formal-ai 0.277.0

//! Low-priority memory maintenance planning **and** self-generalization.
//!
//! Dreaming is deliberately split into a pure planner and an explicit apply
//! helper. The planner can run by default in background contexts because it
//! only reads memory and proposes work. Physical deletion remains a caller
//! decision guarded by the same confirmation/backup flow as other maintenance
//! commands.
//!
//! Beyond garbage collection, dreaming is where Formal AI *learns from its own
//! stored experience* (issue #540). While idle, the planner recalculates which
//! topics the user interacts with most, extracts the durable requirements the
//! user has stated on those topics, and **generalizes** them into
//! [`MetaAlgorithmAmendment`] records — changes baked into how similar future
//! tasks are solved so the user never has to repeat a requirement. Because the
//! amendment can re-derive the specific task/test-run records it subsumes, those
//! specifics become safe to forget under storage pressure while the generalized
//! amendment is retained forever. This is the issue's core rule: "as soon as
//! [a generalization] is working, [the] specific algorithm can be forgotten",
//! yet "our general meta algorithm must keep changes that allow it to solve all
//! other tasks".

use std::collections::{BTreeMap, BTreeSet};

use crate::memory::{MemoryEvent, MemoryStore};
use support::{
    contains_any, estimate_event_bytes, lower_opt, normalized, percent_ceil,
    required_reclaim_bytes, searchable_text, selected_bytes,
};

pub mod cues;
mod learning;
pub mod lexicon;
mod support;

#[derive(Debug, Clone, PartialEq, Eq)]
pub struct DreamingConfig {
    /// Background planning is on by default; disabling it returns an empty plan.
    pub daydreaming_enabled: bool,
    /// Desired free space after satisfying the next known incoming write.
    pub target_free_ratio_percent: u8,
    /// Total capacity of the storage area that holds the memory file.
    pub storage_capacity_bytes: Option<u64>,
    /// Current free bytes in the storage area.
    pub free_bytes: Option<u64>,
    /// Bytes the caller expects to need for the next write/fetch.
    pub incoming_bytes: u64,
}

impl Default for DreamingConfig {
    fn default() -> Self {
        Self {
            daydreaming_enabled: true,
            target_free_ratio_percent: 20,
            storage_capacity_bytes: None,
            free_bytes: None,
            incoming_bytes: 0,
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum DreamingDurability {
    /// Raw user/assistant/system experience that cannot be reconstructed from a
    /// public source.
    IrreplaceableRaw,
    /// Promoted lessons, skill ledgers, and learned experience.
    RetainedLearning,
    /// Events already attached to a soft-deleted conversation.
    DeletedConversation,
    /// Public-source cache or tool output that can be fetched again.
    RecomputableCache,
    /// Summaries and conclusions that can be derived from retained inputs.
    RecomputableIntermediate,
}

impl DreamingDurability {
    #[must_use]
    pub const fn is_reclaimable(self) -> bool {
        matches!(
            self,
            Self::DeletedConversation | Self::RecomputableCache | Self::RecomputableIntermediate
        )
    }

    const fn pressure_priority(self) -> u8 {
        match self {
            Self::DeletedConversation => 0,
            Self::RecomputableCache => 1,
            Self::RecomputableIntermediate => 2,
            Self::RetainedLearning | Self::IrreplaceableRaw => 9,
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum DreamingActionKind {
    PurgeDeletedConversation,
    RemoveDuplicateRecomputable,
    EvictLowUseRecomputable,
    /// A specific task/test-run record that a retained meta-algorithm amendment
    /// can reproduce, so it is forgotten first under pressure.
    ForgetCoveredSpecific,
}

/// How often the user has interacted with one topic, recalculated from the
/// current memory links. Topics are the unit dreaming learns about.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct TopicFrequency {
    pub topic: String,
    /// Total events attributed to the topic.
    pub interactions: usize,
    /// Events that look like concrete tasks / test runs on the topic.
    pub task_events: usize,
    /// Events that state a durable user requirement on the topic.
    pub requirement_events: usize,
}

/// A durable requirement the user stated on a topic. Dreaming remembers these so
/// the user never has to repeat them.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct LearnedRequirement {
    pub topic: String,
    pub statement: String,
    pub source_event_ids: Vec<String>,
    pub occurrences: usize,
}

/// A generalization baked into Formal AI's meta-algorithm: when solving tasks on
/// `topic`, always apply `rule`.
///
/// Materialized into memory as a retained, never-reclaimable learning record;
/// the specifics it `covered_event_ids` subsumes become safe to forget under
/// pressure.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct MetaAlgorithmAmendment {
    pub id: String,
    pub topic: String,
    pub rule: String,
    pub source_requirement_ids: Vec<String>,
    pub covered_event_ids: Vec<String>,
}

/// A prior task replayed by dreaming against the generalized meta-algorithm.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct DreamingCandidateTask {
    pub topic: String,
    pub source_event_id: String,
    pub input: String,
    pub expected_output: String,
    pub simulated_output: String,
    pub passed: bool,
}

/// A recurring task structure mined without relying on natural-language cue
/// words. Patterns are retained learning and can seed later amendments.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct DreamingPattern {
    pub topic: String,
    pub structure: String,
    pub occurrences: usize,
    pub source_event_ids: Vec<String>,
}

/// A brand-new task dreamed up on a most-used topic while idle.
///
/// It is built from a mined pattern and solved through the production
/// amendment path. Applying the plan retains it as a `dreaming_trial` event,
/// so patterns and topic frequencies have a real consumer instead of being
/// write-only statistics.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct DreamingSynthesizedTask {
    pub topic: String,
    pub structure: String,
    pub input: String,
    pub answer: String,
}

#[derive(Debug, Clone, PartialEq, Eq)]
pub struct DreamingAction {
    pub kind: DreamingActionKind,
    pub event_id: String,
    pub conversation_id: Option<String>,
    pub estimated_bytes: u64,
    pub usage_count: usize,
    pub reason: String,
}

#[derive(Debug, Clone, PartialEq, Eq)]
pub struct DreamingEventObservation {
    pub event_id: String,
    pub durability: DreamingDurability,
    pub usage_count: usize,
    pub estimated_bytes: u64,
    pub duplicate_key: Option<String>,
    /// The topic this event belongs to, if one could be recalculated.
    pub topic: Option<String>,
    /// True when a retained meta-algorithm amendment can reproduce this record,
    /// so it is safe to forget first under pressure.
    pub covered_by_amendment: bool,
}

#[derive(Debug, Clone, PartialEq, Eq)]
pub struct DreamingPlan {
    pub daydreaming_enabled: bool,
    pub target_free_ratio_percent: u8,
    pub storage_capacity_bytes: Option<u64>,
    pub free_bytes: Option<u64>,
    pub incoming_bytes: u64,
    pub target_free_bytes: Option<u64>,
    pub required_reclaim_bytes: u64,
    pub selected_reclaim_bytes: u64,
    pub total_reclaimable_bytes: u64,
    pub requires_bigger_storage: bool,
    pub actions: Vec<DreamingAction>,
    pub observations: Vec<DreamingEventObservation>,
    /// Topics ranked by recalculated interaction frequency (most-used first).
    pub topics: Vec<TopicFrequency>,
    /// Durable user requirements recovered from memory.
    pub learned_requirements: Vec<LearnedRequirement>,
    /// Meta-algorithm generalizations baked in from the learned requirements.
    pub amendments: Vec<MetaAlgorithmAmendment>,
    /// Prior tasks discovered on frequent topics and replayed in simulation.
    pub candidate_tasks: Vec<DreamingCandidateTask>,
    /// Recurring structures mined across task inputs independently of language.
    pub patterns: Vec<DreamingPattern>,
    /// New tasks synthesized from patterns on the most-used topics and solved
    /// through the production amendment path while dreaming.
    pub synthesized_tasks: Vec<DreamingSynthesizedTask>,
}

impl DreamingPlan {
    #[must_use]
    pub fn event_usage(&self, event_id: &str) -> Option<usize> {
        self.observations
            .iter()
            .find(|observation| observation.event_id == event_id)
            .map(|observation| observation.usage_count)
    }

    /// The recalculated interaction count for a topic, if it was observed.
    #[must_use]
    pub fn topic_interactions(&self, topic: &str) -> Option<usize> {
        self.topics
            .iter()
            .find(|frequency| frequency.topic == topic)
            .map(|frequency| frequency.interactions)
    }

    /// The amendment learned for a topic, if any.
    #[must_use]
    pub fn amendment_for(&self, topic: &str) -> Option<&MetaAlgorithmAmendment> {
        self.amendments
            .iter()
            .find(|amendment| amendment.topic == topic)
    }
}

#[derive(Debug, Clone, PartialEq, Eq, Default)]
pub struct DreamingOutcome {
    pub removed_events: usize,
    pub estimated_reclaimed_bytes: u64,
    /// Meta-algorithm amendments newly materialized into the store.
    pub learned_amendments: usize,
    /// Recurring task structures newly materialized as retained learning.
    pub learned_patterns: usize,
    /// Failed replay candidates preserved as `dreaming_candidate_failure`
    /// records so future dreaming rounds can keep learning from them.
    pub recorded_failures: usize,
    /// Synthesized trials on the most-used topics materialized as
    /// `dreaming_trial` records.
    pub recorded_trials: usize,
}

#[must_use]
pub fn plan_memory_dreaming(events: &[MemoryEvent], config: &DreamingConfig) -> DreamingPlan {
    let target_free_ratio_percent = config.target_free_ratio_percent.min(100);
    let target_free_bytes = config
        .storage_capacity_bytes
        .map(|capacity| percent_ceil(capacity, target_free_ratio_percent));
    let required_reclaim_bytes =
        required_reclaim_bytes(target_free_bytes, config.free_bytes, config.incoming_bytes);

    if !config.daydreaming_enabled {
        return DreamingPlan {
            daydreaming_enabled: false,
            target_free_ratio_percent,
            storage_capacity_bytes: config.storage_capacity_bytes,
            free_bytes: config.free_bytes,
            incoming_bytes: config.incoming_bytes,
            target_free_bytes,
            required_reclaim_bytes,
            selected_reclaim_bytes: 0,
            total_reclaimable_bytes: 0,
            requires_bigger_storage: required_reclaim_bytes > 0,
            actions: Vec::new(),
            observations: Vec::new(),
            topics: Vec::new(),
            learned_requirements: Vec::new(),
            amendments: Vec::new(),
            candidate_tasks: Vec::new(),
            patterns: Vec::new(),
            synthesized_tasks: Vec::new(),
        };
    }

    let deleted_conversations = deleted_conversation_ids(events);
    let usage_counts = usage_counts(events);
    let mut observations = Vec::with_capacity(events.len());
    let mut reclaimable_candidates = Vec::new();
    let mut duplicate_groups: BTreeMap<String, Vec<usize>> = BTreeMap::new();

    for (index, event) in events.iter().enumerate() {
        let durability = classify_event(event, &deleted_conversations);
        let estimated_bytes = estimate_event_bytes(event);
        let duplicate_key = duplicate_key(event, durability);
        if let Some(key) = duplicate_key.clone() {
            duplicate_groups.entry(key).or_default().push(index);
        }
        if durability.is_reclaimable() && !event.id.is_empty() {
            reclaimable_candidates.push(index);
        }
        observations.push(DreamingEventObservation {
            event_id: event.id.clone(),
            durability,
            usage_count: usage_counts[index],
            estimated_bytes,
            duplicate_key,
            topic: learning::event_topic(event),
            covered_by_amendment: false,
        });
    }

    // Self-generalization: learn topics and durable requirements from the same
    // memory links, then bake each requirement into a meta-algorithm amendment.
    let learning = learning::learn_from_memory(events, &observations, &deleted_conversations);
    let topics = learning.topics;
    let learned_requirements = learning.requirements;
    let amendments = learning.amendments;
    let candidate_tasks = learning.candidate_tasks;
    let patterns = learning.patterns;
    let synthesized_tasks = learning.synthesized_tasks;
    let covered_event_ids: BTreeSet<&str> = amendments
        .iter()
        .flat_map(|amendment| amendment.covered_event_ids.iter().map(String::as_str))
        .collect();
    for observation in &mut observations {
        observation.covered_by_amendment =
            covered_event_ids.contains(observation.event_id.as_str());
    }

    let total_reclaimable_bytes = reclaimable_candidates
        .iter()
        .map(|index| observations[*index].estimated_bytes)
        .sum();
    let mut actions = Vec::new();
    let mut selected_event_ids = BTreeSet::new();

    // Deleted-conversation purges and duplicate restructuring are planned even
    // without storage pressure — deliberately (issue #540 §4). Neither loses
    // information: purged events belong to conversations the user already
    // deleted explicitly, and deduplication always retains one canonical copy
    // of the recomputable record. They are consented housekeeping (nothing is
    // applied without --apply/--confirm or persisted auto-free-space consent);
    // only the pressure loop below is sized to "just enough for the next
    // write" and stops at the reclaim target.
    for event_index in deleted_event_indices(events, &deleted_conversations) {
        push_action_once(
            &mut actions,
            &mut selected_event_ids,
            DreamingAction {
                kind: DreamingActionKind::PurgeDeletedConversation,
                event_id: events[event_index].id.clone(),
                conversation_id: events[event_index].conversation_id.clone(),
                estimated_bytes: observations[event_index].estimated_bytes,
                usage_count: observations[event_index].usage_count,
                reason: String::from("event belongs to a conversation already marked deleted"),
            },
        );
    }

    for group in duplicate_groups.values() {
        if group.len() < 2 {
            continue;
        }
        let keep = group
            .iter()
            .copied()
            .max_by(|left, right| {
                usage_counts[*left]
                    .cmp(&usage_counts[*right])
                    .then_with(|| {
                        observations[*left]
                            .estimated_bytes
                            .cmp(&observations[*right].estimated_bytes)
                    })
                    .then_with(|| right.cmp(left))
            })
            .unwrap_or(group[0]);
        for event_index in group.iter().copied().filter(|index| *index != keep) {
            push_action_once(
                &mut actions,
                &mut selected_event_ids,
                DreamingAction {
                    kind: DreamingActionKind::RemoveDuplicateRecomputable,
                    event_id: events[event_index].id.clone(),
                    conversation_id: events[event_index].conversation_id.clone(),
                    estimated_bytes: observations[event_index].estimated_bytes,
                    usage_count: observations[event_index].usage_count,
                    reason: format!(
                        "duplicate recomputable event; retained {} with usage {}",
                        events[keep].id, usage_counts[keep]
                    ),
                },
            );
        }
    }

    let mut selected_reclaim_bytes = selected_bytes(&actions);
    if required_reclaim_bytes > selected_reclaim_bytes {
        let mut pressure_candidates = reclaimable_candidates
            .into_iter()
            .filter(|index| !selected_event_ids.contains(events[*index].id.as_str()))
            .collect::<Vec<_>>();
        pressure_candidates.sort_by(|left, right| {
            // Specifics a retained amendment can reproduce are forgotten first.
            observations[*right]
                .covered_by_amendment
                .cmp(&observations[*left].covered_by_amendment)
                .then_with(|| {
                    observations[*left]
                        .usage_count
                        .cmp(&observations[*right].usage_count)
                })
                .then_with(|| {
                    observations[*left]
                        .durability
                        .pressure_priority()
                        .cmp(&observations[*right].durability.pressure_priority())
                })
                .then_with(|| {
                    observations[*right]
                        .estimated_bytes
                        .cmp(&observations[*left].estimated_bytes)
                })
                .then_with(|| left.cmp(right))
        });

        for event_index in pressure_candidates {
            if selected_reclaim_bytes >= required_reclaim_bytes {
                break;
            }
            let (kind, reason) = if observations[event_index].covered_by_amendment {
                (
                    DreamingActionKind::ForgetCoveredSpecific,
                    String::from(
                        "specific task/test-run record reproducible from a retained meta-algorithm amendment",
                    ),
                )
            } else {
                (
                    DreamingActionKind::EvictLowUseRecomputable,
                    String::from(
                        "lowest-use recomputable event selected to satisfy the free-space target",
                    ),
                )
            };
            push_action_once(
                &mut actions,
                &mut selected_event_ids,
                DreamingAction {
                    kind,
                    event_id: events[event_index].id.clone(),
                    conversation_id: events[event_index].conversation_id.clone(),
                    estimated_bytes: observations[event_index].estimated_bytes,
                    usage_count: observations[event_index].usage_count,
                    reason,
                },
            );
            selected_reclaim_bytes = selected_bytes(&actions);
        }
    }

    selected_reclaim_bytes = selected_bytes(&actions);
    DreamingPlan {
        daydreaming_enabled: true,
        target_free_ratio_percent,
        storage_capacity_bytes: config.storage_capacity_bytes,
        free_bytes: config.free_bytes,
        incoming_bytes: config.incoming_bytes,
        target_free_bytes,
        required_reclaim_bytes,
        selected_reclaim_bytes,
        total_reclaimable_bytes,
        requires_bigger_storage: required_reclaim_bytes > selected_reclaim_bytes,
        actions,
        observations,
        topics,
        learned_requirements,
        amendments,
        candidate_tasks,
        patterns,
        synthesized_tasks,
    }
}

#[must_use]
pub fn apply_dreaming_plan(store: &mut MemoryStore, plan: &DreamingPlan) -> DreamingOutcome {
    let selected_ids = plan
        .actions
        .iter()
        .map(|action| action.event_id.as_str())
        .collect::<BTreeSet<_>>();

    // Bake each learned generalization into memory as a retained learning record
    // *before* forgetting the specifics it covers. Applying an unchanged plan
    // twice must not duplicate amendments, so we skip ids already present.
    let existing_ids: BTreeSet<String> = store
        .events()
        .iter()
        .map(|event| event.id.clone())
        .collect();
    let new_amendments: Vec<MemoryEvent> = plan
        .amendments
        .iter()
        .filter(|amendment| !existing_ids.contains(&amendment.id))
        .map(amendment_event)
        .collect();
    let learned_amendments = new_amendments.len();
    let new_patterns = plan
        .patterns
        .iter()
        .map(pattern_event)
        .filter(|event| !existing_ids.contains(&event.id))
        .collect::<Vec<_>>();
    let learned_patterns = new_patterns.len();
    // Failed simulations are learning material, not noise: preserve each one as
    // a retained record so later dreaming rounds (and refinement) can consume it.
    let new_failures = plan
        .candidate_tasks
        .iter()
        .filter(|candidate| !candidate.passed)
        .map(candidate_failure_event)
        .filter(|event| !existing_ids.contains(&event.id))
        .collect::<Vec<_>>();
    let recorded_failures = new_failures.len();
    let new_trials = plan
        .synthesized_tasks
        .iter()
        .map(synthesized_trial_event)
        .filter(|event| !existing_ids.contains(&event.id))
        .collect::<Vec<_>>();
    let recorded_trials = new_trials.len();

    if selected_ids.is_empty()
        && new_amendments.is_empty()
        && new_patterns.is_empty()
        && new_failures.is_empty()
        && new_trials.is_empty()
    {
        return DreamingOutcome {
            removed_events: 0,
            estimated_reclaimed_bytes: 0,
            learned_amendments: 0,
            learned_patterns: 0,
            recorded_failures: 0,
            recorded_trials: 0,
        };
    }

    let initial_len = store.len();
    let mut retained = store
        .events()
        .iter()
        .filter(|event| !selected_ids.contains(event.id.as_str()))
        .cloned()
        .collect::<Vec<_>>();
    let removed_events = initial_len - retained.len();
    retained.extend(new_amendments);
    retained.extend(new_patterns);
    retained.extend(new_failures);
    retained.extend(new_trials);
    *store = MemoryStore::from_events(retained);
    DreamingOutcome {
        removed_events,
        estimated_reclaimed_bytes: selected_bytes(&plan.actions),
        learned_amendments,
        learned_patterns,
        recorded_failures,
        recorded_trials,
    }
}

/// Preserve a failed replay as retained learning material. The record keeps
/// the diverging outputs so the next dreaming round's refinement pass (and any
/// human inspecting memory) can see exactly what the meta-algorithm still gets
/// wrong.
fn candidate_failure_event(candidate: &DreamingCandidateTask) -> MemoryEvent {
    let identity = format!("{}\0{}", candidate.source_event_id, candidate.input);
    MemoryEvent {
        id: crate::engine::stable_id("dreaming_failure", &identity),
        kind: Some(String::from("dreaming_candidate_failure")),
        role: Some(String::from("system")),
        intent: Some(String::from("generalize")),
        inputs: Some(candidate.input.clone()),
        outputs: Some(candidate.expected_output.clone()),
        content: Some(format!(
            "Replay of {} on topic {} diverged from the stored output; kept for refinement. Simulated: {}",
            candidate.source_event_id, candidate.topic, candidate.simulated_output
        )),
        demo_label: Some(candidate.topic.clone()),
        evidence: vec![candidate.source_event_id.clone()],
        ..MemoryEvent::default()
    }
}

/// Materialize a synthesized trial dreamed on a most-used topic.
fn synthesized_trial_event(trial: &DreamingSynthesizedTask) -> MemoryEvent {
    let identity = format!("{}\0{}", trial.topic, trial.input);
    MemoryEvent {
        id: crate::engine::stable_id("dreaming_trial", &identity),
        kind: Some(String::from("dreaming_trial")),
        role: Some(String::from("system")),
        intent: Some(String::from("solve")),
        inputs: Some(trial.input.clone()),
        outputs: Some(trial.answer.clone()),
        content: Some(format!(
            "Trial synthesized from recurring structure {} on most-used topic {}",
            trial.structure, trial.topic
        )),
        demo_label: Some(trial.topic.clone()),
        evidence: Vec::new(),
        ..MemoryEvent::default()
    }
}

fn pattern_event(pattern: &DreamingPattern) -> MemoryEvent {
    let identity = format!("{}\0{}", pattern.topic, pattern.structure);
    MemoryEvent {
        id: crate::engine::stable_id("dreaming_pattern", &identity),
        kind: Some(String::from("dreaming_pattern")),
        role: Some(String::from("system")),
        intent: Some(String::from("generalize")),
        inputs: Some(format!("topic={}", pattern.topic)),
        outputs: Some(format!("structure={}", pattern.structure)),
        content: Some(format!(
            "Recurring structure {} observed {} time(s)",
            pattern.structure, pattern.occurrences
        )),
        demo_label: Some(pattern.topic.clone()),
        evidence: pattern.source_event_ids.clone(),
        ..MemoryEvent::default()
    }
}

/// Render a learned generalization as a durable, never-reclaimable memory event.
fn amendment_event(amendment: &MetaAlgorithmAmendment) -> MemoryEvent {
    MemoryEvent {
        id: amendment.id.clone(),
        kind: Some(String::from("meta_algorithm_amendment")),
        role: Some(String::from("system")),
        intent: Some(String::from("generalize")),
        content: Some(amendment.rule.clone()),
        inputs: Some(format!("topic={}", amendment.topic)),
        outputs: Some(format!("rule={}", amendment.rule)),
        demo_label: Some(amendment.topic.clone()),
        conversation_title: Some(amendment.topic.clone()),
        evidence: amendment
            .source_requirement_ids
            .iter()
            .cloned()
            .chain([String::from("recipe:data/meta/dreaming-recipe.lino")])
            .collect(),
        ..MemoryEvent::default()
    }
}

#[must_use]
pub fn render_dreaming_plan(plan: &DreamingPlan) -> String {
    let mut lines = Vec::new();
    lines.push(String::from("memory_dreaming_plan"));
    lines.push(format!("  enabled: {}", plan.daydreaming_enabled));
    lines.push(format!(
        "  target_free_ratio_percent: {}",
        plan.target_free_ratio_percent
    ));
    if let Some(capacity) = plan.storage_capacity_bytes {
        lines.push(format!("  storage_capacity_bytes: {capacity}"));
    }
    if let Some(free) = plan.free_bytes {
        lines.push(format!("  free_bytes: {free}"));
    }
    if let Some(target) = plan.target_free_bytes {
        lines.push(format!("  target_free_bytes: {target}"));
    }
    lines.push(format!("  incoming_bytes: {}", plan.incoming_bytes));
    lines.push(format!(
        "  required_reclaim_bytes: {}",
        plan.required_reclaim_bytes
    ));
    lines.push(format!(
        "  selected_reclaim_bytes: {}",
        plan.selected_reclaim_bytes
    ));
    lines.push(format!(
        "  total_reclaimable_bytes: {}",
        plan.total_reclaimable_bytes
    ));
    lines.push(format!(
        "  requires_bigger_storage: {}",
        plan.requires_bigger_storage
    ));
    if plan.actions.is_empty() {
        lines.push(String::from("  action: none"));
    } else {
        for action in &plan.actions {
            lines.push(format!(
                "  action {:?} event={} bytes={} usage={} reason={}",
                action.kind,
                action.event_id,
                action.estimated_bytes,
                action.usage_count,
                action.reason
            ));
        }
    }

    if plan.topics.is_empty() {
        lines.push(String::from("  topic: none"));
    } else {
        for topic in &plan.topics {
            lines.push(format!(
                "  topic {} interactions={} tasks={} requirements={}",
                topic.topic, topic.interactions, topic.task_events, topic.requirement_events
            ));
        }
    }

    for requirement in &plan.learned_requirements {
        lines.push(format!(
            "  learned_requirement topic={} occurrences={} statement={}",
            requirement.topic, requirement.occurrences, requirement.statement
        ));
    }

    for amendment in &plan.amendments {
        lines.push(format!(
            "  meta_algorithm_amendment id={} topic={} covers={} rule={}",
            amendment.id,
            amendment.topic,
            amendment.covered_event_ids.len(),
            amendment.rule
        ));
    }
    for candidate in &plan.candidate_tasks {
        lines.push(format!(
            "  candidate_task topic={} source={} passed={}",
            candidate.topic, candidate.source_event_id, candidate.passed
        ));
    }
    for pattern in &plan.patterns {
        lines.push(format!(
            "  learned_pattern topic={} occurrences={} structure={}",
            pattern.topic, pattern.occurrences, pattern.structure
        ));
    }
    for trial in &plan.synthesized_tasks {
        lines.push(format!(
            "  synthesized_trial topic={} structure={} input={}",
            trial.topic, trial.structure, trial.input
        ));
    }

    lines.join("\n")
}

/// Compose the meta-algorithm recipe **as data**.
///
/// The result is the base `data/meta/dreaming-recipe.lino` document plus one
/// `meta_amendment` record per retained amendment currently in memory. The
/// dreaming runtime writes this next to the memory file after every run, so
/// learning literally changes the recipe artifact the amendments cite instead
/// of leaving it static.
#[must_use]
pub fn compose_recipe_with_amendments(events: &[MemoryEvent]) -> String {
    let base = lexicon::load_data_document(
        "dreaming-recipe.lino",
        include_str!("../data/meta/dreaming-recipe.lino"),
    );
    let mut composed = base.trim_end().to_owned();
    for amendment in crate::dreaming_application::retained_amendments(events) {
        let _ = std::fmt::Write::write_fmt(
            &mut composed,
            format_args!(
                "\n  meta_amendment {}\n    topic: {}\n    rule: {}\n    applied_by: fn_solve_with_standing_requirements\n",
                amendment.id, amendment.topic, amendment.rule
            ),
        );
    }
    composed.push('\n');
    composed
}

fn push_action_once(
    actions: &mut Vec<DreamingAction>,
    selected_event_ids: &mut BTreeSet<String>,
    action: DreamingAction,
) {
    if action.event_id.is_empty() || !selected_event_ids.insert(action.event_id.clone()) {
        return;
    }
    actions.push(action);
}

fn deleted_conversation_ids(events: &[MemoryEvent]) -> BTreeSet<String> {
    events
        .iter()
        .filter(|event| event.kind.as_deref() == Some("conversation_deleted"))
        .filter_map(|event| event.conversation_id.as_deref())
        .map(ToOwned::to_owned)
        .collect()
}

fn deleted_event_indices(
    events: &[MemoryEvent],
    deleted_conversations: &BTreeSet<String>,
) -> Vec<usize> {
    events
        .iter()
        .enumerate()
        .filter(|(_, event)| {
            event
                .conversation_id
                .as_deref()
                .is_some_and(|id| deleted_conversations.contains(id))
        })
        .map(|(index, _)| index)
        .collect()
}

fn classify_event(
    event: &MemoryEvent,
    deleted_conversations: &BTreeSet<String>,
) -> DreamingDurability {
    if event
        .conversation_id
        .as_deref()
        .is_some_and(|id| deleted_conversations.contains(id))
    {
        return DreamingDurability::DeletedConversation;
    }

    // Durability cues are grounded as data (multilingual) in
    // data/meta/dreaming-lexicon.lino instead of hardcoded English keywords.
    let cues = lexicon::lexicon();
    let kind = lower_opt(event.kind.as_deref());
    let tool = lower_opt(event.tool.as_deref());
    let content = lower_opt(event.content.as_deref());
    let evidence = event.evidence.join("\n").to_lowercase();
    if contains_any(&kind, &cues.learning_kind_cues)
        || contains_any(&content, &cues.learning_content_cues)
        || evidence.contains("learning")
    {
        return DreamingDurability::RetainedLearning;
    }

    if contains_any(&kind, &cues.cache_kind_cues) || contains_any(&tool, &cues.cache_tool_cues) {
        return DreamingDurability::RecomputableCache;
    }

    if contains_any(&kind, &cues.intermediate_kind_cues) {
        return DreamingDurability::RecomputableIntermediate;
    }

    DreamingDurability::IrreplaceableRaw
}

fn duplicate_key(event: &MemoryEvent, durability: DreamingDurability) -> Option<String> {
    if !matches!(
        durability,
        DreamingDurability::RecomputableCache | DreamingDurability::RecomputableIntermediate
    ) {
        return None;
    }
    Some(format!(
        "kind={}|role={}|intent={}|tool={}|inputs={}|outputs={}|content={}",
        normalized(event.kind.as_deref()),
        normalized(event.role.as_deref()),
        normalized(event.intent.as_deref()),
        normalized(event.tool.as_deref()),
        normalized(event.inputs.as_deref()),
        normalized(event.outputs.as_deref()),
        normalized(event.content.as_deref()),
    ))
}

fn usage_counts(events: &[MemoryEvent]) -> Vec<usize> {
    let searchable = events.iter().map(searchable_text).collect::<Vec<_>>();
    events
        .iter()
        .enumerate()
        .map(|(target_index, target)| {
            // Usage = read accesses counted at recall time (issue #494) plus
            // citations from other events; either alone undercounts.
            let accessed = usize::try_from(target.access_count).unwrap_or(usize::MAX);
            if target.id.is_empty() {
                return accessed;
            }
            let cited: usize = searchable
                .iter()
                .enumerate()
                .filter(|(index, _)| *index != target_index)
                .map(|(_, text)| text.matches(&target.id).count())
                .sum();
            accessed.saturating_add(cited)
        })
        .collect()
}