arXiv Machine Learning

From Prediction to Self: Developmental Conditions for Agency in Minimal Neural Systems

arXiv:2606. 05605v1 Announce Type: new Abstract: How does a system that merely predicts the world come to distinguish its own causal influence from everything else?

arXiv AI

Jun 30

From Detecting Agency to Doing Work: Self-Caused Credit Builds a Durable Behavioral Self in a Minimal Spiking Agent

arXiv:2606. 30191v1 Announce Type: new Abstract: How does an agent that can tell self from world come to be durably shaped by that distinction?

By Haoliang Han

agents benchmarks

arXiv Machine Learning

Jul 14

Emergent Generalization by Representation Learning in Artificial Neural Networks

arXiv:2607. 10430v1 Announce Type: cross Abstract: Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity.

By Hardik Rajpal, Dan Goodman

safety

arXiv AI

Jun 16

LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure

arXiv:2606. 15306v1 Announce Type: cross Abstract: We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared across those tasks and use it to improve future decisions.

By Daksh Mittal, Tommaso Castellani, Thomson Yen, Naimeng Ye, Fangyu Wu, Minghui Chen, Tiffany Cai, Emmanouil Koukoumidis, William Zeng, Hongseok Namkoong

llms agents

arXiv Machine Learning

Jun 30

Predictive Objectives Discard Exogenous Control-Relevant Features: A Controlled Mechanistic Study

arXiv:2606. 30068v1 Announce Type: new Abstract: Joint-embedding predictive (JEPA-style) objectives learn representations by predicting future latents.

By Ayan Pendharkar

rag agents

arXiv AI

Jul 16

Grounded world models in biological organisms and future embodied AI

arXiv:2607. 13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.

By Giovanni Pezzulo, Davide Nuzzi, Marco D'Alessandro, Riccardo Proietti, Roberto Bottini, Paul Cisek

agents robotics multimodal

arXiv AI

Jun 11

Implicit Neural Representations of Individual Behavior

arXiv:2606. 12200v1 Announce Type: cross Abstract: We study policy representation learning from unlabeled multi-policy behavioral data.

By Andrew Kang, Priya Narasimhan

agents robotics

arXiv Machine Learning

Jul 15

Constructed Reality, Contested Priors: Decoupling and the Architecture of Cognitive Relapse Under the Free Energy Principle

arXiv:2607. 11958v1 Announce Type: new Abstract: Under the free energy principle, a predictive system does not observe reality directly; it maintains a generative model of the world and experiences that model's best current hypothesis.

By MD Ibrahim Hossain Ridoy

diffusion

arXiv AI

Jun 19

Sensorimotor World Models: Perception for Action via Inverse Dynamics

arXiv:2606. 20104v1 Announce Type: cross Abstract: Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions.

By Petr Ivashkov, Randall Balestriero, Bernhard Sch\"olkopf

arXiv Machine Learning

Aug 7

Observation-Grounded Self-Predictive Reinforcement Learning for Visual Continuous Control

arXiv:2608. 05989v1 Announce Type: new Abstract: Sample-efficient policy learning from pixels is a long-standing challenge in reinforcement learning (RL).

By Xinwei Liu, Junyuan Liang, Jianting Zhang, Wuhui Chen

diffusion reinforcement-learning benchmarks

arXiv AI

Jul 29

Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models

arXiv:2607. 25907v1 Announce Type: cross Abstract: Activation steering controls model behavior by editing internal activations at inference time.

By Deepanshu Mody, Samarth Agarwal, Utkarsh Mittal, Dipesh Mahato

llms safety

arXiv Machine Learning

Jul 24

Perspective Latents as an Architectural Condition for Causal Emergence in Active Inference Agents

arXiv:2607. 20708v1 Announce Type: new Abstract: A recent line of work measures causal emergence in reinforcement learning agents through Integrated Information Decomposition, reporting that $\Phi_r$ grows with training and tracks reward improvement.

By Hongju Pae

agents reinforcement-learning

arXiv AI

Jul 15

Human-AI Agent Interaction as a Neuroplastic Training Environment

arXiv:2607. 12823v1 Announce Type: new Abstract: Interaction with AI agents has become one of the most frequent activities of everyday digital life.

By Eranga Bandara, Ross Gore, Asanga Gunaratna, Ravi Mukkamala, Nihal Siriwardanagea, Gihan Siriwardanagea, Sachini Rajapakse, Isurunima Kularathna, Pramoda Karunarathna, Chalani Rajapakse, Sachin Shetty, Christopher K. Rhea, Ng Wee Keong, Kasun De Zoysa, Amin Hass, Shaifali Kaushik, Wathsala Herath, Preston Samuel, Anita H. Clayton, Atmaram Yarlagadd

llms agents

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