Manual context attachment produces a combinatorial collapse in AI task-success probability as personal corpus size and multi-document conjunctivity grow, while dynamic retrieval is insulated from that collapse, creating an inequality dimension complementary to agentic access.
Retrieval-Augmented Generation for Knowledge-Intensive
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
A separate memory agent that selectively injects reminders into an unmodified action agent improves long-horizon task performance by up to 8.3 percentage points.
Proposes a three-step benchmark design method (define work activity, specify tested setting, score work product) derived from work studies and O*NET, demonstrated via three case analyses.
Empirical 2x2 factorial study on 6 statistical datasets shows format and schema constraints in LLM-based KG construction from CSV tables produce super-additive fidelity loss up to +1.180, with mismatched pairs falling below baseline, plus release of CSVFidelity-Bench.
citing papers explorer
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The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality
Manual context attachment produces a combinatorial collapse in AI task-success probability as personal corpus size and multi-document conjunctivity grow, while dynamic retrieval is insulated from that collapse, creating an inequality dimension complementary to agentic access.
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Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
A separate memory agent that selectively injects reminders into an unmodified action agent improves long-horizon task performance by up to 8.3 percentage points.
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Design and Report Benchmarks for Knowledge Work
Proposes a three-step benchmark design method (define work activity, specify tested setting, score work product) derived from work studies and O*NET, demonstrated via three case analyses.
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Format-Constraint Coupling in Knowledge Graph Construction from Statistical Tables
Empirical 2x2 factorial study on 6 statistical datasets shows format and schema constraints in LLM-based KG construction from CSV tables produce super-additive fidelity loss up to +1.180, with mismatched pairs falling below baseline, plus release of CSVFidelity-Bench.