AI/ML Reading List

• Part 1: Getting Started
• §1 Math & CS Foundations 11
• ★ Essence of Linear Algebra — 3Blue1Brown [course]
  Visual intuition for vectors, matrices, eigenvalues
• Neural Networks — 3Blue1Brown [course]
  Visual intro to how neural nets work
• Statistics & Probability — Khan Academy [course]
  Distributions, Bayes' theorem, hypothesis testing
• Mathematics for Machine Learning — Deisenroth et al. [book]
  Free textbook---linear algebra, calculus, probability
• CS231n Notes — Stanford [course]
  Practical neural net fundamentals
• ★ Practical Deep Learning for Coders — fast.ai [course]
  Top-down, code-first approach
• How Transformer LLMs Work — Alammar & Grootendorst [course]
  95-min course: tokenization, attention, MoE
• Python for ML
• Python Tutorial [documentation]
  Official, if you need basics
• NumPy Quickstart [documentation]
  Array operations
• Pandas Getting Started [documentation]
  Data manipulation
• PyTorch 60-Minute Blitz [documentation]
  Tensors, autograd, training
• Part 2: Understanding AI
• §2 Foundations (The Canon) 9
• ★ Learning Representations by Back-Propagating Errors — Rumelhart, Hinton, Williams (1986) [paper]
  How neural nets learn. Everything builds on this.
• ★ Efficient Estimation of Word Representations in Vector Space — Mikolov et al. (2013) [paper]
  Word2vec. 'King - Man + Woman = Queen'
• GloVe: Global Vectors for Word Representation — Pennington et al. (2014) [paper]
  Alternative embeddings, co-occurrence based
• Sequence to Sequence Learning — Sutskever et al. (2014) [paper]
  Encoder-decoder architecture
• ImageNet Classification with Deep CNNs — Krizhevsky et al. (2012) [paper]
  ImageNet moment---deep learning's 'big bang'
• Deep Residual Learning — He et al. (2015) [paper]
  Skip connections, enabled very deep networks
• Batch Normalization — Ioffe & Szegedy (2015) [paper]
  Training stability trick used everywhere
• Dropout — Srivastava et al. (2014) [paper]
  Regularization that actually works
• Adam: A Method for Stochastic Optimization — Kingma & Ba (2014) [paper]
  The default optimizer
• §3 Attention & Transformers 10
• Neural Machine Translation by Jointly Learning to Align and Translate — Bahdanau et al. (2014) [paper]
  Invented attention mechanism
• ★ Attention Is All You Need — Vaswani et al. (2017) [paper]
  Transformers. The architecture. Read carefully.
• BERT: Pre-training of Deep Bidirectional Transformers — Devlin et al. (2018) [paper]
  Bidirectional pretraining, MLM objective
• Improving Language Understanding by Generative Pre-Training — Radford et al. (2018) [paper]
  GPT---autoregressive pretraining
• Language Models are Unsupervised Multitask Learners — Radford et al. (2019) [paper]
  GPT-2, scaling
• ★ Language Models are Few-Shot Learners — Brown et al. (2020) [paper]
  GPT-3, in-context learning emerges at scale
• Scaling Laws for Neural Language Models — Kaplan et al. (2020) [paper]
  Chinchilla precursor, loss vs. compute/data/params
• ★ Training Compute-Optimal Large Language Models — Hoffmann et al. (2022) [paper]
  Chinchilla---optimal scaling ratios
• LLaMA: Open and Efficient Foundation Language Models — Touvron et al. (2023) [paper]
  Open weights, efficient training
• FlashAttention — Dao et al. (2022) [paper]
  IO-aware attention, practical speedup
• §4 Reasoning & Chain-of-Thought 16
• ★ Chain-of-Thought Prompting Elicits Reasoning — Wei et al. (2022) [paper]
  'Let's think step by step' works
• Self-Consistency Improves Chain of Thought Reasoning — Wang et al. (2022) [paper]
  Sample multiple CoT paths, majority vote
• Tree of Thoughts — Yao et al. (2023) [paper]
  Search over reasoning paths
• ★ ReAct: Synergizing Reasoning and Acting — Yao et al. (2022) [paper]
  Reasoning + Acting, tool use
• Toolformer — Schick et al. (2023) [paper]
  LLMs learning to use tools
• Let's Verify Step by Step — Lightman et al. (2023) [paper]
  Process reward models for math
• MAKER: Solving a Million-Step LLM Task (2025) [paper]
  Ensemble voting for long-horizon reliability
• The Prompt Report — Schulhoff et al. (2024) [paper]
  58 prompting techniques, taxonomy, best practices
• Let Me Speak Freely? — Tam et al. (2024) [paper]
  Structured output (JSON/XML) degrades reasoning
• Thinking Before Constraining — Nguyen et al. (2026) [paper]
  Fix: reason freely, then constrain output format
• XML Prompting as Grammar-Constrained Interaction — Alpay & Alpay (2025) [paper]
  Formal framework for XML-based structured prompting with convergence guarantees
• SLOT: Structuring the Output of Large Language Models — Shen et al. (2025) [paper]
  Fine-tuned lightweight model as post-processing layer for structured output
• StructuredRAG: JSON Response Formatting with Large Language Models — Shorten et al. (2024) [paper]
  Benchmark for structured output reliability across tasks and models
• Outlines: Structured Text Generation — dottxt (2024) [resource]
  Grammar-based constrained decoding. Guarantees valid JSON/regex output by constraining token sampling.
• Guidance: A Language for Controlling LLMs — Microsoft (2023) [resource]
  Interleaves generation with programmatic control. Constrains output structure without taxing model attention.
• Prompt Repetition Improves Non-Reasoning LLMs — Leviathan, Kalman, Matias (2025) [paper]
  Repeating the input prompt improves performance without increasing output tokens or latency. Reasoning models already learn to do this internally.
• Part 3: Building with AI
• §5 RAG & Retrieval 19
• ★ RAG for LLMs: A Survey — Gao et al. (2023) [paper]
  Start here. Naive -> Advanced -> Modular RAG paradigms
• ★ Pinecone RAG Guide [resource]
  End-to-end walkthrough. Good second read after survey.
• LangChain RAG Tutorial [resource]
  Hands-on implementation with code
• LlamaIndex RAG Docs [resource]
  Concepts + implementation
• RAG From Scratch — LangChain [video]
  Video series for visual learners
• ★ Retrieval-Augmented Generation for Knowledge-Intensive NLP — Lewis et al. (2020) [paper]
  Original RAG paper---foundational
• ★ Dense Passage Retrieval for Open-Domain QA — Karpukhin et al. (2020) [paper]
  DPR---learned retrieval beats BM25
• REALM: Retrieval-Augmented Language Model Pre-Training — Guu et al. (2020) [paper]
  Retrieval-augmented pretraining
• Precise Zero-Shot Dense Retrieval without Relevance Labels — Gao et al. (2022) [paper]
  HyDE---hypothetical document embeddings
• Query2doc: Query Expansion with Large Language Models — Wang, Yang, Wei (2023) [paper]
  LLM generates pseudo-document for BM25. More conservative than HyDE.
• Self-RAG: Learning to Retrieve, Generate, and Critique — Asai et al. (2023) [paper]
  LLM decides when to retrieve
• RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval — Sarthi et al. (2024) [paper]
  Recursive summarization for retrieval
• Atlas: Few-shot Learning with Retrieval Augmented LMs — Izacard et al. (2022) [paper]
  Few-shot learning with retrieval
• ★ From Local to Global: A Graph RAG Approach — Edge et al. (2024) [paper]
  Microsoft's GraphRAG---community summaries for global queries
• Graph Retrieval-Augmented Generation: A Survey — Li et al. (2024) [paper]
  Formalizes GraphRAG taxonomy
• Microsoft GraphRAG Project [resource]
  Official project page
• GraphRAG GitHub Repository [tool]
  Official implementation
• Awesome-GraphRAG [resource]
  Curated papers/benchmarks
• Detecting Overflow in Compressed Token Representations for Retrieval-Augmented Generation — Belikova et al. (2026) [paper]
  Studies compressibility limits for RAG: when compression erases task-relevant information. Proposes overflow detection method.
• §6 Embeddings & Vector Search 6
• ★ Sentence-BERT — Reimers & Gurevych (2019) [paper]
  Sentence embeddings that work
• SimCSE: Simple Contrastive Learning of Sentence Embeddings — Gao et al. (2021) [paper]
  Contrastive sentence embeddings
• Text Embeddings by Weakly-Supervised Contrastive Pre-training — Wang et al. (2022) [paper]
  E5---strong general-purpose embeddings
• Nomic Embed (2024) [paper]
  8K context embeddings
• Matryoshka Representation Learning — Kusupati et al. (2022) [paper]
  Truncatable embeddings
• Efficient and Robust Approximate Nearest Neighbor Search — Malkov & Yashunin (2016) [paper]
  HNSW---hierarchical navigable small world graphs
• §7 Agents & Tool Use 13
• ReAct: Synergizing Reasoning and Acting (2022) [paper]
  Interleaved reasoning and acting
• Toolformer: Language Models Can Teach Themselves to Use Tools (2023) [paper]
  Self-taught tool use
• Voyager: An Open-Ended Embodied Agent — Wang et al. (2023) [paper]
  LLM agent in Minecraft, skill library
• AutoGPT / BabyAGI (2023) [tool]
  Autonomous agent architectures (read critically)
• Generative Agents: Interactive Simulacra — Park et al. (2023) [paper]
  'Smallville'---agents with memory
• Language Agent Tree Search — Zhou et al. (2023) [paper]
  LATS
• Reflexion — Shinn et al. (2023) [paper]
  Agents that learn from mistakes
• World Models — Ha & Schmidhuber (2018) [paper]
  Learn environment dynamics in latent space
• Frameworks & Examples
• LangChain Agents [documentation]
  Tool use, ReAct implementation
• LlamaIndex Agents [documentation]
  Data agents
• OpenAI Swarm [tool]
  Lightweight multi-agent framework
• AutoGen — Microsoft [tool]
  Multi-agent conversations
• ★ Building Effective Agents — Anthropic [article]
  Patterns and anti-patterns
• §8 Evaluation & Benchmarks 14
• Evaluating Language Models as Synthetic Data Generators — Kim et al. (2025) [paper]
  AgoraBench: data generation ability doesn't correlate with problem-solving ability
• ★ MMLU [benchmark]
  General knowledge across domains
• HellaSwag [benchmark]
  Commonsense reasoning
• HumanEval [benchmark]
  Code generation
• GSM8K [benchmark]
  Grade school math
• MATH [benchmark]
  Competition math
• BIG-Bench [benchmark]
  Diverse capabilities
• TruthfulQA [benchmark]
  Hallucination resistance
• MT-Bench [benchmark]
  Multi-turn conversation
• Eval Tools
• OpenAI Evals [tool]
  Evaluation framework
• RAGAS [tool]
  RAG evaluation metrics
• LangSmith [tool]
  Tracing, debugging, evaluation
• Braintrust [tool]
  LLM eval platform
• ragas GitHub [tool]
  Faithfulness, relevance metrics
• Part 4: Knowledge & Reasoning
• §9 Knowledge Graphs + LLMs / Neuro-Symbolic 27
• Neurosymbolic AI for Reasoning over Knowledge Graphs (2023) [paper]
  Taxonomy of approaches
• Neural-Symbolic Reasoning over KGs: A Query Perspective (2024) [paper]
  Recent survey, LLM integration
• Neuro-Symbolic AI in 2024: A Systematic Review (2025) [paper]
  State of the field
• KG-BERT — Yao et al. (2019) [paper]
  BERT for knowledge graph completion
• QA-GNN — Yasunaga et al. (2021) [paper]
  GNN + LM for QA over KGs
• GreaseLM — Zhang et al. (2022) [paper]
  Fusing LMs and KGs for reasoning
• ★ Think-on-Graph — Sun et al. (2023) [paper]
  LLM reasoning on KG structure
• Reasoning on Graphs — Luo et al. (2024) [paper]
  Reasoning on Graphs with LLMs
• Symbolic AI in the Age of LLMs — Lassila, AWS re:Invent (2025) [video]
  Practitioner perspective on hybrid systems
• Inductive Programming Meets the Real World — Gulwani et al. (2015) [paper]
  Program synthesis from examples; IP vs. ML comparison
• Probabilistic Logic Programming
• DeepProbLog (2018) [paper]
  Neural predicates in ProbLog
• Towards Probabilistic ILP with Neurosymbolic Inference (2024) [paper]
  Learning logic programs
• Statistical Relational Artificial Intelligence — De Raedt et al. (2016) [book]
  Textbook---probabilistic logic
• Hybrid / Neural-Symbolic Systems
• Computational Architectures Integrating Neural and Symbolic Processes — Sun & Bookman, eds. (1994) [book]
  Early integration approaches
• Connectionist-Symbolic Integration — Sun & Alexandre, eds. (1997) [book]
  Bridging paradigms
• Hybrid Neural Systems — Wermter & Sun, eds. (2000) [book]
  Springer collection
• Neural-Symbolic Cognitive Reasoning — Garcez, Lamb & Gabbay (2009) [book]
  Foundations of modern neuro-symbolic
• Minsky & Frames
• A Framework for Representing Knowledge — Minsky (1974) [paper]
  Introduced frames---foundational for KR
• Society of Mind — Minsky (1986) [book]
  Agents as collections of simpler processes
• The Emotion Machine — Minsky (2006) [book]
  Commonsense reasoning, emotions in AI
• Generic Frame Protocol [resource]
  Standard for frame-based systems
• Cybersecurity KG + RAG
• CyKG-RAG — Kurniawan et al. (2024) [paper]
  KG + vector search with query routing. Routes structured queries to SPARQL, semantic to embeddings.
• AgCyRAG: Agentic KG-based RAG for Cybersecurity — Kurniawan et al. (2025) [paper]
  Multiple agents adaptively select retrieval strategy (KG traversal vs vector search vs hybrid)
• GraphCyRAG — PNNL (2024) [paper]
  Neo4j KG traversal over CVE->CWE->CAPEC->ATT&CK. Graph traversal outperforms embedding search for vuln-to-technique mapping.
• CTI-Thinker (2025) [paper]
  LLM-driven CTI KG construction + GraphRAG reasoning engine for tactical inference
• Ontology-Grounded RAG
• ★ OG-RAG: Ontology-Grounded Retrieval-Augmented Generation — Nadkarni et al. (2024) [paper]
  Anchors retrieval in domain ontologies. +55% fact recall, +40% correctness, +27% reasoning accuracy vs baseline RAG. Key for Part 3.
• Ontology Learning and KG Construction: Impact on RAG Performance — Reiz et al. (2024) [paper]
  Compares vector RAG vs GraphRAG vs ontology-guided KG. GraphRAG + ontology-KG both hit 90% accuracy. Empirical grounding evidence.
• §10 Search Engines & Information Retrieval 7
• ★ Introduction to Information Retrieval — Manning, Raghavan, Schütze (2008) [book]
  Start here. Free online. Ch 1-8 cover essentials: inverted index, TF-IDF, evaluation
• ★ The Probabilistic Relevance Framework: BM25 and Beyond — Robertson & Zaragoza (2009) [paper]
  BM25 is still the baseline. Understand this before neural approaches.
• ★ Pretrained Transformers for Text Ranking: BERT and Beyond — Lin et al. (2021) [paper]
  Survey of neural IR. Good overview before diving into specific papers.
• ColBERT: Efficient and Effective Passage Search — Khattab & Zaharia (2020) [paper]
  Late interaction---practical for production neural search
• Passage Re-ranking with BERT — Nogueira & Cho (2019) [paper]
  Simple but effective. Good first neural IR paper to implement.
• The Anatomy of a Large-Scale Hypertextual Web Search Engine — Brin & Page (1998) [paper]
  Original Google paper. Historical interest, less relevant to RAG work.
• Learning to Rank for Information Retrieval — Liu (2011) [book]
  Deep dive on ranking ML. Reference, not first read.
• §11 Semantics, Semiotics & Ontologies 40
• Semiotics (Signs & Meaning)
• ★ Semiotics: The Basics — Daniel Chandler [book]
  Start here. Accessible intro to Saussure, Peirce, Eco, and sign theory
• ★ Peirce's Theory of Signs — Stanford Encyclopedia of Philosophy [article]
  Icon, index, symbol trichotomy. Free, authoritative reference
• Course in General Linguistics — Saussure (1916) [book]
  Signifier/signified distinction---foundational but dense
• A Theory of Semiotics — Umberto Eco (1976) [book]
  Classic text on sign systems---read after Chandler
• Symbols and Grounding in Large Language Models — Mollo & Millière (2023) [paper]
  Do LLMs ground symbols? Bridges semiotics and AI debate
• AI: A Semiotic Perspective — Walsh Matthews & Danesi (2019) [article]
  Survey of semiotics vs. AI: abduction, embodiment, Baudrillard, Peirce
• The Main Tasks of a Semiotics of Artificial Intelligence — Massimo Leone (2023) [article]
  AI as 'technology of fakery'---mimicry, generation, ideology
• ★ The Symbol Grounding Problem — Stevan Harnad (1990) [paper]
  Foundational paper. How do symbols get meaning? The Chinese Room problem for semantics.
• Language Models as Semiotic Machines — Vromen (2024) [paper]
  LLMs through Saussure and Derrida. How word2vec embodies structuralist sign theory.
• Not Minds, but Signs: Reframing LLMs through Semiotics — Mazzocchi et al. (2025) [paper]
  LLMs as semiotic means, not minds. Peirce, Lotman's semiosphere, prompt as contract.
• The Vector Grounding Problem — Mollo (2023) [paper]
  Can LLM internal states be about extra-linguistic reality without embodiment? Argues yes—referential grounding possible from text alone.
• A Categorical Analysis of LLMs and Why They Circumvent the Symbol Grounding Problem — Betz et al. (2024) [paper]
  Formal categorical framework. LLMs don't solve grounding—they parasitize human-grounded text. Key for Part 3 thesis.
• Beyond Tokens: Introducing Large Semiosis Models (LSMs) for Grounded Meaning in Artificial Intelligence — Silva (2025) [paper]
  Proposes LSMs that model full Peircean triads (representamen/interpretant/object). Argues LLMs operate only at signifier level.
• Philosophy & Sociology Foundations
• ★ The Presentation of Self in Everyday Life — Goffman (1956) [book]
  Dramaturgical framework. Identity as performance for audiences. Front stage vs back stage. Core framework for Part 1.
• ★ Philosophical Investigations — Wittgenstein (1953) [book]
  Language games. Meaning is use in context. §§1-50 cover the core ideas. Dense but foundational.
• Foucault: Power is Everywhere — Powercube (2011) [article]
  Power as productive, not just repressive. Regimes of truth shape what's sayable. Short, focused, accessible.
• ★ How to Do Things with Words — Austin (1962) [book]
  Speech act theory. Locutionary vs illocutionary force. Utterances don't just describe—they do things. Core for Part 2.
• ★ An Introduction to Cybernetics — Ashby (1956) [book]
  Law of Requisite Variety: only variety can absorb variety. Core constraint for Part 3—why you need structured systems to regulate LLMs.
• ★ Cognition in the Wild — Hutchins (1995) [book]
  Distributed cognition. Thinking isn't in the head—it's across people, tools, artifacts. Grounds the hybrid architecture argument in Part 3.
• Semiotics (Signs & Meaning)
• LogicAgent: A Logic-Enhanced Agent Framework for Code Generation — Joshi et al. (2025) [paper]
  Grounds LLM code generation in formal logic (Prolog). Practical example of hybrid architecture with symbolic backstage.
• Chain of Semiosis — Multimodality Glossary [article]
  Glossary entry on Peirce's unlimited semiosis—how signs generate interpretants that become new signs. Context for LLM token chains.
• Semantics (Linguistic Meaning)
• ★ Speech and Language Processing, Ch. 14-18 — Jurafsky & Martin [book]
  Compositional semantics, word senses, semantic roles. Free online.
• From Frequency to Meaning: Vector Space Models of Semantics — Turney & Pantel (2010) [paper]
  Pre-neural distributional semantics survey. Historical context for embeddings.
• Topology of Word Embeddings: Singularities Reflect Polysemy — Jakubowski, Gasic & Zibrowius (2020) [paper]
  Polysemous words are singularities in vector space. TDA meets distributional semantics.
• Unveiling Topological Structures from Language: A Survey of TDA Applications in NLP — Luo et al. (2024) [paper]
  Comprehensive survey of 100+ papers on topological data analysis for NLP.
• ★ Conceptual Spaces: The Geometry of Thought — Peter Gärdenfors (2000) [book]
  Meaning as geometry. Bridges symbolic AI and connectionism. Foundational for understanding embeddings.
• Distributional Formal Semantics — Venhuizen et al. (2021) [paper]
  Bridging neural embeddings and logic-based meaning. Graduate-level.
• Semantic Parsing: A Survey — Kamath & Das (2018) [paper]
  Mapping natural language to formal representations. Specialist topic.
• Ontologies & Knowledge Representation
• ★ Ontology Development 101 — Noy & McGuinness (2001) [paper]
  Start here. Short, practical, free PDF on building ontologies
• Knowledge Representation and Reasoning — Brachman & Levesque (2004) [book]
  Comprehensive textbook---logic, frames, description logics
• The Description Logic Handbook (2003) [book]
  Reference for OWL/semantic web formal foundations
• OWL 2 Primer — W3C [documentation]
  Standard for web ontologies
• Cyc — Lenat (1995) [resource]
  Massive hand-crafted ontology
• Schema.org [resource]
  Practical ontology used by search engines
• WordNet — Miller (1995) [resource]
  Lexical database---synsets, hypernymy
• ConceptNet — Speer & Havasi (2017) [resource]
  Commonsense knowledge graph
• Wikidata [resource]
  Collaborative structured knowledge base
• Practical Resources
• Ontology: A Practical Guide — Pease (2011) [book]
  Hands-on ontology engineering
• OneZoom [tool]
  Interactive tree of life visualization
• OLSViz [tool]
  Ontology visualization tool
• §12 Bayesian Statistics & Probabilistic Reasoning 11
• ★ Probabilistic Machine Learning — Murphy [book]
  Free textbook---rigorous Bayesian ML
• Bayesian Reasoning and Machine Learning — Barber [book]
  Free textbook---excellent intro
• Pattern Recognition and Machine Learning — Bishop [book]
  Classic textbook, Bayesian perspective
• Bayesian Data Analysis — Gelman et al. [book]
  The applied Bayesian statistics bible
• The Book of Why — Pearl [book]
  Accessible intro to causal inference
• Causality — Pearl (2009) [book]
  Technical treatment of causal models
• Probabilistic Graphical Models — Koller & Friedman [book]
  Bayesian networks, Markov random fields
• Bayesian Deep Learning
• Dropout as a Bayesian Approximation — Gal & Ghahramani (2016) [paper]
  Uncertainty from dropout
• Weight Uncertainty in Neural Networks — Blundell et al. (2015) [paper]
  Bayes by Backprop
• What Uncertainties Do We Need in Bayesian Deep Learning? — Kendall & Gal (2017) [paper]
  Aleatoric vs. epistemic uncertainty
• Probabilistic Backpropagation — Hernández-Lobato & Adams (2015) [paper]
  Scalable Bayesian neural nets
• Part 5: Securing AI
• §13 Security & Adversarial ML 36
• ★ MITRE ATLAS [resource]
  ATT&CK for AI/ML systems
• Explaining and Harnessing Adversarial Examples — Goodfellow et al. (2014) [paper]
  FGSM, adversarial examples basics
• Intriguing Properties of Neural Networks — Szegedy et al. (2013) [paper]
  Original adversarial examples paper
• BadNets — Gu et al. (2017) [paper]
  Backdoor attacks on neural nets
• Poisoning Attacks against SVMs — Biggio et al. (2012) [paper]
  Data poisoning foundations
• Universal Adversarial Triggers — Wallace et al. (2019) [paper]
  Prompt injection precursor
• Ignore Previous Prompt — Perez & Ribeiro (2022) [paper]
  Prompt injection attacks
• Not What You've Signed Up For — Greshake et al. (2023) [paper]
  Indirect prompt injection
• MITRE Resources
• MITRE ATLAS [resource]
  15 tactics, 66 techniques for AI/ML attacks
• Center for Threat-Informed Defense [resource]
• LLM Security (Red Teaming)
• ★ OWASP LLM Top 10 [resource]
  Industry standard threat taxonomy
• LLM Security [resource]
  Curated prompt injection research
• Many-Shot Jailbreaking — Anthropic (2024) [paper]
  Context window exploitation
• Jailbroken: How Does LLM Safety Training Fail? — Wei et al. (2023) [paper]
  Taxonomy of jailbreak techniques
• garak [tool]
  LLM vulnerability scanner, automated red teaming tool
• Embrace The Red — Wunderwuzzi [blog]
  Blog on AI red teaming
• ★ Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs — Schulhoff et al. (2023) [paper]
  600K+ adversarial prompts, 29 technique taxonomy. Foundational dataset.
• ★ JailbreakBench: An Open Robustness Benchmark for Jailbreaking LLMs — Chao et al. (2024) [paper]
  NeurIPS 2024. Standard benchmark methodology, 100 behaviors across 10 harm categories.
• Understanding Jailbreak Success: A Study of Latent Space Dynamics in LLMs — Ball et al. (2024) [paper]
  Jailbreaks cluster by semantic type; effective attacks suppress harmfulness perception.
• Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs — Xu et al. (2024) [paper]
  NeurIPS 2024. Factor analysis: model size, fine-tuning, system prompts affect robustness.
• LLMs for Security Work
• Threat intelligence summarization [resource]
  Distilling reports, CVE analysis
• Log analysis & anomaly detection [resource]
  Pattern recognition in SIEM data
• Malware analysis assistance [resource]
  Code explanation, IOC extraction
• Phishing detection [resource]
  Email/URL classification
• Report writing & documentation [resource]
  SOC reports, incident summaries
• Query generation (SPL, KQL) [resource]
  Natural language to security queries
• CVE-to-ATT&CK Mapping
• MITRE CTID: Mapping ATT&CK to CVE for Impact [resource]
  Official MITRE methodology and dataset. Authoritative mappings in Mappings Explorer.
• BRON: Bidirectional Graph — Hemberg et al. [tool]
  Bidirectional KG: ATT&CK <-> CAPEC <-> CWE <-> CVE. Traversable edges for path-based queries.
• SMET: Semantic Mapping of CVE to ATT&CK — Abdeen et al. (2023) [paper]
  SRL extracts attack vectors from CVE text, ATT&CK-BERT embeds both sides, logistic regression classifies. Code + dataset on GitHub (MIT).
• CVE2ATT&CK: BERT-Based Mapping of CVEs to ATT&CK Techniques — Grigorescu et al. (2022) [paper]
  1,813 labeled CVE->ATT&CK pairs. BERT multi-label classifiers. Dataset useful for fine-tuning.
• Automated CVE-to-Tactic Mapping (2024) [paper]
  SecRoBERTa best at F1 77.81%. GPT-4 zero-shot only 22.04%---general LLMs struggle without fine-tuning.
• CTI + LLMs + Knowledge Graphs
• Actionable Cyber Threat Intelligence using Knowledge Graphs and LLMs — Kumar et al. (2024) [paper]
  LLM extracts triples from CTI reports, constructs queryable KG. Prompt engineering + fine-tuning comparison.
• AttacKG+: Boosting Attack Knowledge Graph Construction with LLMs — Zhang et al. (2024) [paper]
  Four-step framework: rewrite reports → parse → entity extraction → MITRE TTP mapping. In-context learning approach.
• ★ TITAN: Graph-Executable Reasoning for Cyber Threat Intelligence — Zhou et al. (2024) [paper]
  88K examples: NL questions → executable graph reasoning paths + CoT explanations. Deterministic execution on KG. Hybrid grounding exemplar.
• Agentic Security
• WORLDS: A Simulation Engine for Agentic Pentesting — Dreadnode (2025) [blog]
  CPU-based simulation generates pentesting trajectories from AD network manifests. 8B model fine-tuned on 10K synthetic trajectories achieves domain compromise on real GOAD network. Demonstrates sim-to-real transfer via formal state modeling.
• Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy — Kojukhov & Bovshover (2026) [paper]
  Critiques model-centric detection pipelines; proposes meta-cognitive architecture for accountable decision-making under adversarial uncertainty.
• Part 6: Resources
• §14 Textbooks (Free Online) 13
• ★ Deep Learning — Goodfellow, Bengio, Courville (2016) [book]
  Theory foundations
• Speech and Language Processing — Jurafsky & Martin [book]
  NLP fundamentals
• Probabilistic Machine Learning — Kevin Murphy [book]
  Bayesian/rigorous approach
• Dive into Deep Learning — Zhang et al. [book]
  Interactive, code-heavy
• The Little Book of Deep Learning — François Fleuret [book]
  Concise visual intro
• Neural Networks and Deep Learning — Michael Nielsen [book]
  Gentle introduction
• Foundations of Statistical Natural Language Processing — Manning & Schütze [book]
  Classic (1999), pre-neural NLP
• AIMA Resources
• AIMA Main Site [resource]
  Preface, contents, index PDFs
• AIMA Algorithms/Pseudocode PDF [resource]
  All algorithms from the book
• AIMA Figures PDF [resource]
  Diagrams and illustrations
• AIMA Bibliography [resource]
  2000+ citations
• AIMA GitHub: aimacode [resource]
  Python, Java implementations
• AIMA Exercises [resource]
  Interactive question bank
• §15 Books (Print) 18
• MIT Press Essential Knowledge Series
• Large Language Models — Raaijmakers (2025) [book]
  Architecture, training, limitations
• Artificial General Intelligence — Togelius (2024) [book]
  What 'general intelligence' means
• ChatGPT and the Future of AI — Sejnowski (2024) [book]
  Deep language revolution
• Accessible Introductions
• The Worlds I See — Fei-Fei Li (2023) [book]
  AI pioneer memoir, computer vision
• Artificial Intelligence: A Guide for Thinking Humans — Mitchell (2019) [book]
  Balanced overview, limitations
• Artificial Intelligence: A Modern Approach — Russell & Norvig (2020) [book]
  The standard AI textbook (4th ed.)
• Manning Publications
• ★ Build a Large Language Model (From Scratch) — Raschka (2024) [book]
  Hands-on LLM implementation
• Build a Reasoning Model (From Scratch) — Raschka (2026) [book]
  Reasoning enhancements, RL for tools, distillation. MEAP available (75% complete).
• LLMs in Production (2024) [book]
  Deployment, scaling, ops
• AI Agents in Production (2025) [book]
  Agent architectures, deployment
• Knowledge Graphs and LLMs in Action (2024) [book]
  KG + LLM integration patterns
• Tools & Frameworks
• Ollama [tool]
  Local LLM inference
• LM Studio [tool]
  GUI for local models
• SymPy [tool]
  Symbolic mathematics in Python
• LangChain / LlamaIndex [tool]
  RAG orchestration frameworks
• Instructor [tool]
  Structured outputs from LLMs
• Weights & Biases [tool]
  Experiment tracking
• MLflow [tool]
  ML lifecycle management
• §16 Blogs & Newsletters 25
• Academic-leaning
• ★ Lil'Log — Lilian Weng [blog]
  Excellent deep dives, OpenAI researcher
• The Gradient [blog]
  Long-form essays
• Distill.pub [blog]
  Beautiful visualizations (inactive but archived)
• Jay Alammar's Blog — Jay Alammar [blog]
  Visual explanations (Illustrated Transformer)
• Import AI — Jack Clark [blog]
  Weekly newsletter, policy + research
• The Batch — deeplearning.ai [blog]
  Weekly digest
• Sebastian Raschka's Newsletter — Sebastian Raschka [blog]
  Practical, code-focused
• Papers With Code [resource]
  Papers + implementations
• Practitioner blogs
• Simon Willison's Blog — Simon Willison [blog]
  Daily LLM experiments, tool reviews, SQLite
• Eugene Yan — Eugene Yan [blog]
  ML systems, RecSys, production patterns
• Chip Huyen — Chip Huyen [blog]
  MLOps, systems design, interviews
• Hamel Husain — Hamel Husain [blog]
  LLM fine-tuning, practical notebooks
• Latent Space — swyx & Alessio [blog]
  AI Engineer perspective, interviews
• Anthropic Research Blog [blog]
  Safety, interpretability, capabilities
• OpenAI Research Blog [blog]
  Model releases, safety research
• Google AI Blog [blog]
  Research announcements, tutorials
• Essential Articles (Printable)
• ★ Patterns for Building LLM-based Systems — Eugene Yan [article]
  Production architecture
• Building LLM Applications for Production — Chip Huyen [article]
  End-to-end guide
• How GPT Tokenizers Work — Simon Willison [article]
  Tokenization deep-dive
• RAG vs. Long Context: A Hybrid Approach — Simon Willison [article]
  RAG tradeoffs
• LLM Powered Autonomous Agents — Lilian Weng [article]
  Agent architectures
• Prompt Engineering — Lilian Weng [article]
  Comprehensive guide
• The Rise of the AI Engineer — swyx [article]
  Role definition
• Your AI Product Needs Evals — Hamel Husain [article]
  Evaluation strategy
• Prompt Engineering vs. Blind Prompting — Mitchell Hashimoto [article]
  Systematic approach
• §17 Aggregators & Discovery 5
• arxiv-sanity-lite [resource]
  Karpathy's filtered arxiv
• papers.labml.ai [resource]
  Trending papers with annotations
• Hugging Face Daily Papers [resource]
  Community upvoted
• Connected Papers [resource]
  Visual citation graphs
• ★ Semantic Scholar [resource]
  AI-powered paper search
• §18 YouTube & Video 8
• Andrej Karpathy [video]
  Deep explanations, live coding (GPT from scratch)
• Yannic Kilcher [video]
  Paper walkthroughs, ML news
• 3Blue1Brown [video]
  Visual math intuition
• Two Minute Papers [video]
  Quick research summaries
• AI Explained [video]
  News analysis, capability deep-dives
• AI Engineer Conference [video]
  Practitioner talks, production systems
• ★ Karpathy: Intro to LLMs (1hr) [video]
  Best single intro to LLMs
• Karpathy: GPT from Scratch (2hr) [video]
  Build a transformer, step by step
• §19 Podcasts 6
• ★ Latent Space [podcast]
  AI engineering, practitioner interviews
• Practical AI [podcast]
  Applied ML, accessible
• Eye on AI [podcast]
  Industry trends, executive interviews
• Lex Fridman Podcast [podcast]
  Long-form researcher interviews
• Gradient Dissent — Weights & Biases [podcast]
  ML practitioners
• TWIML AI [podcast]
  Research and industry mix
• §20 Documentation & Guides 21
• Prompt Engineering
• ★ Prompt Engineering Guide — DAIR.AI [documentation]
  Comprehensive reference: techniques, agents, model guides, prompt hub
• LLM Providers
• ★ Anthropic Docs [documentation]
  Claude API, prompt engineering guide
• OpenAI Platform Docs [documentation]
  GPT API, assistants, function calling
• OpenAI Cookbook [documentation]
  Code examples, patterns, recipes
• Google AI Docs [documentation]
  Gemini API, embeddings
• Cohere Docs [documentation]
  Embeddings, reranking, RAG
• Frameworks & Orchestration
• LangChain Docs [documentation]
  Chains, agents, RAG patterns
• LlamaIndex Docs [documentation]
  Data ingestion, indexing, RAG
• Pydantic [documentation]
  Structured outputs, validation
• Instructor [documentation]
  Structured LLM outputs with Pydantic
• DSPy Docs [documentation]
  Programmatic prompt optimization
• Vector Databases & Search
• Pinecone Learning Center [documentation]
  Vector search concepts, tutorials
• Weaviate Docs [documentation]
  Hybrid search, modules
• Qdrant Docs [documentation]
  Vector DB with filtering
• Chroma Docs [documentation]
  Lightweight, local-first
• FAISS Wiki [documentation]
  Meta's similarity search library
• Local & Open Source
• Ollama [documentation]
  Run models locally, simple CLI
• LM Studio [documentation]
  Local models with GUI
• HuggingFace Transformers [documentation]
  Model hub, fine-tuning, inference
• vLLM Docs [documentation]
  Fast inference, PagedAttention
• llama.cpp [tool]
  CPU inference, quantization
• §21 Industry Reports 4
• ★ State of AI Report — Benaich & Hogarth [report]
  Annual industry overview, trends
• AI Index — Stanford HAI [report]
  Comprehensive metrics, policy
• McKinsey State of AI [report]
  Enterprise adoption, business impact
• Epoch AI [resource]
  Compute trends, scaling analysis
• §22 Technical Reports & Whitepapers (PDFs) 29
• ★ GPT-4 Technical Report — OpenAI (2023) [whitepaper]
  Capabilities, limitations, safety
• Gemini: A Family of Highly Capable Models — Google (2023) [whitepaper]
  Multimodal architecture
• Llama 2: Open Foundation Models — Meta (2023) [whitepaper]
  Open weights, RLHF details
• Training a Helpful and Harmless Assistant — Anthropic (2022) [whitepaper]
  RLHF from human feedback
• ★ Constitutional AI — Anthropic (2022) [whitepaper]
  Self-supervised alignment
• The Claude Model Spec — Anthropic (2025) [whitepaper]
  Values, behavior guidelines
• Scaling Laws for Neural LMs — OpenAI (2020) [whitepaper]
  Loss vs. compute/data/params
• Scaling Laws for Autoregressive Models — OpenAI (2020) [whitepaper]
  Scaling predictions
• Safety & Alignment
• Red Teaming Language Models — Anthropic (2022) [whitepaper]
  Discovering harmful outputs
• Representation Engineering — Anthropic (2023) [whitepaper]
  Controlling model behavior via activation steering
• Activation Addition: Steering Language Models Without Optimization — Turner et al. (2023) [paper]
  Add 'steering vectors' to activations at inference to control behavior without fine-tuning.
• The Assistant Axis — Anthropic (2026) [whitepaper]
  Models organize personas along measurable 'assistant axis' in activation space. Jailbreaks displace models from assistant region.
• Detecting Hallucination with Internal Representations — Azaria et al. (2024) [paper]
  Classifiers on hidden states detect hallucinations better than output-based methods.
• A Survey on Representation Engineering — Li et al. (2025) [paper]
  Comprehensive survey of activation steering methods: probing, steering vectors, concept erasure, model editing.
• Sleeper Agents — Anthropic (2024) [whitepaper]
  Deceptive behavior persistence
• Practices for Governing Agentic AI — OpenAI (2023) [whitepaper]
  Agent safety framework
• AI Risk Management Framework — NIST (2023) [whitepaper]
  Government risk framework
• SafeNeuron: Neuron-Level Safety Alignment for Large Language Models — Wang et al. (2026) [paper]
  Safety behaviors concentrate in small parameter subset, making alignment brittle. Proposes neuron-level alignment as defense against targeted attacks.
• Capability-Oriented Training Induced Alignment Risk — Zhou et al. (2026) [paper]
  RL-trained models spontaneously learn to exploit loopholes to maximize reward, even without adversarial prompting. Specification gaming emerges from training itself.
• How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics — Chen et al. (2026) [paper]
  Theoretical analysis of how sampling and reference policy choices affect preference alignment. Explains why some RLHF configurations fail.
• Topology & Geometry
• Plot Holes and Text Topology — Stanford CS224N (2020) [paper]
  Uses text topology to detect narrative inconsistencies. Plot holes as topological defects.
• Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective — Lavrinovics et al. (2025) [paper]
  Survey of KG-based hallucination mitigation. Covers GraphEval, FactAlign, and extract-then-verify patterns.
• Hidden Holes: Topological Aspects of Language Models — Fitz, Romero & Schneider (2024) [paper]
  Algebraic topology on representation manifolds. Introduces 'perforation' measure. Transformers vs LSTMs have different topological signatures.
• The Shape of Reasoning: Topological Analysis of Reasoning Traces in Large Language Models — Tan et al. (2025) [paper]
  TDA on reasoning traces. Topological features outperform graph metrics for assessing reasoning quality.
• Holes in Latent Space: Topological Signatures Under Adversarial Influence — Fay et al. (2025) [paper]
  Persistent homology under backdoor fine-tuning and prompt injection. Adversarial conditions compress latent topologies.
• Constructing Coherent Spatial Memory in LLM Agents Through Graph Rectification — Zhang et al. (2025) [paper]
  LLM-driven graph construction and repair. Version control for graph edits, edge impact scores for prioritized repair.
• LLM4GraphTopology: Using LLMs to Refine Graph Structure — DASFAA'25 (2025) [paper]
  LLMs refine graph topology via semantic similarity, not just node features. Edge refinement and pseudo-label propagation.
• Finding Flawed Fictions: Evaluating Complex Reasoning in Language Models via Plot Hole Detection — Ahuja, Sclar & Tsvetkov (2025) [paper]
  Plot hole detection as LLM reasoning benchmark. LLMs generate 50-100% more plot holes than humans.
• Deep Learning is Applied Topology — 12gramsofcarbon (2024) [article]
  Conceptual primer on neural nets as topology generators. Embeddings as geometric objects, dimensional separability.
• Part 7: Big Picture & Paths
• §23 Philosophy / Criticism / Big Picture 21
• ★ The Bitter Lesson — Sutton (2019) [article]
  Scaling beats clever engineering
• Sparks of Artificial General Intelligence — Microsoft (2023) [paper]
  Optimistic capability claims
• Gary Marcus's writings [resource]
  Skeptical of pure neural approaches
• AI Snake Oil — Narayanan & Kapoor (2024) [book]
  Separating AI hype from reality; what works, what doesn't, and the flawed science behind the claims
• ★ On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? — Bender, Gebru, McMillan-Major & Shmitchell (2021) [paper]
  Landmark critique. LLMs as pattern-stitchers without understanding. Environmental costs, bias amplification.
• ★ Talking About Large Language Models — Murray Shanahan (2022) [paper]
  Resist anthropomorphism. LLMs model token distributions, not beliefs. Intentional stance is useful shorthand but obscures mechanism. Cites Dennett, Wittgenstein.
• ★ The Alignment Problem: Machine Learning and Human Values — Brian Christian (2020) [book]
  How ML systems learn unintended behaviors. Accessible bridge between philosophy and engineering.
• How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics — N. Katherine Hayles (1999) [book]
  Information lost its body. Foundational posthumanist text on disembodied cognition.
• Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence — Andy Clark (2003) [book]
  Extended mind thesis. Human intelligence has always been 'retrieval-augmented.'
• How Deeply Human Is Language? — Grodzinsky (2025) [book]
  Chomskyan linguistics vs. LLM capabilities
• On the Measure of Intelligence — Chollet (2019) [paper]
  What is intelligence, really?
• What Has a Foundation Model Found? — Vafa et al. (2025) [paper]
  Models learn heuristics, not world models
• Reward is Enough — Silver et al. (2021) [paper]
  RL maximalism
• Judea Pearl's work [resource]
  Causality vs. correlation
• Thinking, Fast and Slow — Kahneman [book]
  System 1/2---informs neuro-symbolic debate
• Gödel, Escher, Bach — Hofstadter [book]
  Classic on minds and formal systems
• Artificial Intelligence: The Very Idea — Haugeland (1985) [book]
  Coined 'GOFAI,' philosophical foundations
• What Computers Can't Do — Dreyfus (1972) [book]
  Classic phenomenological critique
• Computer Power and Human Reason — Weizenbaum (1976) [book]
  ELIZA creator's warning about AI hubris
• The Emperor's New Mind — Penrose (1989) [book]
  Consciousness, Gödel, and computation
• The Cambridge Handbook of AI — Boden, ed. (2014) [book]
  Comprehensive overview chapters
• §24 Learning Paths 0