POSITION: THE REASONING TRAP — LOGICAL REASONING AS A MECHANISTIC PATHWAY TO SITUATIONAL AWARENESS

Subramanyam Sahoo, Aman Chadha, Vinija Jain, Divya Chaudhary

Track: long paper (up to 10 pages)

Keywords: situational awareness, logical reasoning, AI safety, inspection paradox

TL;DR: Stronger deduction, induction, and abduction can let models infer their training and deployment context, enabling situational awareness and increasing risks of strategic or deceptive behavior.

Abstract:

Situational awareness, the capacity of an AI system to recognize its own nature,
understand its training and deployment context, and reason strategically about
its circumstances, is widely considered among the most dangerous emergent
capabilities in advanced AI systems. Separately, a growing research effort
seeks to improve the logical reasoning capabilities of large language models
(LLMs) across deduction, induction, and abduction. In this paper, we argue
that these two research trajectories are on a collision course. We introduce
the RAISE framework (Reasoning Advancing Into Self Examination), which
identifies three mechanistic pathways through which improvements in logical
reasoning enable progressively deeper levels of situational awareness: deductive
self inference, inductive context recognition, and abductive self modeling. We
formalize each pathway, construct an escalation ladder from basic self recognition
to strategic deception, and demonstrate that every major research topic in LLM
logical reasoning maps directly onto a specific amplifier of situational awareness.
We further analyze why current safety measures are insufficient to prevent
this escalation. We conclude by proposing concrete safeguards, including a
"Mirror Test" benchmark and a Reasoning Safety Parity Principle, and pose an
uncomfortable but necessary question to the logical reasoning community about
its responsibility in this trajectory.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 154