acceptodds
Under review as a conference paper at ICLR 2027

CHRONODOSSIER: AUDITING CONSTRUCTION AND TEMPORAL RECOVERABILITY OF LONG-FORM PERSONA MEMORY

Abstract

Long-horizon assistants rely on stored records to remain factually and temporally consistent as circumstances change. Their reliability depends on preserving state when a record is written and recovering the relevant evidence when that record is later queried. Long-form memory benchmarks evaluate records after construction, leaving record-construction failures confounded with evidence-access failures. Persona dossiers provide a controlled setting for separating these stages because they combine persistent attributes with dated, changing states. CHRONODOSSIER links construction and use across 311 synthetic persona dossiers of 1k–10k words produced by seven generators. Each dossier is built from a dated timeline, and every passage remains linked to the life phase it describes. A frozen question set is then answered by five models using either the full dossier or retrieved passages. Restricting each passage writer to source fields assigned to its life phase eliminates all detected reuse of fields assigned to other phases while preserving measured coverage of the intended fields. Compared with the frontier pipelines, the open-source pipelines exhibit model-specific bottlenecks in semantic concentration, answer completion, inference, unsupported-request handling, and retrieval robustness. Downstream evaluation reveals a distinct limitation in evidence use. For two frontier answer models, retrieved-context accuracy falls by 26–34 percentage points from 1k to 10k words. Among explicitly supported questions, time-dependent items trail timeless items by 12 percentage points under retrieval, although the top three retrieved passages contain the correct life phase for every unique frontier item. CHRONODOSSIER evaluates record construction alongside downstream use and traces failures to construction, evidence access, or answer formation.

open until 14 Dec 2026

est. 32% chance this paper gets accepted at ICLR 2027.

Reject 68%Accept 32%

What do you think this paper will get?

All positions stay anonymous.

Related papers

Loading the map…

Discussion (0)

Sign in to comment.