:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Prakki, Rithvik
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Machine Learning
Online Access:	https://arxiv.org/abs/2410.00240
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Active Inference for Self-Organizing Multi-LLM Systems: A Bayesian Thermodynamic Approach to Adaptation
by: Prakki, Rithvik
Published: (2024)

Anubuddhi: A Multi-Agent AI System for Designing and Simulating Quantum Optics Experiments
by: Rithvik, S. K.
Published: (2025)

Demonstrating Onboard Inference for Earth Science Applications with Spectral Analysis Algorithms and Deep Learning
by: Zilberstein, Itai, et al.
Published: (2025)

A Modular Dataset to Demonstrate LLM Abstraction Capability
by: Atanas, Adam, et al.
Published: (2025)

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques
by: Sharma, Asankhaya
Published: (2025)

A Proposal for Networks Capable of Continual Learning
by: Erden, Zeki Doruk, et al.
Published: (2025)

Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach
by: Hou, Muhan, et al.
Published: (2025)

Harnessing Discrete Representations For Continual Reinforcement Learning
by: Meyer, Edan, et al.
Published: (2023)

Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following
by: Wei, Ran, et al.
Published: (2023)

When a Robot is More Capable than a Human: Learning from Constrained Demonstrators
by: Li, Xinhu, et al.
Published: (2025)

Thermodynamic Focusing for Inference-Time Search: Practical Methods for Target-Conditioned Sampling and Prompted Inference
by: Zhang, Zhan
Published: (2025)

On Sequential Bayesian Inference for Continual Learning
by: Kessler, Samuel, et al.
Published: (2023)

Transductive Active Learning: Theory and Applications
by: Hübotter, Jonas, et al.
Published: (2024)

Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery
by: Karimi, Zohre, et al.
Published: (2024)

Active Learning for Continual Learning: Keeping the Past Alive in the Present
by: Park, Jaehyun, et al.
Published: (2025)

Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements
by: Li, Haoran, et al.
Published: (2025)

CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
by: Liang, Anthony, et al.
Published: (2025)

A Practical Approach to Causal Inference over Time
by: Cinquini, Martina, et al.
Published: (2024)

Active Constraint Learning in High Dimensions from Demonstrations
by: Qiu, Zheng, et al.
Published: (2025)

Membership Inference Attacks on Discrete Diffusion Language Models
by: Kasivelrajan, Shailesh
Published: (2026)

Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning
by: Zhu, Yuanyang, et al.
Published: (2024)

HorNets: Learning from Discrete and Continuous Signals with Routing Neural Networks
by: Koloski, Boshko, et al.
Published: (2025)

INSIGHTS: Demonstration-Based Summaries of Time Series Predictors
by: Porat, Bar Eini, et al.
Published: (2026)

Learning to Answer from Correct Demonstrations
by: Joshi, Nirmit, et al.
Published: (2025)

Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
by: Kelleher, John D., et al.
Published: (2025)

Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions from Expert Demonstrations
by: Peng, Baiyu, et al.
Published: (2024)

An Efficient Continual Learning Framework for Multivariate Time Series Prediction Tasks with Application to Vehicle State Estimation
by: Hosseinzadeh, Arvin, et al.
Published: (2025)

Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation
by: Zhang, Ziniu, et al.
Published: (2025)

Can Continuous-Time Diffusion Models Generate and Solve Globally Constrained Discrete Problems? A Study on Sudoku
by: Drozdova, Mariia
Published: (2026)

Inner Loop Inference for Pretrained Transformers: Unlocking Latent Capabilities Without Training
by: Lys, Jonathan, et al.
Published: (2026)

Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
by: Hübotter, Jonas, et al.
Published: (2024)

Demonstration Guided Multi-Objective Reinforcement Learning
by: Lu, Junlin, et al.
Published: (2024)

Inverse Reinforcement Learning by Estimating Expertise of Demonstrators
by: Beliaev, Mark, et al.
Published: (2024)

Understanding the Dynamics of Demonstration Conflict in In-Context Learning
by: Jiao, Difan, et al.
Published: (2026)

Learning Quadruped Walking from Seconds of Demonstration
by: Zhang, Ruipeng, et al.
Published: (2026)

Escaping the Verifier: Learning to Reason via Demonstrations
by: Cai, Locke, et al.
Published: (2025)

Are Human-generated Demonstrations Necessary for In-context Learning?
by: Li, Rui, et al.
Published: (2023)

Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer
by: Liu, Zhenrong, et al.
Published: (2025)

Value of Information and Reward Specification in Active Inference and POMDPs
by: Wei, Ran
Published: (2024)

Active Inference with Reusable State-Dependent Value Profiles
by: Poschl, Jacob
Published: (2025)