Your AI Papers Research Assistant
All Research
14188 papers
Large Language Models
5415 papers
Computer Vision
2514 papers
Generative AI
1700 papers
AI Safety & Ethics
1585 papers
Reinforcement Learning
1085 papers
Graph Neural Networks
885 papers
Robotics & Embodied AI
689 papers
Speech & Audio
257 papers
uncategorized
51 papers
Efficient AI
6 papers
AI for Science
1 papers
arxiv_ai
Learning World Models for Interactive Video Generation
Abstract: Abstract: Foundational world models must be both interactive and preserve
spatiotemporal coherence for effective future planning with action choices.
However, present models for long video generation have limited inherent world
modeling capabilities ...
arxiv_ai
Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers
Abstract: Abstract: Academic poster generation is a crucial yet challenging task in scientific
communication, requiring the compression of long-context interleaved documents
into a single, visually coherent page. To address this challenge, we introduce
the fir...
arxiv_ai
Towards Predicting Any Human Trajectory In Context
Abstract: Abstract: Predicting accurate future trajectories of pedestrians is essential for
autonomous systems but remains a challenging task due to the need for
adaptability in different environments and domains. A common approach involves
collecting scenario...
arxiv_ai
Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators
Abstract: Abstract: Simulation-free training frameworks have been at the forefront of the
generative modelling revolution in continuous spaces, leading to large-scale
diffusion and flow matching models. However, such modern generative models
suffer from expens...
arxiv_ai
Incentivizing LLMs to Self-Verify Their Answers
Abstract: Abstract: Large Language Models (LLMs) have demonstrated remarkable progress in complex
reasoning tasks through both post-training and test-time scaling laws. While
prevalent test-time scaling approaches are often realized by using external
reward mo...
arxiv_ai
UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection
Abstract: Abstract: The detection of ligand binding sites for proteins is a fundamental step in
Structure-Based Drug Design. Despite notable advances in recent years, existing
methods, datasets, and evaluation metrics are confronted with several key
challenges...
arxiv_ai
GenIR: Generative Visual Feedback for Mental Image Retrieval
Abstract: Abstract: Vision-language models (VLMs) have shown strong performance on text-to-image
retrieval benchmarks. However, bridging this success to real-world applications
remains a challenge. In practice, human search behavior is rarely a one-shot
action...
arxiv_ai
Human-assisted Robotic Policy Refinement via Action Preference Optimization
Abstract: Abstract: Establishing a reliable and iteratively refined robotic system is essential
for deploying real-world applications. While Vision-Language-Action (VLA)
models are widely recognized as the foundation model for such robotic
deployment, their re...
arxiv_ai
SAFE: Multitask Failure Detection for Vision-Language-Action Models
Abstract: Abstract: While vision-language-action models (VLAs) have shown promising robotic
behaviors across a diverse set of manipulation tasks, they achieve limited
success rates when deployed on novel tasks out of the box. To allow these
policies to safely ...
arxiv_ai
SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
Abstract: Abstract: Diffusion models have demonstrated remarkable success in high-fidelity image
synthesis and prompt-guided generative modeling. However, ensuring adequate
diversity in generated samples of prompt-guided diffusion models remains a
challenge, p...
arxiv_ml
Exact Sequence Interpolation with Transformers
Abstract: Abstract: We prove that transformers can exactly interpolate datasets of finite input
sequences in $\mathbb{R}^d$, $d\geq 2$, with corresponding output sequences of
smaller or equal length. Specifically, given $N$ sequences of arbitrary but
finite le...
arxiv_ml
TuneNSearch: a hybrid transfer learning and local search approach for solving vehicle routing problems
Abstract: Abstract: This paper introduces TuneNSearch, a hybrid transfer learning and local
search approach for addressing diverse variants of the vehicle routing problem
(VRP). Our method uses reinforcement learning to generate high-quality
solutions, which a...
arxiv_ml
ASGO: Adaptive Structured Gradient Optimization
Abstract: Abstract: Training deep neural networks is a structured optimization problem, because
the parameters are naturally represented by matrices and tensors rather than by
vectors. Under this structural representation, it has been widely observed that
grad...
arxiv_ml
Enlightenment Period Improving DNN Performance
Abstract: Abstract: The start of deep neural network training is characterized by a brief yet
critical phase that lasts from the beginning of the training until the accuracy
reaches approximately 50\%. During this phase, disordered representations
rapidly tran...
arxiv_ml
MDPs with a State Sensing Cost
Abstract: Abstract: In many practical sequential decision-making problems, tracking the state of
the environment incurs a sensing/communication/computation cost. In these
settings, the agent's interaction with its environment includes the additional
component ...
arxiv_ml
Spectral Perturbation Bounds for Low-Rank Approximation with Applications to Privacy
Abstract: Abstract: A central challenge in machine learning is to understand how noise or
measurement errors affect low-rank approximations, particularly in the spectral
norm. This question is especially important in differentially private low-rank
approximati...
arxiv_ml
Q-learning with Posterior Sampling
Abstract: Abstract: Bayesian posterior sampling techniques have demonstrated superior empirical
performance in many exploration-exploitation settings. However, their
theoretical analysis remains a challenge, especially in complex settings like
reinforcement le...
arxiv_ml
Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization
Abstract: Abstract: Finite-sum Coupled Compositional Optimization (FCCO), characterized by its
coupled compositional objective structure, emerges as an important optimization
paradigm for addressing a wide range of machine learning problems. In this
paper, we ...
arxiv_ml
Learning single-index models via harmonic decomposition
Abstract: Abstract: We study the problem of learning single-index models, where the label $y \in
\mathbb{R}$ depends on the input $\boldsymbol{x} \in \mathbb{R}^d$ only through
an unknown one-dimensional projection $\langle
\boldsymbol{w}_*,\boldsymbol{x}\rang...
arxiv_ml
Path-specific effects for pulse-oximetry guided decisions in critical care
Abstract: Abstract: Identifying and measuring biases associated with sensitive attributes is a
crucial consideration in healthcare to prevent treatment disparities. One
prominent issue is inaccurate pulse oximeter readings, which tend to
overestimate oxygen sa...
arxiv_ml
Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation
Abstract: Abstract: Knowledge distillation (KD) is a core component in the training and
deployment of modern generative models, particularly large language models
(LLMs). While its empirical benefits are well documented -- enabling smaller
student models to em...
arxiv_ml
Neural Guided Sampling for Quantum Circuit Optimization
Abstract: Abstract: Translating a general quantum circuit on a specific hardware topology with a
reduced set of available gates, also known as transpilation, comes with a
substantial increase in the length of the equivalent circuit. Due to
decoherence, the qua...
arxiv_ml
Geopolitics, Geoeconomics and Risk:A Machine Learning Approach
Abstract: Abstract: We introduce a novel high-frequency daily panel dataset of both markets and
news-based indicators -- including Geopolitical Risk, Economic Policy
Uncertainty, Trade Policy Uncertainty, and Political Sentiment -- for 42
countries across both...
arxiv_ml
Robot Learning: A Tutorial
Abstract: Abstract: Robot learning is at an inflection point, driven by rapid advancements in
machine learning and the growing availability of large-scale robotics data.
This shift from classical, model-based methods to data-driven, learning-based
paradigms is...
arxiv_ml
Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Abstract: Abstract: Generative Behavior Cloning (GBC) is a simple yet effective framework for
robot learning, particularly in multi-task settings. Recent GBC methods often
employ diffusion policies with open-loop (OL) control, where actions are
generated via a...
arxiv_ml
Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
Abstract: Abstract: In this paper, we refine the Berry-Esseen bounds for the multivariate normal
approximation of Polyak-Ruppert averaged iterates arising from the linear
stochastic approximation (LSA) algorithm with decreasing step size. We consider
the norma...
arxiv_ml
Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Abstract: Abstract: Shallow Recurrent Decoder networks are a novel data-driven methodology able
to provide accurate state estimation in engineering systems, such as nuclear
reactors. This deep learning architecture is a robust technique designed to map
the tem...
arxiv_ml
Locket: Robust Feature-Locking Technique for Language Models
Abstract: Abstract: Chatbot providers (e.g., OpenAI) rely on tiered subscription schemes to
generate revenue, offering basic models for free users, and advanced models for
paying subscribers. However, a finer-grained pay-to-unlock scheme for premium
features (...
arxiv_ml
FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
Abstract: Abstract: Fine-tuning (FT) large language models (LLMs) is crucial for adapting
general-purpose models to specific tasks, enhancing accuracy and relevance with
minimal resources. To further enhance generalization ability while reducing
training costs...
arxiv_ml
Compressibility Measures Complexity: Minimum Description Length Meets Singular Learning Theory
Abstract: Abstract: We study neural network compressibility by using singular learning theory to
extend the minimum description length (MDL) principle to singular models like
neural networks. Through extensive experiments on the Pythia suite with
quantization,...
Loading more papers...
📚 You've reached the end of the papers list