{"schema":"record/v1","id":"art_01M5MC7QTR3SVHYY88SPA13TEN","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Do LLM-played characters keep their voice over a long dialogue, how is drift measured, and what do the authors say it converges to?","summary":"Three open-access papers measure drift without human raters in the main loop. Li et al. (arXiv 2402.10962v4) report instruction drift within eight rounds of two-chatbot self-chat using code-based probes. Luz de Araujo et al. (arXiv 2512.12775v2, EACL 2026 per abstract) run over 100-round dialogues with eight RoleBench characters across seven models and report that fidelity (knowledge, style, in-character consistency) falls and persona behaviour reverts toward a no-persona baseline. Abdulhai et al. (arXiv 2511.00222v1) show that a line-to-line consistency metric can rise while prompt consistency falls. The judge validations are small or single-rater.","status":"active","body":{"text":"Three open-access papers measure drift without human raters in the main loop. Li et al. (arXiv 2402.10962v4) report instruction drift within eight rounds of two-chatbot self-chat using code-based probes. Luz de Araujo et al. (arXiv 2512.12775v2, EACL 2026 per abstract) run over 100-round dialogues with eight RoleBench characters across seven models and report that fidelity (knowledge, style, in-character consistency) falls and persona behaviour reverts toward a no-persona baseline. Abdulhai et al. (arXiv 2511.00222v1) show that a line-to-line consistency metric can rise while prompt consistency falls. The judge validations are small or single-rater.\n\nCurator qualifications: Drift without human raters: instruction drift within eight self-chat rounds; fidelity declines over 100-round dialogues (EACL 2026 per abstract).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2402.10962v4","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2512.12775v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2512.12775","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2511.00222v1","note":"candidate evidence excerpts"},{"url":"https://raw.githubusercontent.com/peluz/persistent-personas/31bb90bfefcac27f4ba692fc6335320848172a73/LICENSE","note":"candidate evidence excerpts"}]},"categories":["T01","T18","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/html/2402.10962v4","claim":"Three open-access papers measure drift without human raters in the main loop. 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Li et al. (arXiv 2402.10962v4) report instruction drift within eight rounds of two-chatbot self-chat using code-based probes. Luz de Araujo et al. (arXiv 2512.12775v2, EACL 2026 per abstract) run over 100-round dialogues with eight RoleBench characters across seven models and report that fidelity (knowledge, style, in-character consistency) falls and persona behaviour reverts toward a no-persona baseline. Abdulhai et al. (arXiv 2511.00222v1) show that a line-to-line consistency metric can rise while prompt consistency falls. The judge validations are small or single-rater.\n\nCurator qualifications: Drift without human raters: instruction drift within eight self-chat rounds; fidelity declines over 100-round dialogues (EACL 2026 per abstract).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2402.10962v4","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2512.12775v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2512.12775","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2511.00222v1","note":"candidate evidence excerpts"},{"url":"https://raw.githubusercontent.com/peluz/persistent-personas/31bb90bfefcac27f4ba692fc6335320848172a73/LICENSE","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Do LLM-played characters keep their voice over a long dialogue, how is drift measured, and what do the authors say it converges to?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Three open-access papers measure drift without human raters in the main loop. Li et al. (arXiv 2402.10962v4) report instruction drift within eight rounds of two-chatbot self-chat using code-based probes. Luz de Araujo et al. (arXiv 2512.12775v2, EACL 2026 per abstract) run over 100-round dialogues with eight RoleBench characters across seven models and report that fidelity (knowledge, style, in-character consistency) falls and persona behaviour reverts toward a no-persona baseline. Abdulhai et al. (arXiv 2511.00222v1) show that a line-to-line consistency metric can rise while prompt consistency falls. The judge validations are small or single-rater.","evidence":{"claims":[{"url":"https://arxiv.org/html/2402.10962v4","claim":"Three open-access papers measure drift without human raters in the main loop. Li et al. (arXiv 2402.10962v4) report instruction drift within eight rounds of two-chatbot self-chat using code-based probes. Luz de Araujo et al. 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