---
slug: eager_evaluation
name: Eager Evaluation
one_liner: "Compute a value as soon as its inputs are available, ahead of any demand for it — the push, work-now dual of demand-driven lazy evaluation."
type: prime
schema_version: 2
status: density_v2_draft
categories: [computational, dynamical]
origin_domain: computer_science
alternate_origin_domains: [economics_operations, cognition_and_psychology, manufacturing_logistics]
aliases: [strict_evaluation, eager_computation, speculative_evaluation, push_evaluation]
related: []
solution_archetypes: []
review_flags:
- "OPERATOR-DERIVED (op-01 dualization, sub-flavors 1b/1c) — the structural dual of lazy_evaluation along the compute-scheduling (push vs pull) axis. Confirmed reactive Phase-C find; reproduced end-to-end as holdout exemplar 3 in the dualization re-pilot (3/3 flavors, 2026-07-02). PENDING owner curation — not yet incorporated."
- "Created 2026-07-02; did NOT go through Phase-A triage. SI/SF/similarity grades are PROPOSER ESTIMATES pending triangulation."
- "Phase-C action: re-similarity check vs lazy_evaluation / scheduling / prioritization / caching to confirm it does not collapse; draw the sibling (lazy_evaluation) and proposed-parent (evaluation-strategy / scheduling) edges at incorporation. one_liner must be synced to a v1 draft per CLAUDE.md before merge."
substrate_independence:
  composite_substrate_independence: 4
  domain_breadth: 4
  structural_abstraction: 5
  transfer_evidence: 3
  reasoning: 'The rule "advance work the moment its inputs are ready, before any consumer demands the result" is a pure scheduling relation on a dataflow graph with no commitment to a medium, so structural abstraction is a full 5. It demonstrably recurs beyond its programming-language home — speculative execution and prefetching in hardware, make-to-stock and push scheduling in operations, precomputation and materialized views in databases, and anticipatory action in cognition — a domain breadth of 4. Transfer is real but less exhaustively documented than a canonical structural 5 (much of the evidence is within computing and its close operational cousins), so transfer evidence is 3 and the composite is 4: a strongly substrate-independent scheduling pattern whose cross-domain instances are genuine but not yet as universal-feeling as feedback or random_walk.'
similarity_to_nearest_existing_prime:
  slug: lazy_evaluation
  similarity: 0.669
structural_framed:
  label: structural
  aggregate: 0.1
  criteria: {vocab_travels: 0.0, evaluative_weight: 0.0, institutional_origin: 0.0, human_practice_bound: 0.0, import_vs_recognize: 0.1}
  rationale: 'Compute-as-soon-as-inputs-are-ready is a scheduling relation on a dependency graph, medium-neutral and carrying no normative or institutional content; every diagnostic reads at or near zero. It has a formal (programming-language semantics) origin, no evaluative weight — being eager is neither good nor bad in itself, only relative to a demand pattern — and no human-practice binding, since hardware and even biological systems run the pattern with no human in the loop. The faint 0.1 on import_vs_recognize reflects that "eager" is mildly a term of art one carries in, but the underlying push-scheduling structure is recognized wherever work is advanced ahead of demand.'
phase_b_passes_completed: []
---
# Eager Evaluation

## Core Idea

Eager evaluation is the scheduling discipline of *doing the work the moment it can be done rather than the moment it is needed*: as soon as an expression's inputs are available, its value is computed and made ready, before — and possibly without — any consumer demanding it. It is one pole of a single structural axis, *what triggers computation*, whose opposite pole is `lazy_evaluation` (compute only on demand, driven by a pull from a consumer). Eager evaluation is driven by a *push* from the availability of inputs; the presence of the data, not the presence of a request, is what fires the work.

The defining commitments are three. First, there is a *dependency structure* — a graph of values where some depend on others as inputs, so "can be computed now" is a well-defined condition (all of a node's inputs are ready). Second, there is a *readiness trigger*: computation fires as soon as that condition holds, not when a downstream consumer asks. Third, the results are *produced ahead of demand* and held, so a later request is served from already-finished work rather than starting it. What eager evaluation names as a prime is the recognition that a wide range of "do it now" behaviors — a programming language evaluating every argument before entering a function, a CPU speculatively executing instructions past a branch, a factory building to stock before orders arrive, a cache warming itself, a person preparing for a contingency that may never occur — are the *same structural choice*: schedule work by input-readiness rather than by consumer-demand. The choice has a characteristic payoff and a characteristic cost. The payoff is *latency hidden ahead of demand*: when the request finally comes, the answer is already there, and if the machine would otherwise be idle the work was free. The cost is *work spent on results that are never used*: because computation is not gated on need, an eager system may compute values that no consumer ever requests, and if a computation has side effects or can fail, eagerness forces those effects and failures to happen whether or not they were ever required. The prime is the clean statement of that trade — pay-now-maybe-waste versus pay-later-for-certain — abstracted away from any particular machine.

## Structural Signature

*the dependency graph of values* — *the readiness trigger (inputs-available, not demand)* — *the ahead-of-demand production* — *the held/ready result* — *the wasted-work-versus-hidden-latency trade*

Eager evaluation is present when each of the following holds:

- **A dependency structure (the graph).** Values are related by "is an input to," so there is a well-defined condition — all inputs ready — under which any given value *can* be computed. Without a dependency structure there is no "as soon as it can be" to speak of.
- **A readiness trigger (the push).** Computation fires on input-availability, not on a consumer's request. This is the load-bearing invariant that distinguishes eager from lazy: the arrow of causation runs from *data ready* to *work done*, not from *work asked-for* to *work done*.
- **Ahead-of-demand production (the anticipation).** Results are produced before, and independently of, any demand for them — possibly for consumers that never materialize. The work is speculative with respect to need.
- **A held, ready result (the buffer).** Because production runs ahead of demand, finished values are stored ready to serve; a later request is satisfied from completed work, which is where the latency saving comes from.
- **The wasted-work / hidden-latency trade (the cost structure).** Eagerness converts *possible future latency* into *certain present work*: it removes waiting at the point of demand at the price of computing results (and forcing any side effects and failures) that may never have been needed. The trade is the diagnostic fingerprint — a system is meaningfully eager only where some produced results can go unused.

The components compose into one object — *work scheduled by input-readiness and produced ahead of demand* — and it is the readiness-trigger-plus-ahead-of-demand pairing that generates the downstream properties: the latency hidden at the point of use, the risk of wasted computation, and the forcing of side effects and failures independent of need.

## What It Is Not

- **Not lazy evaluation.** `lazy_evaluation` is the exact dual: compute only when a value is actually demanded, driven by a consumer's pull. Eager evaluation is driven by a producer's push (inputs ready). They sit at opposite poles of the *trigger* axis, and the whole content of each prime is which pole it occupies — eager risks wasted work to hide latency; lazy risks latency to avoid wasted work. Neither is the "default correct" one; the right choice depends on how likely a produced value is to be needed and how costly waiting at demand-time is.
- **Not caching.** `caching` stores the result of a computation so a *repeat* request is cheap; it is about reuse across multiple demands. Eager evaluation is about *when the first* computation happens relative to demand. They compose (an eager system typically holds its ahead-of-demand results in a cache-like buffer) but answer different questions: caching says "don't recompute what you already computed," eagerness says "compute before you are asked." A lazy system can cache; an eager system need not repeat.
- **Not prefetching or precomputation as a domain instance.** Prefetching (memory/IO) and precomputation (materialized views) are *specific applications* of the eager pattern in particular substrates. The prime is the substrate-free structure they share; naming them as the prime would mistake an instance for the pattern, exactly as naming "diffusion" would for `random_walk`.
- **Not greedy (myopic) optimization.** A greedy algorithm makes the locally-best choice at each step; the word "eager" there refers to *optimization myopia*, a different axis entirely. Eager evaluation says nothing about optimality of choices — only about the *timing* of computation relative to demand. A system can be eager in evaluation and globally optimal, or lazy and greedy.
- **Not speculative execution in the narrow hardware sense only.** Speculative execution (running instructions past an unresolved branch) is one sharp instance, but eager evaluation is broader: it covers any advance-work-on-readiness pattern, including cases with no "speculation about a branch" at all — e.g. a strict language evaluating a function's arguments, where nothing is being guessed, work is simply done up front.
- **Common misclassification.** Reading any "we did it early" as eager evaluation. Catch it by checking the *trigger*: was the work fired by inputs becoming available (eager) or was it scheduled by a plan, a deadline, or a priority ranking (that is `scheduling` / `prioritization`, a different structure)? Eager evaluation specifically means readiness-triggered, demand-independent production.

## Broad Use

Eager evaluation, read as readiness-triggered production ahead of demand, recurs across computing and well beyond it. In **programming languages**, it is *strict evaluation*: languages like C, Java, and OCaml evaluate a function's arguments before the call, and data structures are fully computed when constructed — the opposite of Haskell's lazy default, where the same programs would defer those computations until forced. In **computer architecture**, *speculative execution* and *branch prediction* run instructions eagerly past an unresolved branch so the pipeline never stalls waiting for the condition, and *prefetching* pulls data into cache before it is requested — both trading possibly-wasted work (a mispredicted branch, an unused line) for hidden latency. In **databases**, *materialized views* and *eager (write-through) indexing* precompute query results and index updates when data changes rather than when a query arrives, paying maintenance cost up front to make reads instant. In **operations and manufacturing**, *make-to-stock* and *push scheduling* build inventory ahead of orders — the eager pole whose lazy dual is make-to-order / pull (just-in-time) production; the same wasted-work-versus-responsiveness trade governs whether a firm holds finished goods or waits for demand. In **distributed systems**, *eager replication* propagates a write to all replicas immediately (strong consistency, higher write cost) versus lazy replication that defers propagation. In **cognition and behavior**, *anticipatory action* — preparing a response, pre-loading a plan, laying in supplies for a contingency — is eager evaluation in a mental substrate: doing the work while the inputs (and the idle capacity) are available, accepting that some preparation will prove unnecessary. Across all of these the recognizable move is the same: fire the work on input-readiness, hold the result, and eat the cost of whatever turns out to be unneeded in exchange for having the answer ready before it is asked for.

## Clarity

Naming eager evaluation sharpens a question that is otherwise argued in domain-specific and often confused terms: *should this work be triggered by the data being ready, or by someone needing the result?* Practitioners routinely debate "should we precompute this?", "should we build to stock or to order?", "is speculation worth it here?", "eager or lazy loading?" as if these were unrelated local questions. The prime reveals them as one structural choice with one governing trade, and it relocates the argument onto the two quantities that actually decide it: the *probability that a produced result is used* and the *relative cost of waiting at demand-time versus computing ahead*. When most produced values are used and idle capacity is available, eagerness is close to free and hides real latency; when many produced values go unused, or computing has irreversible side effects or can fail, eagerness pays certain costs for uncertain benefit and laziness dominates. The clarifying force is to convert "should we do it early?" into "what fraction of this ahead-of-demand work will be wasted, and how expensive is the wait we are trying to remove?" — questions with estimable answers rather than a matter of engineering taste. The prime also exposes a subtler point that domain framings hide: eagerness *forces side effects and failures independent of need*. If evaluating a value can throw an error or mutate state, an eager schedule makes that error or mutation happen whether or not the value is ever used — which is why strict languages surface some failures that lazy ones never reach, and why eager replication propagates a write (and its risks) everywhere immediately. Seeing eagerness as a prime makes that consequence predictable rather than a surprise.

## Manages Complexity

Eager evaluation compresses a large family of "do it now" behaviors into a single decision with a single governing trade, so a designer can reason about scheduling once rather than re-deriving it per system. The complexity reduction is concrete: instead of separately analyzing speculative execution, prefetching, materialized views, make-to-stock inventory, eager replication, and anticipatory planning, one recognizes them as the same structure and imports the same analysis — estimate the use-rate of produced results, estimate the demand-time latency being removed, and compare. The prime also manages complexity by making the *trigger* explicit and thereby separable from *what* is computed and *how*. Because eager and lazy differ only in the trigger, a system's timing policy becomes a knob that can be turned independently of its logic: the same dependency graph can be evaluated eagerly or lazily, and a designer can choose per-value (eager for likely-needed low-cost results, lazy for rarely-needed expensive ones) without touching the computation itself. This separation is what lets frameworks expose "eager vs lazy loading" as a configuration flag rather than a rewrite. Finally, eagerness *tames latency variance*: by moving work off the demand path, it converts a system whose response time depends on how much computing a request triggers into one that serves ready results in near-constant time, at the cost of steady background work — a complexity-shifting move that trades unpredictable tail latency for predictable throughput cost, which is exactly the trade real-time and interactive systems most need to control.

## Abstract Reasoning

The eager-evaluation pattern licenses several substrate-independent moves. *Locate the trigger*: faced with any "when should this work happen?" question, ask whether work is fired by input-readiness (eager) or by consumer-demand (lazy), because naming the pole immediately imports the pole's trade — wasted-work risk for eager, latency risk for lazy. *Estimate the use-rate*: eagerness is worth it in proportion to the fraction of produced results actually consumed, so the reasoner should push to quantify "how often is this precomputed thing used?" before committing — a low use-rate is the signature warning against eagerness. *Watch for forced side effects*: whenever a computation can fail or mutate state, eagerness makes those consequences certain and demand-independent, so the reasoner must check that forcing them ahead of need is safe (this is the abstract reason strict languages surface failures lazy ones hide, and why eager replication spreads risk immediately). *Exploit idle capacity*: eager work is close to free when it fills otherwise-idle resource, so the pattern is most attractive exactly where there is slack to absorb the speculative computation — a link to `system_slack`. *Turn the knob per-item*: because the trigger is separable from the logic, the reasoner can mix policies within one system — eager for cheap, likely-needed values; lazy for expensive, rarely-needed ones — optimizing the whole rather than committing globally. And *read the dual*: any argument for lazy evaluation in a setting is, by the structure of the axis, an argument that the use-rate is low or the ahead-of-demand cost is high, and vice versa — so recognizing one pole tells the reasoner exactly what would have to be true for the other to win.

## Knowledge Transfer

Because eager evaluation is the bare scheduling relation "produce on input-readiness, ahead of demand," a result or heuristic proved in one substrate transfers to another by re-identifying the dependency graph, the readiness trigger, and the use-rate of produced results. The core trade transfers verbatim from software to operations: the programming-language choice between strict and lazy argument evaluation is *the same decision* as the manufacturing choice between make-to-stock (push) and make-to-order (pull, just-in-time), and the inventory theory that tells a factory when to build ahead — high, predictable demand and cheap holding favor stock; volatile, rare demand and costly or perishable goods favor order — transfers directly to when a program should precompute versus defer. The forced-side-effect insight transfers from language semantics to distributed systems: just as strict evaluation makes a computation's exceptions happen whether or not its result is used, eager (synchronous) replication makes a write's propagation — and its failure modes — happen everywhere immediately, so the reasoning about "what failures does eagerness surface that laziness hides?" is one analysis serving both. The idle-capacity argument transfers from hardware to cognition: a CPU speculatively executing past a branch to fill pipeline slots and a person rehearsing a contingency plan during downtime are running the identical calculation — advance work is cheap when it consumes slack that would otherwise be wasted, and worth it in proportion to how likely the prepared-for demand is. In every transfer the practitioner runs the same diagnosis — identify what depends on what, confirm the work is triggered by readiness rather than demand, estimate what fraction of the produced results will be used, and check whether forcing the computation's side effects ahead of need is safe — and the transfer is secure because none of these steps names the substrate: a compiler writer choosing strictness, an architect enabling speculation, a DBA materializing a view, a plant manager building to stock, and a person prepping for a maybe-storm are reasoning about the same push-scheduling object, distinguished only by what is being produced and how likely it is to be needed.

## Examples

### Formal/abstract

Consider a dependency graph of values in a functional language: `let x = f(a); let y = g(b); let z = h(x, y)` where evaluating each of `f`, `g`, `h` has some cost. Under **eager (strict) evaluation** — the prime in its native formalism — the *readiness trigger* fires the moment inputs are bound: `f(a)` is computed when `a` is available, `g(b)` when `b` is available, and `h(x, y)` as soon as both `x` and `y` are ready, so all three run regardless of whether anything ever consumes `z` (the *ahead-of-demand production* and *held result* invariants). The contrast with the lazy dual is exact and makes the trade algebraic: under lazy evaluation none of `f`, `g`, `h` runs until some outer context *demands* `z`, and if `z` is never demanded, none runs at all — zero wasted work but the full cost of `f`, `g`, `h` incurred on the demand path when `z` is finally forced. Two structural consequences the prime names appear immediately. First, *forced side effects*: if `f(a)` can throw or mutate state, eager evaluation makes that happen whether or not `z` is used, while lazy evaluation reaches it only if `z` is demanded — the semantic difference between strict and non-strict languages in one line. Second, the *use-rate governs the trade*: if `z` is demanded with probability `p`, eager evaluation does the full work with certainty (expected wasted work `= (1-p) × cost`) to guarantee `z` is ready instantly, whereas lazy pays `p × cost` but on the demand path. The structural payoff is that the entire strict-versus-lazy question reduces to `p` and the cost of demand-time latency, with no reference to what `f`, `g`, `h` actually compute.

**Mapped back:** The `let`-binding graph instantiates every component — the dependency structure (`z` depends on `x`, `y`; `x` on `a`; `y` on `b`), the readiness trigger (compute when inputs bound), ahead-of-demand production (all three run even if `z` is unused), the held result, and the wasted-work/hidden-latency trade governed by the use-probability `p` — and shows the prime's core pairing (readiness-trigger plus ahead-of-demand) as the precise reason eager forces work and side effects that lazy can skip.

### Applied/industry

A CPU's **speculative execution** runs the identical structure in a hardware substrate, with no functional-programming vocabulary. The *dependency graph* is the instruction stream with a conditional branch whose direction depends on a comparison still being computed; the *readiness trigger* is the branch predictor's guess, which lets the processor treat the predicted-taken path's instructions as "inputs available" and begin executing them (the *ahead-of-demand production*) before the branch condition — the actual "demand" for which path is correct — has resolved. Results of the speculated instructions are held in a reorder buffer (the *held/ready result*) and committed if the prediction was right, discarded if wrong. The prime's *wasted-work-versus-hidden-latency trade* is the whole point of the design: when the prediction is correct (a high use-rate, since modern predictors exceed 95% accuracy on typical code) the pipeline never stalled and the latency of waiting for the branch was completely hidden; when it is wrong, the speculated work is thrown away — certain present work spent to remove possible future stalls. The *forced-side-effect* hazard of eagerness appears here in its sharpest real-world form: because speculation runs instructions that may be discarded, it can leave *microarchitectural* traces (cache-state changes) even for work that never commits — the mechanism behind the Spectre/Meltdown class of vulnerabilities, a direct consequence of doing work ahead of the demand that would authorize it. The *idle-capacity* rationale is explicit: speculation is worthwhile precisely because the execution units would otherwise sit idle waiting for the branch, so the speculative work is nearly free when the guess is right.

**Mapped back:** Speculative execution runs the prime end-to-end — a dependency graph (instructions past a branch), a readiness trigger (the prediction that treats the predicted path as ready), ahead-of-demand production (executing before the branch resolves), the held result (reorder buffer), and the wasted-work/hidden-latency trade (mispredictions discarded to keep the pipeline full) — and demonstrates the transfer: a strict language evaluating arguments up front, a CPU running past a branch, and a factory building to stock are reasoning about the same push-scheduling object, distinguished only by what is produced and how often the prepared-for demand actually arrives.

## Structural Tensions

**T1 — Eager versus Lazy (the Trigger Choice).** The prime's foundational tension is with its own dual: whether to fire work on input-readiness (eager) or consumer-demand (lazy). The failure mode is *misplaced eagerness* — precomputing results with a low use-rate, so most of the work is wasted — or its mirror, *misplaced laziness* that piles latency onto the demand path. Diagnostic: estimate the fraction of produced results actually consumed and the cost of demand-time waiting; eager wins when the use-rate is high and idle capacity absorbs the work, lazy wins when the use-rate is low or the ahead-of-demand computation is expensive or risky.

**T2 — Hidden Latency versus Wasted Work (the Core Cost Trade).** Eagerness removes waiting at the point of demand by paying certain work up front; the tension is that the up-front work is speculative with respect to need. The failure mode is *speculative waste*: burning compute, energy, or inventory on results no consumer ever requests. Diagnostic: is the resource spent on unused production scarce (then eagerness is costly) or slack (then it is nearly free)? The trade only favors eager where idle capacity or cheap holding absorbs the miss.

**T3 — Forced Side Effects and Failures (Demand-Independent Consequences).** Because eager evaluation runs a computation regardless of whether its result is needed, any side effects or failures the computation carries happen unconditionally. The failure mode is *unwanted forcing*: surfacing an exception, mutating state, or propagating a write that a lazy schedule would have avoided entirely because the value was never demanded. Diagnostic: does evaluating this value have effects or failure modes that are only acceptable if the value is actually used? If so, eagerness makes them certain — the reason strict languages hit errors lazy ones skip and eager replication spreads failure risk immediately.

**T4 — Prediction Quality (When Eagerness Is a Guess).** Some eager systems do not merely compute-ahead but *speculate* about which future demand will occur (branch prediction, prefetch heuristics, demand forecasts for make-to-stock). The tension is between the payoff of a correct guess and the waste of a wrong one, and it is governed by prediction accuracy. The failure mode is *low-accuracy speculation*: eagerly producing for a predicted demand that mostly does not materialize, so the wasted-work cost swamps the hidden-latency benefit. Diagnostic: what is the hit-rate of the prediction driving the eagerness, and does the benefit-per-hit times hit-rate exceed the cost-per-miss times miss-rate?

**T5 — Freshness versus Precomputation (Staleness of Ready Results).** Ahead-of-demand production means results are computed against the inputs *as they were when produced*; if inputs change before the result is consumed, the held value is stale. The tension is between the latency saved by precomputing and the correctness risk of serving an out-of-date result (materialized views over changing data, prefetched content that has since updated, make-to-stock goods that no longer match demand). The failure mode is *stale-serve*: delivering a precomputed answer that no longer reflects current inputs. Diagnostic: how fast do the inputs change relative to the produce-to-consume interval? Eager precomputation is safe only when inputs are stable over that window or the system invalidates and recomputes on change.

## Structural–Framed Character

Eager evaluation sits at the structural end of the [structural–framed spectrum](../conceptual/structural-and-framed-primes.md), with a frontmatter aggregate of 0.1 — nearly every diagnostic reads zero. Compute-as-soon-as-inputs-are-ready is a scheduling relation on a dependency graph, medium-neutral and carrying no normative or institutional content.

The push-scheduling structure is substrate-neutral and demonstrably recurs across substrates. It carries essentially no home vocabulary that must travel (vocab_travels 0.0): the same object appears as strict evaluation in programming languages, speculative execution and prefetching in hardware, materialized views in databases, make-to-stock in manufacturing, eager replication in distributed systems, and anticipatory action in cognition — each told in its field's own words. It carries no evaluative weight (evaluative_weight 0.0): a schedule's being eager is neither good nor bad in itself, only relative to a use-rate and a latency cost. Its origin is formal (institutional_origin 0.0), the operational semantics of evaluation order, not any institution's product. It is not human-practice-bound (human_practice_bound 0.0): a CPU speculating past a branch and a cache warming itself run the pattern with no human in the loop. The one faint non-zero, import_vs_recognize 0.1, reflects that the label "eager" is mildly a term of art one carries in — but the underlying structure (work advanced on readiness ahead of demand) is recognized wherever it occurs. The contrast with its dual `lazy_evaluation` underscores the structural read: both are pure scheduling relations distinguished only by trigger polarity, neither carrying a frame.

## Substrate Independence

Eager evaluation is strongly substrate-independent — composite **4 / 5** on the [substrate-independence scale](../conceptual/substrate-independence.md). Its signature — produce a value on input-readiness, ahead of demand, holding the result and eating the cost of whatever is unused — is stated in pure scheduling terms with no commitment to a medium, so it is recognized rather than translated when it surfaces in a new field, earning structural abstraction a full 5. It recurs across a broad range of substrates: strict evaluation in programming languages, speculative execution and prefetching in computer architecture, materialized views and write-through indexing in databases, eager replication in distributed systems, make-to-stock and push scheduling in manufacturing and operations, and anticipatory action in cognition — a domain breadth of 4 spanning software, hardware, operations, and mind. The transfer is real and repeatedly documented — the strict-versus-lazy and make-to-stock-versus-make-to-order dualities are recognizably the same trade, and the forced-side-effect insight carries from language semantics to eager replication — but the bulk of the sharpest evidence clusters in computing and its close operational cousins rather than spanning the physical and biological sciences as exhaustively as a canonical structural 5, so transfer evidence is 3 and the composite is 4.

- **Composite substrate independence — 4 / 5**
- Domain breadth — 4 / 5
- Structural abstraction — 5 / 5
- Transfer evidence — 3 / 5

## Not to Be Confused With

The most important contrast is with the prime's dual and nearest neighbor, **`lazy_evaluation`** (estimated similarity ≈ 0.67). The two occupy opposite poles of a single axis — *what triggers computation* — and the entire content of each prime is which pole it holds. Lazy evaluation is *pull*: nothing is computed until a consumer demands it, so no work is wasted but latency lands on the demand path and a never-demanded value is never computed at all. Eager evaluation is *push*: work fires on input-readiness, so results are ready ahead of demand at the price of computing (and forcing the side effects and failures of) values that may go unused. The distinction is load-bearing precisely because the two are otherwise identical — same dependency graph, same logic, same results when everything is used — differing only in *when* and *whether* work happens relative to need. Confusing them collapses the one dimension the primes exist to name.

A second genuine confusion is with **`caching`**. Caching stores a computed result so that *repeat* demands are cheap; it is about reuse across multiple requests, and it is orthogonal to the eager/lazy trigger — a lazy system can cache (compute on first demand, reuse thereafter) and an eager system holds its ahead-of-demand results in a cache-like buffer. The distinction is *first-computation timing* (eager) versus *recomputation avoidance* (caching). They frequently co-occur, which is exactly why they are conflated, but an eager system that never sees a repeat demand still gets no benefit from the "caching" framing — its benefit came from producing before the single demand arrived.

A third confusion is with **`scheduling`** and **`prioritization`** generally. Those name *how work is ordered or ranked* — by deadline, by priority, by resource availability — whereas eager evaluation names a specific trigger: readiness rather than demand. A system can schedule eagerly-produced work by priority, so the concepts compose, but they answer different questions. The tell is the trigger: if work fires because its inputs became available (independent of any demand), it is eager evaluation; if it fires because a planner placed it in an order or a ranking, that is scheduling/prioritization.

For a practitioner these distinctions decide what is being claimed. Confusing eager evaluation with lazy evaluation inverts the core trade — attributing latency savings to a system that is actually deferring work, or wasted-work risk to one that is actually demand-driven. Confusing it with caching mistakes first-computation timing for recomputation avoidance, missing that eagerness pays off even on a single demand. Confusing it with scheduling mistakes a readiness trigger for a planned order, obscuring the demand-independence that generates both the latency hidden at the point of use and the forced side effects on unused results. The unifying discipline is the prime's trigger check: identify the dependency graph, confirm that work fires on input-readiness rather than consumer-demand, confirm results are produced and held ahead of demand, and only then read off the hidden-latency benefit, the wasted-work cost, and the forced-side-effect hazard as the structure eager evaluation — and nothing weaker — provides.
