Pointer Language: A Future Architecture for Symbolic Logic AI

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This paper proposes a "Pointer Language" architecture for symbolic logic AI. This architecture deconstructs content atoms and their reference, quantity, and modification relations in natural language into a pointer network that, in internal representation, does not depend on linear word order and constrains reasoning chains with a local directed trace structure. Pointers are described by fields such as group, level, source, target, parameter, reference parameter, and weight, and weight is used as a soft constraint to handle ambiguity, relation ordering, and some dynamic collocations. Natural language input undergoes two-stage segmentation of coarse segmentation and fine segmentation, and is then mapped to pointer chains by a semi-explicit translator; the reasoning stage is driven by an activation table, energy diffusion, target anchoring, and decay mechanisms, searching for usable chains within limited depth and width through explicit-stack depth-first search, and splitting complex problems with self-calls. Under ideal conditions, index access is approximately O(1), input-output translation is approximately O(L); thinking cost is approximately O(Q), where the complexity during thinking is originally O((W2)Q), and the total cost is approximately (using ** to denote exponentiation) O(L+QW2). In the worst case, Q may grow exponentially with the maximum level C and the number of attempts per level D, but is limited by the upper bounds of C and D and target-anchoring pruning. Memory demand grows approximately linearly with the total number of pointers; under the assumptions in the main text, it is estimated at about 6–12 GB, but this estimate highly depends on assumptions such as the total number of atoms, average number of outgoing pointers, and index table size, and is a design inference rather than a measured result. Compared with Transformer, under conditions such as coverage by loaded pointer libraries, correct knowledge base, acyclic chains, and correct translation, this architecture does not perform probabilistic sampling, reasoning paths are traceable, and the generation process, when translation is correct, basically avoids probabilistic fabrication (affected by training data); however, erroneous training data can still lead to memory errors. Its limitations include dependence on an external tokenizer and translator, inability to handle "island atoms," relatively weak performance on generative tasks, development and tuning requiring low-level language support, and expansion possibly causing waiting, etc. This paper is a theoretical design; performance estimates are based on ideal conditions and the assumptions in the main text and have not yet been verified by large-scale implementation and benchmark tests. This paper holds that Pointer Language is an exploration combining symbolic structures with weight-based soft constraints, which may provide a possible path for interpretable, low-compute AI, but its generality and actual performance still require further examination. 关键词: 指针语言;符号主义;语义网络;活性表;动态认知引擎;局部有向迹图;CPU 推理; Pointer Language;Symbolism;Semantic Network; Activation Table;Dynamic Cognitive Engine;Local Directed Trace Graph;CPU Inference;