Complete Integrated Master List — Dependency-Ordered

Foundations to Frontiers

Philosophy, Mathematics, Logic, AI, Life Sciences, and Alignment
Reordered by logical dependence: easiest prerequisites first

★ NEW entries from curriculum review
↑ MOVED earlier from original position
Phase 0

Mathematical Preliminaries

Optional Warm-Up

If you need to refresh pre-calculus or basic proof techniques before Phase 1.

Proofs & Mathematical Thinking
  • ◆
    How to Prove It — Daniel J. Velleman
    A gentle introduction to proof techniques; builds the rigorous thinking needed for all later math.
  • ◆
    Book of Proof — Richard Hammack (free online)
    Another excellent, accessible resource for learning to write proofs.
Precalculus
  • ◆
    Precalculus — James Stewart
    Covers functions, trigonometry, and other fundamentals; useful if your calculus is rusty.
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Phase 1

The Theoretical Bedrock

Philosophy of Mind · Mereology · Philosophy of Language & Pragmatics · Philosophy of Action · Mathematics · Logic · Biology

Goal: Understand representations as vectors in high-dimensional space and learning as moving down a slope. Grasp the logical foundations of meaning and semantics. Ground your intuitions about agency before formalizing them. Internal order matters: Mind → Mereology → Language/Pragmatics → Action → Math → Logic → Biology. Each step prepares the vocabulary for the next.

1a — Philosophy of Mind & Language (Core)
  • ◆
    Routledge History of Philosophy Vol. 10
    Completes your historical grounding; essential for understanding the trajectory of 20th-century thought.
  • ◆
    Philosophy of Mind — Jaegwon Kim
    The classic introduction to mind-body problems, mental causation, and physicalism; foundational for any discussion of consciousness and AI.
  • ◆
    Zhao Tingyang (2026) — "A Philosophical Review on Metalanguage, the Grammar of Thought, and the 'Grammar' of Large Language Models"
    Critiques LLMs for lacking genuine understanding and proposes a "grammar of thought"; bridges philosophy of mind and the semantics gap.
  • ◆
    UNED Research (Feb 2026) — "A rigorous philosophical examination of the notion of understanding and semantics in artificial intelligence language models"
    Connects Frege, Russell, Wittgenstein with modern Transformers; defines operational criteria for "understanding."
  • ◆
    Peter Carruthers (2024) — Human Motives: Hedonism, Altruism, and the Science of Affect (Oxford)
    Explores fundamental human motivations; directly relevant to the "utility functions" of your pragmatic subsystems.
  • ◆
    Peter Carruthers (2025) — Explaining our Actions: A Critique of Common-Sense Theorizing (Cambridge)
    Critiques how we understand and explain action; essential for modeling how agents justify themselves.
  • ◆
    Peter Carruthers (2026) — Innateness in Mind (Cambridge Elements)
    Explores innate cognitive endowments; relevant to what is built-in vs. learned in your subsystems.
  • ◆
    Bettina Bergo (2025) — The Missed Conversation: Husserl, Freud, and the Cognitive Sciences (Oxford)
    Bridges phenomenology, psychoanalysis, and cognitive neuroscience; argues consciousness is emergent from deeper fields — a major resource for your emergentist philosophy.
  • ◆
    Batisti & Vidal (2025) — "Post-Cognitivism and the Indissoluble Bonding of Languaging, Embodiment, and Thinking"
    Argues for the indissoluble link between language, embodiment, and social interaction; supports your critique of purely formal semantics.
  • ◆
    The Tacit Dimension — Michael Polanyi (1966)
    The source for "tacit knowledge"; essential for understanding critiques of AI's limitations.
1b — Mereology: The Logic of Parts and Wholes ↑ Moved from Phase 6
Why moved here Mereology belongs immediately after philosophy of mind and before philosophy of language, action, and formal logic — not at the end of the curriculum. It requires zero mathematical prerequisites and is essentially applied ontology: what does it mean for something to be a part of something else? You need this vocabulary when reading Carruthers on mental states, when reading Austin on actions, and later when reading about compositional semantics and category theory. Leaving it in Phase 6 created a situation where you were implicitly using mereological concepts throughout the curriculum without ever having made them explicit. It also connects naturally to the emergentism thread running from Bergo (above) through to Maturana & Varela later — you cannot think clearly about emergence without a prior account of composition.
  • ◆
    Parts: A Study in Ontology — Peter Simons (1987) ↑ MOVED
    The classic modern treatment. Develops a rigorous formal theory of part-whole relations and explores its implications for metaphysics, including the structure of individuals, the problem of composition, and the relation between mereology and set theory. Read this before formal logic — you will import its vocabulary constantly.
  • ◆
    Mereology and the Sciences — Claudio Calosi & Pierluigi Graziani, eds. (2014) ↑ MOVED
    Connects mereology to physics, biology, and cognitive science. Shows how part-whole reasoning applies to spacetime, organisms, and complex systems. Directly relevant to your emergentist framework and to understanding how different levels of organization relate.
1c — Philosophy of Language & Pragmatics ↑ Pragmatics moved from Phase 7
Why pragmatics moved here Austin, Searle, and Grice are foundational 20th-century analytic philosophy — they belong alongside Wittgenstein, not after category theory. The speech act tradition is a prerequisite for understanding compositional semantics (Phase 7's Enriched Meanings), not a consequence of it. Lepore & Stojnić, who edit the Oxford Handbook of Contemporary Philosophy of Language listed here, are themselves working directly in this tradition. Putting Grice after Mac Lane was a dependency inversion. The advanced pragmatics applications (Evolutionary Pragmatics, Inflammatory Language) remain in Phase 7 where they belong — those genuinely require evolution and category theory background respectively.
  • ◆
    Ernie Lepore & Una Stojnić, eds. (2025) — The Oxford Handbook of Contemporary Philosophy of Language
    State-of-the-art coverage of speech acts, discourse dynamics, meta-semantics; essential reference for your general semantics + pragmatics synthesis.
  • ◆
    How to Do Things with Words — J.L. Austin ↑ MOVED
    The origin of speech act theory. Austin's distinction between locutionary, illocutionary, and perlocutionary acts is the conceptual skeleton for almost everything that follows in pragmatics and in AI communication design.
  • ◆
    Speech Acts — John Searle ↑ MOVED
    The foundational text systematizing speech act theory. Searle's sincerity conditions and satisfaction conditions map directly onto alignment problems around AI commitments and stated intent.
  • ◆
    Studies in the Way of Words — H.P. Grice ↑ MOVED
    Collected papers on meaning, implicature, and conversation. The cooperative maxims are the clearest pre-formal account of how agents communicate under shared expectations — which is exactly the problem of AI-human alignment.
  • ◆
    Daniel Vanderveken — works on illocutionary logic ↑ MOVED
    Formalizes illocutionary force and success/satisfaction conditions. Read after Austin and Searle, before modal logic.
1d — Philosophy of Action ★ New Insertion
This phase is inserted between Philosophy of Language and Logic. Mind tells you what a mental state is; Language tells you how mental states are expressed and communicated; Action tells you how mental states produce behavior; Logic then gives you tools to formalize all three. By the time you reach formal decision theory in Phase 3, you will encounter utility functions as attempted formalizations of concepts you have already examined philosophically — and found partially wanting.

Core through-line to watch for: the unresolved tension between reasons and causation as the basis of agency. This tension is not resolved in the philosophical literature, and it appears almost verbatim in alignment debates about whether you want your AI to have genuine normative understanding versus just the right causal structure.
  • ◆
    Intention — G.E.M. Anscombe (1957) ★ NEW
    Short, dense, and foundational. Her central question: what distinguishes intentional action from mere happening? Her answer — that intentional actions are those to which "why?" applies in a particular way — is directly load-bearing for alignment. Read first.
  • ◆
    Essays on Actions and Events — Donald Davidson (1980) ★ NEW
    Prioritize "Actions, Reasons and Causes" and "Agency." Davidson argues that reasons are causes — this sounds obvious until you see how much philosophical work that claim is doing. This is where philosophy of action connects to philosophy of mind.
  • ◆
    The Importance of What We Care About — Harry Frankfurt (1988) ★ NEW
    Prioritize "Freedom of the Will and the Concept of a Person" and "Identification and Wholeheartedness." Frankfurt's hierarchy of desires maps almost uncannily onto alignment problems around preference learning and reflective stability.
  • ◆
    Intention, Plans, and Practical Reason — Michael Bratman (1987) ★ NEW
    Develops a "planning theory" of agency where intentions persist and constrain future deliberation. Probably the most directly relevant text to how AI agents are actually architected — and almost nobody in the technical alignment community engages with it seriously.
  • ◆
    Self-Constitution: Agency, Identity, and Integrity — Christine Korsgaard (2009) ★ NEW
    Argues that agency requires constituting yourself as a unified agent. Connects directly to questions about AI self-modification, corrigibility, and value stability.
  • ◆
    Actions — Jennifer Hornsby (1980) & selected essays on trying ★ NEW
    Useful complement to the Anscombe/Davidson core; her work on "trying" as the most basic act-description is worth raiding.
1e — Mathematics Core
  • ◆
    Linear Algebra Done Right — Sheldon Axler or Introduction to Linear Algebra — Gilbert Strang
    Both are essential for understanding vector representations and transformations. Axler is conceptual/proof-based; Strang is applied.
  • ◆
    Vector Calculus — Marsden & Tromba
    Covers gradients, multiple integrals, and the fundamental theorem of calculus in higher dimensions; directly relevant to understanding learning as moving down a slope.
1f — Logic Foundations
  • ◆
    A Mathematical Introduction to Logic — Herbert Enderton
    The standard text for propositional and first-order logic; necessary for all subsequent logic work.
  • ◆
    Set Theory: An Introduction to Independence Proofs — Kenneth Kunen (reference)
    You already have set theory background; keep this for deep dives.
  • ◆
    Computability and Logic — Boolos, Burgess & Jeffrey
    Covers computability, modal logic, and more; useful for understanding the limits of computation.
  • ◆
    An Introduction to Modal Logic — Brian Chellas ↑ MOVED from Phase 3
    Alethic and epistemic modal logics (necessity, possibility, knowledge) are natural extensions of first-order logic and have no dependency on AI or decision theory. Move the deontic (obligation) and action-logic portions forward to Phase 3 where they connect to agency, but the core modal apparatus belongs here, immediately after Enderton.
1g — Life Sciences Core: General Biology & Autopoiesis ↑ Autopoiesis moved from Phase 6
Why Autopoiesis moved here Maturana & Varela's Autopoiesis and Cognition (1972/1980) is a biology and systems theory text. It does not depend on transformers, category theory, or alignment literature. It belongs here alongside general biology, where it provides the conceptual vocabulary of self-producing systems that you will need when the MDPI critique of AI (Phase 6) invokes autopoiesis as something AI currently lacks. Reading it in Phase 6 for the first time while trying to absorb alignment literature is like being introduced to a key term in a bibliography rather than in the text.
  • ◆
    Bio-Concepts — Wiley (March 2026)
    A brand-new, visually engaging introduction to biology for non-majors. Covers cell chemistry, cell structure, evolution, body systems, and ecology. Provides the essential biological context for any discussion of embodied intelligence.
  • ◆
    Autopoiesis and Cognition — Humberto Maturana & Francisco Varela (1972/1980) ↑ MOVED from Phase 6
    The origin of autopoiesis theory — self-producing systems. Read here, with fresh biology vocabulary in hand, rather than later when you would be learning the concept for the first time in the context of an AI critique. Central to your emergentist framework.
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Phase 2

Reasoning Under Uncertainty

Probability · Statistics · Causal Inference · Evolutionary Theory

Goal: Master the mathematics of uncertainty and counterfactuals — the bridge between ethics and technical AI. Understand the ultimate framework for "why" intelligence exists: evolution.

Core
  • ◆
    Reasons and Persons — Derek Parfit
    A masterpiece on ethics, personal identity, and rationality; provides deep philosophical grounding for agent foundations. No mathematical prerequisites; pure philosophy. Read at the top of Phase 2 before the math begins.
  • ◆
    A First Course in Probability — Sheldon Ross
    Standard introduction; covers distributions, expectations, and limit theorems.
  • ◆
    Introduction to Statistical Learning — James et al.
    Applied ML perspective on statistics; modern and practical.
  • ◆
    The Book of Why / Causality — Judea Pearl
    The foundational works on causal inference and counterfactuals; essential for understanding agency and alignment.
  • ◆
    Elements of Information Theory — Thomas Cover & Joy Thomas
    The classic; covers entropy, mutual information, channel capacity — fundamental for communication and compression.
  • ◆
    Measure Theory — Donald Cohn
    Provides the rigorous foundation for probability; useful if you go deep into probability theory.
Life Sciences Core: Evolutionary Theory
  • ◆
    Fundamentals of Evolutionary Biology — Supreet Saini (2026, CRC Press)
    Written specifically for quantitative thinkers; introduces evolution with semi-quantitative treatment, graphical illustrations, and MATLAB coding exercises. Perfect bridge from math to biology.
  • ◆
    Encyclopedia of Evolutionary Biology, 2nd Ed. — Wolf & Russo, eds. (2026, Elsevier, 4 vols.)
    Comprehensive reference covering evolutionary genetics, genomics, macroevolution, speciation, human evolution, evolution of behavior, game theory, and kin selection — directly relevant to multi-agent systems and alignment.
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Phase 3

Classical AI & Agency

Foundational AI · Decision Theory · Game Theory · Deontic & Action Logic

Goal: Understand "What should agents do?" (Parfit) meeting "What can agents calculate?" (AI). Having completed Phase 1d, you now encounter formal decision theory as a formalization of concepts already examined philosophically — including the reasons/causation tension from Davidson, the planning structures from Bratman, and the hierarchy of desires from Frankfurt. Notice how often the formal tools are incomplete formalizations of the philosophical problems.

Core
  • ◆
    Artificial Intelligence: A Modern Approach — Stuart Russell & Peter Norvig
    The canonical survey of AI; covers everything from search to planning to learning.
  • ◆
    The Foundations of Statistics — Leonard Savage (classic) / Decision Theory: An Introduction — Parmigiani & Inoue (modern)
    Covers expected utility, Bayesian decision theory, and the foundations of rational choice.
  • ◆
    Game Theory — Maschler, Solan & Zamir (comprehensive) / Multiagent Systems — Shoham & Leyton-Brown (algorithmic)
    Essential for modeling strategic interaction among agents.
Logic for Agency — Deontic & Action Logic
  • ◆
    An Introduction to Modal Logic — Brian Chellas (deontic & action logic portions)
    The alethic/epistemic modal logic core was moved to Phase 1f. Return here for the deontic modalities (obligation, permission, prohibition) and action logic — these connect directly to normative AI behavior and are best understood against the decision-theoretic background you now have.
  • ◆
    Temporal Logic
    Covered in the same texts; crucial for reasoning about time and change in agent planning.
  • ◆
    Conditional Logic (counterfactuals)
    Already covered in Pearl's Causality; also see relevant chapters in Priest's An Introduction to Non-Classical Logic.
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Phase 4

The Deep Learning Stack

Foundational ML · Neural Networks · Optimization · Numerical Methods · Cell Biology · Neuroscience

Goal: Build technical fluency in how neural networks actually work. Understand the fundamental unit of life: the cell, and the architecture of the brain.

Core
  • ◆
    Pattern Recognition and Machine Learning — Christopher Bishop
    The standard text for ML fundamentals; covers probability, linear models, neural networks, and graphical models.
  • ◆
    Neural Networks — Simon Haykin
    A comprehensive treatment of neural networks from a signal processing perspective.
  • ◆
    Deep Learning — Goodfellow, Bengio & Courville
    The deep learning bible; covers architectures, optimization, and applications.
  • ◆
    Convex Optimization — Stephen Boyd & Lieven Vandenberghe
    The standard reference for convex optimization; essential for understanding training algorithms.
Mathematics Deepening
  • ◆
    Understanding Analysis — Stephen Abbott
    Accessible introduction to real analysis; provides rigorous foundation for optimization and convergence.
  • ◆
    Introductory Functional Analysis with Applications — Erwin Kreyszig
    Covers Banach and Hilbert spaces, operators, and spectral theory; essential for understanding neural networks as functions in infinite-dimensional spaces.
  • ◆
    Numerical Analysis — Richard Burden & J. Douglas Faires
    Covers algorithms for solving mathematical problems on computers; important for implementation.
  • ◆
    Mathematical Foundations for Deep Learning — Mehdi Ghayoumi (2026, Chapman and Hall/CRC)
    A unified reference tying together linear algebra, calculus, probability, optimization, information theory, graph theory, differential geometry, topology, harmonic analysis, dynamical systems, and quantum computing in the context of deep learning.
Life Sciences Core: Cell Biology
  • ◆
    Cell Biology: Basics to Breakthroughs — Aruljothi et al. (2026, Bentham Books)
    Covers classical cell structure to cutting-edge topics like stem cells, cancer signaling, and molecular imaging.
  • ◆
    Histology and Cell Biology: An Introduction to Pathology, 6th Ed. — Kierszenbaum & Tres (2026, Elsevier)
    Connects cell biology to how the body functions and malfunctions; integrates microscopic anatomy with physiology.
Neuroscience Foundation
  • ◆
    Neuroscience: Exploring the Brain, 5th Ed. — Bear, Connors & Paradiso (July 2026, Jones & Bartlett)
    The gold standard introductory neuroscience textbook; covers neurons to cognition, beautifully illustrated. Read cover to cover to ground your understanding of the only working example of general intelligence.
  • ◆
    Fundamental Neuroscience for Basic and Clinical Applications, 6th Ed. — Duane E. Haines (2026, Elsevier)
    Clinical focus; use as a companion for deeper dives into specific systems and pathways.
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Phase 5

Modern LLMs & Scaling

Transformers · Scaling Laws · Information Theory · Advanced Math · Human Body

Goal: Understand why "next token prediction" is so powerful and how modern LLMs work. Understand the architecture of the most complex system we know: the human body.

Core
  • ◆
    "Attention Is All You Need" — Vaswani et al. (2017)
    The foundational transformer paper.
  • ◆
    The Transformer Architecture — Denis Rothman (or similar)
    A comprehensive guide to transformers in practice.
  • ◆
    Scaling Laws Papers — Kaplan et al. (2020); Hoffmann et al. (2022, "Chinchilla")
    Empirical studies on how model performance scales with size and data.
  • ◆
    Elements of Information Theory — Cover & Thomas
    Revisit as needed for understanding compression and capacity.
New Empirical Papers (2026)
  • ◆
    Google Research (Jan 2026) — "Society of Thought: How Internal Debate Doubles AI Accuracy on Complex Tasks"
    Shows that top reasoning models develop emergent "internal personas" that debate each other; direct empirical validation of your "society of sub-agents" idea.
  • ◆
    "Agentic Reasoning for Large Language Models" (2026 survey paper)
    A comprehensive roadmap of multi-agent reasoning, covering planning, tool use, and collective intelligence.
Mathematics for Representations
  • ◆
    Introduction to Graph Theory — Douglas West
    Covers networks, trees, connectivity; essential for understanding attention mechanisms as graph operations.
  • ◆
    A First Course in Harmonic Analysis — Anton Deitmar
    Covers Fourier series and transforms, wavelets; relevant for understanding convolutional and attentional filters.
  • ◆
    Introduction to Smooth Manifolds — John Lee / Mathematical Foundations of AI: Basics of Manifold Theory — Momiao Xiong (2026)
    Essential for manifold learning and representation geometry.
  • ◆
    Topology — James Munkres (point-set)
    Covers continuity, compactness, connectedness; relevant for understanding the shape of data.
  • ◆
    Differential Equations, Dynamical Systems, and an Introduction to Chaos — Hirsch, Smale & Devaney
    Covers stability, attractors, bifurcations; essential for understanding recurrent networks and iterative processes.
Life Sciences Core: The Human Body
  • ◆
    Anthony's Textbook of Anatomy & Physiology, 22nd Ed. — Kevin T. Patton et al. (Sept 2026, Elsevier)
    The definitive, comprehensive text covering cells to organ systems, homeostasis, and genetics. Uses a "Big Picture" approach, aligning with your goal of understanding emergent complexity.
  • ◆
    Hole's Essentials of Human Anatomy and Physiology, 3rd Ed. — Welsh & Prentice-Craver (2026, McGraw Hill)
    A more streamlined, visually rich introduction; useful as a companion for first pass.
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Phase 6

Modern Alignment & Agent Foundations

Alignment Theory · Agent Foundations · Category Theory · Advanced Logic · AI-Specific Philosophy

Goal: Master the core alignment literature and connect it to your architectural vision. The mereology you read in Phase 1b now pays off in category theory; the philosophy of action from Phase 1d pays off in discussions of goal-directed behavior; Bratman's planning structures appear almost verbatim in agent foundations work. The advanced logic and substructural logics here are genuinely hard prerequisites on each other — read them in the order given.

Core Alignment
  • ◆
    Human Compatible — Stuart Russell
    A landmark book on AI alignment, arguing that we must build machines with uncertain objectives.
  • ◆
    Alignment Forum sequences — Embedded Agency & Reframing Superintelligence
    Foundational online sequences on agent foundations and alignment.
  • ◆
    The Alignment Problem — Brian Christian (optional)
    A narrative overview of the alignment field.
Category Theory (Compositional Intelligence)
  • ◆
    Categories for the Working Mathematician — Saunders Mac Lane
    The canonical text; essential for understanding categorical concepts. Your mereology background (Phase 1b) will make the part-whole / functor / composition vocabulary click faster than it otherwise would.
  • ◆
    Conceptual Mathematics — F. William Lawvere & Stephen Schanuel
    An accessible introduction to category theory.
  • ◆
    Sheaves in Geometry and Logic — Mac Lane & Moerdijk
    Advanced text on topos theory; relevant for deep connections between logic, geometry, and semantics.
Advanced Logic & Semantics
  • ◆
    An Introduction to Non-Classical Logic, 2nd Ed. — Graham Priest
    Covers intuitionistic, many-valued, paraconsistent, relevance, modal, and conditional logics. A one-stop resource for most non-classical logics. Prerequisite: your Phase 1f logic foundations.
  • ◆
    An Introduction to Substructural Logics — Greg Restall
    Covers linear, affine, and relevant logics. Read after Priest.
  • ◆
    Situations and Attitudes — Jon Barwise & John Perry
    Foundational text on situation semantics; alternative to possible-worlds semantics. Read after non-classical logic.
  • ◆
    Model-Theoretic Logics — Barwise & Feferman, eds.
    Advanced reference on infinitary and generalized quantifier logics.
New Architectures Papers (2026)
  • ◆
    GRACE: Breaking Up with Normatively Monolithic Agency — Jahn et al. (Feb 2026)
    Neuro-symbolic architecture separating normative (ethical) reasoning from instrumental decision-making; ethics as a distinct subsystem.
  • ◆
    "Toward Culture-Inspired Artificial Intelligence" — Younas & Zeng (Feb 2026)
    Addresses the "Cultural Cognition Gap"; proposes frameworks for cultural plurality in AI.
  • ◆
    Ben Goertzel (March 2026) — "The Path to Robust deAGI" (SCALE 23x presentation)
    Visionary talk on decentralized AGI, modular architectures, and structural alignment.
  • ◆
    EU Project CAVAA (Counterfactual Assessment and Valuation for Awareness Architecture)
    Engineering "awareness" and counterfactual reasoning; tackles perception, memory, simulation, and ethical frameworks.
  • ◆
    Cisco Outshift Whitepaper (Jan 2026) — "From connection to cognition: Scaling out superintelligence"
    Blueprint for "horizontal scaling" of intelligence — agents and humans sharing intent and reasoning collectively.
Modern Philosophy (AI Specific)
  • ◆
    AI Pluralism — P. Anand Rao, ed. (2026, Palgrave MacMillan)
    Introduces "AI Pluralism" — embracing differentiation of AI agents to enable genuine dialectical exchange. Directly on point for your project.
  • ◆
    Cognitive Symbiosis — Peter Eidos (2026, PhilArchive)
    Develops "Cognitive Symbiosis" where human-AI interaction becomes co-cognition; proposes "relational identity" arising from interaction.
  • ◆
    The Ethics of Artificial Intelligence — Sven Nyholm (2026, Hackett Publishing)
    Comprehensive introduction to AI ethics, covering consciousness, responsibility, meaning in life, and value alignment.
  • ◆
    MDPI (Feb 2026) — "What Artificial Intelligence May Be Missing" (Philosophies)
    Argues that AI lacks tacit knowledge, abduction, and autopoiesis. Having read Maturana & Varela in Phase 1g, you can now engage this critique properly rather than encountering autopoiesis for the first time here.
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Phase 7

Future Architectures — Pluralistic & Pragmatic AI

Optional Extensions · Advanced Pragmatics & Semantics · Research Frontiers
Advanced Pragmatics & Semantics
Note: The foundational pragmatics texts (Austin, Searle, Grice) were moved to Phase 1c. These entries are the applications that genuinely require prior background — category theory for Enriched Meanings, evolution for Evolutionary Pragmatics, and the full philosophy of language context for Inflammatory Language.
  • ◆
    Enriched Meanings — Ash Asudeh & Gianluca Giorgolo (2020, Oxford)
    Uses category theory to model semantics; a perfect bridge between the category theory you learned in Phase 6 and your semantics core. This placement is now correct — it requires Mac Lane as background.
  • ◆
    Evolutionary Pragmatics — Bart Geurts & Richard Moore (2025, Oxford)
    Connects pragmatics to evolution; requires the evolutionary biology background from Phase 2. Placement here is correct.
  • ◆
    Inflammatory Language — Una Stojnić & Ernie Lepore (2025, Oxford)
    A cutting-edge application of philosophy of language to real-world social dynamics; shows how your toolkit can address urgent problems.
Cutting-Edge Research
  • ◆
    Multi-agent debate frameworks
    Track recent work beyond the Google paper (search arXiv, AAAI, NeurIPS).
  • ◆
    Cognitive pluralism in AI
    Search recent proceedings of AAAI, NeurIPS, ACL, etc.
  • ◆
    Elementary Applied Topology — Herbert Edelsbrunner
    Covers persistent homology and shape of data (Topological Data Analysis); relevant for understanding representation geometry.
Additional Advanced Math (If Needed)
  • ◆
    Ideals, Varieties, and Algorithms — Cox, Little & O'Shea (computational algebraic geometry)
    May be relevant for understanding loss landscapes.
  • ◆
    K-Theory and Sheaf Theory
    Advanced topics; for deep theoretical connections.
  • ◆
    Homotopy Type Theory (HoTT) — free online
    A modern synthesis of type theory, homotopy theory, and category theory.