Geometric Motifs for Selecting and Routing Coding-Agent Training Data
This paper extends the trajectory ledger by annotating sessions with behavioral motifs and geometric features. The goal is to condition training data generation on the kind of behavior a session exhibits, not merely on final outcome.
Paper workspace
Live draft structure
Artifacts
Markdown paper source
Behavioral motif paper mapped from KARL source. Public PDF render pending.
source-only
Editable source
Paper source exists. Results should remain tied to exact snapshot IDs before PDF publication.
Source anchors
karl/paper/behavioral-motifs-paper.md
karl trajectory annotation/routing scripts
evo-cube-output/karl-trajectory-intelligence/stage0-research.md
Method tags
Ingest intersections
Status
Paper drafted; motif routing results should be kept tied to exact evaluation snapshots.
Key claims
01
Recurring agent behaviors can be represented as compact motifs.
02
Geometry-conditioned routing can improve specificity when quota-balanced.
03
Unconstrained routing can degrade quality by over-selecting easy residual sessions.
Public reading note
Public abstract safe; results should cite exact snapshots when released.
Standard skeleton
What this paper must keep proving
problem
High-scoring sessions are not all the same; training data needs to preserve the behavioral pattern that made a session valuable.
method
Annotate sessions with symbolic motifs and geometric scalars, then route them to specialized training-data lenses.
implementation
KARL motif annotation, yield scoring, quota-balanced routing, and quality verification.
data
Real multi-project coding sessions summarized as motif labels and geometry features.
evaluation
Transition-pressure prediction, effect sizes against random selection, and quota-balanced routing quality.
references
Conditional memory, Engram-style pattern storage, agent trajectory learning, reward routing.
openQuestions
Whether motif-conditioned training improves downstream agent behavior under official benchmarks.
Checkpoints and references
Proof chain
Claim checkpoint
central-claim slot
Every central claim must point to a proof anchor or remain labeled as speculative.
Implementation checkpoint
implementation-map slot
Every method should identify the code path, harness, schema, or protocol that embodies it.
Evidence checkpoint
evidence-manifest slot
Every reported result should point to run IDs, packet IDs, data snapshots, commits, or review artifacts.
Reference checkpoint
references slot
Every external claim should resolve to a cited paper, benchmark, standard, or documented prior system.
Release checkpoint
release-gate slot
Every PDF needs a named condition before it can move from draft to citation-ready.