Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale
  Learning   In modern machine learning, pattern recognition replaces realtime semantic
reasoning. The mapping from input to output is learned with fixed semantics by
training outcomes deliberately. This is an expensive and static approach which
depends heavily on the availability of a very particular kind of prior raining
data to make inferences in a single step. Conventional semantic network
approaches, on the other hand, base multi-step reasoning on modal logics and
handcrafted ontologies, which are ad hoc, expensive to construct, and fragile
to inconsistency. Both approaches may be enhanced by a hybrid approach, which
completely separates reasoning from pattern recognition. In this report, a
quasi-linguistic approach to knowledge representation is discussed, motivated
by spacetime structure. Tokenized patterns from diverse sources are integrated
to build a lightly constrained and approximately scale-free network. This is
then be parsed with very simple recursive algorithms to generate
`brainstorming' sets of reasoned knowledge.

7
The categories of this paper are: hep-th
2
The categories of this paper are: math
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th