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SUMMARY:The Ontic-Epistemic Distinction: Implications for Robust General I
 ntelligence - Shreya Ishita
DTSTART;TZID=US/Central:20260717T164000
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DTSTAMP:20260726T090844Z
UID:pretalx-iacap-2026-D97D7V@pretalx.iacapconf.org
DESCRIPTION:The pursuit of Artificial General Intelligence (AGI) is largel
 y predicated on the “Scaling Hypothesis”\, the observation that model 
 performance predictably improves with more scale\, compute & parameter cou
 nt. This paradigm presupposes a substrate independent\, functionalist view
  of cognition\, where sufficiently large syntactic processing is expected 
 to eventually yield semantic understanding & generality. My paper challeng
 es this assumption by analyzing the emergence of a persistent tension betw
 een the appearance of fluency & reality of robustness as these models scal
 e. I use failures such as the reversal curse\, where models fail to genera
 lize the symmetry in identity relations (A=B -> B=A)\,  and plateauing per
 formance on novel reasoning challenges (ARC-AGI) to argue that these limit
 ations represent a fundamental architectural barrier in achieving general 
 intelligence: the lack of ontic grounding.\n\nSynthesizing Stevan Harnad
 ’s "Symbol Grounding Problem" with Evan Thompson's intrinsic normativity
  framework in autopoietic systems\, I argue that true generality requires 
 "Sense-Making"\, a process distinct from "Information Processing"\, whereb
 y an agent’s internal states are causally coupled to its environment via
  thermodynamic or survival stakes. Current LLMs\, lacking this intrinsic n
 ormativity\, remain epistemic instruments rather than ontic agents. By def
 ining this "Ontic Gap\," I offer a framework for distinguishing between th
 e simulation of reasoning and true understanding\, with urgent implication
 s for AI safety and governance.
LOCATION:Apollo Auditorium
URL:https://pretalx.iacapconf.org/iacap-2026/talk/D97D7V/
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