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ARXIV:2604.18026 · BLACK-BOX OPTIMIZATION · SUBMITTED 21 APR · 02:39 UTC · FRESHNESS STALE
ARXIV:2604.18026BLACK-BOX OPTIMIZATIONSUBMITTED 21 APR · 02:39 UTCFRESHNESS STALEEnze Pan · arXiv
RASP-Tuner: A retrieval-augmented soft prompt system for efficient context-aware black-box optimization in non-stationary environments, outperforming traditional methods.
Opportunity summary
Pain RASP-Tuner: A retrieval-augmented soft prompt system for efficient context-aware black-box optimization in non-stationary environments, outperforming traditional methods.
Evidence 40 refs | 3 sources | 67% coverage
Blocker Evidence unverified
RASP-Tuner: A retrieval-augmented soft prompt system for efficient context-aware black-box optimization in non-stationary environments, outperforming traditional methods. When contexts revisit a small set of latent regimes, an optimizer that discards history pays repeated adaptation…
Many deployed systems expose black-box objectives whose minimizing configuration shifts with an externally observed context. When contexts revisit a small set of latent regimes, an optimizer that discards history pays repeated adaptation cost; when…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. On nine synthetic non-stationary benchmarks, an adversarial-context sanity check, and three tabular real-world streams (Section on real-world experiments), RASP-Tuner improves or matches cumulative regret…
Black-Box Optimization moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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RASP-Tuner: A retrieval-augmented soft prompt system for efficient context-aware black-box optimization in non-stationary environments, outperforming traditional methods.
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10.48550/arXiv.2604.18026RASP-Tuner: A retrieval-augmented soft prompt system for efficient context-aware black-box optimization in non-stationary environments, outperforming traditional methods.
Abstract
Many deployed systems expose black-box objectives whose minimizing configuration shifts with an externally observed context. When contexts revisit a small set of latent regimes, an optimizer that discards history pays repeated adaptation cost; when each step must remain inexpensive, full Gaussian-process (GP) refits at high observation counts are difficult to sustain. We cast online tuning as context-conditioned regret minimization and present RASP-Tuner, which instantiates a decomposition motivated by first principles: (i) identify a regime proxy by retrieving similar past contexts; (ii) predict short-horizon loss with a mixture-of-experts surrogate whose input concatenates parameters, context, and a retrieved soft prompt; (iii) adapt chiefly in a low-dimensional prompt subspace, invoking full surrogate updates only when scalarized error or disagreement spikes. A RealErrorComposer maps heterogeneous streaming metrics to [0,1] via EMA-stabilized logistic scores, supplying a single differentiable training target. On nine synthetic non-stationary benchmarks, an adversarial-context sanity check, and three tabular real-world streams (Section on real-world experiments), RASP-Tuner improves or matches cumulative regret relative to our GP-UCB and CMA-ES implementations on seven of nine synthetic tasks under paired tests at horizon T=100, while recording 8-12 times lower wall-clock per step than sliding-window GP-UCB on identical hardware. Idealized analysis in a cluster-separated, strongly convex regime model (RA-GD) supplies sufficient conditions for bounded dynamic regret; the deployed pipeline violates several of these premises, and we articulate which gaps remain open.
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Proof status
unverified40 refs; 3 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Dimensions overall score 6.0
PROBLEM
RASP-Tuner: A retrieval-augmented soft prompt system for efficient context-aware black-box optimization in non-stationary environments, outperforming traditional methods. When contexts revisit a small set of latent regimes, an optimizer that discards history pays repeated adapta...
METHOD
Many deployed systems expose black-box objectives whose minimizing configuration shifts with an externally observed context. When contexts revisit a small set of latent regimes, an optimizer that discards history pays repeated adaptation cost; when each step must remain inexpens...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. On nine synthetic non-stationary benchmarks, an adversarial-context sanity check, and three tabular real-world streams (Section on real-world experiments), RASP-Tuner improves or matches cumulative regret...
WHY NOW
Black-Box Optimization moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 22, "author": "Enze Pan", "title": "RASP-Tuner: Retrieval-Augmented Soft Prompts for Context-Aware Black-Box Optimization in Non-Stationary Environments"
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RASP-Tuner: A retrieval-augmented soft prompt system for efficient context-aware black-box optimization in non-stationary environments, outperforming traditional methods.
Segment
Black-Box Optimization
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Commercial read
6.0/10 public viability
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3/3 checks · 100%
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missing
reason
passport_row_missing
proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
OpportunityKernel evidence_receipt
40 refs / 3 sources / 67% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
40 references, 3 sources, 67% evidence coverage.
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Buyer clarity
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Refresh defensibility bars with source receipts.
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missing
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No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Prototype owner missing.
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Operator workflow not sourced.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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