LLM information retrieval shows a U-shaped performance drop as words are fragmented by inserted whitespace, attributed to a disordered transition between word-level and character-level processing modes.
org/abs/2504.02733
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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A prompt perturbation approach builds comparison graphs from LLM judgments, filters inconsistent cycles or ties, and aggregates more reliable rankings.
NoisyAgent trains LLM agents with controlled user and tool noise to improve robustness in stochastic environments while also boosting clean-benchmark performance.
citing papers explorer
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The Text Uncanny Valley: Non-Monotonic Performance Degradation in LLM Information Retrieval
LLM information retrieval shows a U-shaped performance drop as words are fragmented by inserted whitespace, attributed to a disordered transition between word-level and character-level processing modes.
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Prompt Perturbation for Reliable LLM Evaluation over Comparison Graphs
A prompt perturbation approach builds comparison graphs from LLM judgments, filters inconsistent cycles or ties, and aggregates more reliable rankings.
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Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments
NoisyAgent trains LLM agents with controlled user and tool noise to improve robustness in stochastic environments while also boosting clean-benchmark performance.