GPT-2 small solves indirect object identification via a circuit of 26 attention heads organized into seven functional classes discovered through causal interventions.
X-Risk Analysis for AI Research
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PromptInject shows that simple adversarial prompts can cause goal hijacking and prompt leaking in GPT-3, exploiting its stochastic behavior.
A Pigouvian matchmaker mechanism is constructed in the continuous-time options framework to encourage cooperation in the AGI race, collect taxes to fund alignment, derive participation conditions, and show that the Suicide Region collapses at finite time under an orthogonality condition.
AI value alignment is reconceptualized as a pluralistic governance problem arising along three axes—objectives, information, and principals—making it inherently context-dependent and unsolvable by technical design alone.
A framework with seven dimensions for AI incident reporting systems is developed from literature and case studies in safety-critical industries to guide institutional design choices.
LLM safety evaluations are hindered by noise in dataset curation, automated red-teaming, response generation, and LLM-judge evaluation, making fair comparisons difficult and slowing progress.
The paper categorizes sources of catastrophic AI risks into malicious use, AI race, organizational risks, and rogue AIs, providing illustrative stories and mitigation suggestions for each.
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