Research-Stack/docs/famm/FAMM_Stigmergic_Route_Memory.md

FAMM — Stigmergic Route Memory

Status: RESEARCH_NOTE
Claim level: architecture bridge / conceptual alignment
Primary stack anchor: FAMM = frustration-aligned memory management
Related concepts: stigmergy, slime-trail memory, basin memory, route scars, frustration timing, topology-aware scheduling

Source Anchor

Current project status defines FAMM as frustration-aligned memory management: it stores failed, partial, and successful routes as basin/frustration signals that bias future search.

This note adds a cognition/biology bridge: stigmergic memory.

In stigmergy, agents do not need a central planner or complete internal map. They leave traces in a medium. Those traces alter the environment, and the altered environment biases later action.

FAMM is the computational analogue:

route traversal -> trace / scar -> basin or frustration signal -> biased future search

Core Definition

FAMM is frustration-aligned route memory.

It records successes, failures, partial traversals, torsion, basins, and phase deltas so that future search is biased away from bad routes and toward lawful attractors.

Compact form:

FAMM = scars becoming navigation

or:

FAMM = route outcomes encoded as future-routing pressure

Stigmergic Bridge

The slime-trail model of memory is useful because it reframes memory as an environmental trace rather than a stored object.

A slime mold does not need a complete internal map if its trail changes the field of future traversal. The medium remembers by being changed.

FAMM performs the same move inside the research stack:

failed route   -> avoid / penalize basin
partial route  -> preserve as near-miss / torsion signal
successful route -> reinforce basin / attractor
ambiguous route -> quarantine / uncertainty field

The important object is not only the trace. It is the route bias induced by the trace.

Difference from AMMR / AVMR

FAMM should not be collapsed into AMMR or AVMR.

AMMR = auditable append-only structured history / receipt chain
AVMR = hierarchical vector-state accumulation / merge history
FAMM = frustration-aligned routing bias derived from prior traversal outcomes

AMMR preserves what happened.
AVMR aggregates vector state.
FAMM changes where the system searches next.

FAMM Load

Existing NII driver notes frame FAMM-aware scheduling through timing/load terms such as:

L_famm = Sigma^2 + I_lock + Delta_phi

where the broad roles are:

• Sigma^2 = torsional stress from manifold state
• I_lock = interlocking energy
• Delta_phi = phase delta / route mismatch pressure

A more implementation-facing sketch also represents FAMM timing as:

structure FAMMTiming where
  torsionalStress : Q16_16
  interlockingEnergy : Q16_16
  laplacianEnergy : Q16_16

def computeFAMMLoad (t : FAMMTiming) : Q16_16 :=
  t.torsionalStress + t.interlockingEnergy + t.laplacianEnergy

The exact implementation may vary by module, but the architectural point is stable:

higher FAMM load = route history indicates stress, lock, torsion, or phase mismatch

Scheduling Interpretation

FAMM-aware scheduling should route work according to historical scar geometry.

A scheduler should ask:

Has this route failed before?
Did it partially work?
Did it produce torsion?
Did it land in a stable basin?
Did it create downstream regret or desync?
Does another route have lower frustration load?

This turns memory into routing pressure.

Seven-Pattern Mapping

FAMM maps cleanly into the Unified Function Layer:

CHAIN:
route attempt -> outcome -> scar -> future scheduling decision

FEEDBACK:
route outcomes change future route selection

GRADIENT:
frustration basins create search pressure fields

MASS:
accumulated route scars, basin weight, regret magnitude, load score

ENTROPY:
successful FAMM reduces blind search disorder; failed FAMM increases routing noise

COUPLING:
agent state couples to route history and basin geometry

SCALING:
route-memory pressure must remain tractable as corpus, graph, or agent count grows

Use in Current Stack

FAMM belongs wherever the system has to learn from traversal, not merely store records.

Examples:

• equation-pattern classification unknown bucket review
• ENE artifact routing
• N-gate adversary traversal
• compression-chain selection
• Hutter/corpus stress testing
• Jupiter-box degraded-channel routing
• topology-aware scheduling
• FPGA/SRAM route and memory-bank decisions

Guardrail

FAMM should not hallucinate certainty.

A scar is not proof. A basin is not truth. A successful route is not universal validity.

FAMM is a search-bias mechanism. It should preserve uncertainty, provenance, and receipt links so that route memory remains auditable.

Best Line

FAMM is the mathematics of scars becoming navigation.