Unverified50% confidenceFactExact time
In language modeling, OrderGPT adaptively recovers a dependency structure close to left-to-right.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe OrderGPT paper proposes that a "single adapted order" between left-to-right and arbitrary order is a neglected middle ground, and presents a method that lets the model learn the generation order itself.75% similarUnverifiedCausal language models are the foundational architecture of current mainstream large models (such as the GPT series, LLaMA, etc.), predicting the next token step by step from left to right given an existing token sequence, with each step only able to see the left-side context.68% similarUnverifiedOrderGPT's training mechanism includes extracting dependency signals from random prefixes, aggregating them into a dependency graph and deriving priorities, and using a lightweight OrderMLP to predict the order.66% similarUnverifiedOn tabular data, OrderGPT discovers decompositions that place statistically and semantically related fields near each other, while improving downstream modeling metrics across all domains.63% similarUnverifiedThe ReAct pattern interweaves the language model's reasoning (Chain-of-Thought) with action calls to external tools within the same loop61% similar
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