arXiv AI
Jun 10
Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization
arXiv:2606. 10086v1 Announce Type: new Abstract: This paper develops a theory of exploratory adaptation under AI-assisted optimization.
Predictive Assistance and the Temporal Dynamics of Exploratory Compression
arXiv:2606. 10094v1 Announce Type: new Abstract: Classical theories of cognition describe problem solving as exploratory search through structured problem spaces in which repeated interaction gradually compresses search into efficient representational structures.
Related stories
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.
arXiv Machine Learning
Jun 24
Parallel Manifold Steering: Efficient Adaptation of Large Associative Memories via Residual Energy Shaping
arXiv:2606. 24396v1 Announce Type: new Abstract: Large Transformer models function as Dense Associative Memories (DAMs), retrieving knowledge via high-dimensional attractor dynamics driven by the self-attention mechanism \citep{ramsauer2020hopfield, wu2024attention}.
arXiv Machine Learning
Jun 30
A Path-Space Formulation of Prediction in World Models: From a Single Action to Prediction, Planning, and Irreversibility
arXiv:2606. 28751v1 Announce Type: new Abstract: We propose a path-space formulation of prediction in AI world models.
arXiv Computation and Language
3d ago
EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making
arXiv:2609.38334v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-mode...
arXiv Computer Vision
3d ago
The Planning Limits of Latent World Models
arXiv:2609.39235v1 Announce Type: cross Abstract: World models offer a promising way to help robots understand how the physical world evolves and plan complex behaviours through imagination. Yet exis...
arXiv AI
Sep 4
FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience
arXiv:2609. 03241v1 Announce Type: cross Abstract: A reasoning model can improve from its own on-policy experience, but this inner loop is fragile: terminal verifiers provide reliable yet sparse supervision, while dense same-model guidance can reinforce false confidence or overconcentrate learning on a narrow solution mode.
arXiv Machine Learning
Jun 18
Decomposing Prediction Mechanisms for In-Context Recall
arXiv:2507. 01414v2 Announce Type: replace Abstract: We introduce a new family of toy problems that combine features of linear-regression-style continuous in-context learning (ICL) with discrete associative recall.
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.
arXiv AI
Sep 16
World Models for Embodied Intelligence: From Plausible to Controllable to Actionable
arXiv:2609.16697v1 Announce Type: cross Abstract: World models connect perception and decision-making in embodied intelligence by maintaining hidden state, anticipating consequences, comparing interv...
arXiv AI
Sep 10
Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks
The paper introduces Feedback‑Enriched Environments (FEEs) as a new approach to training large language models as autonomous agents for long‑horizon tasks. By shifting from action guidance to observation enrichment during later stages of exploration, FEEs improve performance across SciWorld and BFCL benchmarks with various Qwen3 model scales and RL algorithms. The study shows that FEEs stabilize training, promote proactive exploration, embed environmental guidance into policy weights, and highlight intra‑group feedback consistency as key for stable optimization.
arXiv AI
4d ago
Self-discovering RL in the Era of Experience: Is Learning History an Asset or a Burden?
arXiv:2609.35897v1 Announce Type: new Abstract: The pursuit of recursive self-improvement (RSI) toward general intelligence is divided between macro-level language model scaling and the interaction-d...