Position: AI Is Not Ready for Strategic Conflicts

arXiv:2609.16189v1 Announce Type: new Abstract: Open-ended strategic wargames are high-stakes LM-based social simulations: they model adversaries, institutions, escalation, plan brittleness, doctrine, and crisis response. Language models (LMs) are attractive because they can play agents, generate scenario branches, adjudicate ambiguous actions, and summarize lessons, but the same affordances make open-ended roles dangerous: model language determines both what an actor attempts and what becomes simulated reality. This position paper argues that no LM-enabled wargame should inform planning, doctrine, policy, or crisis response without an auditable safety case, and that the proper use of open-ended wargames today is to stress-test decision-influencing LM agents. We identify five failure modes: decision laundering, adjudication opacity, role collapse, escalation-through-adjudication, and failure of strategic imagination. Ordinary benchmarks cannot establish safety for these settings. Wargames can expose failures as stress tests; they are not themselves safety cases for consequential use.

LMs in strategic conflicts pose significant risks.

The paper identifies critical AI safety failure modes in LM-enabled strategic wargames that simulate high-stakes conflicts, warning against their use in policy without rigorous safety audits. This highlights significant risks of misinformed decisions and escalation, potentially worsening conflict outcomes. The paper calls for cautious, stress-testing use only.

This is a position paper, not an empirical incident report, arguing that language models are unsafe for informing real strategic or military decisions without rigorous safety cases. It surfaces genuine risks in a high-stakes domain but offers no evidence of actual deployment or harm, only conceptual failure modes.

The paper warns that LM-based wargames have dangerous affordances. It argues that they should not inform planning without a safety case. This implies significant risks to strategic decision-making.

SOURCE     arXiv cs.AI (tier 1)
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DOCUMENT   42bc98e4-c107-420d-ac7d-0bdf276c1dda
           https://arxiv.org/abs/2609.16189
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EVIDENCE   2 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -52  (Adverse)
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CONFIDENCE 78%