aiqbench analysis
TechPulse take: This paper introduces PairPref, a new benchmark that exposes a critical gap in memory-augmented AI models regarding contextual preference application. While systems can successfully retrieve stored preferences, they struggle significantly to determine when those preferences are actually appropriate for the current situation, often applying them indiscriminately even when conditions have changed. The findings highlight a fundamental limitation in current reasoning capabilities where retrieval success does not equate to correct contextual judgment.
PairPref: When Should Memory Guide the Answer? A Benchmark for Contextual Preference Use
arXiv:2609.34526v1 Announce Type: new Abstract: Memory-augmented assistants use retrieved preferences to guide their responses. A small change in the situation can change whether a preference is appropriate while barely affecting its retrieval similarity. Memory benchmarks typically test whether systems store and retrieve preferences, with less attention to when those preferences should apply. We introduce PairPref, a benchmark of contextual preference use. Each pair changes only the situation, keeping the preference, request, and four candidate replies fixed. The preference remains valid in both situations. In the selection track, models must choose the reply that applies the preference only where appropriate. In the free-generation track, they must decide when to apply it without seeing candidate replies. Both tracks use the same 1,227 pairs across 45 preferences and eight situation categories. We evaluate eight models, most of which achieve selection scores ($\Delta$) of 51 to 65 points. In free generation, however, both responses are appropriate for their respective situations in only 3.6\% to 18.3\% of pairs. Models continue to apply the preference in both situations even with fewer retrieved memories, alternative presentation formats, and a stricter prompt. These results show that models still struggle to judge when user preferences apply and respond accordingly.
Title:PairPref: When Should Memory Guide the Answer? A Benchmark for Contextual Preference Use
Abstract:Memory-augmented assistants use retrieved preferences to guide their responses. A small change in the situation can change whether a preference is appropriate while barely affecting its retrieval similarity. Memory benchmarks typically test whether systems store and retrieve preferences, with less attention to when those preferences should apply. We introduce PairPref, a benchmark of contextual preference use. Each pair changes only the situation, keeping the preference, request, and four candidate replies fixed. The preference remains valid in both situations. In the selection track, models must choose the reply that applies the preference only where appropriate. In the free-generation track, they must decide when to apply it without seeing candidate replies. Both tracks use the same 1,227 pairs across 45 preferences and eight situation categories. We evaluate eight models, most of which achieve selection scores ($\Delta$) of 51 to 65 points. In free generation, however, both responses are appropriate for their respective situations in only 3.6\% to 18.3\% of pairs. Models continue to apply the preference in both situations even with fewer retrieved memories, alternative presentation formats, and a stricter prompt. These results show that models still struggle to judge when user preferences apply and respond accordingly.
Artificial Intelligence (cs.AI)
arXiv:2609.34526 [cs.AI]
(or arXiv:2609.34526v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.34526 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 302 words
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