FOCAL cuts token use by 60% and VLM calls by 72% on desktop streams while raising key recall from 0.38 to 0.61 and staying robust to task switches that break baselines.
Zhang, Joshua B
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LLMs perform substantially better as pragmatic listeners judging language than as speakers generating it, revealing weak alignment between the two roles.
citing papers explorer
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FOCAL: Filtered On-device Continuous Activity Logging for Efficient Personal Desktop Summarization
FOCAL cuts token use by 60% and VLM calls by 72% on desktop streams while raising key recall from 0.38 to 0.61 and staying robust to task switches that break baselines.
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How Hypocritical Is Your LLM judge? Listener-Speaker Asymmetries in the Pragmatic Competence of Large Language Models
LLMs perform substantially better as pragmatic listeners judging language than as speakers generating it, revealing weak alignment between the two roles.