arXiv:2606. 05528v1 Announce Type: new Abstract: Existing frameworks assess whether AI systems might be conscious but provide no guidance on what to do with that assessment.
arXiv:2609.35618v2 Announce Type: replace Abstract: The question of AI consciousness is one of the most urgent pre-emptive problems in philosophy and computer science, yet progress is hampered by a c...
arXiv:2608. 19215v1 Announce Type: new Abstract: Given deep uncertainty about the possibility of artificial consciousness, it is unclear how we should treat potentially sentient AI.
The report examines how software architects view the growing use of AI development agents in their field. A focus group of 22 industry and academic participants discussed current practices, trust, validation, governance, and educational implications, concluding that decision‑making, accountability, and guardrail authoring remain human responsibilities. They introduced the concept of harness engineering—building systems that govern AI‑assisted creation—and identified criticality and cognitive debt as key factors for calibrating human oversight.
arXiv:2606. 16319v1 Announce Type: new Abstract: Modern AI systems exhibit structural failures that capability scaling alone does not reliably fix: they optimize under-specified objectives with no architectural mechanism to question whether the objective should be optimized at all.
The paper proposes a five‑layer implementation architecture for the S3Q theory of consciousness, mapping its three core conditions—grounded sensorimotor situatedness, internal simulation via a world model, and structural coherence between predictions and observations—to existing computational primitives. It integrates these components into a single pipeline that processes continuous, differentiable per‑object slot vectors, includes a developmental bootstrap sequence, and yields falsifiable predictions that cannot be produced by any subset of the architecture alone. The model further suggests that a basic sense of self emerges from linking actions to outcomes, and that behavior can be categorized into hesitation, curiosity, or avoidance based on outcome unexpectedness and valence. "whyItMatters":"The architecture provides a concrete, testable framework that unifies theoretical conditions of consciousness with practical computational components, enabling empirical investigation of machine consciousness."
arXiv:2606. 23991v1 Announce Type: new Abstract: What is an agent?
arXiv:2606. 13196v1 Announce Type: new Abstract: Recent AI systems can generate texts, software architectures, hypotheses, designs, and scientific workflows that appear creative.
arXiv:2606. 13196v2 Announce Type: replace Abstract: Recent AI systems can generate texts, software architectures, hypotheses, designs, and scientific workflows that appear creative.
The Digital Consciousness Model (DCM) is introduced as a systematic, probabilistic framework for evaluating evidence of consciousness in AI systems, drawing on multiple leading theories of consciousness. Initial results indicate that, while the evidence suggests 2024 large language models (LLMs) are not conscious, this conclusion is not decisive and is weaker than evidence against consciousness in simpler AI systems.
The paper proposes a new interdisciplinary field called Cognitive Infrastructure Studies (CIS) to examine how AI systems act as invisible, foundational cognitive infrastructures that shape what people can know and do in digital societies. It argues that these infrastructures, through anticipatory personalization and adaptive invisibility, automate relevance judgments and shift epistemic agency to non‑human systems. CIS offers methodological tools, such as infrastructure breakdown experiments, to uncover the hidden cognitive dependencies created by AI preprocessing across individual, collective, and societal levels.
arXiv:2509. 14474v3 Announce Type: replace Abstract: The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-level performance versus replicating human-like cognitive processes.
The paper argues that the current debate on machine consciousness rests on the mistaken assumption that AI systems are already the kind of entities that could possess consciousness. By distinguishing between phenomenal consciousness, introspective report, and human projective introspection, it introduces the AI Consciousness Fallacy, showing that generative models can produce first‑person linguistic traces without being conscious. It then proposes Causal Liability Theory (CLT), with CLT‑I defining liability closure as a criterion for identifying a bearer of consciousness and CLT‑II suggesting that liability closure is both necessary and sufficient for minimal phenomenal subjecthood, and demonstrates experimentally that these distinctions are tractable and can separate causal bearer structure from first‑person performance.