id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.01941 | Can LLMs Maintain Fundamental Abilities under KV Cache Compression? | cs.CL cs.AI | This paper investigates an under-explored challenge in large language models
(LLMs): the impact of KV cache compression methods on LLMs' fundamental
capabilities. While existing methods achieve impressive compression ratios on
long-context benchmarks, their effects on core model capabilities remain
understudied. We p... |
2502.01942 | Boundary-Driven Table-Filling with Cross-Granularity Contrastive
Learning for Aspect Sentiment Triplet Extraction | cs.CL cs.AI | The Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect
terms, opinion terms, and their corresponding sentiment polarity from a given
sentence. It remains one of the most prominent subtasks in fine-grained
sentiment analysis. Most existing approaches frame triplet extraction as a 2D
table-filling p... |
2502.01943 | DAMA: Data- and Model-aware Alignment of Multi-modal LLMs | cs.CV | Direct Preference Optimization (DPO) has shown effectiveness in aligning
multi-modal large language models (MLLM) with human preferences. However,
existing methods exhibit an imbalanced responsiveness to the data of varying
hardness, tending to overfit on the easy-to-distinguish data while underfitting
on the hard-to... |
2502.01946 | HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for
Multi-session Radar SLAM | cs.RO cs.CV | Recently, radars have been widely featured in robotics for their robustness
in challenging weather conditions. Two commonly used radar types are spinning
radars and phased-array radars, each offering distinct sensor characteristics.
Existing datasets typically feature only a single type of radar, leading to the
devel... |
2502.01949 | LAYOUTDREAMER: Physics-guided Layout for Text-to-3D Compositional Scene
Generation | cs.CV cs.AI cs.GR | Recently, the field of text-guided 3D scene generation has garnered
significant attention. High-quality generation that aligns with physical
realism and high controllability is crucial for practical 3D scene
applications. However, existing methods face fundamental limitations: (i)
difficulty capturing complex relatio... |
2502.01951 | On the Emergence of Position Bias in Transformers | cs.LG | Recent studies have revealed various manifestations of position bias in
transformer architectures, from the "lost-in-the-middle" phenomenon to
attention sinks, yet a comprehensive theoretical understanding of how attention
masks and positional encodings shape these biases remains elusive. This paper
introduces a nove... |
2502.01953 | Local minima of the empirical risk in high dimension: General theorems
and convex examples | stat.ML cs.LG math.ST stat.TH | We consider a general model for high-dimensional empirical risk minimization
whereby the data $\mathbf{x}_i$ are $d$-dimensional isotropic Gaussian vectors,
the model is parametrized by $\mathbf{\Theta}\in\mathbb{R}^{d\times k}$, and
the loss depends on the data via the projection
$\mathbf{\Theta}^\mathsf{T}\mathbf{x... |
2502.01954 | Constrained belief updates explain geometric structures in transformer
representations | cs.LG | What computational structures emerge in transformers trained on next-token
prediction? In this work, we provide evidence that transformers implement
constrained Bayesian belief updating -- a parallelized version of partial
Bayesian inference shaped by architectural constraints. To do this, we
integrate the model-agno... |
2502.01956 | DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement
Learning Agents | cs.RO cs.AI cs.LG | In this paper, we address the challenge of long-horizon visual planning tasks
using Hierarchical Reinforcement Learning (HRL). Our key contribution is a
Discrete Hierarchical Planning (DHP) method, an alternative to traditional
distance-based approaches. We provide theoretical foundations for the method
and demonstra... |
2502.01959 | MATCNN: Infrared and Visible Image Fusion Method Based on Multi-scale
CNN with Attention Transformer | cs.CV | While attention-based approaches have shown considerable progress in
enhancing image fusion and addressing the challenges posed by long-range
feature dependencies, their efficacy in capturing local features is compromised
by the lack of diverse receptive field extraction techniques. To overcome the
shortcomings of ex... |
2502.01960 | MPIC: Position-Independent Multimodal Context Caching System for
Efficient MLLM Serving | cs.LG | The context caching technique is employed to accelerate the Multimodal Large
Language Model (MLLM) inference by prevailing serving platforms currently.
However, this approach merely reuses the Key-Value (KV) cache of the initial
sequence of prompt, resulting in full KV cache recomputation even if the prefix
differs s... |
2502.01961 | Hierarchical Consensus Network for Multiview Feature Learning | cs.CV cs.LG | Multiview feature learning aims to learn discriminative features by
integrating the distinct information in each view. However, most existing
methods still face significant challenges in learning view-consistency
features, which are crucial for effective multiview learning. Motivated by the
theories of CCA and contra... |
2502.01962 | Memory Efficient Transformer Adapter for Dense Predictions | cs.CV | While current Vision Transformer (ViT) adapter methods have shown promising
accuracy, their inference speed is implicitly hindered by inefficient memory
access operations, e.g., standard normalization and frequent reshaping. In this
work, we propose META, a simple and fast ViT adapter that can improve the
model's mem... |
2502.01968 | Token Cleaning: Fine-Grained Data Selection for LLM Supervised
Fine-Tuning | cs.CL cs.AI | Recent studies show that in supervised fine-tuning (SFT) of large language
models (LLMs), data quality matters more than quantity. While most data
cleaning methods concentrate on filtering entire samples, the quality of
individual tokens within a sample can vary significantly. After pre-training,
even in high-quality... |
2502.01969 | Mitigating Object Hallucinations in Large Vision-Language Models via
Attention Calibration | cs.CV cs.AI | Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning
capabilities but remain highly susceptible to object hallucination, where
models generate responses that are not factually aligned with the visual
content. Recent works attribute this issue to an inherent bias of LVLMs where
vision token att... |
2502.01971 | Bottom-Up Reputation Promotes Cooperation with Multi-Agent Reinforcement
Learning | cs.MA | Reputation serves as a powerful mechanism for promoting cooperation in
multi-agent systems, as agents are more inclined to cooperate with those of
good social standing. While existing multi-agent reinforcement learning methods
typically rely on predefined social norms to assign reputations, the question
of how a popu... |
2502.01972 | Layer Separation: Adjustable Joint Space Width Images Synthesis in
Conventional Radiography | eess.IV cs.AI cs.CV cs.LG | Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by
joint inflammation and progressive structural damage. Joint space width (JSW)
is a critical indicator in conventional radiography for evaluating disease
progression, which has become a prominent research topic in computer-aided
diagnostic (CAD... |
2502.01976 | CITER: Collaborative Inference for Efficient Large Language Model
Decoding with Token-Level Routing | cs.CL cs.AI cs.LG cs.PF | Large language models have achieved remarkable success in various tasks but
suffer from high computational costs during inference, limiting their
deployment in resource-constrained applications. To address this issue, we
propose a novel CITER (Collaborative Inference with Token-lEvel Routing)
framework that enables e... |
2502.01977 | AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations
from LLMs | cs.CV | User interface understanding with vision-language models has received much
attention due to its potential for enabling next-generation software
automation. However, existing UI datasets either only provide large-scale
context-free element annotations or contextualized functional descriptions for
elements at a much sm... |
2502.01979 | Gradient-Regularized Latent Space Modulation in Large Language Models
for Structured Contextual Synthesis | cs.CL | Generating structured textual content requires mechanisms that enforce
coherence, stability, and adherence to predefined constraints while maintaining
semantic fidelity. Conventional approaches often rely on rule-based heuristics
or fine-tuning strategies that lack flexibility and generalizability across
diverse task... |
2502.01980 | Generative Data Mining with Longtail-Guided Diffusion | cs.LG cs.AI | It is difficult to anticipate the myriad challenges that a predictive model
will encounter once deployed. Common practice entails a reactive, cyclical
approach: model deployment, data mining, and retraining. We instead develop a
proactive longtail discovery process by imagining additional data during
training. In par... |
2502.01983 | Diagrammatics of information | math-ph cs.IT math.IT math.MP | We introduce a diagrammatic perspective for Shannon entropy created by the
first author and Mikhail Khovanov and connect it to information theory and
mutual information. We also give two complete proofs that the $5$-term
dilogarithm deforms to the $4$-term infinitesimal dilogarithm.
|
2502.01984 | Efficient Covering Using Reed--Solomon Codes | cs.IT eess.SP math.IT | We propose an efficient algorithm to find a Reed-Solomon (RS) codeword at a
distance within the covering radius of the code from any point in its ambient
Hamming space. To the best of the authors' knowledge, this is the first attempt
of its kind to solve the covering problem for RS codes. The proposed algorithm
lever... |
2502.01985 | Ilargi: a GPU Compatible Factorized ML Model Training Framework | cs.LG cs.DC | The machine learning (ML) training over disparate data sources traditionally
involves materialization, which can impose substantial time and space overhead
due to data movement and replication. Factorized learning, which leverages
direct computation on disparate sources through linear algebra (LA) rewriting,
has emer... |
2502.01986 | DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature
Extraction for Hyperspectral Image Classification | cs.CV eess.IV | Hyperspectral image classification presents challenges due to spectral
redundancy and complex spatial-spectral dependencies. This paper proposes a
novel framework, DCT-Mamba3D, for hyperspectral image classification.
DCT-Mamba3D incorporates: (1) a 3D spectral-spatial decorrelation module that
applies 3D discrete cos... |
2502.01987 | Online Adaptive Traversability Estimation through Interaction for
Unstructured, Densely Vegetated Environments | cs.RO | Navigating densely vegetated environments poses significant challenges for
autonomous ground vehicles. Learning-based systems typically use prior and
in-situ data to predict terrain traversability but often degrade in performance
when encountering out-of-distribution elements caused by rapid environmental
changes or ... |
2502.01988 | ReMiDi: Reconstruction of Microstructure Using a Differentiable
Diffusion MRI Simulator | eess.IV cs.GR cs.LG physics.med-ph | We propose ReMiDi, a novel method for inferring neuronal microstructure as
arbitrary 3D meshes using a differentiable diffusion Magnetic Resonance Imaging
(dMRI) simulator. We first implemented in PyTorch a differentiable dMRI
simulator that simulates the forward diffusion process using a finite-element
method on an ... |
2502.01989 | T-SCEND: Test-time Scalable MCTS-enhanced Diffusion Model | cs.LG | We introduce Test-time Scalable MCTS-enhanced Diffusion Model (T-SCEND), a
novel framework that significantly improves diffusion model's reasoning
capabilities with better energy-based training and scaling up test-time
computation. We first show that na\"ively scaling up inference budget for
diffusion models yields m... |
2502.01990 | Rethinking Timesteps Samplers and Prediction Types | cs.LG cs.CV | Diffusion models suffer from the huge consumption of time and resources to
train. For example, diffusion models need hundreds of GPUs to train for several
weeks for a high-resolution generative task to meet the requirements of an
extremely large number of iterations and a large batch size. Training diffusion
models b... |
2502.01991 | Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study
on Vaccination Debate on Social Media | cs.CL cs.AI cs.CY cs.HC cs.SI | Nowadays, social media is pivotal in shaping public discourse, especially on
polarizing issues like vaccination, where diverse moral perspectives influence
individual opinions. In NLP, data scarcity and complexity of psycholinguistic
tasks, such as identifying morality frames, make relying solely on human
annotators ... |
2502.01992 | FinRLlama: A Solution to LLM-Engineered Signals Challenge at FinRL
Contest 2024 | q-fin.TR cs.LG | In response to Task II of the FinRL Challenge at ACM ICAIF 2024, this study
proposes a novel prompt framework for fine-tuning large language models (LLM)
with Reinforcement Learning from Market Feedback (RLMF). Our framework
incorporates market-specific features and short-term price dynamics to generate
more precise ... |
2502.01993 | One Diffusion Step to Real-World Super-Resolution via Flow Trajectory
Distillation | cs.CV | Diffusion models (DMs) have significantly advanced the development of
real-world image super-resolution (Real-ISR), but the computational cost of
multi-step diffusion models limits their application. One-step diffusion models
generate high-quality images in a one sampling step, greatly reducing
computational overhead... |
2502.01995 | Theoretical and Practical Analysis of Fr\'echet Regression via
Comparison Geometry | stat.ML cs.AI cs.LG | Fr\'echet regression extends classical regression methods to non-Euclidean
metric spaces, enabling the analysis of data relationships on complex
structures such as manifolds and graphs. This work establishes a rigorous
theoretical analysis for Fr\'echet regression through the lens of comparison
geometry which leads t... |
2502.01998 | Data Guard: A Fine-grained Purpose-based Access Control System for Large
Data Warehouses | cs.DB | The last few years have witnessed a spate of data protection regulations in
conjunction with an ever-growing appetite for data usage in large businesses,
thus presenting significant challenges for businesses to maintain compliance.
To address this conflict, we present Data Guard - a fine-grained, purpose-based
access... |
2502.02002 | The Ball-Proximal (="Broximal") Point Method: a New Algorithm,
Convergence Theory, and Applications | math.OC cs.LG stat.ML | Non-smooth and non-convex global optimization poses significant challenges
across various applications, where standard gradient-based methods often
struggle. We propose the Ball-Proximal Point Method, Broximal Point Method, or
Ball Point Method (BPM) for short - a novel algorithmic framework inspired by
the classical... |
2502.02004 | Wavelet-based Positional Representation for Long Context | cs.CL | In the realm of large-scale language models, a significant challenge arises
when extrapolating sequences beyond the maximum allowable length. This is
because the model's position embedding mechanisms are limited to positions
encountered during training, thus preventing effective representation of
positions in longer ... |
2502.02007 | Reasoning Bias of Next Token Prediction Training | cs.CL cs.LG | Since the inception of Large Language Models (LLMs), the quest to efficiently
train them for superior reasoning capabilities has been a pivotal challenge.
The dominant training paradigm for LLMs is based on next token prediction
(NTP). Alternative methodologies, called Critical Token Prediction (CTP),
focused exclusi... |
2502.02009 | LLMSecConfig: An LLM-Based Approach for Fixing Software Container
Misconfigurations | cs.SE cs.AI cs.CR cs.LG | Security misconfigurations in Container Orchestrators (COs) can pose serious
threats to software systems. While Static Analysis Tools (SATs) can effectively
detect these security vulnerabilities, the industry currently lacks automated
solutions capable of fixing these misconfigurations. The emergence of Large
Languag... |
2502.02013 | Layer by Layer: Uncovering Hidden Representations in Language Models | cs.LG cs.AI cs.CL | From extracting features to generating text, the outputs of large language
models (LLMs) typically rely on their final layers, following the conventional
wisdom that earlier layers capture only low-level cues. However, our analysis
shows that intermediate layers can encode even richer representations, often
improving... |
2502.02014 | Analytical Lyapunov Function Discovery: An RL-based Generative Approach | cs.LG cs.AI cs.SC cs.SY eess.SY | Despite advances in learning-based methods, finding valid Lyapunov functions
for nonlinear dynamical systems remains challenging. Current neural network
approaches face two main issues: challenges in scalable verification and
limited interpretability. To address these, we propose an end-to-end framework
using transfo... |
2502.02015 | The Wisdom of Intellectually Humble Networks | cs.SI | People's collectively held beliefs can have significant social implications,
including on democratic processes and policies. Unfortunately, as people
interact with peers to form and update their beliefs, various cognitive and
social biases can hinder their collective wisdom. In this paper, we probe
whether and how th... |
2502.02016 | A Periodic Bayesian Flow for Material Generation | cs.LG cs.AI | Generative modeling of crystal data distribution is an important yet
challenging task due to the unique periodic physical symmetry of crystals.
Diffusion-based methods have shown early promise in modeling crystal
distribution. More recently, Bayesian Flow Networks were introduced to
aggregate noisy latent variables, ... |
2502.02017 | Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via
Topology Alignment | cs.SI cs.AI cs.LG | Recent advances in CV and NLP have inspired researchers to develop
general-purpose graph foundation models through pre-training across diverse
domains. However, a fundamental challenge arises from the substantial
differences in graph topologies across domains. Additionally, real-world graphs
are often sparse and pron... |
2502.02018 | Dual Ensembled Multiagent Q-Learning with Hypernet Regularizer | cs.MA cs.LG | Overestimation in single-agent reinforcement learning has been extensively
studied. In contrast, overestimation in the multiagent setting has received
comparatively little attention although it increases with the number of agents
and leads to severe learning instability. Previous works concentrate on
reducing overest... |
2502.02020 | Causal bandits with backdoor adjustment on unknown Gaussian DAGs | cs.LG stat.ME | The causal bandit problem aims to sequentially learn the intervention that
maximizes the expectation of a reward variable within a system governed by a
causal graph. Most existing approaches assume prior knowledge of the graph
structure, or impose unrealistically restrictive conditions on the graph. In
this paper, we... |
2502.02021 | Multi-illuminant Color Constancy via Multi-scale Illuminant Estimation
and Fusion | cs.CV eess.IV | Multi-illuminant color constancy methods aim to eliminate local color casts
within an image through pixel-wise illuminant estimation. Existing methods
mainly employ deep learning to establish a direct mapping between an image and
its illumination map, which neglects the impact of image scales. To alleviate
this probl... |
2502.02024 | UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image
segmentation | eess.IV cs.CV | Recent advancements have highlighted the Mamba framework, a state-space model
known for its efficiency in capturing long-range dependencies with linear
computational complexity. While Mamba has shown competitive performance in
medical image segmentation, it encounters difficulties in modeling local
features due to th... |
2502.02026 | ContinuouSP: Generative Model for Crystal Structure Prediction with
Invariance and Continuity | cs.LG cond-mat.mtrl-sci | The discovery of new materials using crystal structure prediction (CSP) based
on generative machine learning models has become a significant research topic
in recent years. In this paper, we study invariance and continuity in the
generative machine learning for CSP. We propose a new model, called
ContinuouSP, which e... |
2502.02027 | From Fog to Failure: How Dehazing Can Harm Clear Image Object Detection | cs.CV cs.AI | This study explores the challenges of integrating human visual cue-based
dehazing into object detection, given the selective nature of human perception.
While human vision adapts dynamically to environmental conditions,
computational dehazing does not always enhance detection uniformly. We propose
a multi-stage frame... |
2502.02028 | Fine-tuning Language Models for Recipe Generation: A Comparative
Analysis and Benchmark Study | cs.CL cs.AI | This research presents an exploration and study of the recipe generation task
by fine-tuning various very small language models, with a focus on developing
robust evaluation metrics and comparing across different language models the
open-ended task of recipe generation. This study presents extensive experiments
with ... |
2502.02029 | MORPH-LER: Log-Euclidean Regularization for Population-Aware Image
Registration | cs.CV cs.LG | Spatial transformations that capture population-level morphological
statistics are critical for medical image analysis. Commonly used smoothness
regularizers for image registration fail to integrate population statistics,
leading to anatomically inconsistent transformations. Inverse consistency
regularizers promote g... |
2502.02032 | Heteroscedastic Double Bayesian Elastic Net | stat.ME cs.AI stat.ML | In many practical applications, regression models are employed to uncover
relationships between predictors and a response variable, yet the common
assumption of constant error variance is frequently violated. This issue is
further compounded in high-dimensional settings where the number of predictors
exceeds the samp... |
2502.02033 | On Iso-Dual MDS Codes From Elliptic Curves | cs.IT math.IT | For a linear code $C$ over a finite field, if its dual code $C^{\perp}$ is
equivalent to itself, then the code $C$ is said to be {\it isometry-dual}. In
this paper, we first confirm a conjecture about the isometry-dual MDS elliptic
codes proposed by Han and Ren. Subsequently, two constructions of isometry-dual
maximu... |
2502.02034 | Improving Wireless Federated Learning via Joint Downlink-Uplink
Beamforming over Analog Transmission | cs.IT eess.SP math.IT | Federated learning (FL) over wireless networks using analog transmission can
efficiently utilize the communication resource but is susceptible to errors
caused by noisy wireless links. In this paper, assuming a multi-antenna base
station, we jointly design downlink-uplink beamforming to maximize FL training
convergen... |
2502.02036 | From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for
Telemanipulation | cs.RO cs.AI | This paper presents a teleoperation system for controlling a redundant degree
of freedom robot manipulator using human arm gestures. We propose a GRU-based
Variational Autoencoder to learn a latent representation of the manipulator's
configuration space, capturing its complex joint kinematics. A fully connected
neura... |
2502.02040 | M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer
Inference | cs.CL cs.AI cs.LG | Residual transformations enhance the representational depth and expressive
power of large language models (LLMs). However, applying static residual
transformations across all tokens in auto-regressive generation leads to a
suboptimal trade-off between inference efficiency and generation fidelity.
Existing methods, in... |
2502.02046 | Contextual Memory Reweaving in Large Language Models Using Layered
Latent State Reconstruction | cs.CL | Memory retention challenges in deep neural architectures have ongoing
limitations in the ability to process and recall extended contextual
information. Token dependencies degrade as sequence length increases, leading
to a decline in coherence and factual consistency across longer outputs. A
structured approach is int... |
2502.02047 | AmaSQuAD: A Benchmark for Amharic Extractive Question Answering | cs.CL | This research presents a novel framework for translating extractive
question-answering datasets into low-resource languages, as demonstrated by the
creation of the AmaSQuAD dataset, a translation of SQuAD 2.0 into Amharic. The
methodology addresses challenges related to misalignment between translated
questions and a... |
2502.02048 | Efficient Domain Adaptation of Multimodal Embeddings using Constrastive
Learning | cs.LG cs.CL cs.CV | Recent advancements in machine learning (ML), natural language processing
(NLP), and foundational models have shown promise for real-life applications in
critical, albeit compute-constrainted fields like healthcare.
In such areas, combining foundational models with supervised ML offers
potential for automating task... |
2502.02050 | RECCS: Realistic Cluster Connectivity Simulator for Synthetic Network
Generation | cs.SI | The limited availability of useful ground-truth communities in real-world
networks presents a challenge to evaluating and selecting a "best" community
detection method for a given network or family of networks. The use of
synthetic networks with planted ground-truths is one way to address this
challenge. While severa... |
2502.02051 | Sound Judgment: Properties of Consequential Sounds Affecting
Human-Perception of Robots | cs.RO cs.HC cs.SD eess.AS | Positive human-perception of robots is critical to achieving sustained use of
robots in shared environments. One key factor affecting human-perception of
robots are their sounds, especially the consequential sounds which robots (as
machines) must produce as they operate. This paper explores qualitative
responses from... |
2502.02052 | Multimaterial topology optimization for finite strain elastoplasticity:
theory, methods, and applications | cs.CE math.OC | Plasticity is inherent to many engineering materials such as metals. While it
can degrade the load-carrying capacity of structures via material yielding, it
can also protect structures through plastic energy dissipation. To fully
harness plasticity, here we present the theory, method, and application of a
topology op... |
2502.02054 | RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for
Vision-Based Drone Navigation | cs.RO cs.AI cs.CV cs.LG | This paper introduces a learning-based visual planner for agile drone flight
in cluttered environments. The proposed planner generates collision-free
waypoints in milliseconds, enabling drones to perform agile maneuvers in
complex environments without building separate perception, mapping, and
planning modules. Learn... |
2502.02060 | CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for
Sustainable Maritime Logistics | cs.AI cs.MA | Addressing global challenges such as greenhouse gas emissions and resource
inequity demands advanced AI-driven coordination among autonomous agents. We
propose CH-MARL (Constrained Hierarchical Multiagent Reinforcement Learning), a
novel framework that integrates hierarchical decision-making with dynamic
constraint e... |
2502.02061 | Reason4Rec: Large Language Models for Recommendation with Deliberative
User Preference Alignment | cs.IR | While recent advancements in aligning Large Language Models (LLMs) with
recommendation tasks have shown great potential and promising performance
overall, these aligned recommendation LLMs still face challenges in complex
scenarios. This is primarily due to the current alignment approach focusing on
optimizing LLMs t... |
2502.02063 | CASIM: Composite Aware Semantic Injection for Text to Motion Generation | cs.CV cs.AI cs.GR | Recent advances in generative modeling and tokenization have driven
significant progress in text-to-motion generation, leading to enhanced quality
and realism in generated motions. However, effectively leveraging textual
information for conditional motion generation remains an open challenge. We
observe that current ... |
2502.02066 | Anticipate & Act : Integrating LLMs and Classical Planning for Efficient
Task Execution in Household Environments | cs.RO cs.CL cs.LG | Assistive agents performing household tasks such as making the bed or cooking
breakfast often compute and execute actions that accomplish one task at a time.
However, efficiency can be improved by anticipating upcoming tasks and
computing an action sequence that jointly achieves these tasks.
State-of-the-art methods ... |
2502.02067 | AdaptBot: Combining LLM with Knowledge Graphs and Human Input for
Generic-to-Specific Task Decomposition and Knowledge Refinement | cs.RO cs.AI cs.CL cs.LG | Embodied agents assisting humans are often asked to complete a new task in a
new scenario. An agent preparing a particular dish in the kitchen based on a
known recipe may be asked to prepare a new dish or to perform cleaning tasks in
the storeroom. There may not be sufficient resources, e.g., time or labeled
examples... |
2502.02068 | Robust and Secure Code Watermarking for Large Language Models via
ML/Crypto Codesign | cs.CR cs.CL cs.LG | This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign
watermarking framework that regulates LLM-generated code to avoid intellectual
property rights violations and inappropriate misuse in software development.
High-quality watermarks adhering to the detectability-fidelity-robustness
tri-objective a... |
2502.02069 | LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models | cs.CV | The rapid advancements in vision-language models (VLMs), such as CLIP, have
intensified the need to address distribution shifts between training and
testing datasets. Although prior Test-Time Training (TTT) techniques for VLMs
have demonstrated robust performance, they predominantly rely on tuning text
prompts, a pro... |
2502.02071 | Sequential Multi-objective Multi-agent Reinforcement Learning Approach
for Predictive Maintenance | eess.SY cs.SY | Existing predictive maintenance (PdM) methods typically focus solely on
whether to replace system components without considering the costs incurred by
inspection. However, a well-considered approach should be able to minimize
Remaining Useful Life (RUL) at engine replacement while maximizing inspection
interval. To a... |
2502.02072 | ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for
Detection of Bias, Discrimination and Stereotyping | cs.CL cs.AI cs.CY | The rapid evolution of Large Language Models (LLMs) has transformed natural
language processing but raises critical concerns about biases inherent in their
deployment and use across diverse linguistic and sociocultural contexts. This
paper presents a framework named ASCenD BDS (Adaptable, Stochastic and
Context-aware... |
2502.02074 | Rethinking stance detection: A theoretically-informed research agenda
for user-level inference using language models | cs.CL | Stance detection has emerged as a popular task in natural language processing
research, enabled largely by the abundance of target-specific social media
data. While there has been considerable research on the development of stance
detection models, datasets, and application, we highlight important gaps
pertaining to ... |
2502.02076 | Position Paper: Building Trust in Synthetic Data for Clinical AI | cs.LG cs.CV | Deep generative models and synthetic medical data have shown significant
promise in addressing key challenges in healthcare, such as privacy concerns,
data bias, and the scarcity of realistic datasets. While research in this area
has grown rapidly and demonstrated substantial theoretical potential, its
practical adop... |
2502.02079 | Online Clustering of Dueling Bandits | cs.LG cs.AI | The contextual multi-armed bandit (MAB) is a widely used framework for
problems requiring sequential decision-making under uncertainty, such as
recommendation systems. In applications involving a large number of users, the
performance of contextual MAB can be significantly improved by facilitating
collaboration among... |
2502.02083 | Improving Power Plant CO2 Emission Estimation with Deep Learning and
Satellite/Simulated Data | cs.CV eess.IV | CO2 emissions from power plants, as significant super emitters, contribute
substantially to global warming. Accurate quantification of these emissions is
crucial for effective climate mitigation strategies. While satellite-based
plume inversion offers a promising approach, challenges arise from data
limitations and t... |
2502.02085 | A New Rejection Sampling Approach to $k$-$\mathtt{means}$++ With
Improved Trade-Offs | cs.DS cs.LG | The $k$-$\mathtt{means}$++ seeding algorithm (Arthur & Vassilvitskii, 2007)
is widely used in practice for the $k$-means clustering problem where the goal
is to cluster a dataset $\mathcal{X} \subset \mathbb{R} ^d$ into $k$ clusters.
The popularity of this algorithm is due to its simplicity and provable
guarantee o... |
2502.02088 | IPO: Iterative Preference Optimization for Text-to-Video Generation | cs.CV cs.AI | Video foundation models have achieved significant advancement with the help
of network upgrade as well as model scale-up. However, they are still hard to
meet requirements of applications due to unsatisfied generation quality. To
solve this problem, we propose to align video foundation models with human
preferences f... |
2502.02091 | Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic
Separation | cs.CV | Recent 4D dynamic scene editing methods require editing thousands of 2D
images used for dynamic scene synthesis and updating the entire scene with
additional training loops, resulting in several hours of processing to edit a
single dynamic scene. Therefore, these methods are not scalable with respect to
the temporal ... |
2502.02092 | Sum of Squared Extended {\eta}-{\mu} and {\kappa}-{\mu} RVs: A New
Framework Applied to FR3 and Sub-THz Systems | cs.IT eess.SP math.IT | The analysis of systems operating in future frequency ranges calls for a
proper statistical channel characterization through generalized fading models.
In this paper, we adopt the Extended {\eta}-{\mu} and {\kappa}-{\mu} models to
characterize the propagation in FR3 and the sub-THz band, respectively. For
these model... |
2502.02095 | LongDPO: Unlock Better Long-form Generation Abilities for LLMs via
Critique-augmented Stepwise Information | cs.CL | Long-form generation is crucial for academic writing papers and repo-level
code generation. Despite this, current models, including GPT-4o, still exhibit
unsatisfactory performance. Existing methods that utilize preference learning
with outcome supervision often fail to provide detailed feedback for extended
contexts... |
2502.02096 | Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via
In-the-wild Cascading Flow Optimization | cs.CV | Adversarial attacks are widely used to evaluate model robustness, and in
black-box scenarios, the transferability of these attacks becomes crucial.
Existing generator-based attacks have excellent generalization and
transferability due to their instance-agnostic nature. However, when training
generators for multi-targ... |
2502.02097 | VerteNet -- A Multi-Context Hybrid CNN Transformer for Accurate
Vertebral Landmark Localization in Lateral Spine DXA Images | cs.CV | Lateral Spine Image (LSI) analysis is important for medical diagnosis,
treatment planning, and detailed spinal health assessments. Although modalities
like Computed Tomography and Digital X-ray Imaging are commonly used, Dual
Energy X-ray Absorptiometry (DXA) is often preferred due to lower radiation
exposure, seamle... |
2502.02100 | Topic Modeling in Marathi | cs.CL cs.LG | While topic modeling in English has become a prevalent and well-explored
area, venturing into topic modeling for Indic languages remains relatively
rare. The limited availability of resources, diverse linguistic structures, and
unique challenges posed by Indic languages contribute to the scarcity of
research and appl... |
2502.02103 | Neural Networks Learn Distance Metrics | cs.LG cs.AI stat.ML | Neural networks may naturally favor distance-based representations, where
smaller activations indicate closer proximity to learned prototypes. This
contrasts with intensity-based approaches, which rely on activation magnitudes.
To test this hypothesis, we conducted experiments with six MNIST architectural
variants co... |
2502.02104 | Concept-Aware Latent and Explicit Knowledge Integration for Enhanced
Cognitive Diagnosis | cs.LG | Cognitive diagnosis can infer the students' mastery of specific knowledge
concepts based on historical response logs. However, the existing cognitive
diagnostic models (CDMs) represent students' proficiency via a unidimensional
perspective, which can't assess the students' mastery on each knowledge concept
comprehens... |
2502.02109 | Causally-informed Deep Learning towards Explainable and Generalizable
Outcomes Prediction in Critical Care | cs.LG cs.AI | Recent advances in deep learning (DL) have prompted the development of
high-performing early warning score (EWS) systems, predicting clinical
deteriorations such as acute kidney injury, acute myocardial infarction, or
circulatory failure. DL models have proven to be powerful tools for various
tasks but come with the ... |
2502.02112 | The Induced Matching Distance: A Novel Topological Metric with
Applications in Robotics | math.AT cs.RO | This paper introduces the induced matching distance, a novel topological
metric designed to compare discrete structures represented by a symmetric
non-negative function. We apply this notion to analyze agent trajectories over
time. We use dynamic time warping to measure trajectory similarity and compute
the 0-dimensi... |
2502.02118 | BRIDLE: Generalized Self-supervised Learning with Quantization | cs.LG cs.CV | Self-supervised learning has been a powerful approach for learning meaningful
representations from unlabeled data across various domains, reducing the
reliance on large labeled datasets. Inspired by BERT's success in capturing
deep bidirectional contexts in natural language processing, similar frameworks
have been ad... |
2502.02121 | BILBO: BILevel Bayesian Optimization | cs.LG stat.ML | Bilevel optimization is characterized by a two-level optimization structure,
where the upper-level problem is constrained by optimal lower-level solutions,
and such structures are prevalent in real-world problems. The constraint by
optimal lower-level solutions poses significant challenges, especially in
noisy, const... |
2502.02129 | Deep Neural Cellular Potts Models | cs.LG q-bio.QM | The cellular Potts model (CPM) is a powerful computational method for
simulating collective spatiotemporal dynamics of biological cells. To drive the
dynamics, CPMs rely on physics-inspired Hamiltonians. However, as first
principles remain elusive in biology, these Hamiltonians only approximate the
full complexity of... |
2502.02132 | How Memory in Optimization Algorithms Implicitly Modifies the Loss | cs.LG cs.AI math.OC stat.ML | In modern optimization methods used in deep learning, each update depends on
the history of previous iterations, often referred to as memory, and this
dependence decays fast as the iterates go further into the past. For example,
gradient descent with momentum has exponentially decaying memory through
exponentially av... |
2502.02133 | Synthesis of Model Predictive Control and Reinforcement Learning: Survey
and Classification | eess.SY cs.AI cs.LG cs.SY | The fields of MPC and RL consider two successful control techniques for
Markov decision processes. Both approaches are derived from similar fundamental
principles, and both are widely used in practical applications, including
robotics, process control, energy systems, and autonomous driving. Despite
their similaritie... |
2502.02135 | Standard Neural Computation Alone Is Insufficient for Logical
Intelligence | cs.AI cs.LG | Neural networks, as currently designed, fall short of achieving true logical
intelligence. Modern AI models rely on standard neural
computation-inner-product-based transformations and nonlinear activations-to
approximate patterns from data. While effective for inductive learning, this
architecture lacks the structura... |
2502.02140 | An Information-Theoretic Analysis of Thompson Sampling with Infinite
Action Spaces | stat.ML cs.LG | This paper studies the Bayesian regret of the Thompson Sampling algorithm for
bandit problems, building on the information-theoretic framework introduced by
Russo and Van Roy (2015). Specifically, it extends the rate-distortion analysis
of Dong and Van Roy (2018), which provides near-optimal bounds for linear
bandits... |
2502.02144 | DOC-Depth: A novel approach for dense depth ground truth generation | cs.CV cs.RO | Accurate depth information is essential for many computer vision
applications. Yet, no available dataset recording method allows for fully dense
accurate depth estimation in a large scale dynamic environment. In this paper,
we introduce DOC-Depth, a novel, efficient and easy-to-deploy approach for
dense depth generat... |
2502.02145 | Risk-Aware Driving Scenario Analysis with Large Language Models | cs.AI cs.CL cs.RO | Large Language Models (LLMs) can capture nuanced contextual relationships,
reasoning, and complex problem-solving. By leveraging their ability to process
and interpret large-scale information, LLMs have shown potential to address
domain-specific challenges, including those in autonomous driving systems. This
paper pr... |
2502.02150 | On the Guidance of Flow Matching | cs.CV cs.LG | Flow matching has shown state-of-the-art performance in various generative
tasks, ranging from image generation to decision-making, where guided
generation is pivotal. However, the guidance of flow matching is more general
than and thus substantially different from that of its predecessor, diffusion
models. Therefore... |
2502.02153 | Vulnerability Mitigation for Safety-Aligned Language Models via
Debiasing | cs.AI cs.CL cs.LG | Safety alignment is an essential research topic for real-world AI
applications. Despite the multifaceted nature of safety and trustworthiness in
AI, current safety alignment methods often focus on a comprehensive notion of
safety. By carefully assessing models from the existing safety-alignment
methods, we found that... |
2502.02163 | Progressive Correspondence Regenerator for Robust 3D Registration | cs.CV | Obtaining enough high-quality correspondences is crucial for robust
registration. Existing correspondence refinement methods mostly follow the
paradigm of outlier removal, which either fails to correctly identify the
accurate correspondences under extreme outlier ratios, or select too few
correct correspondences to s... |
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