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Large Language Models: A Mathematical Formulation
从数学底层重新定义大语言模型，为理解LLM提供严谨的理论框架。
arXiv:2601.22170v2 Announce Type: replace-cross Abstract: Large language models (LLMs) process and predict sequences containing text to answer questio…
2
The Value of a Prompt: An LLM-Relative Kolmogorov-Complexity Approach
用列文-柯尔莫哥洛夫复杂度度量提示词价值，给“好提示”一个可计算的理论标尺。
arXiv:2608.16438v1 Announce Type: new Abstract: In a world where valuable artifacts are increasingly created, completed, or processed by LLMs, the cen…
3
Generalized Linear Markov Decision Process
提出广义线性马尔可夫决策过程统一框架，为强化学习理论分析提供新视角，值得关注。
arXiv:2506.00818v2 Announce Type: replace-cross Abstract: Offline reinforcement learning for longitudinal studies often faces two linked challenges: r…
4
Context Compaction Theory
大模型上下文压缩迎来首个统一理论框架，从数学层面揭示信息保留与压缩比的最优平衡，值得关注。
arXiv:2608.01326v1 Announce Type: cross Abstract: Large Language Models (LLMs) have a bounded context window. The context window is the maximum input …
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From Tokens to States: LLMs as a Special Case of World Models and the Continuous Path Beyond
从词元到状态，重新定义LLM与世界模型的关系，开启连续世界模型新路径
arXiv:2606.28127v1 Announce Type: cross Abstract: The AI community has framed the relationship between large language models (LLMs) and world models a…
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Radical AI Interpretability
把AI当智能体来解读，借哲学“激进解释”传统重构可解释性，视角新颖。
arXiv:2606.26523v1 Announce Type: new Abstract: We develop a framework for interpreting AI systems as agents, drawing on the philosophical tradition o…
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Enhancing LLM Safety Through a Theoretical Minimax Game Lens
从博弈论角度破解大模型安全难题，提出理论新框架。
arXiv:2502.05163v2 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) necessitates effective mechanisms to e…
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Green AI: A Unified Theory of Computational Waste
从根源量化AI计算浪费，提出绿色AI统一理论框架，减少环境成本。
Article URL: https://zenodo.org/records/20459312 Comments URL: https://news.ycombinator.com/item?id=48411690 Points: 1 # Comments: 0
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Greed is Good: A Unifying Perspective on Guided Generation
从“贪婪”视角统一引导式生成，为AI生成方法提供全新理论框架，已被NeurIPS 2025接收。
arXiv:2502.08006v3 Announce Type: replace-cross Abstract: Training-free guided generation is a widely used and powerful technique that allows the end …
10
A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula
从易到难课程驱动模型自我迭代，任务中心理论揭示高效训练新路径。
arXiv:2602.10014v3 Announce Type: replace Abstract: Iterative self-improvement fine-tunes an autoregressive large language model (LLM) on reward-verif…
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A Theoretical Framework for Self-Play Theorem Proving Algorithms
自对弈算法在定理证明领域有了严谨的理论根基，为AI数学推理提供新视角。
arXiv:2606.01861v1 Announce Type: new Abstract: Self-play, a type of training algorithm that enables a model to self-improve, has recently shown promi…
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Beyond the Bellman Recursion: A Pontryagin-Guided Framework for Non-Exponential Discounting
用庞特里亚金最优控制原理突破传统强化学习折扣框架，为认知与经济学中的非指数折扣提供新解法。
arXiv:2605.20996v1 Announce Type: new Abstract: Most value-based and actor--critic reinforcement learning methods rely on Bellman-style recursions, ye…
13
Consciousness as Uncommon Self-Knowledge: A Synergistic Information Framework
从信息分解视角重新定义意识：提出“非共同自我知识”作为意识候选标准，摘要揭示协同信息的核心作用。
arXiv:2605.13884v1 Announce Type: cross Abstract: We propose uncommon self-knowledge (USK) as a candidate criterion for consciousness: synergistic inf…