What is recorded here?
A short project summary is easy to search, but may omit the number or exception needed to answer a later question. Xia and colleagues pair a primary abstraction with concrete memory content, then add cue anchors that offer several ways to find the same entry. Their retrieval policy can refine a query, explore connected entries or stop. The work tests whether this combination improves answer quality on conversational-memory benchmarks. Its contribution is the external abstraction-and-cue representation and the specified retrieval methods, distinct from the neural Memora model introduced within MIRAS.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The reported averages depend on model-based judging and specific benchmark settings. Some question categories and word-overlap metrics favor other configurations. The policy retriever uses additional model calls and is slower than semantic retrieval in the measured setup. The trained-policy comparison uses a separate held-out split and is not the configuration behind every headline result. Context reduction is not total cost reduction. Automatically consolidating two entries does not establish that two real people or scientific claims are identical.
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People and systems
Menglin Xia; Xuchao Zhang; Shantanu Dixit; Paramaguru Harimurugan; Rujia Wang; Victor Rühle; Robert Sim; Chetan Bansal; Saravan Rajmohan
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.
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English catalogue narrative
MEMORA: keeping an overview without losing the details
A short project summary is easy to search, but may omit the number or exception needed to answer a later question. Xia and colleagues pair a primary abstraction with concrete memory content, then add cue anchors that offer several ways to find the same entry. Their retrieval policy can refine a query, explore connected entries or stop. The work tests whether this combination improves answer quality on conversational-memory benchmarks. Its contribution is the external abstraction-and-cue representation and the specified retrieval methods, distinct from the neural Memora model introduced within MIRAS.
Scientific scope
The reported averages depend on model-based judging and specific benchmark settings. Some question categories and word-overlap metrics favor other configurations. The policy retriever uses additional model calls and is slower than semantic retrieval in the measured setup. The trained-policy comparison uses a separate held-out split and is not the configuration behind every headline result. Context reduction is not total cost reduction. Automatically consolidating two entries does not establish that two real people or scientific claims are identical.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Menglin Xia; Xuchao Zhang; Shantanu Dixit; Paramaguru Harimurugan; Rujia Wang; Victor Rühle; Robert Sim; Chetan Bansal; Saravan Rajmohan
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.