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.
By Afshin Khadangi
arXiv:2607. 15883v1 Announce Type: cross Abstract: Large language models are broadly capable, yet in sustained one-to-one conversation they still read as flat: competent, responsive, and somehow not quite the presence of a mind.
By Sebastian Cochinescu
The paper introduces the Situated Identity Test (SIT), a framework that assesses whether a language model’s behavior can be traced to a specific developmental lineage rather than merely imitating a persona. SIT requires agents to possess accurate knowledge of their recorded experiences while appropriately ignoring ungrounded information, and it demonstrates that policies based only on compressed profiles are limited in distinguishing between colliding life histories. The authors present SITBench, an evaluation suite with 25 profile‑collision pairs and 10,000 probes across nine model architectures, and provide open‑source tools and pilot results on state‑of‑the‑art foundation models.
By Jun He, Deying Yu
arXiv:2606. 00545v1 Announce Type: new Abstract: Post-trained language models can recognize their own outputs from a sentence or two out of context.
By Asvin G
arXiv:2608. 06485v1 Announce Type: cross Abstract: Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the development of lifelong agents that remain coherent over extended interactions.
By Ming Wang, Peidong Wang, Xiaocui Yang, Daling Wang, Shi Feng, Fiona Fui-Hoon Nah, Ee-Peng Lim
The paper introduces PAI‑Bench, a benchmark designed to evaluate persistent AI agents on how faithfully they adhere to a versioned identity contract. It separates several dimensions—recall, composition, behavioral enactment, resistance, persistence, lineage, and role‑conditioned updates—while keeping scoring oracles independent of the target process. Experiments on synthetic profiles show that explicit cues can significantly alter the presence of identity identifiers, revealing prompt‑dependent component selection and sensitivity to startup cues.
By Zhenyu Zhao, Roy Zhao
arXiv:2607. 00006v1 Announce Type: cross Abstract: Beckmann & Butlin's (2026) ontological framework for the LLM individuation problem inherits an unargued cross-regime co-reference assumption from the persona-vectors literature: that the same direction picks out the same content under prompt-conditioning, gradient-descent fine-tuning, and inference-time steering.
By Shuaizhi Cheng
arXiv:2609.22090v1 Announce Type: new
Abstract: An LLM producing the response pattern associated with a human psychological effect is not the same claim as the LLM possessing that bias. We present Ps...
By Joy Bose
arXiv:2607. 00220v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments.
By Andrea Ferrario
arXiv:2607. 10539v1 Announce Type: new Abstract: Existing approaches to infer user traits and generate responses consistent with a persona rely on static prompting.
By Yan Lin, Yuyang Dai, Jiahui Geng, Yuxia Wang
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...
By Shamil Chandaria, Arvo Mu\~noz Mor\'an, Fernando Rosas, Anil Seth, Henry Shevlin, Marcus Hutter, Thore Graepel, Adam Bales, Iulia Comsa, Murray Shanahan, Ruben Laukkonen, Morten Kringelbach, Chris Frith, Shane Legg
Emergi-PersonaOS is a psychology‑grounded operating system designed to manage persona agents throughout their lifecycle. It structures personas into three layers—dispositional traits, characteristic adaptations, and narrative identity—allowing the system to infer current persona states from situational cues and generate appropriate actions. The OS records experiences, evaluates revision candidates, and controls belief updates through explicit review and traceable evidence, enabling controllable evolution of persona agents over long interactions.
By Haoluan Fu, Keni Chen, Xinyu Jia, Jinpeng Wang, Yuyu Yin