Large language models encode relational bindings via a cell-based representation: a low-dimensional linear subspace in which each cell corresponds to an entity-relation index pair and attributes are retrieved from the matching cell.
arXiv preprint arXiv:2503.09066 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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TRACES learns prefix-level trajectory risk states from LLM hidden representations using weak trajectory-level supervision to enable proactive safety auditing for multi-turn agents.
HyperLens reveals that deeper transformer layers magnify small confidence changes into fine-grained trajectories, allowing quantification of cognitive effort where complex tasks demand more and standard SFT can reduce it.
citing papers explorer
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Cell-Based Representation of Relational Binding in Language Models
Large language models encode relational bindings via a cell-based representation: a low-dimensional linear subspace in which each cell corresponds to an entity-relation index pair and attributes are retrieved from the matching cell.
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TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling
TRACES learns prefix-level trajectory risk states from LLM hidden representations using weak trajectory-level supervision to enable proactive safety auditing for multi-turn agents.
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HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory
HyperLens reveals that deeper transformer layers magnify small confidence changes into fine-grained trajectories, allowing quantification of cognitive effort where complex tasks demand more and standard SFT can reduce it.