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TrajLift: Lifting Memory Recall Trajectories with Latency-Modulated Heat Diffusion — arXiv:2611.12197

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.12197
License (as archived): CC0-1.0
Archived: 2026-11-10T18:43:59.574Z
SHA-256 of the archived text: b174f0d530820e9aa11473e3c8baf28b5e81ecc51b92f5d294ff7bf579a78171
Stable URL of this page: https://metraipolis.com/documento/dc_b68276c5fbcd

How to cite

TrajLift: Lifting Memory Recall Trajectories with Latency-Modulated Heat Diffusion — arXiv:2611.12197 — arXiv (en), https://arxiv.org/abs/2611.12197 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b68276c5fbcd (Metraipolis archive dc_b68276c5fbcd, sha256 b174f0d530820e9aa11473e3c8baf28b5e81ecc51b92f5d294ff7bf579a78171)

BibTeX

@misc{dcb68276c5fbcd,
  title = {TrajLift: Lifting Memory Recall Trajectories with Latency-Modulated Heat Diffusion — arXiv:2611.12197},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.12197},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b68276c5fbcd; sha256 b174f0d530820e9aa11473e3c8baf28b5e81ecc51b92f5d294ff7bf579a78171},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.12197. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Verbal memory retrieval carries rich diagnostic information for Alzheimer's disease through the order, hesitation, and categorical organization of recalled words, yet computational modeling of this retrieval process remains largely unexplored. A key challenge is that timing and hierarchical structure are diagnostically entangled: the diagnostic meaning of retrieval timing depends on its hierarchical context. We propose TrajLift, a spectral framework that models verbal recall as timed trajectories on a hierarchical graph. Our central mechanism is latency-modulated heat diffusion on the semantic hierarchy, a unified operator that lifts each recalled word into a representation of both multi-scale hierarchical structure and continuous retrieval timing. Formal analysis shows that the resulting representation is first-order separable into structural and temporal components with spectral selectivity across hierarchical levels. On synthetic benchmarks requiring structure-time interaction, TrajLift stays near-perfect on deep hierarchies where all baselines fail. On real-world clinical data, we show that TrajLift outperforms diverse baselines by jointly modeling timing and semantic structure, with gains that grow as retrieval timing becomes more informative.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC).
Pagina del lavoro: https://arxiv.org/abs/2611.12197 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-10T18:37:37.684Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).