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ARXIV:2603.01822 · AI AND COGNITIVE SCIENCE · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.01822AI AND COGNITIVE SCIENCESUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
Develop mechanisms for semantic memory foraging in LLMs aligning them with human cognitive patterns.
Opportunity summary
Pain Develop mechanisms for semantic memory foraging in LLMs aligning them with human cognitive patterns.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Develop mechanisms for semantic memory foraging in LLMs aligning them with human cognitive patterns. In humans, efficient and strategic access to this memory store is a critical foundation for a variety of cognitive functions.
Both humans and Large Language Models (LLMs) store a vast repository of semantic memories. In humans, efficient and strategic access to this memory store is a critical foundation for a variety of cognitive functions.
ScienceToStartup currently rates this 2.0/10 on the public viability pass. To this end, we present preliminary results examining SFT as a case study.
AI and Cognitive Science moved forward this cycle; last verified April 2026. Public score 2.0/10.
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Develop mechanisms for semantic memory foraging in LLMs aligning them with human cognitive patterns.
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10.48550/arXiv.2603.01822Develop mechanisms for semantic memory foraging in LLMs aligning them with human cognitive patterns.
Abstract
Both humans and Large Language Models (LLMs) store a vast repository of semantic memories. In humans, efficient and strategic access to this memory store is a critical foundation for a variety of cognitive functions. Such access has long been a focus of psychology and the computational mechanisms behind it are now well characterized. Much of this understanding has been gleaned from a widely-used neuropsychological and cognitive science assessment called the Semantic Fluency Task (SFT), which requires the generation of as many semantically constrained concepts as possible. Our goal is to apply mechanistic interpretability techniques to bring greater rigor to the study of semantic memory foraging in LLMs. To this end, we present preliminary results examining SFT as a case study. A central focus is on convergent and divergent patterns of generative memory search, which in humans play complementary strategic roles in efficient memory foraging. We show that these same behavioral signatures, critical to human performance on the SFT, also emerge as identifiable patterns in LLMs across distinct layers. Potentially, this analysis provides new insights into how LLMs may be adapted into closer cognitive alignment with humans, or alternatively, guided toward productive cognitive \emph{disalignment} to enhance complementary strengths in human-AI interaction.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 2.0
PROBLEM
Develop mechanisms for semantic memory foraging in LLMs aligning them with human cognitive patterns. In humans, efficient and strategic access to this memory store is a critical foundation for a variety of cognitive functions.
METHOD
Both humans and Large Language Models (LLMs) store a vast repository of semantic memories. In humans, efficient and strategic access to this memory store is a critical foundation for a variety of cognitive functions.
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. To this end, we present preliminary results examining SFT as a case study.
WHY NOW
AI and Cognitive Science moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop mechanisms for semantic memory foraging in LLMs aligning them with human cognitive patterns. In humans, efficient and strategic access to this memory store is a critical foundation for a variety of cognitive functions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Both humans and Large Language Models (LLMs) store a vast repository of semantic memories. In humans, efficient and strategic access to this memory store is a critical foundation for a variety of cognitive functions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. To this end, we present preliminary results examining SFT as a case study.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI and Cognitive Science moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop mechanisms for semantic memory foraging in LLMs aligning them with human cognitive patterns.
Segment
AI and Cognitive Science
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Commercial read
2.0/10 public viability
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reason
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proof status
unverified
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Classify regulatory flags before commercialization planning.
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