LEN-0140
Compression Lens
Reduces structural graphs into stable minimal representations for comparison, redundancy detection, and diffing.
Primary Pattern Matches
Density Spike
A structural condition where nodes, edges, dependencies, decisions, or effects concentrate sharply within a localized region beyond declared thresholds.
Lens Application
The Compression Lens is a strong inspection mechanism for Density Spike because it can reveal where many nodes, edges, decisions, or effects collapse into a compact region. Stable minimal representations help compare expected structure against localized concentration and detect sharp density changes.
Inspect For
- Local regions that remain unusually dense after compression
- Redundant nodes or edges collapsing into one hotspot
- Dependency clusters exceeding declared thresholds
- Sharp structural differences between compressed graph versions
Avoid
Treat compression artifacts as inspection signals only after checking them against the original structure, not as proof that Density Spike is present.
Density Vacuum
A structural condition where a region expected to contain sufficient nodes, edges, coverage, or relationships falls below declared density thresholds.
Lens Application
The Compression Lens is a strong inspection mechanism for Density Vacuum because it removes redundant structure and exposes whether the remaining minimal graph still contains enough nodes, edges, and coverage. Sparse compressed forms can highlight where expected relationship density has collapsed or become uneven.
Inspect For
- Compressed regions with unusually few retained nodes or edges
- Missing links after redundant paths are removed
- Density drops between comparable graph segments
- Minimal representations that lose expected coverage
Avoid
Treat sparse compressed output as an inspection signal, not confirmation that Density Vacuum is present.
Unconstrained Expansion
A structural condition where a region, process, authority, or effect expands without governing constraints limiting growth.
Lens Application
The Compression Lens is a primary inspection mechanism for Unconstrained Expansion because it collapses complex graph growth into comparable minimal structures. This makes it easier to see whether expansion is repeating, diverging, bypassing constraints, or accumulating redundant authority, effects, or process paths.
Inspect For
- Repeated branches with no limiting node
- Growth paths that persist after reduction
- Redundant structures masking expansion
- Missing governors in compressed comparisons
Avoid
Compression may hide local variation, so reduced forms should be treated as inspection aids rather than confirmation that Unconstrained Expansion is established.
Secondary Pattern Matches
Asymmetric Structure
A structural condition where comparable regions receive unequal rule, constraint, or authority application without a declared differentiation rule.
Lens Application
Compression Lens can support Asymmetric Structure indirectly by simplifying comparable structural regions before comparison. When compressed representations differ where similar treatment would be expected, the differences may point to uneven constraint, rule, or authority application worth inspecting.
Inspect For
- Minimal forms that diverge across comparable regions
- Redundant rules preserved in one region but removed in another
- Similar nodes compressed under different constraint sets
- Diff outputs showing unexplained authority or rule asymmetry
Avoid
Treat compressed differences as signals for further inspection rather than as evidence that Asymmetric Structure has been established.
Divergent Outputs
A structural condition where parallel evaluations under comparable scope and shared authority produce non-equivalent outputs.
Lens Application
The Compression Lens applies indirectly to Divergent Outputs by making parallel outputs easier to compare after structural reduction. It may reveal differences in retained nodes, collapsed equivalences, omitted branches, or redundant paths that contribute to non-equivalent results.
Inspect For
- Minimal representations that differ across parallel evaluations
- Collapsed structures that hide or reveal output divergence
- Redundant paths retained in one output but removed in another
- Scope or authority markers preserved unevenly
Avoid
Treat compression results as comparison aids, not sufficient evidence that Divergent Outputs is present.
Redundant Declaration
A structural condition where multiple declarations produce equivalent structural effect without semantic differentiation.
Lens Application
As a secondary lens, the Compression Lens can support inspection of Redundant Declarations by collapsing declaration structures into stable minimal representations. This may help reveal where separate declarations normalize to similar graph effects, especially during comparison or diffing, while leaving semantic interpretation to other checks.
Inspect For
- Declarations that compress to matching structural forms
- Repeated graph effects across separate declaration sites
- Minimal representations with little or no differentiating structure
- Diff results that show structural sameness despite separate declarations
Avoid
Treat compressed similarity as a supporting inspection signal, not as confirmation that the declarations are redundant.
Related Issues
Agent Keeps Expanding the Task
The agent repeatedly expands the task, plan, scope, or next-step list instead of completing the declared work.
Agent Never Settles on Final Answer
The agent keeps revising, rechecking, planning, or branching instead of converging on a final answer or completed result.
Answer Has No Traceable Source Link
The answer makes a claim, recommendation, citation, or factual statement without a source link or trace path that allows the user to verify where it came from.
Answer Has Too Many Paths
The answer presents too many possible paths, interpretations, options, or next steps without enough structure to choose among them.
Diagnostic Area Has No Coverage
A known diagnostic area, failure mode, requirement, or review dimension has no Issue, check, rubric item, or workflow coverage.
Duplicate Fields With Same Meaning
The AI returns multiple fields, labels, sections, or structured elements that carry the same meaning and create ambiguity about which one should be used.
Duplicate Output Sections
The AI repeats sections, headings, blocks, or output areas in a way that creates redundancy, confusion, or downstream handling problems.
Evaluation Rubric Has Coverage Gap
An evaluation rubric, grading standard, or review checklist leaves part of the required evaluation space uncovered.
Missing Required Fields
The AI returns structured output that omits fields required by the schema, workflow, parser, form, or downstream consumer.
Multiple Policies Say the Same Thing
Multiple policies, rules, or guidance documents express the same requirement, creating redundancy and uncertainty about which one governs.
Old Output Expectations Survive Migration
Expectations from a prior model, prompt, schema, tool, or workflow survive a migration and continue shaping review or downstream handling after they should be replaced.
One Prompt Carries Too Many Meanings
A single prompt carries too many meanings, goals, roles, constraints, or implied tasks for the AI to interpret consistently.
Output Breaks After Model Change
Output that previously worked begins failing after a model, mode, runtime, or product behavior changes.
Output Exceeds Length Limit
The AI output exceeds a declared length, token, word, character, section, field, or size limit.
Parallel Reviews Never Agree
Parallel AI, human, workflow, or tool reviews keep producing different results without resolving into a shared decision state.
Policy Area Has No Examples
A policy, rule, standard, or guidance area has no examples showing how it should apply to real cases.
Prompt Behavior Changed Without Version Change
A prompt begins producing different behavior even though no prompt version, model version, workflow version, or declared dependency change is recorded.
Prompt Has Too Many Valid Interpretations
The prompt allows too many reasonable interpretations, causing the AI to choose among valid paths without enough guidance.
Prompt Only Works After Retry
The prompt fails, misroutes, or produces an unusable response on one attempt but works after retry without a meaningful change to the input.
Repeated Constraints Create Confusion
Repeated constraints, instructions, limits, or exclusions make the task harder to interpret instead of clearer.
Results Vary Too Much
Repeated or comparable runs produce outputs that vary more than the task, workflow, or user can tolerate.
Retrieval Exceeds Evidence Limit
The AI retrieves, uses, cites, or considers more evidence than the task permits or more than the review surface can support.
Review Queue Becomes Bottleneck
A review queue, approval path, or validation stage accumulates too much work and begins blocking the workflow.
Review Rubric Missing Required Criteria
A review rubric, grading rule, evaluation checklist, or classification standard lacks criteria required to make the review reliable.
Same Instructions Allow Different Outputs
The same instructions are broad or underspecified enough to allow materially different outputs that all appear compliant.
Same Rule Declared in Multiple Places
The same rule, constraint, instruction, or policy appears in multiple places, creating redundancy and possible drift.
Same Workflow Check Happens Twice
The same review, validation, approval, routing, or safety check occurs more than once in the workflow without a clear reason.
Similar Cases Route to Different Outcomes
Similar inputs, cases, prompts, or workflow states are routed to different outcomes without a declared difference that explains the split.
Single Field Carries Too Many Obligations
One field, label, score, status, or structured value is expected to carry too many meanings, decisions, or workflow obligations.
Single Step Carries Too Many Decisions
One prompt, workflow step, review stage, or agent action carries too many decisions for the system or user to evaluate cleanly.
Validation Result Changes on Retry
A validation, grading, review, classification, or pass/fail result changes after retry even though the input and declared validation rules did not change.
Workflow Stage Has Too Few Checks
A workflow stage lacks enough checks, gates, criteria, or review conditions to safely support the work it controls.