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Intelligence Artificielle

How to Navigate Uncertainty About AI Consciousness

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. On the one hand, we could assume insentience but risk doing terrible harms to entities that deserve moral standing. On the other hand, we could assume sentience and instead risk wasting resources…

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Intelligence Artificielle

Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review

arXiv:2608.19214v1 Announce Type: new Abstract: The berth allocation and quay crane assignment problem (BACAP) is a representative port-terminal scheduling problem in maritime transportation and freight logistics, where vessel arrivals, berth positions, service durations, and quay?crane availability are tightly coupled. Under uncertainties such as arrival deviations,…

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Intelligence Artificielle

Active Inference as Context Acquisition for AI Agents

arXiv:2608.19202v1 Announce Type: new Abstract: Interactive AI agents must acquire the right context as efficiently as possible. When a user omits a constraint, preference, file, or task variable, an agent can proceed with a default assumption or spend tokens on a clarifying question, retrieval call, tool call, or prompt trial. We formulate this tradeoff as active inference…

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Intelligence Artificielle

Self- and Other-Labels Induce Bidirectional Bias in LLM Judges

arXiv:2608.18091v1 Announce Type: new Abstract: As LLM-as-a-judge systems become increasingly widespread, self-preference in LLMs -- the tendency to favor one's own outputs -- raises growing concerns about evaluation reliability. However, it has been studied predominantly on generated text, where stylistic features and response quality are inevitably conflated. As a result,…

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Nine Emotion Centroids: A Label-Free Valence Axis That Transfers Across Four Modalities

arXiv:2608.18090v1 Announce Type: new Abstract: Inside a modern language model sits a single internal direction that tracks how positive or negative a sentence feels. We show how to find this valence axis (V-axis) from just 9 emotion category names plus 50 short narrative paragraphs per emotion -- about 1,500 fewer labels than the usual supervised approach -- and that the…

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Latent Space Refusal Anchoring for Low-Resource African Languages: Mechanistic Safety Recovery Without Retraining

arXiv:2608.18089v1 Announce Type: new Abstract: Instruction-tuned models often refuse harmful requests in English but comply with the same requests in Yoruba, Igbo, Igala, and Hausa. This suggests that the refusal mechanism is present in the residual stream but fails to activate for low-resource inputs. Recovering it normally requires labelled target-language data and…

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