Pith. sign in

REVIEW 1 cited by

Reflective Linguistic Programming (RLP): A Stepping Stone in Socially-Aware AGI (SocialAGI)

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2305.12647 v1 pith:3NUVM76G submitted 2023-05-22 cs.AI cs.CLcs.HCcs.LG

classification cs.AIcs.CLcs.HCcs.LG
keywords strategicadvancedbehaviorsboguscognitivecreationdeceptionlinguistic
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper presents Reflective Linguistic Programming (RLP), a unique approach to conversational AI that emphasizes self-awareness and strategic planning. RLP encourages models to introspect on their own predefined personality traits, emotional responses to incoming messages, and planned strategies, enabling contextually rich, coherent, and engaging interactions. A striking illustration of RLP's potential involves a toy example, an AI persona with an adversarial orientation, a demon named `Bogus' inspired by the children's fairy tale Hansel & Gretel. Bogus exhibits sophisticated behaviors, such as strategic deception and sensitivity to user discomfort, that spontaneously arise from the model's introspection and strategic planning. These behaviors are not pre-programmed or prompted, but emerge as a result of the model's advanced cognitive modeling. The potential applications of RLP in socially-aware AGI (Social AGI) are vast, from nuanced negotiations and mental health support systems to the creation of diverse and dynamic AI personas. Our exploration of deception serves as a stepping stone towards a new frontier in AGI, one filled with opportunities for advanced cognitive modeling and the creation of truly human `digital souls'.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support

    cs.IR 2025-07 conditional novelty 5.0 of 10

    The paper proposes reorienting group recommender systems from one-shot preference aggregation to chat-based, agentic decision support powered by large language models.

Pith tools