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cs.LG2026
Learning When to Abort in Transformer Planning: A Mechanistic Study
Shi Feng, Qimin Zhong, Siwei Wang +4
Planning does not by itself imply the ability to recognise errors, but recognising errors can improve planning capacity. In this paper, we study this phenomenon in Transformer plan…
cs.LG2026
Looped World Models
Hongyuan Adam Lu, Z. L. Victor Wei, Qun Zhang +28
Current world models face a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding error…
cs.LG2026
Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement
Qimin Zhong, Hao Liao, Haiming Qin +4
Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervis…