Do Neurons Dream of Primitive Operators? Wake-Sleep Compression Rediscovers Schank's Event Semantics
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866915934455726080 |
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| author | Balogh, Peter |
| author_facet | Balogh, Peter |
| contents | We show that they do. Roger Schank's conceptual dependency theory proposed that all human events decompose into primitive operations -- ATRANS (transfer of possession), PTRANS (physical movement), MTRANS (information transfer), and others -- hand-coded from linguistic intuition. We ask: can the same primitives be discovered automatically through compression pressure alone?
We adapt DreamCoder's wake-sleep library learning to event state transformations. Given events as before/after world-state pairs, the system searches for operator compositions explaining each event (wake), then extracts recurring patterns as library entries under Minimum Description Length (sleep). Starting from four generic primitives, it discovers operators mapping to Schank's core: MOVE_PROP_has = ATRANS, CHANGE_location = PTRANS, SET_knows = MTRANS, SET_consumed = INGEST, plus compound operators (e.g., "mail" = ATRANS composed with PTRANS) and novel emotional-state operators absent from Schank's taxonomy.
We validate on synthetic events, ATOMIC (Sap et al., 2019), and GLUCOSE (Mostafazadeh et al., 2020). On synthetic data, the discovered library achieves MDL within 4% of Schank's hand-coded primitives at 100% coverage (vs. Schank's 81%). On ATOMIC, Schank covers only 10%; on GLUCOSE, 31%. The discovered library covers 100% of both, dominated by mental/emotional operators -- CHANGE_wants (20%), CHANGE_feels (18%), CHANGE_is (18%) -- none in Schank's original taxonomy.
Libraries discovered from one corpus transfer to the other with under 1 bit/event degradation despite different annotation schemes and domains, suggesting the operators are information-theoretically determined structure, not dataset artifacts. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_25975 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Do Neurons Dream of Primitive Operators? Wake-Sleep Compression Rediscovers Schank's Event Semantics Balogh, Peter Machine Learning Artificial Intelligence Computation and Language We show that they do. Roger Schank's conceptual dependency theory proposed that all human events decompose into primitive operations -- ATRANS (transfer of possession), PTRANS (physical movement), MTRANS (information transfer), and others -- hand-coded from linguistic intuition. We ask: can the same primitives be discovered automatically through compression pressure alone? We adapt DreamCoder's wake-sleep library learning to event state transformations. Given events as before/after world-state pairs, the system searches for operator compositions explaining each event (wake), then extracts recurring patterns as library entries under Minimum Description Length (sleep). Starting from four generic primitives, it discovers operators mapping to Schank's core: MOVE_PROP_has = ATRANS, CHANGE_location = PTRANS, SET_knows = MTRANS, SET_consumed = INGEST, plus compound operators (e.g., "mail" = ATRANS composed with PTRANS) and novel emotional-state operators absent from Schank's taxonomy. We validate on synthetic events, ATOMIC (Sap et al., 2019), and GLUCOSE (Mostafazadeh et al., 2020). On synthetic data, the discovered library achieves MDL within 4% of Schank's hand-coded primitives at 100% coverage (vs. Schank's 81%). On ATOMIC, Schank covers only 10%; on GLUCOSE, 31%. The discovered library covers 100% of both, dominated by mental/emotional operators -- CHANGE_wants (20%), CHANGE_feels (18%), CHANGE_is (18%) -- none in Schank's original taxonomy. Libraries discovered from one corpus transfer to the other with under 1 bit/event degradation despite different annotation schemes and domains, suggesting the operators are information-theoretically determined structure, not dataset artifacts. |
| title | Do Neurons Dream of Primitive Operators? Wake-Sleep Compression Rediscovers Schank's Event Semantics |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2603.25975 |