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2502.03382
|
High-Fidelity Simultaneous Speech-To-Speech Translation
|
cs.CL cs.SD eess.AS
|
We introduce Hibiki, a decoder-only model for simultaneous speech
translation. Hibiki leverages a multistream language model to synchronously
process source and target speech, and jointly produces text and audio tokens to
perform speech-to-text and speech-to-speech translation. We furthermore address
the fundamental challenge of simultaneous interpretation, which unlike its
consecutive counterpart, where one waits for the end of the source utterance to
start translating, adapts its flow to accumulate just enough context to produce
a correct translation in real-time, chunk by chunk. To do so, we introduce a
weakly-supervised method that leverages the perplexity of an off-the-shelf text
translation system to identify optimal delays on a per-word basis and create
aligned synthetic data. After supervised training, Hibiki performs adaptive,
simultaneous speech translation with vanilla temperature sampling. On a
French-English simultaneous speech translation task, Hibiki demonstrates
state-of-the-art performance in translation quality, speaker fidelity and
naturalness. Moreover, the simplicity of its inference process makes it
compatible with batched translation and even real-time on-device deployment. We
provide examples as well as models and inference code.
|
2502.03383
|
Transformers and Their Roles as Time Series Foundation Models
|
cs.LG cs.AI
|
We give a comprehensive analysis of transformers as time series foundation
models, focusing on their approximation and generalization capabilities. First,
we demonstrate that there exist transformers that fit an autoregressive model
on input univariate time series via gradient descent. We then analyze MOIRAI, a
multivariate time series foundation model capable of handling an arbitrary
number of covariates. We prove that it is capable of automatically fitting
autoregressive models with an arbitrary number of covariates, offering insights
into its design and empirical success. For generalization, we establish bounds
for pretraining when the data satisfies Dobrushin's condition. Experiments
support our theoretical findings, highlighting the efficacy of transformers as
time series foundation models.
|
2502.03386
|
A Structured Reasoning Framework for Unbalanced Data Classification
Using Probabilistic Models
|
cs.LG
|
This paper studies a Markov network model for unbalanced data, aiming to
solve the problems of classification bias and insufficient minority class
recognition ability of traditional machine learning models in environments with
uneven class distribution. By constructing joint probability distribution and
conditional dependency, the model can achieve global modeling and reasoning
optimization of sample categories. The study introduced marginal probability
estimation and weighted loss optimization strategies, combined with
regularization constraints and structured reasoning methods, effectively
improving the generalization ability and robustness of the model. In the
experimental stage, a real credit card fraud detection dataset was selected and
compared with models such as logistic regression, support vector machine,
random forest and XGBoost. The experimental results show that the Markov
network performs well in indicators such as weighted accuracy, F1 score, and
AUC-ROC, significantly outperforming traditional classification models,
demonstrating its strong decision-making ability and applicability in
unbalanced data scenarios. Future research can focus on efficient model
training, structural optimization, and deep learning integration in large-scale
unbalanced data environments and promote its wide application in practical
applications such as financial risk control, medical diagnosis, and intelligent
monitoring.
|
2502.03387
|
LIMO: Less is More for Reasoning
|
cs.CL cs.AI
|
We present a fundamental discovery that challenges our understanding of how
complex reasoning emerges in large language models. While conventional wisdom
suggests that sophisticated reasoning tasks demand extensive training data
(>100,000 examples), we demonstrate that complex mathematical reasoning
abilities can be effectively elicited with surprisingly few examples. Through
comprehensive experiments, our proposed model LIMO demonstrates unprecedented
performance in mathematical reasoning. With merely 817 curated training
samples, LIMO achieves 57.1% accuracy on AIME and 94.8% on MATH, improving from
previous SFT-based models' 6.5% and 59.2% respectively, while only using 1% of
the training data required by previous approaches. LIMO demonstrates
exceptional out-of-distribution generalization, achieving 40.5% absolute
improvement across 10 diverse benchmarks, outperforming models trained on 100x
more data, challenging the notion that SFT leads to memorization rather than
generalization. Based on these results, we propose the Less-Is-More Reasoning
Hypothesis (LIMO Hypothesis): In foundation models where domain knowledge has
been comprehensively encoded during pre-training, sophisticated reasoning
capabilities can emerge through minimal but precisely orchestrated
demonstrations of cognitive processes. This hypothesis posits that the
elicitation threshold for complex reasoning is determined by two key factors:
(1) the completeness of the model's encoded knowledge foundation during
pre-training, and (2) the effectiveness of post-training examples as "cognitive
templates" that show the model how to utilize its knowledge base to solve
complex reasoning tasks. To facilitate reproducibility and future research in
data-efficient reasoning, we release LIMO as a comprehensive open-source suite
at https://github.com/GAIR-NLP/LIMO.
|
2502.03391
|
Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise
Sufficient Reasons
|
cs.LG cs.LO
|
*Minimal sufficient reasons* represent a prevalent form of explanation - the
smallest subset of input features which, when held constant at their
corresponding values, ensure that the prediction remains unchanged. Previous
*post-hoc* methods attempt to obtain such explanations but face two main
limitations: (1) Obtaining these subsets poses a computational challenge,
leading most scalable methods to converge towards suboptimal, less meaningful
subsets; (2) These methods heavily rely on sampling out-of-distribution input
assignments, potentially resulting in counterintuitive behaviors. To tackle
these limitations, we propose in this work a self-supervised training approach,
which we term *sufficient subset training* (SST). Using SST, we train models to
generate concise sufficient reasons for their predictions as an integral part
of their output. Our results indicate that our framework produces succinct and
faithful subsets substantially more efficiently than competing post-hoc
methods, while maintaining comparable predictive performance.
|
2502.03393
|
CAPE: Covariate-Adjusted Pre-Training for Epidemic Time Series
Forecasting
|
cs.LG
|
Accurate forecasting of epidemic infection trajectories is crucial for
safeguarding public health. However, limited data availability during emerging
outbreaks and the complex interaction between environmental factors and disease
dynamics present significant challenges for effective forecasting. In response,
we introduce CAPE, a novel epidemic pre-training framework designed to harness
extensive disease datasets from diverse regions and integrate environmental
factors directly into the modeling process for more informed decision-making on
downstream diseases. Based on a covariate adjustment framework, CAPE utilizes
pre-training combined with hierarchical environment contrasting to identify
universal patterns across diseases while estimating latent environmental
influences. We have compiled a diverse collection of epidemic time series
datasets and validated the effectiveness of CAPE under various evaluation
scenarios, including full-shot, few-shot, zero-shot, cross-location, and
cross-disease settings, where it outperforms the leading baseline by an average
of 9.9% in full-shot and 14.3% in zero-shot settings. The code will be released
upon acceptance.
|
2502.03395
|
Benchmarking Time Series Forecasting Models: From Statistical Techniques
to Foundation Models in Real-World Applications
|
cs.LG cs.AI
|
Time series forecasting is essential for operational intelligence in the
hospitality industry, and particularly challenging in large-scale, distributed
systems. This study evaluates the performance of statistical, machine learning
(ML), deep learning, and foundation models in forecasting hourly sales over a
14-day horizon using real-world data from a network of thousands of restaurants
across Germany. The forecasting solution includes features such as weather
conditions, calendar events, and time-of-day patterns. Results demonstrate the
strong performance of ML-based meta-models and highlight the emerging potential
of foundation models like Chronos and TimesFM, which deliver competitive
performance with minimal feature engineering, leveraging only the pre-trained
model (zero-shot inference). Additionally, a hybrid PySpark-Pandas approach
proves to be a robust solution for achieving horizontal scalability in
large-scale deployments.
|
2502.03396
|
Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS
Digital Twin
|
cs.LG cs.AI cs.ET
|
Creating a Digital Twin (DT) for Healthcare Intelligent Transportation
Systems (HITS) is a hot research trend focusing on enhancing HITS management,
particularly in emergencies where ambulance vehicles must arrive at the crash
scene on time and track their real-time location is crucial to the medical
authorities. Despite the claim of real-time representation, a temporal
misalignment persists between the physical and virtual domains, leading to
discrepancies in the ambulance's location representation. This study proposes
integrating AI predictive models, specifically Support Vector Regression (SVR)
and Deep Neural Networks (DNN), within a constructed mock DT data pipeline
framework to anticipate the medical vehicle's next location in the virtual
world. These models align virtual representations with their physical
counterparts, i.e., metaphorically offsetting the synchronization delay between
the two worlds. Trained meticulously on a historical geospatial dataset, SVR
and DNN exhibit exceptional prediction accuracy in MATLAB and Python
environments. Through various testing scenarios, we visually demonstrate the
efficacy of our methodology, showcasing SVR and DNN's key role in significantly
reducing the witnessed gap within the HITS's DT. This transformative approach
enhances real-time synchronization in emergency HITS by approximately 88% to
93%.
|
2502.03397
|
SPRI: Aligning Large Language Models with Context-Situated Principles
|
cs.CL cs.AI
|
Aligning Large Language Models to integrate and reflect human values,
especially for tasks that demand intricate human oversight, is arduous since it
is resource-intensive and time-consuming to depend on human expertise for
context-specific guidance. Prior work has utilized predefined sets of rules or
principles to steer the behavior of models (Bai et al., 2022; Sun et al.,
2023). However, these principles tend to be generic, making it challenging to
adapt them to each individual input query or context. In this work, we present
Situated-PRInciples (SPRI), a framework requiring minimal or no human effort
that is designed to automatically generate guiding principles in real-time for
each input query and utilize them to align each response. We evaluate SPRI on
three tasks, and show that 1) SPRI can derive principles in a complex
domain-specific task that leads to on-par performance as expert-crafted ones;
2) SPRI-generated principles lead to instance-specific rubrics that outperform
prior LLM-as-a-judge frameworks; 3) using SPRI to generate synthetic SFT data
leads to substantial improvement on truthfulness. We release our code and model
generations at https://github.com/honglizhan/SPRI-public.
|
2502.03400
|
DenseReviewer: A Screening Prioritisation Tool for Systematic Review
based on Dense Retrieval
|
cs.IR
|
Screening is a time-consuming and labour-intensive yet required task for
medical systematic reviews, as tens of thousands of studies often need to be
screened. Prioritising relevant studies to be screened allows downstream
systematic review creation tasks to start earlier and save time. In previous
work, we developed a dense retrieval method to prioritise relevant studies with
reviewer feedback during the title and abstract screening stage. Our method
outperforms previous active learning methods in both effectiveness and
efficiency. In this demo, we extend this prior work by creating (1) a web-based
screening tool that enables end-users to screen studies exploiting
state-of-the-art methods and (2) a Python library that integrates models and
feedback mechanisms and allows researchers to develop and demonstrate new
active learning methods. We describe the tool's design and showcase how it can
aid screening. The tool is available at https://densereviewer.ielab.io. The
source code is also open sourced at https://github.com/ielab/densereviewer.
|
2502.03403
|
Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin
Networks
|
cs.CR cs.AI
|
Task offloading management in 6G vehicular networks is crucial for
maintaining network efficiency, particularly as vehicles generate substantial
data. Integrating secure communication through authentication introduces
additional computational and communication overhead, significantly impacting
offloading efficiency and latency. This paper presents a unified framework
incorporating lightweight Identity-Based Cryptographic (IBC) authentication
into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs).
Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning
(DRL), our approach optimizes authenticated offloading decisions to minimize
latency and enhance resource allocation. Performance evaluation under varying
network sizes, task sizes, and data rates reveals that IBC authentication can
reduce offloading efficiency by up to 50% due to the added overhead. Besides,
increasing network size and task size can further reduce offloading efficiency
by up to 91.7%. As a countermeasure, increasing the transmission data rate can
improve the offloading performance by as much as 63%, even in the presence of
authentication overhead. The code for the simulations and experiments detailed
in this paper is available on GitHub for further reference and reproducibility
[1].
|
2502.03405
|
Deep Clustering via Probabilistic Ratio-Cut Optimization
|
cs.LG cs.CV
|
We propose a novel approach for optimizing the graph ratio-cut by modeling
the binary assignments as random variables. We provide an upper bound on the
expected ratio-cut, as well as an unbiased estimate of its gradient, to learn
the parameters of the assignment variables in an online setting. The clustering
resulting from our probabilistic approach (PRCut) outperforms the Rayleigh
quotient relaxation of the combinatorial problem, its online learning
extensions, and several widely used methods. We demonstrate that the PRCut
clustering closely aligns with the similarity measure and can perform as well
as a supervised classifier when label-based similarities are provided. This
novel approach can leverage out-of-the-box self-supervised representations to
achieve competitive performance and serve as an evaluation method for the
quality of these representations.
|
2502.03407
|
Detecting Strategic Deception Using Linear Probes
|
cs.LG
|
AI models might use deceptive strategies as part of scheming or misaligned
behaviour. Monitoring outputs alone is insufficient, since the AI might produce
seemingly benign outputs while their internal reasoning is misaligned. We thus
evaluate if linear probes can robustly detect deception by monitoring model
activations. We test two probe-training datasets, one with contrasting
instructions to be honest or deceptive (following Zou et al., 2023) and one of
responses to simple roleplaying scenarios. We test whether these probes
generalize to realistic settings where Llama-3.3-70B-Instruct behaves
deceptively, such as concealing insider trading (Scheurer et al., 2023) and
purposely underperforming on safety evaluations (Benton et al., 2024). We find
that our probe distinguishes honest and deceptive responses with AUROCs between
0.96 and 0.999 on our evaluation datasets. If we set the decision threshold to
have a 1% false positive rate on chat data not related to deception, our probe
catches 95-99% of the deceptive responses. Overall we think white-box probes
are promising for future monitoring systems, but current performance is
insufficient as a robust defence against deception. Our probes' outputs can be
viewed at data.apolloresearch.ai/dd and our code at
github.com/ApolloResearch/deception-detection.
|
2502.03409
|
Verification and Synthesis Methods for High-Order Control Barrier
Functions
|
eess.SY cs.SY
|
High-order control barrier functions (HOCBFs) can be used to provide
autonomous systems with safety, though computational methods to verify and
synthesize these functions remain lacking. In this work, we address this need
by formulating SOS programs that verify and synthesize HOCBFs, such that
continued safety is always guaranteed forward in time. We first propose a
verification SOS program for systems with (i) one or multiple HOCBFs, (ii) a
control Lyapunov function (CLF), and (iii) input constraints, and we show that
a solution to this problem guarantees that the online implementation of the
system is always safe. Next, we propose a sequence of SOS programs that
synthesize the class K functions used in an HOCBF, and we show that this
sequence of problems ensures that a system is guaranteed to remain safe while
running. After that, a synthesis framework is given that ensures real-time
safety for systems with (i) multiple HOCBFs, (ii) a CLF, and (iii) input
constraints. Our developments are illustrated in numerical simulations for a
system with seven HOCBFs of maximum relative degree two, with 14 total unknown
class K functions, all of which are successfully synthesized in a way that
produces safe autonomy.
|
2502.03411
|
Cryptocurrency Network Analysis
|
cs.SI cs.CY cs.NI
|
Cryptocurrency network analysis consists of applying the tools and methods of
social network analysis to transactional data issued from cryptocurrencies. The
main difference with most online social networks is that users do not exchange
textual content but instead value -- in systems designed mainly as
cryptocurrency, such as Bitcoin -- or digital items and services in more
permissive systems based on smart contracts such as Ethereum.
|
2502.03412
|
Deep Reinforcement Learning-Based Optimization of Second-Life Battery
Utilization in Electric Vehicles Charging Stations
|
eess.SY cs.LG cs.SY
|
The rapid rise in electric vehicle (EV) adoption presents significant
challenges in managing the vast number of retired EV batteries. Research
indicates that second-life batteries (SLBs) from EVs typically retain
considerable residual capacity, offering extended utility. These batteries can
be effectively repurposed for use in EV charging stations (EVCS), providing a
cost-effective alternative to new batteries and reducing overall planning
costs. Integrating battery energy storage systems (BESS) with SLBs into EVCS is
a promising strategy to alleviate system overload. However, efficient operation
of EVCS with integrated BESS is hindered by uncertainties such as fluctuating
EV arrival and departure times and variable power prices from the grid. This
paper presents a deep reinforcement learning-based (DRL) planning framework for
EV charging stations with BESS, leveraging SLBs. We employ the advanced soft
actor-critic (SAC) approach, training the model on a year's worth of data to
account for seasonal variations, including weekdays and holidays. A tailored
reward function enables effective offline training, allowing real-time
optimization of EVCS operations under uncertainty.
|
2502.03417
|
From Features to Transformers: Redefining Ranking for Scalable Impact
|
cs.LG
|
We present LiGR, a large-scale ranking framework developed at LinkedIn that
brings state-of-the-art transformer-based modeling architectures into
production. We introduce a modified transformer architecture that incorporates
learned normalization and simultaneous set-wise attention to user history and
ranked items. This architecture enables several breakthrough achievements,
including: (1) the deprecation of most manually designed feature engineering,
outperforming the prior state-of-the-art system using only few features
(compared to hundreds in the baseline), (2) validation of the scaling law for
ranking systems, showing improved performance with larger models, more training
data, and longer context sequences, and (3) simultaneous joint scoring of items
in a set-wise manner, leading to automated improvements in diversity. To enable
efficient serving of large ranking models, we describe techniques to scale
inference effectively using single-pass processing of user history and set-wise
attention. We also summarize key insights from various ablation studies and A/B
tests, highlighting the most impactful technical approaches.
|
2502.03418
|
Think or Step-by-Step? UnZIPping the Black Box in Zero-Shot Prompts
|
cs.CL
|
Zero-shot prompting techniques have significantly improved the performance of
Large Language Models (LLMs). However, we lack a clear understanding of why
zero-shot prompts are so effective. For example, in the prompt "Let's think
step-by-step," is "think" or "step-by-step" more crucial to its success?
Existing interpretability methods, such as gradient-based and attention-based
approaches, are computationally intensive and restricted to open-source models.
We introduce the ZIP score (Zero-shot Importance of Perturbation score), a
versatile metric applicable to both open and closed-source models, based on
systematic input word perturbations. Our experiments across four recent LLMs,
seven widely-used prompts, and several tasks, reveal interesting patterns in
word importance. For instance, while both 'step-by-step' and 'think' show high
ZIP scores, which one is more influential depends on the model and task. We
validate our method using controlled experiments and compare our results with
human judgments, finding that proprietary models align more closely with human
intuition regarding word significance. These findings enhance our understanding
of LLM behavior and contribute to developing more effective zero-shot prompts
and improved model analysis.
|
2502.03420
|
Can Text-to-Image Generative Models Accurately Depict Age? A Comparative
Study on Synthetic Portrait Generation and Age Estimation
|
cs.CV
|
Text-to-image generative models have shown remarkable progress in producing
diverse and photorealistic outputs. In this paper, we present a comprehensive
analysis of their effectiveness in creating synthetic portraits that accurately
represent various demographic attributes, with a special focus on age,
nationality, and gender. Our evaluation employs prompts specifying detailed
profiles (e.g., Photorealistic selfie photo of a 32-year-old Canadian male),
covering a broad spectrum of 212 nationalities, 30 distinct ages from 10 to 78,
and balanced gender representation. We compare the generated images against
ground truth age estimates from two established age estimation models to assess
how faithfully age is depicted. Our findings reveal that although text-to-image
models can consistently generate faces reflecting different identities, the
accuracy with which they capture specific ages and do so across diverse
demographic backgrounds remains highly variable. These results suggest that
current synthetic data may be insufficiently reliable for high-stakes
age-related tasks requiring robust precision, unless practitioners are prepared
to invest in significant filtering and curation. Nevertheless, they may still
be useful in less sensitive or exploratory applications, where absolute age
precision is not critical.
|
2502.03421
|
Investigating Corporate Social Responsibility Initiatives: Examining the
case of corporate Covid-19 response
|
cs.IR
|
In todays age of freely available information, policy makers have to take
into account a huge amount of information while making decisions affecting
relevant stakeholders. While increase in the amount of information sources and
documents increases credibility of decisions based on the corpus of available
text, it is challenging for policymakers to make sense of this information.
This paper demonstrates how policy makers can implement some of the most
popular topic recognition methods, Latent Dirichlet Allocation, Deep
Distributed Representation method, text summarization approaches, Word Based
Sentence Ranking method and TextRank for sentence extraction method, to sum up
the content of large volume of documents to understand the gist of the overload
of information. We have applied popular NLP methods to corporate press releases
during the early period and advanced period of Covid-19 pandemic which has
resulted in a global unprecedented health and socio-economic crisis, when
policymaking and regulations have become especially important to standardize
corporate practices for employee and social welfare in the face of similar
future unseen crises. The steps undertaken in this study can be replicated to
yield insights from relevant documents in any other social decision-making
context.
|
2502.03422
|
Concept Based Explanations and Class Contrasting
|
cs.CV
|
Explaining deep neural networks is challenging, due to their large size and
non-linearity. In this paper, we introduce a concept-based explanation method,
in order to explain the prediction for an individual class, as well as
contrasting any two classes, i.e. explain why the model predicts one class over
the other. We test it on several openly available classification models trained
on ImageNet1K, as well as on a segmentation model trained to detect tumor in
stained tissue samples. We perform both qualitative and quantitative tests. For
example, for a ResNet50 model from pytorch model zoo, we can use the
explanation for why the model predicts a class 'A' to automatically select six
dataset crops where the model does not predict class 'A'. The model then
predicts class 'A' again for the newly combined image in 71\% of the cases
(works for 710 out of the 1000 classes). The code including an .ipynb example
is available on git:
https://github.com/rherdt185/concept-based-explanations-and-class-contrasting.
|
2502.03424
|
Prediction of the Most Fire-Sensitive Point in Building Structures with
Differentiable Agents for Thermal Simulators
|
cs.LG
|
Fire safety is a critical area of research in civil and mechanical
engineering, particularly in ensuring the structural stability of buildings
during fire events. The Most Fire-Sensitive Point (MFSP) in a structure is the
location where a fire would cause the greatest impact on structural stability.
Accurate prediction of the MFSP is vital for streamlining structural
assessments and optimizing the design process. This paper presents a novel
framework for MFSP prediction using a neural network-based approach that
integrates fire dynamics and finite element analysis through a differentiable
agent model. The framework focuses on predicting the Maximum Interstory Drift
Ratio (MIDR), a key indicator of structural performance under fire conditions.
By leveraging the differentiable agent model, we efficiently generate labeled
data for MFSP and directly train a predictor for this critical metric. To
achieve this, we generated extensive simulation data encompassing structural
and fire scenarios and employed graph neural networks to represent the building
structures. Transfer learning was applied to optimize the training process, and
an edge update mechanism was introduced to dynamically adjust edge attributes,
reflecting property changes under fire conditions. The proposed model was
rigorously evaluated on simulation data, demonstrating strong performance in
accurately predicting both MIDR and MFSP, thus advancing fire safety analysis
for building structures.
|
2502.03426
|
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID
Preserving Pose Transfer
|
cs.CV cs.AI
|
Pose-Guided Person Image Synthesis (PGPIS) generates images that maintain a
subject's identity from a source image while adopting a specified target pose
(e.g., skeleton). While diffusion-based PGPIS methods effectively preserve
facial features during pose transformation, they often struggle to accurately
maintain clothing details from the source image throughout the diffusion
process. This limitation becomes particularly problematic when there is a
substantial difference between the source and target poses, significantly
impacting PGPIS applications in the fashion industry where clothing style
preservation is crucial for copyright protection. Our analysis reveals that
this limitation primarily stems from the conditional diffusion model's
attention modules failing to adequately capture and preserve clothing patterns.
To address this limitation, we propose human-parsing-guided attention
diffusion, a novel approach that effectively preserves both facial and clothing
appearance while generating high-quality results. We propose a
human-parsing-aware Siamese network that consists of three key components: dual
identical UNets (TargetNet for diffusion denoising and SourceNet for source
image embedding extraction), a human-parsing-guided fusion attention (HPFA),
and a CLIP-guided attention alignment (CAA). The HPFA and CAA modules can embed
the face and clothes patterns into the target image generation adaptively and
effectively. Extensive experiments on both the in-shop clothes retrieval
benchmark and the latest in-the-wild human editing dataset demonstrate our
method's significant advantages over 13 baseline approaches for preserving both
facial and clothes appearance in the source image.
|
2502.03429
|
On Fairness of Unified Multimodal Large Language Model for Image
Generation
|
cs.CL cs.AI
|
Unified multimodal large language models (U-MLLMs) have demonstrated
impressive performance in visual understanding and generation in an end-to-end
pipeline. Compared with generation-only models (e.g., Stable Diffusion),
U-MLLMs may raise new questions about bias in their outputs, which can be
affected by their unified capabilities. This gap is particularly concerning
given the under-explored risk of propagating harmful stereotypes. In this
paper, we benchmark the latest U-MLLMs and find that most exhibit significant
demographic biases, such as gender and race bias. To better understand and
mitigate this issue, we propose a locate-then-fix strategy, where we audit and
show how the individual model component is affected by bias. Our analysis shows
that bias originates primarily from the language model. More interestingly, we
observe a "partial alignment" phenomenon in U-MLLMs, where understanding bias
appears minimal, but generation bias remains substantial. Thus, we propose a
novel balanced preference model to balance the demographic distribution with
synthetic data. Experiments demonstrate that our approach reduces demographic
bias while preserving semantic fidelity. We hope our findings underscore the
need for more holistic interpretation and debiasing strategies of U-MLLMs in
the future.
|
2502.03430
|
A Temporal Convolutional Network-Based Approach and a Benchmark Dataset
for Colonoscopy Video Temporal Segmentation
|
cs.CV eess.IV
|
Following recent advancements in computer-aided detection and diagnosis
systems for colonoscopy, the automated reporting of colonoscopy procedures is
set to further revolutionize clinical practice. A crucial yet underexplored
aspect in the development of these systems is the creation of computer vision
models capable of autonomously segmenting full-procedure colonoscopy videos
into anatomical sections and procedural phases. In this work, we aim to create
the first open-access dataset for this task and propose a state-of-the-art
approach, benchmarked against competitive models. We annotated the publicly
available REAL-Colon dataset, consisting of 2.7 million frames from 60 complete
colonoscopy videos, with frame-level labels for anatomical locations and
colonoscopy phases across nine categories. We then present ColonTCN, a
learning-based architecture that employs custom temporal convolutional blocks
designed to efficiently capture long temporal dependencies for the temporal
segmentation of colonoscopy videos. We also propose a dual k-fold
cross-validation evaluation protocol for this benchmark, which includes model
assessment on unseen, multi-center data.ColonTCN achieves state-of-the-art
performance in classification accuracy while maintaining a low parameter count
when evaluated using the two proposed k-fold cross-validation settings,
outperforming competitive models. We report ablation studies to provide
insights into the challenges of this task and highlight the benefits of the
custom temporal convolutional blocks, which enhance learning and improve model
efficiency. We believe that the proposed open-access benchmark and the ColonTCN
approach represent a significant advancement in the temporal segmentation of
colonoscopy procedures, fostering further open-access research to address this
clinical need.
|
2502.03433
|
Analyzing Political Discourse on Discord during the 2024 U.S.
Presidential Election
|
cs.SI
|
Social media networks have amplified the reach of social and political
movements, but most research focuses on mainstream platforms such as X, Reddit,
and Facebook, overlooking Discord. As a rapidly growing, community-driven
platform with optional decentralized moderation, Discord offers unique
opportunities to study political discourse. This study analyzes over 30 million
messages from political servers on Discord discussing the 2024 U.S. elections.
Servers were classified as Republican-aligned, Democratic-aligned, or unaligned
based on their descriptions. We tracked changes in political conversation
during key campaign events and identified distinct political valence and
implicit biases in semantic association through embedding analysis. We observed
that Republican servers emphasized economic policies, while Democratic servers
focused on equality-related and progressive causes. Furthermore, we detected an
increase in toxic language, such as sexism, in Republican-aligned servers after
Kamala Harris's nomination. These findings provide a first look at political
behavior on Discord, highlighting its growing role in shaping and understanding
online political engagement.
|
2502.03435
|
Taking a Big Step: Large Learning Rates in Denoising Score Matching
Prevent Memorization
|
stat.ML cs.LG
|
Denoising score matching plays a pivotal role in the performance of
diffusion-based generative models. However, the empirical optimal score--the
exact solution to the denoising score matching--leads to memorization, where
generated samples replicate the training data. Yet, in practice, only a
moderate degree of memorization is observed, even without explicit
regularization. In this paper, we investigate this phenomenon by uncovering an
implicit regularization mechanism driven by large learning rates. Specifically,
we show that in the small-noise regime, the empirical optimal score exhibits
high irregularity. We then prove that, when trained by stochastic gradient
descent with a large enough learning rate, neural networks cannot stably
converge to a local minimum with arbitrarily small excess risk. Consequently,
the learned score cannot be arbitrarily close to the empirical optimal score,
thereby mitigating memorization. To make the analysis tractable, we consider
one-dimensional data and two-layer neural networks. Experiments validate the
crucial role of the learning rate in preventing memorization, even beyond the
one-dimensional setting.
|
2502.03438
|
BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic
Theorem Proving
|
cs.AI
|
Recent advancements in large language models (LLMs) have spurred growing
interest in automatic theorem proving using Lean4, where effective tree search
methods are crucial for navigating proof search spaces. While the existing
approaches primarily rely on value functions and Monte Carlo Tree Search
(MCTS), the potential of simpler methods like Best-First Search (BFS) remains
underexplored. This paper investigates whether BFS can achieve competitive
performance in large-scale theorem proving tasks. We present
\texttt{BFS-Prover}, a scalable expert iteration framework, featuring three key
innovations. First, we implement strategic data filtering at each expert
iteration round, excluding problems solvable via beam search node expansion to
focus on harder cases. Second, we improve the sample efficiency of BFS through
Direct Preference Optimization (DPO) applied to state-tactic pairs
automatically annotated with compiler error feedback, refining the LLM's policy
to prioritize productive expansions. Third, we employ length normalization in
BFS to encourage exploration of deeper proof paths. \texttt{BFS-Prover}
achieves a score of $71.31$ on the MiniF2F test set and therefore challenges
the perceived necessity of complex tree search methods, demonstrating that BFS
can achieve competitive performance when properly scaled.
|
2502.03439
|
Linearized Optimal Transport pyLOT Library: A Toolkit for Machine
Learning on Point Clouds
|
stat.ML cs.LG cs.MS stat.CO
|
The pyLOT library offers a Python implementation of linearized optimal
transport (LOT) techniques and methods to use in downstream tasks. The pipeline
embeds probability distributions into a Hilbert space via the Optimal Transport
maps from a fixed reference distribution, and this linearization allows
downstream tasks to be completed using off the shelf (linear) machine learning
algorithms. We provide a case study of performing ML on 3D scans of lemur
teeth, where the original questions of classification, clustering, dimension
reduction, and data generation reduce to simple linear operations performed on
the LOT embedded representations.
|
2502.03444
|
Masked Autoencoders Are Effective Tokenizers for Diffusion Models
|
cs.CV cs.AI cs.LG
|
Recent advances in latent diffusion models have demonstrated their
effectiveness for high-resolution image synthesis. However, the properties of
the latent space from tokenizer for better learning and generation of diffusion
models remain under-explored. Theoretically and empirically, we find that
improved generation quality is closely tied to the latent distributions with
better structure, such as the ones with fewer Gaussian Mixture modes and more
discriminative features. Motivated by these insights, we propose MAETok, an
autoencoder (AE) leveraging mask modeling to learn semantically rich latent
space while maintaining reconstruction fidelity. Extensive experiments validate
our analysis, demonstrating that the variational form of autoencoders is not
necessary, and a discriminative latent space from AE alone enables
state-of-the-art performance on ImageNet generation using only 128 tokens.
MAETok achieves significant practical improvements, enabling a gFID of 1.69
with 76x faster training and 31x higher inference throughput for 512x512
generation. Our findings show that the structure of the latent space, rather
than variational constraints, is crucial for effective diffusion models. Code
and trained models are released.
|
2502.03449
|
Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion
Prior and Differentiable Physics
|
cs.CV
|
Recent advances in large models have significantly advanced image-to-3D
reconstruction. However, the generated models are often fused into a single
piece, limiting their applicability in downstream tasks. This paper focuses on
3D garment generation, a key area for applications like virtual try-on with
dynamic garment animations, which require garments to be separable and
simulation-ready. We introduce Dress-1-to-3, a novel pipeline that reconstructs
physics-plausible, simulation-ready separated garments with sewing patterns and
humans from an in-the-wild image. Starting with the image, our approach
combines a pre-trained image-to-sewing pattern generation model for creating
coarse sewing patterns with a pre-trained multi-view diffusion model to produce
multi-view images. The sewing pattern is further refined using a differentiable
garment simulator based on the generated multi-view images. Versatile
experiments demonstrate that our optimization approach substantially enhances
the geometric alignment of the reconstructed 3D garments and humans with the
input image. Furthermore, by integrating a texture generation module and a
human motion generation module, we produce customized physics-plausible and
realistic dynamic garment demonstrations. Project page:
https://dress-1-to-3.github.io/
|
2502.03450
|
A Schema-Guided Reason-while-Retrieve framework for Reasoning on Scene
Graphs with Large-Language-Models (LLMs)
|
cs.LG cs.AI cs.MA cs.RO
|
Scene graphs have emerged as a structured and serializable environment
representation for grounded spatial reasoning with Large Language Models
(LLMs). In this work, we propose SG-RwR, a Schema-Guided Retrieve-while-Reason
framework for reasoning and planning with scene graphs. Our approach employs
two cooperative, code-writing LLM agents: a (1) Reasoner for task planning and
information queries generation, and a (2) Retriever for extracting
corresponding graph information following the queries. Two agents collaborate
iteratively, enabling sequential reasoning and adaptive attention to graph
information. Unlike prior works, both agents are prompted only with the scene
graph schema rather than the full graph data, which reduces the hallucination
by limiting input tokens, and drives the Reasoner to generate reasoning trace
abstractly.Following the trace, the Retriever programmatically query the scene
graph data based on the schema understanding, allowing dynamic and global
attention on the graph that enhances alignment between reasoning and retrieval.
Through experiments in multiple simulation environments, we show that our
framework surpasses existing LLM-based approaches in numerical Q\&A and
planning tasks, and can benefit from task-level few-shot examples, even in the
absence of agent-level demonstrations. Project code will be released.
|
2502.03454
|
Kineto-Dynamical Planning and Accurate Execution of Minimum-Time
Maneuvers on Three-Dimensional Circuits
|
cs.RO
|
Online planning and execution of minimum-time maneuvers on three-dimensional
(3D) circuits is an open challenge in autonomous vehicle racing. In this paper,
we present an artificial race driver (ARD) to learn the vehicle dynamics, plan
and execute minimum-time maneuvers on a 3D track. ARD integrates a novel
kineto-dynamical (KD) vehicle model for trajectory planning with economic
nonlinear model predictive control (E-NMPC). We use a high-fidelity vehicle
simulator (VS) to compare the closed-loop ARD results with a minimum-lap-time
optimal control problem (MLT-VS), solved offline with the same VS. Our ARD sets
lap times close to the MLT-VS, and the new KD model outperforms a literature
benchmark. Finally, we study the vehicle trajectories, to assess the
re-planning capabilities of ARD under execution errors. A video with the main
results is available as supplementary material.
|
2502.03459
|
SKI Models: Skeleton Induced Vision-Language Embeddings for
Understanding Activities of Daily Living
|
cs.CV
|
The introduction of vision-language models like CLIP has enabled the
development of foundational video models capable of generalizing to unseen
videos and human actions. However, these models are typically trained on web
videos, which often fail to capture the challenges present in Activities of
Daily Living (ADL) videos. Existing works address ADL-specific challenges, such
as similar appearances, subtle motion patterns, and multiple viewpoints, by
combining 3D skeletons and RGB videos. However, these approaches are not
integrated with language, limiting their ability to generalize to unseen action
classes. In this paper, we introduce SKI models, which integrate 3D skeletons
into the vision-language embedding space. SKI models leverage a
skeleton-language model, SkeletonCLIP, to infuse skeleton information into
Vision Language Models (VLMs) and Large Vision Language Models (LVLMs) through
collaborative training. Notably, SKI models do not require skeleton data during
inference, enhancing their robustness for real-world applications. The
effectiveness of SKI models is validated on three popular ADL datasets for
zero-shot action recognition and video caption generation tasks.
|
2502.03460
|
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language
Model Training
|
cs.LG cs.AI cs.CL
|
Small language models (SLMs) have attracted considerable attention from both
academia and industry due to their broad range of applications in edge devices.
To obtain SLMs with strong performance, conventional approaches either
pre-train the models from scratch, which incurs substantial computational
costs, or compress/prune existing large language models (LLMs), which results
in performance drops and falls short in comparison to pre-training. In this
paper, we investigate the family of acceleration methods that involve both
structured pruning and model training. We found 1) layer-wise adaptive pruning
(Adapt-Pruner) is extremely effective in LLMs and yields significant
improvements over existing pruning techniques, 2) adaptive pruning equipped
with further training leads to models comparable to those pre-training from
scratch, 3) incremental pruning brings non-trivial performance gain by
interleaving pruning with training and only removing a small portion of neurons
($\sim$5%) at a time. Experimental results on LLaMA-3.1-8B demonstrate that
Adapt-Pruner outperforms conventional pruning methods, such as LLM-Pruner,
FLAP, and SliceGPT, by an average of 1%-7% in accuracy on commonsense
benchmarks. Additionally, Adapt-Pruner restores the performance of
MobileLLM-125M to 600M on the MMLU benchmark with 200$\times$ fewer tokens via
pruning from its larger counterparts, and discovers a new 1B model that
surpasses LLaMA-3.2-1B in multiple benchmarks.
|
2502.03461
|
Do Large Language Model Benchmarks Test Reliability?
|
cs.LG cs.CL
|
When deploying large language models (LLMs), it is important to ensure that
these models are not only capable, but also reliable. Many benchmarks have been
created to track LLMs' growing capabilities, however there has been no similar
focus on measuring their reliability. To understand the potential ramifications
of this gap, we investigate how well current benchmarks quantify model
reliability. We find that pervasive label errors can compromise these
evaluations, obscuring lingering model failures and hiding unreliable behavior.
Motivated by this gap in the evaluation of reliability, we then propose the
concept of so-called platinum benchmarks, i.e., benchmarks carefully curated to
minimize label errors and ambiguity. As a first attempt at constructing such
benchmarks, we revise examples from fifteen existing popular benchmarks. We
evaluate a wide range of models on these platinum benchmarks and find that,
indeed, frontier LLMs still exhibit failures on simple tasks such as
elementary-level math word problems. Analyzing these failures further reveals
previously unidentified patterns of problems on which frontier models
consistently struggle. We provide code at
https://github.com/MadryLab/platinum-benchmarks
|
2502.03465
|
Seeing World Dynamics in a Nutshell
|
cs.CV cs.AI cs.GR cs.MM
|
We consider the problem of efficiently representing casually captured
monocular videos in a spatially- and temporally-coherent manner. While existing
approaches predominantly rely on 2D/2.5D techniques treating videos as
collections of spatiotemporal pixels, they struggle with complex motions,
occlusions, and geometric consistency due to absence of temporal coherence and
explicit 3D structure. Drawing inspiration from monocular video as a projection
of the dynamic 3D world, we explore representing videos in their intrinsic 3D
form through continuous flows of Gaussian primitives in space-time. In this
paper, we propose NutWorld, a novel framework that efficiently transforms
monocular videos into dynamic 3D Gaussian representations in a single forward
pass. At its core, NutWorld introduces a structured spatial-temporal aligned
Gaussian (STAG) representation, enabling optimization-free scene modeling with
effective depth and flow regularization. Through comprehensive experiments, we
demonstrate that NutWorld achieves high-fidelity video reconstruction quality
while enabling various downstream applications in real-time. Demos and code
will be available at https://github.com/Nut-World/NutWorld.
|
2502.03467
|
Where AI Assurance Might Go Wrong: Initial lessons from engineering of
critical systems
|
cs.CY cs.AI cs.SE
|
We draw on our experience working on system and software assurance and
evaluation for systems important to society to summarise how safety engineering
is performed in traditional critical systems, such as aircraft flight control.
We analyse how this critical systems perspective might support the development
and implementation of AI Safety Frameworks. We present the analysis in terms
of: system engineering, safety and risk analysis, and decision analysis and
support.
We consider four key questions: What is the system? How good does it have to
be? What is the impact of criticality on system development? and How much
should we trust it? We identify topics worthy of further discussion. In
particular, we are concerned that system boundaries are not broad enough, that
the tolerability and nature of the risks are not sufficiently elaborated, and
that the assurance methods lack theories that would allow behaviours to be
adequately assured.
We advocate the use of assurance cases based on Assurance 2.0 to support
decision making in which the criticality of the decision as well as the
criticality of the system are evaluated. We point out the orders of magnitude
difference in confidence needed in critical rather than everyday systems and
how everyday techniques do not scale in rigour.
Finally we map our findings in detail to two of the questions posed by the
FAISC organisers and we note that the engineering of critical systems has
evolved through open and diverse discussion. We hope that topics identified
here will support the post-FAISC dialogues.
|
2502.03469
|
A Capability Approach to AI Ethics
|
cs.CY cs.AI
|
We propose a conceptualization and implementation of AI ethics via the
capability approach. We aim to show that conceptualizing AI ethics through the
capability approach has two main advantages for AI ethics as a discipline.
First, it helps clarify the ethical dimension of AI tools. Second, it provides
guidance to implementing ethical considerations within the design of AI tools.
We illustrate these advantages in the context of AI tools in medicine, by
showing how ethics-based auditing of AI tools in medicine can greatly benefit
from our capability-based approach.
|
2502.03478
|
From In Silico to In Vitro: A Comprehensive Guide to Validating
Bioinformatics Findings
|
q-bio.GN cs.CE
|
The integration of bioinformatics predictions and experimental validation
plays a pivotal role in advancing biological research, from understanding
molecular mechanisms to developing therapeutic strategies. Bioinformatics tools
and methods offer powerful means for predicting gene functions, protein
interactions, and regulatory networks, but these predictions must be validated
through experimental approaches to ensure their biological relevance. This
review explores the various methods and technologies used for experimental
validation, including gene expression analysis, protein-protein interaction
verification, and pathway validation. We also discuss the challenges involved
in translating computational predictions to experimental settings and highlight
the importance of collaboration between bioinformatics and experimental
research. Finally, emerging technologies, such as CRISPR gene editing,
next-generation sequencing, and artificial intelligence, are shaping the future
of bioinformatics validation and driving more accurate and efficient biological
discoveries.
|
2502.03480
|
Foundation for unbiased cross-validation of spatio-temporal models for
species distribution modeling
|
stat.AP cs.LG
|
Species Distribution Models (SDMs) often suffer from spatial autocorrelation
(SAC), leading to biased performance estimates. We tested cross-validation (CV)
strategies - random splits, spatial blocking with varied distances,
environmental (ENV) clustering, and a novel spatio-temporal method - under two
proposed training schemes: LAST FOLD, widely used in spatial CV at the cost of
data loss, and RETRAIN, which maximizes data usage but risks reintroducing SAC.
LAST FOLD consistently yielded lower errors and stronger correlations. Spatial
blocking at an optimal distance (SP 422) and ENV performed best, achieving
Spearman and Pearson correlations of 0.485 and 0.548, respectively, although
ENV may be unsuitable for long-term forecasts involving major environmental
shifts. A spatio-temporal approach yielded modest benefits in our moderately
variable dataset, but may excel with stronger temporal changes. These findings
highlight the need to align CV approaches with the spatial and temporal
structure of SDM data, ensuring rigorous validation and reliable predictive
outcomes.
|
2502.03482
|
Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted
Prostate Cancer MRI Diagnosis
|
eess.IV cs.AI cs.CV cs.CY cs.HC cs.LG
|
Despite the growing interest in human-AI decision making, experimental
studies with domain experts remain rare, largely due to the complexity of
working with domain experts and the challenges in setting up realistic
experiments. In this work, we conduct an in-depth collaboration with
radiologists in prostate cancer diagnosis based on MRI images. Building on
existing tools for teaching prostate cancer diagnosis, we develop an interface
and conduct two experiments to study how AI assistance and performance feedback
shape the decision making of domain experts. In Study 1, clinicians were asked
to provide an initial diagnosis (human), then view the AI's prediction, and
subsequently finalize their decision (human-AI team). In Study 2 (after a
memory wash-out period), the same participants first received aggregated
performance statistics from Study 1, specifically their own performance, the
AI's performance, and their human-AI team performance, and then directly viewed
the AI's prediction before making their diagnosis (i.e., no independent initial
diagnosis). These two workflows represent realistic ways that clinical AI tools
might be used in practice, where the second study simulates a scenario where
doctors can adjust their reliance and trust on AI based on prior performance
feedback. Our findings show that, while human-AI teams consistently outperform
humans alone, they still underperform the AI due to under-reliance, similar to
prior studies with crowdworkers. Providing clinicians with performance feedback
did not significantly improve the performance of human-AI teams, although
showing AI decisions in advance nudges people to follow AI more. Meanwhile, we
observe that the ensemble of human-AI teams can outperform AI alone, suggesting
promising directions for human-AI collaboration.
|
2502.03484
|
Dementia Classification Using Acoustic Speech and Feature Selection
|
eess.AS cs.LG cs.SD
|
Dementia is a general term for a group of syndromes that affect cognitive
functions such as memory, thinking, reasoning, and the ability to perform daily
tasks. The number of dementia patients is increasing as the population ages,
and it is estimated that over 10 million people develop dementia each year.
Dementia progresses gradually, and the sooner a patient receives help and
support, the better their chances of maintaining their functional abilities.
For this reason, early diagnosis of dementia is important. In recent years,
machine learning models based on naturally spoken language have been developed
for the early diagnosis of dementia. These methods have proven to be
user-friendly, cost-effective, scalable, and capable of providing extremely
fast diagnoses. This study utilizes the well-known ADReSS challenge dataset for
classifying healthy controls and Alzheimer's patients. The dataset contains
speech recordings from a picture description task featuring a kitchen scene,
collected from both healthy controls and dementia patients. Unlike most
studies, this research does not segment the audio recordings into active speech
segments; instead, acoustic features are extracted from entire recordings. The
study employs Ridge linear regression, Extreme Minimal Learning Machine, and
Linear Support Vector Machine machine learning models to compute feature
importance scores based on model outputs. The Ridge model performed best in
Leave-One-Subject-Out cross-validation, achieving a classification accuracy of
87.8%. The EMLM model, proved to be effective in both cross-validation and the
classification of a separate test dataset, with accuracies of 85.3% and 79.2%,
respectively. The study's results rank among the top compared to other studies
using the same dataset and acoustic feature extraction for dementia diagnosis.
|
2502.03487
|
Artificial Intelligence and Legal Analysis: Implications for Legal
Education and the Profession
|
cs.CY cs.AI
|
This article reports the results of a study examining the ability of legal
and non-legal Large Language Models to perform legal analysis using the
Issue-Rule-Application-Conclusion framework. LLMs were tested on legal
reasoning tasks involving rule analysis and analogical reasoning. The results
show that LLMs can conduct basic IRAC analysis, but are limited by brief
responses lacking detail, an inability to commit to answers, false confidence,
and hallucinations. The study compares legal and nonlegal LLMs, identifies
shortcomings, and explores traits that may hinder their ability to think like a
lawyer. It also discusses the implications for legal education and practice,
highlighting the need for critical thinking skills in future lawyers and the
potential pitfalls of overreliance on artificial intelligence AI resulting in a
loss of logic, reasoning, and critical thinking skills.
|
2502.03490
|
Examining Two Hop Reasoning Through Information Content Scaling
|
cs.AI cs.LG
|
Prior work has found that transformers have an inconsistent ability to learn
to answer latent two-hop questions -- questions of the form "Who is Bob's
mother's boss?" We study why this is the case by examining how transformers'
capacity to learn datasets of two-hop questions and answers (two-hop QA) scales
with their size, motivated by prior work on transformer knowledge capacity for
simple factual memorization. We find that capacity scaling and generalization
both support the hypothesis that latent two-hop QA requires transformers to
learn each fact twice, while two-hop QA with chain of thought does not. We also
show that with appropriate dataset parameters, it is possible to "trap" very
small models in a regime where they memorize answers to two-hop questions
independently, even though they would perform better if they could learn to
answer them with function composition. Our findings show that measurement of
capacity scaling can complement existing interpretability methods, though there
are challenges in using it for this purpose.
|
2502.03492
|
Teaching Language Models to Critique via Reinforcement Learning
|
cs.LG cs.AI cs.CL
|
Teaching large language models (LLMs) to critique and refine their outputs is
crucial for building systems that can iteratively improve, yet it is
fundamentally limited by the ability to provide accurate judgments and
actionable suggestions. In this work, we study LLM critics for code generation
and propose $\texttt{CTRL}$, a framework for $\texttt{C}$ritic
$\texttt{T}$raining via $\texttt{R}$einforcement $\texttt{L}$earning, which
trains a critic model to generate feedback that maximizes correction
performance for a fixed generator model without human supervision. Our results
demonstrate that critics trained with $\texttt{CTRL}$ significantly enhance
pass rates and mitigate compounding errors across both base and stronger
generator models. Furthermore, we show that these critic models act as accurate
generative reward models and enable test-time scaling through iterative
critique-revision, achieving up to 106.1% relative improvements across
challenging code generation benchmarks.
|
2502.03493
|
MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from
Monocular Endoscopic Images
|
eess.IV cs.CV
|
Depth estimation from monocular endoscopic images presents significant
challenges due to the complexity of endoscopic surgery, such as irregular
shapes of human soft tissues, as well as variations in lighting conditions.
Existing methods primarily estimate the depth information from RGB images
directly, and often surffer the limited interpretability and accuracy. Given
that RGB and depth images are two views of the same endoscopic surgery scene,
in this paper, we introduce a novel concept referred as ``meta feature
embedding (MetaFE)", in which the physical entities (e.g., tissues and surgical
instruments) of endoscopic surgery are represented using the shared features
that can be alternatively decoded into RGB or depth image. With this concept,
we propose a two-stage self-supervised learning paradigm for the monocular
endoscopic depth estimation. In the first stage, we propose a temporal
representation learner using diffusion models, which are aligned with the
spatial information through the cross normalization to construct the MetaFE. In
the second stage, self-supervised monocular depth estimation with the
brightness calibration is applied to decode the meta features into the depth
image. Extensive evaluation on diverse endoscopic datasets demonstrates that
our approach outperforms the state-of-the-art method in depth estimation,
achieving superior accuracy and generalization. The source code will be
publicly available.
|
2502.03499
|
Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and
Multi-Task Learning
|
q-bio.GN cs.AI cs.LG
|
Large Language Models (LLMs) demonstrate remarkable generalizability across
diverse tasks, yet genomic foundation models (GFMs) still require separate
finetuning for each downstream application, creating significant overhead as
model sizes grow. Moreover, existing GFMs are constrained by rigid output
formats, limiting their applicability to various genomic tasks. In this work,
we revisit the transformer-based auto-regressive models and introduce Omni-DNA,
a family of cross-modal multi-task models ranging from 20 million to 1 billion
parameters. Our approach consists of two stages: (i) pretraining on DNA
sequences with next token prediction objective, and (ii) expanding the
multi-modal task-specific tokens and finetuning for multiple downstream tasks
simultaneously. When evaluated on the Nucleotide Transformer and GB benchmarks,
Omni-DNA achieves state-of-the-art performance on 18 out of 26 tasks. Through
multi-task finetuning, Omni-DNA addresses 10 acetylation and methylation tasks
at once, surpassing models trained on each task individually. Finally, we
design two complex genomic tasks, DNA2Function and Needle-in-DNA, which map DNA
sequences to textual functional descriptions and images, respectively,
indicating Omni-DNA's cross-modal capabilities to broaden the scope of genomic
applications. All the models are available through
https://huggingface.co/collections/zehui127
|
2502.03500
|
Efficient Image Restoration via Latent Consistency Flow Matching
|
eess.IV cs.AI stat.AP
|
Recent advances in generative image restoration (IR) have demonstrated
impressive results. However, these methods are hindered by their substantial
size and computational demands, rendering them unsuitable for deployment on
edge devices. This work introduces ELIR, an Efficient Latent Image Restoration
method. ELIR operates in latent space by first predicting the latent
representation of the minimum mean square error (MMSE) estimator and then
transporting this estimate to high-quality images using a latent consistency
flow-based model. Consequently, ELIR is more than 4x faster compared to the
state-of-the-art diffusion and flow-based approaches. Moreover, ELIR is also
more than 4x smaller, making it well-suited for deployment on
resource-constrained edge devices. Comprehensive evaluations of various image
restoration tasks show that ELIR achieves competitive results, effectively
balancing distortion and perceptual quality metrics while offering improved
efficiency in terms of memory and computation.
|
2502.03501
|
Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for
Medical Segmentation
|
eess.IV cs.LG
|
In this paper, we aim to address the unmet demand for automated prompting and
enhanced human-model interactions of SAM and SAM2 for the sake of promoting
their widespread clinical adoption. Specifically, we propose Proxy Prompt (PP),
auto-generated by leveraging non-target data with a pre-annotated mask. We
devise a novel 3-step context-selection strategy for adaptively selecting the
most representative contextual information from non-target data via vision
mamba and selective maps, empowering the guiding capability of non-target
image-mask pairs for segmentation on target image/video data. To reinforce
human-model interactions in PP, we further propose a contextual colorization
module via a dual-reverse cross-attention to enhance interactions between
target features and contextual-embedding with amplifying distinctive features
of user-defined object(s). Via extensive evaluations, our method achieves
state-of-the-art performance on four public datasets and yields comparable
results with fully-trained models, even when trained with only 16 image masks.
|
2502.03502
|
DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with
Video Diffusion Prior
|
eess.IV cs.AI cs.GR
|
Video super-resolution (VSR) aims to reconstruct a high-resolution (HR) video
from a low-resolution (LR) counterpart. Achieving successful VSR requires
producing realistic HR details and ensuring both spatial and temporal
consistency. To restore realistic details, diffusion-based VSR approaches have
recently been proposed. However, the inherent randomness of diffusion, combined
with their tile-based approach, often leads to spatio-temporal inconsistencies.
In this paper, we propose DC-VSR, a novel VSR approach to produce spatially and
temporally consistent VSR results with realistic textures. To achieve spatial
and temporal consistency, DC-VSR adopts a novel Spatial Attention Propagation
(SAP) scheme and a Temporal Attention Propagation (TAP) scheme that propagate
information across spatio-temporal tiles based on the self-attention mechanism.
To enhance high-frequency details, we also introduce Detail-Suppression
Self-Attention Guidance (DSSAG), a novel diffusion guidance scheme.
Comprehensive experiments demonstrate that DC-VSR achieves spatially and
temporally consistent, high-quality VSR results, outperforming previous
approaches.
|
2502.03503
|
Two in context learning tasks with complex functions
|
stat.ML cs.AI cs.LG
|
We examine two in context learning (ICL) tasks with mathematical functions in
several train and test settings for transformer models. Our study generalizes
work on linear functions by showing that small transformers, even models with
attention layers only, can approximate arbitrary polynomial functions and hence
continuous functions under certain conditions. Our models also can approximate
previously unseen classes of polynomial functions, as well as the zeros of
complex functions. Our models perform far better on this task than LLMs like
GPT4 and involve complex reasoning when provided with suitable training data
and methods. Our models also have important limitations; they fail to
generalize outside of training distributions and so don't learn class forms of
functions. We explain why this is so.
|
2502.03504
|
Immersion for AI: Immersive Learning with Artificial Intelligence
|
q-bio.NC cs.AI cs.HC
|
This work reflects upon what Immersion can mean from the perspective of an
Artificial Intelligence (AI). Applying the lens of immersive learning theory,
it seeks to understand whether this new perspective supports ways for AI
participation in cognitive ecologies. By treating AI as a participant rather
than a tool, it explores what other participants (humans and other AIs) need to
consider in environments where AI can meaningfully engage and contribute to the
cognitive ecology, and what the implications are for designing such learning
environments. Drawing from the three conceptual dimensions of immersion -
System, Narrative, and Agency - this work reinterprets AIs in immersive
learning contexts. It outlines practical implications for designing learning
environments where AIs are surrounded by external digital services, can
interpret a narrative of origins, changes, and structural developments in data,
and dynamically respond, making operational and tactical decisions that shape
human-AI collaboration. Finally, this work suggests how these insights might
influence the future of AI training, proposing that immersive learning theory
can inform the development of AIs capable of evolving beyond static models.
This paper paves the way for understanding AI as an immersive learner and
participant in evolving human-AI cognitive ecosystems.
|
2502.03505
|
Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using
Deep Learning
|
eess.IV cs.AI cs.LG
|
This study introduces a motion-based learning network with a global-local
self-attention module (MoGLo-Net) to enhance 3D reconstruction in handheld
photoacoustic and ultrasound (PAUS) imaging. Standard PAUS imaging is often
limited by a narrow field of view and the inability to effectively visualize
complex 3D structures. The 3D freehand technique, which aligns sequential 2D
images for 3D reconstruction, faces significant challenges in accurate motion
estimation without relying on external positional sensors. MoGLo-Net addresses
these limitations through an innovative adaptation of the self-attention
mechanism, which effectively exploits the critical regions, such as
fully-developed speckle area or high-echogenic tissue area within successive
ultrasound images to accurately estimate motion parameters. This facilitates
the extraction of intricate features from individual frames. Additionally, we
designed a patch-wise correlation operation to generate a correlation volume
that is highly correlated with the scanning motion. A custom loss function was
also developed to ensure robust learning with minimized bias, leveraging the
characteristics of the motion parameters. Experimental evaluations demonstrated
that MoGLo-Net surpasses current state-of-the-art methods in both quantitative
and qualitative performance metrics. Furthermore, we expanded the application
of 3D reconstruction technology beyond simple B-mode ultrasound volumes to
incorporate Doppler ultrasound and photoacoustic imaging, enabling 3D
visualization of vasculature. The source code for this study is publicly
available at: https://github.com/guhong3648/US3D
|
2502.03506
|
Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent
Reinforcement Learning
|
cs.MA cs.LG
|
The Centralized Training with Decentralized Execution (CTDE) paradigm is
widely used in cooperative multi-agent reinforcement learning. However, due to
the representational limitations of traditional monotonic value decomposition
methods, algorithms can underestimate optimal actions, leading policies to
suboptimal solutions. To address this challenge, we propose Optimistic
$\epsilon$-Greedy Exploration, focusing on enhancing exploration to correct
value estimations. The underestimation arises from insufficient sampling of
optimal actions during exploration, as our analysis indicated. We introduce an
optimistic updating network to identify optimal actions and sample actions from
its distribution with a probability of $\epsilon$ during exploration,
increasing the selection frequency of optimal actions. Experimental results in
various environments reveal that the Optimistic $\epsilon$-Greedy Exploration
effectively prevents the algorithm from suboptimal solutions and significantly
improves its performance compared to other algorithms.
|
2502.03508
|
Elucidation of the Concept of Consciousness from the Theory of Non-Human
Communication Agents
|
q-bio.NC cs.AI cs.HC
|
This article focuses on elucidating the concept of consciousness from a
relational and post-phenomenological theory of non-human communication agents
(ANHC). Specifically, we explore the contributions of Thomas Metzinger s Self
Model Theory, Katherine Hayles conceptualizations of non-conscious cognitive
processes centered on knowledge processing phenomena shared between biological
and technical systems and Lenore and Manuel Blum s theoretical perspective on
computation, which defines consciousness as an emergent phenomenon of complex
computational systems, arising from the appropriate organization of their
inorganic materiality. Building on interactions with non-human cognitive
agents, among other factors, the explainability of sociotechnical systems
challenges the humanistic common sense of modern philosophy and science. This
critical integration of various approaches ultimately questions other concepts
associated with consciousness, such as autonomy, freedom, and mutual
responsibility. The aim is to contribute to a necessary discussion for
designing new frameworks of understanding that pave the way toward an ethical
and pragmatic approach to addressing contemporary challenges in the design,
regulation, and interaction with ANHC. Such frameworks, in turn, enable a more
inclusive and relational understanding of agency in an interconnected world.
|
2502.03510
|
Mapping and Localization Using LiDAR Fiducial Markers
|
cs.CV
|
LiDAR sensors are essential for autonomous systems, yet LiDAR fiducial
markers (LFMs) lag behind visual fiducial markers (VFMs) in adoption and
utility. Bridging this gap is vital for robotics and computer vision but
challenging due to the sparse, unstructured nature of 3D LiDAR data and
2D-focused fiducial marker designs. This dissertation proposes a novel
framework for mapping and localization using LFMs is proposed to benefit a
variety of real-world applications, including the collection of 3D assets and
training data for point cloud registration, 3D map merging, Augmented Reality
(AR), and many more.
First, an Intensity Image-based LiDAR Fiducial Marker (IFM) system is
introduced, using thin, letter-sized markers compatible with VFMs. A detection
method locates 3D fiducials from intensity images, enabling LiDAR pose
estimation. Second, an enhanced algorithm extends detection to 3D maps,
increasing marker range and facilitating tasks like 3D map merging. This method
leverages both intensity and geometry, overcoming limitations of geometry-only
detection approaches. Third, a new LFM-based mapping and localization method
registers unordered, low-overlap point clouds. It employs adaptive threshold
detection and a two-level graph framework to solve a maximum a-posteriori (MAP)
problem, optimizing point cloud and marker poses. Additionally, the
Livox-3DMatch dataset is introduced, improving learning-based multiview point
cloud registration methods.
Extensive experiments with various LiDAR models in diverse indoor and outdoor
scenes demonstrate the effectiveness and superiority of the proposed framework.
|
2502.03511
|
An Empirical Exploration of ChatGPT's Ability to Support Problem
Formulation Tasks for Mission Engineering and a Documentation of its
Performance Variability
|
cs.SE cs.AI cs.CL
|
Systems engineering (SE) is evolving with the availability of generative
artificial intelligence (AI) and the demand for a systems-of-systems
perspective, formalized under the purview of mission engineering (ME) in the US
Department of Defense. Formulating ME problems is challenging because they are
open-ended exercises that involve translation of ill-defined problems into
well-defined ones that are amenable for engineering development. It remains to
be seen to which extent AI could assist problem formulation objectives. To that
end, this paper explores the quality and consistency of multi-purpose Large
Language Models (LLM) in supporting ME problem formulation tasks, specifically
focusing on stakeholder identification. We identify a relevant reference
problem, a NASA space mission design challenge, and document ChatGPT-3.5's
ability to perform stakeholder identification tasks. We execute multiple
parallel attempts and qualitatively evaluate LLM outputs, focusing on both
their quality and variability. Our findings portray a nuanced picture. We find
that the LLM performs well in identifying human-focused stakeholders but poorly
in recognizing external systems and environmental factors, despite explicit
efforts to account for these. Additionally, LLMs struggle with preserving the
desired level of abstraction and exhibit a tendency to produce solution
specific outputs that are inappropriate for problem formulation. More
importantly, we document great variability among parallel threads, highlighting
that LLM outputs should be used with caution, ideally by adopting a stochastic
view of their abilities. Overall, our findings suggest that, while ChatGPT
could reduce some expert workload, its lack of consistency and domain
understanding may limit its reliability for problem formulation tasks.
|
2502.03512
|
YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing
Multi-Objective Optimization based DPO for Text-to-Image Alignment
|
cs.AI
|
Precise alignment in Text-to-Image (T2I) systems is crucial to ensure that
generated visuals not only accurately encapsulate user intents but also conform
to stringent ethical and aesthetic benchmarks. Incidents like the Google Gemini
fiasco, where misaligned outputs triggered significant public backlash,
underscore the critical need for robust alignment mechanisms. In contrast,
Large Language Models (LLMs) have achieved notable success in alignment.
Building on these advancements, researchers are eager to apply similar
alignment techniques, such as Direct Preference Optimization (DPO), to T2I
systems to enhance image generation fidelity and reliability.
We present YinYangAlign, an advanced benchmarking framework that
systematically quantifies the alignment fidelity of T2I systems, addressing six
fundamental and inherently contradictory design objectives. Each pair
represents fundamental tensions in image generation, such as balancing
adherence to user prompts with creative modifications or maintaining diversity
alongside visual coherence. YinYangAlign includes detailed axiom datasets
featuring human prompts, aligned (chosen) responses, misaligned (rejected)
AI-generated outputs, and explanations of the underlying contradictions.
|
2502.03540
|
Path Planning for Masked Diffusion Model Sampling
|
cs.LG cs.AI
|
In this paper, we explore how token unmasking order influences generative
quality in masked diffusion models (MDMs). We derive an expanded evidence lower
bound (ELBO) that introduces a planner to select which tokens to unmask at each
step. Our analysis reveals that alternative unmasking strategies can enhance
generation performance. Building on this, we propose Path Planning (P2), a
sampling framework that uses a pre-trained BERT model or the denoiser itself to
guide unmasking decisions. P2 generalizes all known MDM sampling strategies and
significantly improves performance across diverse domains, including language
generation (in-context learning, code generation, story infilling, mathematical
reasoning, reverse curse correction) and biological sequence generation
(protein and RNA sequences).
|
2502.03544
|
Gold-medalist Performance in Solving Olympiad Geometry with
AlphaGeometry2
|
cs.AI cs.LG
|
We present AlphaGeometry2, a significantly improved version of AlphaGeometry
introduced in Trinh et al. (2024), which has now surpassed an average gold
medalist in solving Olympiad geometry problems. To achieve this, we first
extend the original AlphaGeometry language to tackle harder problems involving
movements of objects, and problems containing linear equations of angles,
ratios, and distances. This, together with other additions, has markedly
improved the coverage rate of the AlphaGeometry language on International Math
Olympiads (IMO) 2000-2024 geometry problems from 66% to 88%. The search process
of AlphaGeometry2 has also been greatly improved through the use of Gemini
architecture for better language modeling, and a novel knowledge-sharing
mechanism that combines multiple search trees. Together with further
enhancements to the symbolic engine and synthetic data generation, we have
significantly boosted the overall solving rate of AlphaGeometry2 to 84% for
$\textit{all}$ geometry problems over the last 25 years, compared to 54%
previously. AlphaGeometry2 was also part of the system that achieved
silver-medal standard at IMO 2024 https://dpmd.ai/imo-silver. Last but not
least, we report progress towards using AlphaGeometry2 as a part of a fully
automated system that reliably solves geometry problems directly from natural
language input.
|
2502.03545
|
Proportional Selection in Networks
|
cs.GT cs.AI cs.MA cs.SI
|
We address the problem of selecting $k$ representative nodes from a network,
aiming to achieve two objectives: identifying the most influential nodes and
ensuring the selection proportionally reflects the network's diversity. We
propose two approaches to accomplish this, analyze them theoretically, and
demonstrate their effectiveness through a series of experiments.
|
2502.03549
|
Kronecker Mask and Interpretive Prompts are Language-Action Video
Learners
|
cs.CV
|
Contrastive language-image pretraining (CLIP) has significantly advanced
image-based vision learning. A pressing topic subsequently arises: how can we
effectively adapt CLIP to the video domain? Recent studies have focused on
adjusting either the textual or visual branch of CLIP for action recognition.
However, we argue that adaptations of both branches are crucial. In this paper,
we propose \textbf{CLAVER}: a \textbf{C}ontrastive
\textbf{L}anguage-\textbf{A}ction \textbf{V}ideo Learn\textbf{er}, designed to
shift CLIP's focus from the alignment of static visual objects and concrete
nouns to the alignment of dynamic action behaviors and abstract verbs.
Specifically, we introduce a novel Kronecker mask attention for temporal
modeling. Our tailored Kronecker mask offers three benefits 1) it expands the
temporal receptive field for each token, 2) it serves as an effective
spatiotemporal heterogeneity inductive bias, mitigating the issue of
spatiotemporal homogenization, and 3) it can be seamlessly plugged into
transformer-based models. Regarding the textual branch, we leverage large
language models to generate diverse, sentence-level and semantically rich
interpretive prompts of actions, which shift the model's focus towards the verb
comprehension. Extensive experiments on various benchmarks and learning
scenarios demonstrate the superiority and generality of our approach.
|
2502.03550
|
TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy
Constraint
|
cs.LG cs.RO
|
Model-based reinforcement learning algorithms that combine model-based
planning and learned value/policy prior have gained significant recognition for
their high data efficiency and superior performance in continuous control.
However, we discover that existing methods that rely on standard SAC-style
policy iteration for value learning, directly using data generated by the
planner, often result in \emph{persistent value overestimation}. Through
theoretical analysis and experiments, we argue that this issue is deeply rooted
in the structural policy mismatch between the data generation policy that is
always bootstrapped by the planner and the learned policy prior. To mitigate
such a mismatch in a minimalist way, we propose a policy regularization term
reducing out-of-distribution (OOD) queries, thereby improving value learning.
Our method involves minimum changes on top of existing frameworks and requires
no additional computation. Extensive experiments demonstrate that the proposed
approach improves performance over baselines such as TD-MPC2 by large margins,
particularly in 61-DoF humanoid tasks. View qualitative results at
https://darthutopian.github.io/tdmpc_square/.
|
2502.03551
|
Online Learning Algorithms in Hilbert Spaces with $\beta-$ and
$\phi-$Mixing Sequences
|
stat.ML cs.LG math.FA
|
In this paper, we study an online algorithm in a reproducing kernel Hilbert
spaces (RKHS) based on a class of dependent processes, called the mixing
process. For such a process, the degree of dependence is measured by various
mixing coefficients. As a representative example, we analyze a strictly
stationary Markov chain, where the dependence structure is characterized by the
\(\beta-\) and \(\phi-\)mixing coefficients. For these dependent samples, we
derive nearly optimal convergence rates. Our findings extend existing error
bounds for i.i.d. observations, demonstrating that the i.i.d. case is a special
instance of our framework. Moreover, we explicitly account for an additional
factor introduced by the dependence structure in the Markov chain.
|
2502.03552
|
Can Cross Encoders Produce Useful Sentence Embeddings?
|
cs.CL cs.IR
|
Cross encoders (CEs) are trained with sentence pairs to detect relatedness.
As CEs require sentence pairs at inference, the prevailing view is that they
can only be used as re-rankers in information retrieval pipelines. Dual
encoders (DEs) are instead used to embed sentences, where sentence pairs are
encoded by two separate encoders with shared weights at training, and a loss
function that ensures the pair's embeddings lie close in vector space if the
sentences are related. DEs however, require much larger datasets to train, and
are less accurate than CEs. We report a curious finding that embeddings from
earlier layers of CEs can in fact be used within an information retrieval
pipeline. We show how to exploit CEs to distill a lighter-weight DE, with a
5.15x speedup in inference time.
|
2502.03553
|
Efficient Global Neural Architecture Search
|
cs.CV
|
Neural architecture search (NAS) has shown promise towards automating neural
network design for a given task, but it is computationally demanding due to
training costs associated with evaluating a large number of architectures to
find the optimal one. To speed up NAS, recent works limit the search to network
building blocks (modular search) instead of searching the entire architecture
(global search), approximate candidates' performance evaluation in lieu of
complete training, and use gradient descent rather than naturally suitable
discrete optimization approaches. However, modular search does not determine
network's macro architecture i.e. depth and width, demanding manual trial and
error post-search, hence lacking automation. In this work, we revisit NAS and
design a navigable, yet architecturally diverse, macro-micro search space. In
addition, to determine relative rankings of candidates, existing methods employ
consistent approximations across entire search spaces, whereas different
networks may not be fairly comparable under one training protocol. Hence, we
propose an architecture-aware approximation with variable training schemes for
different networks. Moreover, we develop an efficient search strategy by
disjoining macro-micro network design that yields competitive architectures in
terms of both accuracy and size. Our proposed framework achieves a new
state-of-the-art on EMNIST and KMNIST, while being highly competitive on the
CIFAR-10, CIFAR-100, and FashionMNIST datasets and being 2-4x faster than the
fastest global search methods. Lastly, we demonstrate the transferability of
our framework to real-world computer vision problems by discovering competitive
architectures for face recognition applications.
|
2502.03566
|
CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally
|
cs.CV cs.LG
|
CLIP (Contrastive Language-Image Pretraining) has become a popular choice for
various downstream tasks. However, recent studies have questioned its ability
to represent compositional concepts effectively. These works suggest that CLIP
often acts like a bag-of-words (BoW) model, interpreting images and text as
sets of individual concepts without grasping the structural relationships. In
particular, CLIP struggles to correctly bind attributes to their corresponding
objects when multiple objects are present in an image or text. In this work, we
investigate why CLIP exhibits this BoW-like behavior. We find that the correct
attribute-object binding information is already present in individual text and
image modalities. Instead, the issue lies in the cross-modal alignment, which
relies on cosine similarity. To address this, we propose Linear Attribute
Binding CLIP or LABCLIP. It applies a linear transformation to text embeddings
before computing cosine similarity. This approach significantly improves CLIP's
ability to bind attributes to correct objects, thereby enhancing its
compositional understanding. The code is available at
https://github.com/kdariina/CLIP-not-BoW-unimodally.
|
2502.03568
|
Code Simulation as a Proxy for High-order Tasks in Large Language Models
|
cs.LG cs.AI
|
Many reasoning, planning, and problem-solving tasks share an intrinsic
algorithmic nature: correctly simulating each step is a sufficient condition to
solve them correctly. We collect pairs of naturalistic and synthetic reasoning
tasks to assess the capabilities of Large Language Models (LLM). While
naturalistic tasks often require careful human handcrafting, we show that
synthetic data is, in many cases, a good proxy that is much easier to collect
at scale. We leverage common constructs in programming as the counterpart of
the building blocks of naturalistic reasoning tasks, such as straight-line
programs, code that contains critical paths, and approximate and redundant
instructions. We further assess the capabilities of LLMs on sorting problems
and repeated operations via sorting algorithms and nested loops. Our synthetic
datasets further reveal that while the most powerful LLMs exhibit relatively
strong execution capabilities, the process is fragile: it is negatively
affected by memorisation and seems to rely heavily on pattern recognition. Our
contribution builds upon synthetically testing the reasoning capabilities of
LLMs as a scalable complement to handcrafted human-annotated problems.
|
2502.03569
|
Controllable Sequence Editing for Counterfactual Generation
|
cs.LG q-bio.GN q-bio.PE
|
Sequence models generate counterfactuals by modifying parts of a sequence
based on a given condition, enabling reasoning about "what if" scenarios. While
these models excel at conditional generation, they lack fine-grained control
over when and where edits occur. Existing approaches either focus on univariate
sequences or assume that interventions affect the entire sequence globally.
However, many applications require precise, localized modifications, where
interventions take effect only after a specified time and impact only a subset
of co-occurring variables. We introduce CLEF, a controllable sequence editing
model for counterfactual reasoning about both immediate and delayed effects.
CLEF learns temporal concepts that encode how and when interventions should
influence a sequence. With these concepts, CLEF selectively edits relevant time
steps while preserving unaffected portions of the sequence. We evaluate CLEF on
cellular and patient trajectory datasets, where gene regulation affects only
certain genes at specific time steps, or medical interventions alter only a
subset of lab measurements. CLEF improves immediate sequence editing by up to
36.01% in MAE compared to baselines. Unlike prior methods, CLEF enables
one-step generation of counterfactual sequences at any future time step,
outperforming baselines by up to 65.71% in MAE. A case study on patients with
type 1 diabetes mellitus shows that CLEF identifies clinical interventions that
shift patient trajectories toward healthier outcomes.
|
2502.03571
|
A Multi-Task Learning Approach to Linear Multivariate Forecasting
|
cs.LG cs.AI
|
Accurate forecasting of multivariate time series data is important in many
engineering and scientific applications. Recent state-of-the-art works ignore
the inter-relations between variates, using their model on each variate
independently. This raises several research questions related to proper
modeling of multivariate data. In this work, we propose to view multivariate
forecasting as a multi-task learning problem, facilitating the analysis of
forecasting by considering the angle between task gradients and their balance.
To do so, we analyze linear models to characterize the behavior of tasks. Our
analysis suggests that tasks can be defined by grouping similar variates
together, which we achieve via a simple clustering that depends on
correlation-based similarities. Moreover, to balance tasks, we scale gradients
with respect to their prediction error. Then, each task is solved with a linear
model within our MTLinear framework. We evaluate our approach on challenging
benchmarks in comparison to strong baselines, and we show it obtains on-par or
better results on multivariate forecasting problems. The implementation is
available at: https://github.com/azencot-group/MTLinear
|
2502.03576
|
Clone-Resistant Weights in Metric Spaces: A Framework for Handling
Redundancy Bias
|
cs.LG cs.GT
|
We are given a set of elements in a metric space. The distribution of the
elements is arbitrary, possibly adversarial. Can we weigh the elements in a way
that is resistant to such (adversarial) manipulations? This problem arises in
various contexts. For instance, the elements could represent data points,
requiring robust domain adaptation. Alternatively, they might represent tasks
to be aggregated into a benchmark; or questions about personal political
opinions in voting advice applications. This article introduces a theoretical
framework for dealing with such problems. We propose clone-proof representation
functions as a solution concept. These functions distribute importance across
elements of a set such that similar objects (``clones'') share (some of) their
weights, thus avoiding a potential bias introduced by their multiplicity. Our
framework extends the maximum uncertainty principle to accommodate general
metric spaces and includes a set of axioms - symmetry, continuity, and
clone-proofness - that guide the construction of representation functions.
Finally, we address the existence of representation functions satisfying our
axioms in the significant case of Euclidean spaces and propose a general method
for their construction.
|
2502.03587
|
Stein Discrepancy for Unsupervised Domain Adaptation
|
cs.LG stat.ML
|
Unsupervised domain adaptation (UDA) leverages information from a labeled
source dataset to improve accuracy on a related but unlabeled target dataset. A
common approach to UDA is aligning representations from the source and target
domains by minimizing the distance between their data distributions. Previous
methods have employed distances such as Wasserstein distance and maximum mean
discrepancy. However, these approaches are less effective when the target data
is significantly scarcer than the source data. Stein discrepancy is an
asymmetric distance between distributions that relies on one distribution only
through its score function. In this paper, we propose a novel UDA method that
uses Stein discrepancy to measure the distance between source and target
domains. We develop a learning framework using both non-kernelized and
kernelized Stein discrepancy. Theoretically, we derive an upper bound for the
generalization error. Numerical experiments show that our method outperforms
existing methods using other domain discrepancy measures when only small
amounts of target data are available.
|
2502.03589
|
HACK: Homomorphic Acceleration via Compression of the Key-Value Cache
for Disaggregated LLM Inference
|
cs.DC cs.LG
|
Disaggregated Large Language Model (LLM) inference has gained popularity as
it separates the computation-intensive prefill stage from the memory-intensive
decode stage, avoiding the prefill-decode interference and improving resource
utilization. However, transmitting Key-Value (KV) data between the two stages
can be a bottleneck, especially for long prompts. Additionally, the computation
time overhead for prefill and decode is key for optimizing Job Completion Time
(JCT), and KV data size can become prohibitive for long prompts and sequences.
Existing KV quantization methods can alleviate the transmission bottleneck and
reduce memory requirements, but they introduce significant dequantization
overhead, exacerbating the computation time.
We propose Homomorphic Acceleration via Compression of the KV cache (HACK)
for disaggregated LLM inference. HACK eliminates the heavy KV dequantization
step, and directly performs computations on quantized KV data to approximate
and reduce the cost of the expensive matrix-multiplication step. Extensive
trace-driven experiments show that HACK reduces JCT by up to 70.9% compared to
disaggregated LLM inference baseline and by up to 52.3% compared to
state-of-the-art KV quantization methods.
|
2502.03591
|
Clinically-Inspired Hierarchical Multi-Label Classification of Chest
X-rays with a Penalty-Based Loss Function
|
cs.CV cs.AI cs.LG
|
In this work, we present a novel approach to multi-label chest X-ray (CXR)
image classification that enhances clinical interpretability while maintaining
a streamlined, single-model, single-run training pipeline. Leveraging the
CheXpert dataset and VisualCheXbert-derived labels, we incorporate hierarchical
label groupings to capture clinically meaningful relationships between
diagnoses. To achieve this, we designed a custom hierarchical binary
cross-entropy (HBCE) loss function that enforces label dependencies using
either fixed or data-driven penalty types. Our model achieved a mean area under
the receiver operating characteristic curve (AUROC) of 0.903 on the test set.
Additionally, we provide visual explanations and uncertainty estimations to
further enhance model interpretability. All code, model configurations, and
experiment details are made available.
|
2502.03592
|
Solar Panel Mapping via Oriented Object Detection
|
cs.CV
|
Maintaining the integrity of solar power plants is a vital component in
dealing with the current climate crisis. This process begins with analysts
creating a detailed map of a plant with the coordinates of every solar panel,
making it possible to quickly locate and mitigate potential faulty solar
panels. However, this task is extremely tedious and is not scalable for the
ever increasing capacity of solar power across the globe. Therefore, we propose
an end-to-end deep learning framework for detecting individual solar panels
using a rotated object detection architecture. We evaluate our approach on a
diverse dataset of solar power plants collected from across the United States
and report a mAP score of 83.3%.
|
2502.03603
|
Dynamical Landauer principle: Thermodynamic criteria of transmitting
classical information
|
quant-ph cs.IT math-ph math.IT math.MP
|
Transmitting energy and information are two essential aspects of nature.
Recent findings suggest they are closely related, while a quantitative
equivalence between them is still unknown. This thus motivates us to ask: Can
information transmission tasks equal certain energy transmission tasks? We
answer this question positively by bounding various one-shot classical
capacities via different energy transmission tasks. Such bounds provide the
physical implication that, in the one-shot regime, transmitting $n$ bits of
classical information is equivalent to $n\times k_BT\ln2$ transmitted energy.
Unexpectedly, these bounds further uncover a dynamical version of Landauer's
principle, showing the strong link between "transmitting" (rather than
"erasing") information and energy. Finally, in the asymptotic regime, our
findings further provide thermodynamic meanings for
Holevo-Schumacher-Westmoreland Theorem and a series of strong converse
properties as well as no-go theorems.
|
2502.03604
|
Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques
for Efficient LLM Fine-Tuning and Meta-Training
|
cs.LG
|
Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks
using First-Order (FO) optimizers presents significant computational
challenges. Parameter-Efficient Fine-Tuning(PEFT) methods have been proposed to
address these challenges by freezing most model parameters and training only a
small subset. While PEFT is efficient, it may not outperform full fine-tuning
when high task-specific performance is required. Zeroth-Order (ZO) methods
offer an alternative for fine-tuning the entire pre-trained model by
approximating gradients using only the forward pass, thus eliminating the
computational burden of back-propagation in first-order methods. However, when
implementing ZO methods, a hard prompt is crucial, and relying on simple, fixed
hard prompts may not be optimal. In this paper, we propose a bilevel
optimization framework that complements ZO methods with PEFT to mitigate
sensitivity to hard prompts while efficiently and effectively fine-tuning LLMs.
Our Bilevel ZOFO (Zeroth-Order-First-Order) method employs a double-loop
optimization strategy, where only the gradient of the PEFT model and the
forward pass of the base model are required. We provide convergence guarantees
for Bilevel ZOFO. Empirically, we demonstrate that Bilevel ZOFO outperforms
both PEFT and ZO methods in single-task settings while maintaining similar
memory efficiency. Additionally, we show its strong potential for multitask
learning. Compared to current first-order meta-training algorithms for
multitask learning, our method has significantly lower computational demands
while maintaining or improving performance.
|
2502.03607
|
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
|
cs.RO cs.AI cs.LG
|
Recent advances in diffusion models hold significant potential in robotics,
enabling the generation of diverse and smooth trajectories directly from raw
representations of the environment. Despite this promise, applying diffusion
models to motion planning remains challenging due to their difficulty in
enforcing critical constraints, such as collision avoidance and kinematic
feasibility. These limitations become even more pronounced in Multi-Robot
Motion Planning (MRMP), where multiple robots must coordinate in shared spaces.
To address this challenge, this work proposes Simultaneous MRMP Diffusion
(SMD), a novel approach integrating constrained optimization into the diffusion
sampling process to produce collision-free, kinematically feasible
trajectories. Additionally, the paper introduces a comprehensive MRMP benchmark
to evaluate trajectory planning algorithms across scenarios with varying robot
densities, obstacle complexities, and motion constraints. Experimental results
show SMD consistently outperforms classical and learning-based motion planners,
achieving higher success rates and efficiency in complex multi-robot
environments.
|
2502.03608
|
(GG) MoE vs. MLP on Tabular Data
|
cs.LG cs.AI
|
In recent years, significant efforts have been directed toward adapting
modern neural network architectures for tabular data. However, despite their
larger number of parameters and longer training and inference times, these
models often fail to consistently outperform vanilla multilayer perceptron
(MLP) neural networks. Moreover, MLP-based ensembles have recently demonstrated
superior performance and efficiency compared to advanced deep learning methods.
Therefore, rather than focusing on building deeper and more complex deep
learning models, we propose investigating whether MLP neural networks can be
replaced with more efficient architectures without sacrificing performance. In
this paper, we first introduce GG MoE, a mixture-of-experts (MoE) model with a
Gumbel-Softmax gating function. We then demonstrate that GG MoE with an
embedding layer achieves the highest performance across $38$ datasets compared
to standard MoE and MLP models. Finally, we show that both MoE and GG MoE
utilize significantly fewer parameters than MLPs, making them a promising
alternative for scaling and ensemble methods.
|
2502.03609
|
Multivariate Conformal Prediction using Optimal Transport
|
stat.ML cs.LG
|
Conformal prediction (CP) quantifies the uncertainty of machine learning
models by constructing sets of plausible outputs. These sets are constructed by
leveraging a so-called conformity score, a quantity computed using the input
point of interest, a prediction model, and past observations. CP sets are then
obtained by evaluating the conformity score of all possible outputs, and
selecting them according to the rank of their scores. Due to this ranking step,
most CP approaches rely on a score functions that are univariate. The challenge
in extending these scores to multivariate spaces lies in the fact that no
canonical order for vectors exists. To address this, we leverage a natural
extension of multivariate score ranking based on optimal transport (OT). Our
method, OTCP, offers a principled framework for constructing conformal
prediction sets in multidimensional settings, preserving distribution-free
coverage guarantees with finite data samples. We demonstrate tangible gains in
a benchmark dataset of multivariate regression problems and address
computational \& statistical trade-offs that arise when estimating conformity
scores through OT maps.
|
2502.03614
|
A Novel Zero-Touch, Zero-Trust, AI/ML Enablement Framework for IoT
Network Security
|
cs.LG cs.AI cs.CR
|
The IoT facilitates a connected, intelligent, and sustainable society;
therefore, it is imperative to protect the IoT ecosystem. The IoT-based 5G and
6G will leverage the use of machine learning and artificial intelligence
(ML/AI) more to pave the way for autonomous and collaborative secure IoT
networks. Zero-touch, zero-trust IoT security with AI and machine learning (ML)
enablement frameworks offers a powerful approach to securing the expanding
landscape of Internet of Things (IoT) devices. This paper presents a novel
framework based on the integration of Zero Trust, Zero Touch, and AI/ML powered
for the detection, mitigation, and prevention of DDoS attacks in modern IoT
ecosystems. The focus will be on the new integrated framework by establishing
zero trust for all IoT traffic, fixed and mobile 5G/6G IoT network traffic, and
data security (quarantine-zero touch and dynamic policy enforcement). We
perform a comparative analysis of five machine learning models, namely,
XGBoost, Random Forest, K-Nearest Neighbors, Stochastic Gradient Descent, and
Native Bayes, by comparing these models based on accuracy, precision, recall,
F1-score, and ROC-AUC. Results show that the best performance in detecting and
mitigating different DDoS vectors comes from the ensemble-based approaches.
|
2502.03616
|
Noncooperative Equilibrium Selection via a Trading-based Auction
|
cs.GT cs.MA
|
Noncooperative multi-agent systems often face coordination challenges due to
conflicting preferences among agents. In particular, agents acting in their own
self-interest can settle on different equilibria, leading to suboptimal
outcomes or even safety concerns. We propose an algorithm named trading auction
for consensus (TACo), a decentralized approach that enables noncooperative
agents to reach consensus without communicating directly or disclosing private
valuations. TACo facilitates coordination through a structured trading-based
auction, where agents iteratively select choices of interest and provably reach
an agreement within an a priori bounded number of steps. A series of numerical
experiments validate that the termination guarantees of TACo hold in practice,
and show that TACo achieves a median performance that minimizes the total cost
across all agents, while allocating resources significantly more fairly than
baseline approaches.
|
2502.03618
|
The Logical Implication Steering Method for Conditional Interventions on
Transformer Generation
|
cs.LG
|
The field of mechanistic interpretability in pre-trained transformer models
has demonstrated substantial evidence supporting the ''linear representation
hypothesis'', which is the idea that high level concepts are encoded as vectors
in the space of activations of a model. Studies also show that model generation
behavior can be steered toward a given concept by adding the concept's vector
to the corresponding activations. We show how to leverage these properties to
build a form of logical implication into models, enabling transparent and
interpretable adjustments that induce a chosen generation behavior in response
to the presence of any given concept. Our method, Logical Implication Model
Steering (LIMS), unlocks new hand engineered reasoning capabilities by
integrating neuro-symbolic logic into pre-trained transformer models.
|
2502.03619
|
Swarm Characteristic Classification using Robust Neural Networks with
Optimized Controllable Inputs
|
cs.LG
|
Having the ability to infer characteristics of autonomous agents would
profoundly revolutionize defense, security, and civil applications. Our
previous work was the first to demonstrate that supervised neural network time
series classification (NN TSC) could rapidly predict the tactics of swarming
autonomous agents in military contexts, providing intelligence to inform
counter-maneuvers. However, most autonomous interactions, especially military
engagements, are fraught with uncertainty, raising questions about the
practicality of using a pretrained classifier. This article addresses that
challenge by leveraging expected operational variations to construct a richer
dataset, resulting in a more robust NN with improved inference performance in
scenarios characterized by significant uncertainties. Specifically, diverse
datasets are created by simulating variations in defender numbers, defender
motions, and measurement noise levels. Key findings indicate that robust NNs
trained on an enriched dataset exhibit enhanced classification accuracy and
offer operational flexibility, such as reducing resources required and offering
adherence to trajectory constraints. Furthermore, we present a new framework
for optimally deploying a trained NN by the defenders. The framework involves
optimizing defender trajectories that elicit adversary responses that maximize
the probability of correct NN tactic classification while also satisfying
operational constraints imposed on the defenders.
|
2502.03620
|
Efficient Optimal PAC Learning
|
cs.LG
|
Recent advances in the binary classification setting by Hanneke [2016b] and
Larsen [2023] have resulted in optimal PAC learners. These learners leverage,
respectively, a clever deterministic subsampling scheme and the classic
heuristic of bagging Breiman [1996]. Both optimal PAC learners use, as a
subroutine, the natural algorithm of empirical risk minimization. Consequently,
the computational cost of these optimal PAC learners is tied to that of the
empirical risk minimizer algorithm. In this work, we seek to provide an
alternative perspective on the computational cost imposed by the link to the
empirical risk minimizer algorithm. To this end, we show the existence of an
optimal PAC learner, which offers a different tradeoff in terms of the
computational cost induced by the empirical risk minimizer.
|
2502.03621
|
DynVFX: Augmenting Real Videos with Dynamic Content
|
cs.CV
|
We present a method for augmenting real-world videos with newly generated
dynamic content. Given an input video and a simple user-provided text
instruction describing the desired content, our method synthesizes dynamic
objects or complex scene effects that naturally interact with the existing
scene over time. The position, appearance, and motion of the new content are
seamlessly integrated into the original footage while accounting for camera
motion, occlusions, and interactions with other dynamic objects in the scene,
resulting in a cohesive and realistic output video. We achieve this via a
zero-shot, training-free framework that harnesses a pre-trained text-to-video
diffusion transformer to synthesize the new content and a pre-trained Vision
Language Model to envision the augmented scene in detail. Specifically, we
introduce a novel inference-based method that manipulates features within the
attention mechanism, enabling accurate localization and seamless integration of
the new content while preserving the integrity of the original scene. Our
method is fully automated, requiring only a simple user instruction. We
demonstrate its effectiveness on a wide range of edits applied to real-world
videos, encompassing diverse objects and scenarios involving both camera and
object motion.
|
2502.03622
|
AdaPhish: AI-Powered Adaptive Defense and Education Resource Against
Deceptive Emails
|
cs.CR cs.AI
|
Phishing attacks remain a significant threat in the digital age, yet
organizations lack effective methods to tackle phishing attacks without leaking
sensitive information. Phish bowl initiatives are a vital part of cybersecurity
efforts against these attacks. However, traditional phish bowls require manual
anonymization and are often limited to internal use. To overcome these
limitations, we introduce AdaPhish, an AI-powered phish bowl platform that
automatically anonymizes and analyzes phishing emails using large language
models (LLMs) and vector databases. AdaPhish achieves real-time detection and
adaptation to new phishing tactics while enabling long-term tracking of
phishing trends. Through automated reporting, adaptive analysis, and real-time
alerts, AdaPhish presents a scalable, collaborative solution for phishing
detection and cybersecurity education.
|
2502.03623
|
Large Teams Overshadow Individual Recognition
|
cs.SI
|
In an ideal world, every scientist's contribution would be fully recognized,
driving collective scientific progress. In reality, however, only a few
scientists are recognized and remembered. Sociologist Robert Merton first
described this disparity between contribution and recognition as the Matthew
Effect, where citations disproportionately favor established scientists, even
when their contributions are no greater than those of junior peers. Merton's
work, however, did not account for coauthored papers, where citations
acknowledge teams rather than individual authors. How do teams affect reward
systems in science? We hypothesize that teams will divide and obscure
intellectual credit, making it even harder to recognize individual
contributions. To test this, we developed and analyzed the world's first
large-scale observational dataset on author contributions, derived from LaTeX
source files of 1.6 million papers authored by 2 million scientists. We also
quantified individual credits within teams using a validated algorithm and
examined their relationship to contributions, accounting for factors such as
team size, career stage, and historical time. Our findings confirm that teams
amplify the Matthew Effect and overshadow individual contributions. As
scientific research shifts from individual efforts to collaborative teamwork,
this study highlights the urgent need for effective credit assignment practices
in team-based science.
|
2502.03627
|
Sorting the Babble in Babel: Assessing the Performance of Language
Detection Algorithms on the OpenAlex Database
|
cs.CL
|
This project aims to compare various language classification procedures,
procedures combining various Python language detection algorithms and
metadata-based corpora extracted from manually-annotated articles sampled from
the OpenAlex database. Following an analysis of precision and recall
performance for each algorithm, corpus, and language as well as of processing
speeds recorded for each algorithm and corpus type, overall procedure
performance at the database level was simulated using probabilistic confusion
matrices for each algorithm, corpus, and language as well as a probabilistic
model of relative article language frequencies for the whole OpenAlex database.
Results show that procedure performance strongly depends on the importance
given to each of the measures implemented: for contexts where precision is
preferred, using the LangID algorithm on the greedy corpus gives the best
results; however, for all cases where recall is considered at least slightly
more important than precision or as soon as processing times are given any kind
of consideration, the procedure combining the FastSpell algorithm and the
Titles corpus outperforms all other alternatives. Given the lack of truly
multilingual, large-scale bibliographic databases, it is hoped that these
results help confirm and foster the unparalleled potential of the OpenAlex
database for cross-linguistic, bibliometric-based research and analysis.
|
2502.03628
|
The Hidden Life of Tokens: Reducing Hallucination of Large
Vision-Language Models via Visual Information Steering
|
cs.CV cs.AI cs.LG
|
Large Vision-Language Models (LVLMs) can reason effectively over both textual
and visual inputs, but they tend to hallucinate syntactically coherent yet
visually ungrounded contents. In this paper, we investigate the internal
dynamics of hallucination by examining the tokens logits rankings throughout
the generation process, revealing three key patterns in how LVLMs process
information: (1) gradual visual information loss -- visually grounded tokens
gradually become less favored throughout generation, and (2) early excitation
-- semantically meaningful tokens achieve peak activation in the layers earlier
than the final layer. (3) hidden genuine information -- visually grounded
tokens though not being eventually decided still retain relatively high
rankings at inference. Based on these insights, we propose VISTA (Visual
Information Steering with Token-logit Augmentation), a training-free
inference-time intervention framework that reduces hallucination while
promoting genuine information. VISTA works by combining two complementary
approaches: reinforcing visual information in activation space and leveraging
early layer activations to promote semantically meaningful decoding. Compared
to existing methods, VISTA requires no external supervision and is applicable
to various decoding strategies. Extensive experiments show that VISTA on
average reduces hallucination by abount 40% on evaluated open-ended generation
task, and it consistently outperforms existing methods on four benchmarks
across four architectures under three decoding strategies.
|
2502.03629
|
REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image
Transformations
|
cs.CV cs.AI cs.CL cs.LG
|
Existing image editing models struggle to meet real-world demands. Despite
excelling in academic benchmarks, they have yet to be widely adopted for real
user needs. Datasets that power these models use artificial edits, lacking the
scale and ecological validity necessary to address the true diversity of user
requests. We introduce REALEDIT, a large-scale image editing dataset with
authentic user requests and human-made edits sourced from Reddit. REALEDIT
includes a test set of 9300 examples to evaluate models on real user requests.
Our results show that existing models fall short on these tasks, highlighting
the need for realistic training data. To address this, we introduce 48K
training examples and train our REALEDIT model, achieving substantial gains -
outperforming competitors by up to 165 Elo points in human judgment and 92
percent relative improvement on the automated VIEScore metric. We deploy our
model on Reddit, testing it on new requests, and receive positive feedback.
Beyond image editing, we explore REALEDIT's potential in detecting edited
images by partnering with a deepfake detection non-profit. Finetuning their
model on REALEDIT data improves its F1-score by 14 percentage points,
underscoring the dataset's value for broad applications.
|
2502.03638
|
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
|
cond-mat.mtrl-sci cs.LG
|
Generating novel crystalline materials has potential to lead to advancements
in fields such as electronics, energy storage, and catalysis. The defining
characteristic of crystals is their symmetry, which plays a central role in
determining their physical properties. However, existing crystal generation
methods either fail to generate materials that display the symmetries of
real-world crystals, or simply replicate the symmetry information from examples
in a database. To address this limitation, we propose SymmCD, a novel
diffusion-based generative model that explicitly incorporates crystallographic
symmetry into the generative process. We decompose crystals into two components
and learn their joint distribution through diffusion: 1) the asymmetric unit,
the smallest subset of the crystal which can generate the whole crystal through
symmetry transformations, and; 2) the symmetry transformations needed to be
applied to each atom in the asymmetric unit. We also use a novel and
interpretable representation for these transformations, enabling generalization
across different crystallographic symmetry groups. We showcase the competitive
performance of SymmCD on a subset of the Materials Project, obtaining diverse
and valid crystals with realistic symmetries and predicted properties.
|
2502.03639
|
Towards Physical Understanding in Video Generation: A 3D Point
Regularization Approach
|
cs.CV
|
We present a novel video generation framework that integrates 3-dimensional
geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D
point trajectories and align them in pixel space. The resulting 3D-aware video
dataset, PointVid, is then used to fine-tune a latent diffusion model, enabling
it to track 2D objects with 3D Cartesian coordinates. Building on this, we
regularize the shape and motion of objects in the video to eliminate undesired
artifacts, \eg, nonphysical deformation. Consequently, we enhance the quality
of generated RGB videos and alleviate common issues like object morphing, which
are prevalent in current video models due to a lack of shape awareness. With
our 3D augmentation and regularization, our model is capable of handling
contact-rich scenarios such as task-oriented videos. These videos involve
complex interactions of solids, where 3D information is essential for
perceiving deformation and contact. Furthermore, our model improves the overall
quality of video generation by promoting the 3D consistency of moving objects
and reducing abrupt changes in shape and motion.
|
2502.03640
|
Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal
Control
|
cs.RO cs.LG cs.MA math.OC
|
Control policies that can achieve high task performance and satisfy safety
constraints are desirable for any system, including multi-agent systems (MAS).
One promising technique for ensuring the safety of MAS is distributed control
barrier functions (CBF). However, it is difficult to design distributed
CBF-based policies for MAS that can tackle unknown discrete-time dynamics,
partial observability, changing neighborhoods, and input constraints,
especially when a distributed high-performance nominal policy that can achieve
the task is unavailable. To tackle these challenges, we propose DGPPO, a new
framework that simultaneously learns both a discrete graph CBF which handles
neighborhood changes and input constraints, and a distributed high-performance
safe policy for MAS with unknown discrete-time dynamics. We empirically
validate our claims on a suite of multi-agent tasks spanning three different
simulation engines. The results suggest that, compared with existing methods,
our DGPPO framework obtains policies that achieve high task performance
(matching baselines that ignore the safety constraints), and high safety rates
(matching the most conservative baselines), with a constant set of
hyperparameters across all environments.
|
2502.03643
|
Context-Preserving Gradient Modulation for Large Language Models: A
Novel Approach to Semantic Consistency in Long-Form Text Generation
|
cs.CL
|
Maintaining semantic consistency over extended text sequences remains a
fundamental challenge in long-form text generation, where conventional training
methodologies often struggle to prevent contextual drift and coherence
degradation. A novel gradient modulation approach is introduced, designed to
adjust parameter updates dynamically in response to contextual relevance,
ensuring that generated text remains aligned with prior discourse. By
integrating a modulation function that selectively amplifies or attenuates
gradients based on learned contextual dependencies, the proposed method
enhances the stability of model-generated narratives without imposing
significant computational overhead. Comparative evaluations against baseline
models reveal improvements in coherence, contextual retention, and long-range
dependency tracking, demonstrating the effectiveness of modifying the learning
process at the gradient level. The results indicate that sentence structure
variability and lexical diversity benefit from this approach, mitigating
repetitive phrasing and improving adaptability across diverse linguistic
contexts. Statistical validation of coherence metrics further substantiates the
observed enhancements, with a significant reduction in inconsistencies emerging
as a direct consequence of the modulation mechanism. Computational efficiency
assessments confirm that the framework achieves these gains without requiring
substantial modifications to the underlying architecture, ensuring
compatibility with existing optimization workflows.
|
2502.03647
|
Looking for the Inner Music: Probing LLMs' Understanding of Literary
Style
|
cs.CL cs.LG
|
Recent work has demonstrated that language models can be trained to identify
the author of much shorter literary passages than has been thought feasible for
traditional stylometry. We replicate these results for authorship and extend
them to a new dataset measuring novel genre. We find that LLMs are able to
distinguish authorship and genre, but they do so in different ways. Some models
seem to rely more on memorization, while others benefit more from training to
learn author/genre characteristics. We then use three methods to probe one
high-performing LLM for features that define style. These include direct
syntactic ablations to input text as well as two methods that look at model
internals. We find that authorial style is easier to define than genre-level
style and is more impacted by minor syntactic decisions and contextual word
usage. However, some traits like pronoun usage and word order prove significant
for defining both kinds of literary style.
|
2502.03649
|
All-in-One Image Compression and Restoration
|
cs.CV
|
Visual images corrupted by various types and levels of degradations are
commonly encountered in practical image compression. However, most existing
image compression methods are tailored for clean images, therefore struggling
to achieve satisfying results on these images. Joint compression and
restoration methods typically focus on a single type of degradation and fail to
address a variety of degradations in practice. To this end, we propose a
unified framework for all-in-one image compression and restoration, which
incorporates the image restoration capability against various degradations into
the process of image compression. The key challenges involve distinguishing
authentic image content from degradations, and flexibly eliminating various
degradations without prior knowledge. Specifically, the proposed framework
approaches these challenges from two perspectives: i.e., content information
aggregation, and degradation representation aggregation. Extensive experiments
demonstrate the following merits of our model: 1) superior rate-distortion (RD)
performance on various degraded inputs while preserving the performance on
clean data; 2) strong generalization ability to real-world and unseen
scenarios; 3) higher computing efficiency over compared methods. Our code is
available at https://github.com/ZeldaM1/All-in-one.
|
2502.03650
|
Rule-based Evolving Fuzzy System for Time Series Forecasting: New
Perspectives Based on Type-2 Fuzzy Sets Measures Approach
|
stat.ML cs.LG
|
Real-world data contain uncertainty and variations that can be correlated to
external variables, known as randomness. An alternative cause of randomness is
chaos, which can be an important component of chaotic time series. One of the
existing methods to deal with this type of data is the use of the evolving
Fuzzy Systems (eFSs), which have been proven to be a powerful class of models
for time series forecasting, due to their autonomy to handle the data and
highly complex problems in real-world applications. However, due to its working
structure, type-2 fuzzy sets can outperform type-1 fuzzy sets for highly
uncertain scenarios. We then propose ePL-KRLS-FSM+, an enhanced class of
evolving fuzzy modeling approach that combines participatory learning (PL), a
kernel recursive least squares method (KRLS), type-2 fuzzy logic and data
transformation into fuzzy sets (FSs). This improvement allows to create and
measure type-2 fuzzy sets for better handling uncertainties in the data,
generating a model that can predict chaotic data with increased accuracy. The
model is evaluated using two complex datasets: the chaotic time series
Mackey-Glass delay differential equation with different degrees of chaos, and
the main stock index of the Taiwan Capitalization Weighted Stock Index - TAIEX.
Model performance is compared to related state-of-the-art rule-based eFS models
and classical approaches and is analyzed in terms of error metrics, runtime and
the number of final rules. Forecasting results show that the proposed model is
competitive and performs consistently compared with type-1 models, also
outperforming other forecasting methods by showing the lowest error metrics and
number of final rules.
|
2502.03652
|
The Cost of Shuffling in Private Gradient Based Optimization
|
cs.LG
|
We consider the problem of differentially private (DP) convex empirical risk
minimization (ERM). While the standard DP-SGD algorithm is theoretically
well-established, practical implementations often rely on shuffled gradient
methods that traverse the training data sequentially rather than sampling with
replacement in each iteration. Despite their widespread use, the theoretical
privacy-accuracy trade-offs of private shuffled gradient methods
(\textit{DP-ShuffleG}) remain poorly understood, leading to a gap between
theory and practice. In this work, we leverage privacy amplification by
iteration (PABI) and a novel application of Stein's lemma to provide the first
empirical excess risk bound of \textit{DP-ShuffleG}. Our result shows that data
shuffling results in worse empirical excess risk for \textit{DP-ShuffleG}
compared to DP-SGD. To address this limitation, we propose
\textit{Interleaved-ShuffleG}, a hybrid approach that integrates public data
samples in private optimization. By alternating optimization steps that use
private and public samples, \textit{Interleaved-ShuffleG} effectively reduces
empirical excess risk. Our analysis introduces a new optimization framework
with surrogate objectives, adaptive noise injection, and a dissimilarity
metric, which can be of independent interest. Our experiments on diverse
datasets and tasks demonstrate the superiority of \textit{Interleaved-ShuffleG}
over several baselines.
|
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