SPIN Unprocessed July 7, 2026 ai_technology research
PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction
View original on arxiv.orgOverview
arXiv:2607.02881v1 Announce Type: new Abstract: Long-horizon behavior prediction aims to infer a user's next action based on a lengthy historical sequence, playing a crucial role in artificial intelligence field. The rise of large language models (LLMs) offers a promising direction for sequential behavior prediction, yet LLMs struggle with latent behavioral pattern induction and model-intrinsic cognitive biases when tackling long-horizon behavior prediction. Prior memory management methods follo
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Computation and Language
View all →- From Gentlemen to Frontiermen: Masculine Formations in English-Language Fiction (1771--1930)
- TACG: Trajectory-Aware Commit Gating for Diffusion Language Model Decoding
- S-DiverSe: Spanish Diverse Speech
- KARMA: Knowledge graph-based Automated Reasoning Materialization and Alignment
- The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish-Chinese Journalistic Translation
- Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO