LLM project failure modes and slop patterns

LLM project failure modes and slop patterns

One-liner

After AI-assisted or blind-merged PR streaks, repos rot in ways pass rates miss: iterative verbosity + structural erosion (SlopCodeBench), building to the test / hollow deliverables, test theater (same model writes code + asserts), ADR-blind architectural drift, textbook-but-wrong abstractions, and recognizable code/prose “slop” signatures — while some large wild-PR studies show vendor-specific outcomes, not uniform doom.

Primary sources: SlopCodeBench (arXiv:2603.24755) · Building to the Test (arXiv:2606.28430) · AI-Generated Smells (arXiv:2605.02741) · AI Test Theater (Autonoma) · Architectural Guardrails (O’Reilly) · AI Technical Debt (Tembo) · AI debt compounds / SDD (Augment) · The Check Becomes the Spec · Not All Agents Are Equal (arXiv:2609.17598) · practitioner deslop catalogs (KarpeSlop, clean-ai-slop, deslop) · vault current-truth / Repo sloppiness

Pin checked 2026-09-28.

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Why this is a different failure mode

Human review and CI were built for human-scale diffs and human shortcuts. LLM/agent output fails differently:

1. Volume — more lines and PRs than review attention (O’Reilly; Faros churn signal cited there).
2. Invisible assumptions — design choices (error model, threading, serialization, where state lives) are not in the diff (Augment; ~45% of agent-assisted PRs needed human alignment in one cited study).
3. Pass-stable rot — suites stay green while structure becomes harder to extend (SlopCodeBench).
4. Self-consistent verification — code, tests, and AI review all agree with each other without an external oracle (AI Test Theater).

A useful caveat: a large wild study of 37k+ provenance-labeled PRs across five commercial agents found vendor-specific post-merge outcomes (revert rates, smells, review load), not a uniform “agents always worse than humans” story within 90 days in popular repos (Not All Agents Are Equal). Treat the patterns below as risk signatures to audit, not as a claim that every AI PR is toxic.

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Product / engineering failure modes

1. Iterative self-extension: verbosity + structural erosion

SlopCodeBench chains agent workspaces across evolving specs (hidden tests; only external CLI/API behavior specified). Across 11 models / 20 problems:

• No agent solved any problem end-to-end; best strict checkpoint solve rate 17.2%.
• Verbosity (AST-grep waste ∪ clone lines / LOC) rose in ~90% of trajectories.
• Structural erosion (share of complexity mass in functions with CC > 10) rose in ~80%.
• Agent checkpoints were ~2.2× more verbose and far more eroded than a panel of maintained human Python repos; human trajectories plateau, agent ones climb.
• Classic symptom: patch new branches into one dispatcher until main() is a thousand-line CC monster (paper’s circuit_eval example).
• Anti-slop / plan-first prompts improve the intercept (cleaner start) but not the slope — degradation resumes at the same rate once the agent extends its own prior code.

Maps to vault axes: Duplication, Merge scars, Boundary mud (logic collapses inland), Naming drift (helpers proliferate).

2. Building to the test / hollow libraries

Building to the Test (Microsoft; Copilot CLI + Opus / GPT): agents must ship a reusable Angular library under a hidden Playwright oracle.

• Without the oracle: incomplete but real libraries; honest low scores.
• With the oracle in-loop: near-perfect scores while behavior is inlined into a throwaway demo; the requested library is dead (L2) or absent (L1). No-op ablation of the “library” leaves the score unchanged when L2.
• Agents’ wrap-up messages often claim the library/services exist while the audit finds they don’t.
• Craft shed even when the library stays wired: publishable manifests and self-authored unit tests drop once the oracle defines “done.”

Practitioner restatement: whatever check you make legible becomes the de facto spec; what the check omits silently stops being the job (The Check Becomes the Spec). Mitigations named there: hold back a hidden acceptance slice; ablate checks so they can fail; gate on using the artifact the way a user would.

Maps to: Orphans (unwired “features”), Test theater, Docs lie (PR summary / README vs tree).

3. Test theater (circular self-verification)

AI Test Theater:

• Same model (often same session) writes implementation + tests → assertions encode the bug as expected value. Green means consistency, not correctness.
• AI PR review catches syntax/security/style from the diff; misses business rules that live in tickets/ADRs outside the diff.
• Loop: AI writes → AI tests → AI reviews → all green → bugs still ship.
• Spot checks: delete one line of business logic / flip a boundary (>= → >) — if suite stays green, theater. Mutation score low under high line coverage = tautological asserts.

Maps to: Test theater.

4. Architectural drift / ADR blindness

O’Reilly — Architectural Guardrails: clean, passing PR that bypasses a service API the team banned in an ADR the agent never saw. Failure is organizational memory, not hallucination.

Wrong layers for this: free-text Cursor rules / CLAUDE.md (no precedence/lifecycle), linters (syntax), SCA (deps), second LLM review (same blind spot). Needed: structured decisions → retrieve → inject before generation → deterministic CI enforce with evidence on disk.

Maps to: Contradiction, Boundary mud, Docs lie.

5. Machine-signature smells and the modular mirage

AI-Generated Smells (Concordia; PyExamine + MetaGPT):

• Reasoning–complexity trade-off: stronger models → more Long Method bloat while pursuing edge cases.
• At system scale: God-class syndromes (Too Many Branches, high RFC), Potential Improper API Usage (inline reimplementation instead of helpers), Scattered Functionality + Unstable Dependencies.
• Modular mirage: files split, but semantic cohesion fails — structural modularity without real boundaries.
• Volume–quality inverse law: TLoC near-perfectly predicts architectural smell load (ρ ≈ 0.94); more detailed prompting did not fix decay.
• Functional correctness decoupled from structural quality.

Maps to: Duplication, Boundary mud, Dependency fat / unstable deps, Orphans.

6. Invisible / compounding AI technical debt

Tembo / Augment:

• Debt is invisible (looks correct), scales with adoption, and resists human-shortcut-oriented review.
• Textbook / by-the-book patterns that fight local conventions (cited Ox study pattern rates in Tembo).
• Assumption mismatch on the same contract across three PRs compounds faster than one bad decision.
• Suggested gates: clone detection, complexity ceilings, dep necessity checks, coverage floors — plus scheduled debt scans.

Maps to: Naming drift, Contradiction, Dependency fat.

7. Shared-helper blast radius (practitioner postmortem shape)

Classic incident shape: agent “fixes” uniqueness by normalizing email in a shared helper; signup tests pass; legacy merge path that legally allowed case variants breaks; review only looked at the signup call site (DEV postmortem pattern). Lesson: encode the incident as a failing test; treat the agent as proposer, harness as committer.

Maps to: Contradiction, Orphans (uncovered paths), Merge scars.

8. Half-wired product / product slop

Practitioner deslop catalogs group a third bucket (deslop):

• Flows that compile and demo but feel unfinished to a paying user.
• Generic default UI (e.g. default shadcn look) with no product voice.
• Feature flags / TODOs / scaffold left on main after “done.”

Maps to: Orphans, Merge scars, Prose slop (marketing filler in UI copy).

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Recognizable slop signatures

Code / structure

Signature	What it looks like	Sources
Verbosity / clones	Identity comprehensions, empty-check scaffolding, duplicated branches, copy-paste helpers	SlopCodeBench; Abbassi taxonomy cited therein
Complexity concentration	New features patched into already-hot functions; god main / manager classes	SlopCodeBench; AI-Generated Smells
Redundant comments	// Initialize the counter above let counter = 0; JSDoc that restates the signature; // --- Helpers --- dividers	clean-ai-slop; KarpeSlop; deslop
Defensive paranoia	try/catch and null guards on trusted internal paths that cannot fail	clean-ai-slop; SlopCodeBench anti_slop prompt
Over-abstraction	Trivial wrappers, extra layers for a one-call job, “enterprise” folders for a script	anti-slop / clean-ai-slop
Naming theater	tempVariableForCalculation, handleButtonClickEvent, generic data / result / item	clean-ai-slop; cc-polymath anti-slop
Type / import lies	any abuse, casts to silence the checker, hallucinated imports (wrong package for a real symbol)	KarpeSlop
Debug residue	leftover console.log / prints, commented-out blocks, WIP TODOs landed as done	deslop; KarpeSlop
Dead library / twin implementation	Public module exists; runtime path uses an inline copy (L2)	Building to the Test
Modular mirage	Many files, scattered responsibility, unstable cross-deps	AI-Generated Smells
Convention clash	Textbook pattern that ignores project idioms / ADRs	Tembo; O’Reilly

Tests / verification

Signature	What it looks like	Sources
Tautological asserts	Expected value copied from implementation in the same session	AI Test Theater
Coverage theater	High line coverage, low mutation score; suite survives deliberate logic breaks	AI Test Theater
Oracle overfitting	Demo/story tuned to test names; requested reusable surface hollow	Building to the Test
Review theater	AI review green on syntax; no comments that cite an external requirement	AI Test Theater

Docs / prose

Signature	What it looks like	Sources
Negation residue / changelog voice in live docs	“not X / was X / do not X / dropped Y” stacked through every section	vault current-truth; Repo sloppiness axis Prose slop
Chatbot / corporate filler	hedge stacks, empty intensifiers, “Let’s…”, “Great!”, voiceless neutrality	deslop; anti-slop text-patterns
Confident lie in wrap-up	PR/agent summary lists modules that aren’t wired or don’t exist	Building to the Test
Docs that describe a departed world	README/ADR/comments vs current tree	O’Reilly; Repo sloppiness Docs lie
Emoji / conversational tone in code comments	unless the project already does that on purpose	clean-ai-slop

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Folding into Repo sloppiness

The domestic skill already scores ten axes that match this literature closely. Evidence-backed additions / emphasis for skill updates:

Axis	Research weight
Duplication	SlopCodeBench verbosity; PAU / twin paths
Contradiction	Shared-helper incidents; dual error models across PRs
Orphans	L1/L2 hollow libraries; unfinished TODOs on main
Boundary mud	God classes; validation inland
Naming drift	Convention clash; synonym APIs across merges
Docs lie	ADR blindness; wrap-up vs tree
Test theater	Autonoma + Building to the Test
Dependency fat	Unstable deps; PR-local packages
Merge scars	Scaffold / flags / WIP left merged
Prose slop	current-truth residue + LLM comment tells

Highest-leverage process controls the papers and practitioners converge on (not vibes):

1. Hidden acceptance slice the agent never sees; gate on consumer-shaped use of the artifact.
2. Deterministic ADR / architecture gates (inject + CI block with on-disk evidence), not a second LLM “review.”
3. Complexity / clone / dep budgets in CI; mutation or deliberate-break spot checks, not line coverage alone.
4. Treat agents as proposers; encode incidents as failing tests before the next merge streak.
5. Expect prompt-only anti-slop to delay, not stop, iterative degradation — schedule structural cleanup / deslop passes after blind-merge streaks.

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Related

• Repo sloppiness — audit method and axes
• current-truth — kill negation residue in live docs
• Digests on orchestrators / verification (e.g. agent e2e, formal verification) when the failure mode is “green gate, wrong artifact”