Papers and theoretical foundations that shaped causal-memory. This is not a bibliography — it's a map of which ideas ended up in which design decisions.

For the full systematic research documentation (BibTeX, detailed abstracts, methodology critiques, and per-paper design traces), see docs/research/ — organized by theme:

• neuroscience/ — how the brain handles memory, causality, and consolidation
• cognitive-psychology/ — how humans represent and reason about causal knowledge
• computational-ai/ — what the AI field does and where the causal memory gap is
• causal-inference/ — formal foundations (Pearl, Spirtes)

This page provides a quick-reference summary. For depth, follow the links above.

1. The Core Thesis: LLM is a Stateless Function

Reference: insights/09-stateless-function

Every LLM inference call starts from scratch. Memory is not a feature — it's a mandatory injection layer. Causal memory is one specific injection strategy optimized for decision→outcome links.

2. Why Causal? The Compaction Degradation Evidence

Paper: papers/02-compaction-degradation

Real-LLM benchmark (grok-build's production compaction prompt):

Key finding: causal information decays faster than expected under text compaction. The causal table survives because it lives outside the compaction pipeline.

3. Neuroscience

Kumaran, Hassabis & McClelland (2016) — CLS Theory

Key idea: The brain has two memory systems — hippocampus (fast, episodic) and neocortex (slow, semantic).

Design connection: Our dual-table schema (causal_edges + meta_causal_edges) directly copies this architecture. We refuse to compact causal_edges because the hippocampus does not compress episodic traces during initial encoding.

Deep dive: neuroscience/cls-theory.md

Schapiro et al. (2017) — Hippocampal Replay

Key idea: The hippocampus resolves temporal ambiguity via compressed replay during rest — not faithful playback, but structured re-evaluation.

Design connection: The offline consolidation cycle ("sleep") is directly inspired by this — and since v0.9 the replay is real re-evaluation, not playback: reactivation scores feed downscaling (protected edges decay at half rate with lenient GC) and replayed edges are marked via last_accessed_at, closing a replay → consolidate → survive feedback loop across cycles.

Deep dive: neuroscience/hippocampus-temporal.md

Davachi (2006) — Temporal Contiguity

Key idea: The brain defaults to "A happened before B, therefore A caused B" — a heuristic, not a fact.

Design connection: Our confidence levels encode this explicitly: temporal = 0.4 (weak), rule = 0.7 (strong), user_feedback = 0.95 (gold standard). This prevents over-weighting spurious temporal correlations.

Deep dive: neuroscience/temporal-contiguity.md

Diekelmann & Born (2010) — Sleep Consolidation

Key idea: Sleep actively transforms memory via selective reactivation, gist extraction, and synaptic down-selection.

Design connection: The v0.4 consolidation cycle includes: reactivation (priority queue), generalization (meta_causal_edges), and down-selection (confidence decay + garbage collection).

Deep dive: neuroscience/sleep-consolidation.md

4. Cognitive Psychology

Sloman (2005) — Causal Graph Theory

Key idea: Humans use directed acyclic graphs (DAGs) as the default representational format for causal knowledge.

Design connection: causal_edges is a flattened DAG edge list. The relation types (caused, enabled, prevented, no_effect) encode structural constraints that causal models must satisfy.

Deep dive: cognitive-psychology/causal-graph-theory.md

Gerstenberg et al. (2021) — Counterfactual Simulation

Key idea: Humans determine causal responsibility by running mental simulations of counterfactual worlds — "if I hadn't done X, would Y still have happened?"

Design connection: counterfactual_query (v0.9) implements the honest engineering subset: a contrastive/empirical counterfactual that compares recorded outcomes of a decision vs an alternative in similar past situations — explicitly not an SCM simulation of an unobserved world.

Deep dive: cognitive-psychology/counterfactual-simulation.md

Schacter & Addis (2007) — Reconstructive Memory

Key idea: Memory is not playback — it's reconstruction. The hippocampus stores "construction blueprints," not raw footage. Every retrieval reassembles stored components.

Design connection: Implemented (v0.9) as reconstruct_lesson: instead of returning raw edges, the system retrieves the Markov-blanket causal subgraph and the LLM reconstructs a coherent "lessons learned" narrative from compact stubs. The optional calibrate=N mode generates N independent reconstructions and flags disagreement as unreliable memory.

Deep dive: cognitive-psychology/reconstructive-memory.md

5. Computational AI

Wang et al. (2024) — Agent Memory Survey

Key idea: Current LLM agent memory systems are almost entirely RAG-based. None store causal relationships as a primary data structure.

Design connection: This is our primary evidence that causal memory is a genuine market gap, not a feature that existing systems "just haven't gotten around to."

Deep dive: computational-ai/agent-memory-survey.md

Park et al. (2023) — Generative Agents

Key idea: Persistent memory + periodic reflection enables emergent social behavior. But reflection is coarse-grained (text summaries), not decision-level causal links.

Design connection: Generative Agents is the closest precedent. We extend it by making reflection structured (meta_causal_edges) and causal (causal_edges).

Deep dive: computational-ai/generative-agents.md

Goyal & Bengio (2022) — System 2 Inductive Biases

Key idea: System 2 cognition (planning, causal reasoning) requires explicit object-relation-rule representations, not end-to-end implicit encoding.

Design connection: causal-memory is an implementation of this principle. Instead of hoping the LLM "learns" causality, we externalize causal structure into an explicit graph.

Deep dive: computational-ai/system2-explicit-representation.md

6. Causal Inference (Formal Foundations)

Pearl (2009) — Causality

Key idea: The ladder of causation — three levels: association (seeing), intervention (doing), counterfactual (imagining). Each strictly more powerful.

Design connection: Rung 1 is search_causal. Rung 2 is fully implemented: intervention_query predicts effects of similar past actions, with a task_tag-stratified adjustment that warns when the pooled estimate is confounded. Rung 3 ships as the contrastive/empirical engineering subset (counterfactual_query); the SCM form stays out of scope. Pearl provides the formal target.

Deep dive: causal-inference/pearl-causality.md

Spirtes, Glymour & Scheines (2000) — PC Algorithm

Key idea: Automated causal discovery from observational data via conditional independence testing.

Design connection: The miner's meta_causal_edges activation was inspired by PC; since v5 it runs a real (engineering-grade) stratified test: a pattern is promoted only when it replicates in ≥ 2 distinct task_tag strata, single-stratum patterns are marked confounded, and direction flips across strata are flagged simpson — a stratified replication stand-in for conditional independence testing, not the full algorithm.

Deep dive: causal-inference/pc-algorithm.md

BibTeX

All papers: docs/research/references.bib

# Import into Zotero
zotero docs/research/references.bib

Reading Order

1. Start with insights/09 — the "LLM is stateless" premise
2. Read papers/02 — the empirical evidence
3. Read insights/11 — the design this implements
4. Then explore docs/research/ by theme — each paper is connected to a specific design decision

This document is a living artifact. As we implement v0.3+ features, we update the research map with the papers that shaped those decisions.