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This survey presents a unified perspective on self-improving test-time intelligence, connecting test-time adaptation, learning, and scaling for AI systems that refine their behavior during deployment using feedback-driven methods.
The paper proposes a Perception-Centered Architecture (Pera) for persistent language agents that continuously adapt service procedures by perceiving signals from tasks, context, and environmental changes. It organizes existing work and provides insights for building more capable persistent agents.
This paper introduces an evolutionary recurrent decision model for analyzing the development of adaptive and maladaptive behaviors, with applications in AI and cognitive systems.
This paper surveys the evolution of continual learning from parameter-centric methods to system-level adaptation, proposing a tri-axial framework (When, How, Where) to characterize learning across pre-training, post-training, and inference stages.
The article discusses a new approach in AI where memory systems can autonomously rewire themselves, potentially leading to more adaptive and efficient learning.
A new process-level latent variable model (PLVM) predicts future behavioral strategies from partial process traces across tasks, demonstrated in PowerWash Simulator gameplay data.
This paper empirically tests the psychometric reliability of LLM-based user state classification, finding that only 31 of 213 metrics met reliability criteria, questioning trust in real-time adaptive systems.
EvolveMem introduces a self-evolving memory architecture for LLM agents that optimizes retrieval configurations through LLM-powered diagnosis and iterative research cycles, achieving significant performance improvements on benchmarks like LoCoMo and MemBench.