activity
20202026
most citedA Review of Mathematical and Computational Methods in Cancer Dynamics

41 citations · 139 across the 17 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.IT2026

Causal Information Exchange as a Necessary Condition for Adaptive Regulation: A Perturbational Framework for Life and Agency

Brian LuValle, Abicumaran Uthamacumaran, Hector Zenil

Information exchange can support living organisation without providing a sufficient criterion for life. We develop a perturbational framework that distinguishes causal transmission…

q-bio.OT2026

scCTM: Continuous Thought Machine Reconstruction of Glioma Cell Fate Dynamics Attractor Landscapes and Algorithmic Perturbation Signatures

Abicumaran Uthamacumaran

Cell fate plasticity dynamics in glioma ecosystems is usually inferred from static single-cell snapshots, leaving a gap between manifold geometry, predictive modelling, and mechani…

cs.IT2026

Reconstruction of Geometric and Structural Aspects of Undeciphered Messages by Information-theoretic Methods

Hector Zenil, Abicumaran Uthamacumaran

In previous work, we established sufficient conditions for recovering geometry and topology from non-random information, demonstrating reconstruction with the Arecibo message. Unde…

cs.LG2026

What Can Language Models Inherit from Solomonoff Induction? Limits, Attainable Properties, and What Served Conditionals Reveal

Luan Ozelim, Abicumaran Uthamacumaran, Hector Zenil

A familiar line of reasoning runs as follows: language models are flexible approximators trained under a strictly proper loss, so scale brings them closer to Solomonoff induction,…

q-bio.OT2026

Enhancing Clinical Decision Support and Differential Diagnosis with Knowledge Graphs, and Retrieval Augmented Generation in Generative AI

Henri Feto, Abicumaran Uthamacumaran, Hector Zenil

Diagnostic error carries a burden, while unconstrained large language models (LLMs) remain vulnerable to hallucination and weak integration of quantitative laboratory dynamics. We…