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ARXIV:2605.02236 · LLM BEHAVIOR ANALYSIS · SUBMITTED 05 MAY · 20:31 UTC · FRESHNESS STALE
ARXIV:2605.02236LLM BEHAVIOR ANALYSISSUBMITTED 05 MAY · 20:31 UTCFRESHNESS STALEPawel Kaplanski · arXiv
Investigating how perturbation dose affects recursive LLM loops, revealing memory-policy-conditioned persistence and structural dips in model behavior.
Opportunity summary
Pain Investigating how perturbation dose affects recursive LLM loops, revealing memory-policy-conditioned persistence and structural dips in model behavior.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
Investigating how perturbation dose affects recursive LLM loops, revealing memory-policy-conditioned persistence and structural dips in model behavior. The practical question is how much injected text is needed to move a settled loop somewhere else,…
Recursive language-model loops often settle into recognizable attractor-like patterns. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether that move lasts.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. The main result is that persistent redirection in append-mode recursive loops is memory-policy-conditioned. A public repository is linked, so build verification can inspect implementation…
LLM Behavior Analysis moved forward this cycle; last verified May 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.
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Score4.0Analysis summary
Investigating how perturbation dose affects recursive LLM loops, revealing memory-policy-conditioned persistence and structural dips in model behavior.
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Paper Pack
10.48550/arXiv.2605.02236Investigating how perturbation dose affects recursive LLM loops, revealing memory-policy-conditioned persistence and structural dips in model behavior.
Abstract
Recursive language-model loops often settle into recognizable attractor-like patterns. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether that move lasts. We study this in 30-step recursive loops by separating the model from the context-update rule: append, replace, and dialog updates expose different histories to the same generator. The main result is that persistent redirection in append-mode recursive loops is memory-policy-conditioned. Under a 12,000-character tail clip, destination-coherent persistence plateaus near 16 percent and retained source-basin escape near 36 percent at dose 400; neither crosses 50 percent. Under a full-history protocol, retained source-basin escape crosses 50 percent near 400 tokens and saturates at 75-80 percent by 1,500 tokens, while destination-coherent persistence first reaches 0.50 near 1,500 tokens with a Wilson 95 percent CI of [0.41, 0.61]. For raw switching, adversarial continuations yield an ED50 near 40 tokens, with paired-control floors near 35 percent and net switching never reaching +50 percentage points within 5-400 tokens. Replace-mode raw switching is near-saturated but largely reflects state-reset overwrite: insert-mode probes drop it to 12-32 percent. A homogeneous-perturbation control reproduced the high-dose non-monotonic dip in destination-coherent persistence, refuting perturbation heterogeneity as the cause; the dip appears structural, with mechanism unresolved. We report 37 experiments on gpt-4o-mini with within-vendor replication on gpt-4.1-nano. Recursive-loop evaluations should distinguish transient movement from durable escape, subtract stochastic floors, and treat context-update rules as first-class safety-relevant design choices.
Source availability
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Extraction status
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Proof status
unverified0 refs; 4 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 4.0
PROBLEM
Investigating how perturbation dose affects recursive LLM loops, revealing memory-policy-conditioned persistence and structural dips in model behavior. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether that move lasts.
METHOD
Recursive language-model loops often settle into recognizable attractor-like patterns. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether that move lasts.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. The main result is that persistent redirection in append-mode recursive loops is memory-policy-conditioned. A public repository is linked, so build verification can inspect implementation evidence instead...
WHY NOW
LLM Behavior Analysis moved forward this cycle; last verified May 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
Investigating how perturbation dose affects recursive LLM loops, revealing memory-policy-conditioned persistence and structural dips in model behavior. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether that move lasts.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Recursive language-model loops often settle into recognizable attractor-like patterns. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether that move lasts.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. The main result is that persistent redirection in append-mode recursive loops is memory-policy-conditioned. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Behavior Analysis moved forward this cycle; last verified May 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
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Investigating how perturbation dose affects recursive LLM loops, revealing memory-policy-conditioned persistence and structural dips in model behavior.
Segment
LLM Behavior Analysis
Adoption evidence
Public code linked for build inspection
Commercial read
4.0/10 public viability
Direct
Adjacent
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Unknown
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 67% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 4 sources, 67% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Gaps
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People
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Regulatory need unclassified.
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People
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Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.