A paired-history audit and label-equivariant repair support a scoped evaluation methodology and reproducible failure analysis and general superiority of the repaired algorithm, not general superiority of the repaired algorithm.
Abstract
A memory can answer a current query correctly while discarding distinctions required by a later update. We investigate this failure with a paired-history audit: two histories have the same current answer, receive a shared future update, and require different subsequent answers. A pilot evaluates 24 history pairs across six synthetic mechanisms, 12 memory conditions, two repeats, and two model backends. A deterministic frontier selector obtains strict reveal accuracy of 96/96 on DeepSeek and 82/96 on GLM; a structured writer obtains 62 successes with one unresolved outcome and 56/96. The configured four-outcome joint contrast has finite-sample identification intervals of [0.521, 0.542] and [0.292, 0.313], not confidence intervals. A record-level audit distinguishes retained-state adequacy, response delivery, and answer-schema compliance without changing those original scores. It finds 26 and 25 well-formed but semantically wrong structured reveal memories, while all 14 GLM frontier reveal failures contain correct values in the wrong wrapper. Tombstone removal produces 16/16 exact replay failures in the targeted mechanism. Identifier renaming then exposes a separate flaw: original frontier late-reference adequacy falls from 8/8 to 94/320 transformed instances. We provide and test a label-equivariant repair, but it preserves only 2/8 original late-reference answers: eliminating a naming shortcut does not solve unknown future relevance. These results support a scoped evaluation methodology and reproducible failure analysis, not general superiority of the repaired algorithm. Paid pilot evidence, retrospective diagnostics, and new offline tests are reported separately; no independent held-out or natural-task validation is claimed.
Offline replay should estimate what a discovery system could retrieve at a historical point, yet freezing the corpus leaves interaction memory unconstrained. We formalize point-in-time (PIT) discovery through historical state $(D_t, \theta_t, M_{<i})$ and introduce a paired replay that changes only memory availability....
Yi-Xi Zhou, Fan Zhang, Si-Kun Wang et al.· 0 citations
Long-term-memory agents can retrieve relevant information that is inadmissible for the current request because it belongs to another principal, violates policy, or reflects an incompatible lifecycle state. Recall and final-answer accuracy do not reveal this: a route can appear safe by missing required evidence, while a...
Zi Wang, Xing-Qiao Wang, Emmanuel Addai et al.· 0 citations
Where recent studies report that probe-detected errors are resistant to interventions, it is found that in-context binding is a setting in which probes are actionable.
Manas Venkata Sai Ravulapalli, Samrath Singh Chadha, Abhinav M. Hari· 1 citation
Family-level and seed-stability analyses show that example-level accuracy overstates counterfactual consistency: complete four-way family success is rare, and an exploratory follow-up that elicits decomposed semantic evidence fails to improve routing for the cleanly evaluated endpoint.
Yi-Hang Chen, Pinyan Qian, Su Wang et al.· 0 citations
Conversational memory changes during use, so endpoint question answering alone cannot establish how a persistent state accumulates, ages, or incorporates revisions. We introduce LSREP, a Longitudinal State-Replay Evaluation Protocol combining ordered replay, explicit lifecycle schedules, repeated probes, evolving refer...
A persistent agent must decide both what to retain as information arrives and what to surface once a query appears, yet memory evaluations can confound these decisions by comparing methods that differ in both retention and selection. We build a streaming-recall benchmark crossing retention and selection rules and evalu...
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