10d ago
Fourier Preconditioning for Neural Feature Learning
Proposes Fourier-based preconditioning for mutual information-inspired feature learning, proving H-Score invariance properties.
10d ago
Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction
Studies uncertainty quantification via conformal prediction for counterfactual decision-making in high-stakes applications.
10d ago
An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility
Additive deep learning framework for drug discovery that separately models chemical descriptors and molecular graph structure for solubility prediction.
10d ago
Purified OPSD: On-Policy Self-Distillation Without Losing How to Think
Addresses failure of on-policy self-distillation on long chain-of-thought reasoning, proposing method to maintain model thinking capability.
10d ago
Aggregation with Exponential Weights is Optimal in Expectation
Proves aggregation with exponential weights is minimax-rate optimal in expectation for model selection, settling open problem from 2013.
10d ago
Dendritic In-Context Learning in a Single-Layer Spiking Neural Network
Enables in-context learning in spiking neural networks via dendritic computation, making biologically plausible SNNs pass Garg-2022 ICL benchmark.
10d ago
On the Role of Directionality in Structural Generalization
Redesigns symbolic parser backend using CCG directed types for improved structural generalization on SLOG benchmark with 30K parameters.
10d ago
HNSW with Accuracy Guarantees Using Graph Spanners -- A Technical Report
HNSW search framework adding theoretical correctness guarantees to hierarchical navigable small world graphs via graph spanner verification.
10d ago
Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates
Proposes quantum sequence modeling using variational circuits with self-modulating gates and bounded memory for stable long-sequence processing.
10d ago
The Dual Nature of LLM Persona: Aggregated Tendencies and Frame-Dependent Geometry
Studies whether LLM personas from psychometric questionnaires are intrinsic or frame-dependent using geometric analysis on manifolds.
10d ago
Bringing Agentic Search to Earth Observation Data Discovery
NASA deploys agentic search system using LLMs to help geoscience researchers discover relevant datasets and tools from thousands of available resources.
10d ago
WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs
WattGPU predicts power consumption and latency for LLM inference across unseen GPUs without exhaustive profiling, addressing data center energy optimization.
10d ago
Fast Multi-dimensional Refusal Subspaces via RFM-AGOP
arXiv paper on fast multi-dimensional refusal subspace extraction in LLMs for safety and interpretability.
10d ago
Object-centric LeJEPA
arXiv paper on object-centric LeJEPA for more data-efficient self-supervised image representation learning.
10d ago
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
Q-GAIN Python package for machine learning and physics-informed analysis of cold-atom experiment images with classification and detection.
10d ago
LIME: Learning Intent-aware Camera Motion from Egocentric Video
Language-conditioned camera motion learning for egocentric robots to adjust viewpoint based on task intent and user instructions.
10d ago
Optimal Stabilizer Testing and Learning with Limited Quantum Memory
Quantum algorithm for stabilizer state testing and learning with limited coherent quantum memory, dimension-independent complexity analysis.
10d ago
OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
OrbitQuant data-agnostic quantization method for diffusion transformers handling activation shifts across timesteps without recalibration.
10d ago
Controllable Sim Agents with Behavior Latents
CNeVA framework for controllable simulated traffic agents with interpretable behavior latents enabling edge case testing and variable isolation.
10d ago
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
Study of how social structure and audience context affect what LLM agents express in multi-agent debate settings using dual-channel framework.
10d ago
Online Safety Monitoring for LLMs
Real-time safety monitoring framework for LLMs using external verifiers with risk-calibrated thresholds to detect unsafe outputs at deployment.
10d ago
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
LACUNA testbed evaluates parameter-level localization precision for LLM unlearning, addressing memorized sensitive training data removal.
10d ago
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors
Dataset from wearable sensors measuring blood flow and tissue activity to assess mental health conditions like stress and anxiety.
10d ago
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
MetaTT tensor-train adapter for parameter-efficient fine-tuning of transformers with flexible factorization across layers and task dimensions.
10d ago
BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression
BALF framework for parameter-efficient model compression using activation-aware low-rank factorization beyond linear layers.
10d ago
LLM Priors for ERM over Programs
Method using LLM priors to enable efficient program learning through empirical risk minimization with fewer samples and less computation.
10d ago
Geometry as a Missing Axis of Representation Quality: The Variational Geometric Information Bottleneck under Data Scarcity
Framework incorporating latent geometry as explicit representation quality component under data scarcity through variational information bottleneck.
10d ago
On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions
Theoretical analysis of deep neural network approximation rates for symmetric Korobov functions with polynomial dimension dependence.
10d ago
Locality-Aware Continual Unlearning for Diffusion Models
Method for continual unlearning in diffusion models to progressively remove concepts while maintaining generation quality across multiple removal steps.
10d ago
ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
ThreadWeaver enables parallel reasoning in LLMs through adaptive threading to reduce inference latency while maintaining output quality.
10d ago
Adaptively trained Physics-informed Radial Basis Function Neural Networks for Solving Multi-asset Option Pricing Problems
Physics-informed neural networks using radial basis functions for Black-Scholes PDE option pricing with multiple assets.
10d ago
ZENITH: Automated Gradient Norm Informed Stochastic Optimization
ZENITH optimizer for automatic learning rate scheduling in deep vision models with lower computational overhead than existing adaptive optimizers.
10d ago
When Does Predictive Inverse Dynamics Outperform Behavior Cloning?
Theoretical analysis comparing predictive inverse dynamics models to behavior cloning for offline imitation learning with limited demonstrations.
10d ago
Spectral Imbalance Causes Forgetting in Low-Rank Continual Adaptation
Research on spectral imbalance in low-rank continual learning for parameter-efficient model adaptation without catastrophic forgetting.
10d ago
Local exponential stability of mean-field Langevin descent-ascent and associated particle system
Convergence analysis of mean-field Langevin descent-ascent for solving nonconvex-nonconcave two-player games.
10d ago
On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
Analysis of inverse dynamics models for semi-supervised imitation learning from labeled and unlabeled trajectory data.
10d ago
Adaptive Batch Sizes Using Non-Euclidean Gradient Noise Scales for Stochastic Sign and Spectral Descent
Adaptive batch size selection using non-Euclidean gradient noise scales for sign and spectral descent optimizers.
10d ago
On the Role of Computation in Reinforcement Learning
Theoretical framework analyzing how computation budget affects reinforcement learning policy performance beyond parameter count.
10d ago
LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection
Unsupervised time series anomaly detection using learnable fusion of multi-view token representations.
10d ago
QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs
Efficient LLM deployment technique combining token-adaptive layer execution with quantization for reduced computation and memory.
10d ago
$\mu$pscaling small models: Principled warm starts and hyperparameter transfer
Principled approach for upscaling smaller trained models to larger ones with hyperparameter transfer and warm starts.
10d ago
Audio-Visual Continual Test-Time Adaptation without Forgetting
Continual test-time adaptation method for audio-visual models handling distribution shift without catastrophic forgetting.
10d ago
Recursive Models for Long-Horizon Reasoning
Framework enabling language models to overcome context limitations by recursively invoking themselves to solve long-horizon reasoning problems.
10d ago
Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
Neural surrogate model using disentangled latent dynamics for solving parameterized PDEs with temporal extrapolation capability.
10d ago
On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry
Theoretical analysis of self-supervised pre-training using two-stage M-estimation to understand pre-training and fine-tuning dynamics.
10d ago
An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
Lightweight uncertainty quantification method for neural networks using gradient norms and isotropy assumptions.
10d ago
Hyperloop Transformers
Parameter-efficient LLM architecture using looped transformers to improve memory efficiency for edge and on-device deployment.
10d ago
FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Federated fine-tuning framework using Fisher-guided token quantization to reduce communication for LLM adaptation on edge devices.
10d ago
The Transformer as a Polar State Estimator
Geometric interpretation of transformer components showing attention and normalization emerge from polar state estimation.
10d ago
Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
Novel inverse reinforcement learning method using trust region optimization with explicit dual ascent for improved stability.