significantly outperforms various baseline methods, highlighting its
effectiveness in clinical diagnosis tasks.
|
2502.14616
|
Monocular Depth Estimation and Segmentation for Transparent Object with
Iterative Semantic and Geometric Fusion
|
cs.CV
|
Transparent object perception is indispensable for numerous robotic tasks.
However, accurately segmenting and estimating the depth of transparent objects
remain challenging due to complex optical properties. Existing methods
primarily delve into only one task using extra inputs or specialized sensors,
neglecting the valuable interactions among tasks and the subsequent refinement
process, leading to suboptimal and blurry predictions. To address these issues,
we propose a monocular framework, which is the first to excel in both
segmentation and depth estimation of transparent objects, with only a
single-image input. Specifically, we devise a novel semantic and geometric
fusion module, effectively integrating the multi-scale information between
tasks. In addition, drawing inspiration from human perception of objects, we
further incorporate an iterative strategy, which progressively refines initial
features for clearer results. Experiments on two challenging synthetic and
real-world datasets demonstrate that our model surpasses state-of-the-art
monocular, stereo, and multi-view methods by a large margin of about
38.8%-46.2% with only a single RGB input. Codes and models are publicly
available at https://github.com/L-J-Yuan/MODEST.
|
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