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ARXIV:2603.26557 · LLM INFERENCE OPTIMIZATION · SUBMITTED 30 MAR · 22:28 UTC · FRESHNESS STALE
ARXIV:2603.26557LLM INFERENCE OPTIMIZATIONSUBMITTED 30 MAR · 22:28 UTCFRESHNESS STALEJoris Köster · Zixuan Liu · Siavash Khajavi · Zizhan Zheng · arXiv
A framework that reduces LLM inference costs by intelligently reusing answers and escalating complex queries to a stronger model.
Opportunity summary
Pain A framework that reduces LLM inference costs by intelligently reusing answers and escalating complex queries to a stronger model.
Evidence 25 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework that reduces LLM inference costs by intelligently reusing answers and escalating complex queries to a stronger model. In this work, we propose MemBoost, a memory-boosted LLM serving framework that enables a lightweight…
Large Language Models (LLMs) deliver strong performance but incur high inference cost in real-world services, especially under workloads with repeated or near-duplicate queries across users and sessions. In this work, we propose MemBoost, a…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. In this work, we propose MemBoost, a memory-boosted LLM serving framework that enables a lightweight model to reuse previously generated answers and retrieve relevant…
LLM Inference Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.
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A framework that reduces LLM inference costs by intelligently reusing answers and escalating complex queries to a stronger model.
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10.48550/arXiv.2603.26557A framework that reduces LLM inference costs by intelligently reusing answers and escalating complex queries to a stronger model.
Abstract
Large Language Models (LLMs) deliver strong performance but incur high inference cost in real-world services, especially under workloads with repeated or near-duplicate queries across users and sessions. In this work, we propose MemBoost, a memory-boosted LLM serving framework that enables a lightweight model to reuse previously generated answers and retrieve relevant supporting information for cheap inference, while selectively escalating difficult or uncertain queries to a stronger model. Unlike standard retrieval-augmented generation, which primarily grounds a single response, MemBoost is designed for interactive settings by supporting answer reuse, continual memory growth, and cost-aware routing. Experiments across multiple models under simulated workloads show that MemBoost substantially reduces expensive large-model invocations and overall inference cost, while maintaining high answer quality comparable to the strong model baseline.
Source availability
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Extraction status
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Proof status
unverified25 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Dimensions overall score 5.0
PROBLEM
A framework that reduces LLM inference costs by intelligently reusing answers and escalating complex queries to a stronger model. In this work, we propose MemBoost, a memory-boosted LLM serving framework that enables a lightweight model to reuse previously generated answers and...
METHOD
Large Language Models (LLMs) deliver strong performance but incur high inference cost in real-world services, especially under workloads with repeated or near-duplicate queries across users and sessions. In this work, we propose MemBoost, a memory-boosted LLM serving framework t...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. In this work, we propose MemBoost, a memory-boosted LLM serving framework that enables a lightweight model to reuse previously generated answers and retrieve relevant supporting information for cheap infe...
WHY NOW
LLM Inference Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.
while maintaining high answer quality comparable to the strong model baseline.
The abstract highlights this as a key outcome, indicating that cost reduction does not come at the expense of quality.
partial
MemBoost, a memory-boosted LLM serving framework that enables a lightweight model to reuse previously generated answers and retrieve relevant supporting information for cheap inference
This is a core claim stated directly in the abstract and elaborated upon in the method section.
partial
while selectively escalating difficult or uncertain queries to a stronger model.
This is a key component of the MemBoost architecture, explicitly mentioned in the abstract and detailed in the method section.
partial
MemBoost is designed for interactive settings by supporting answer reuse, continual memory growth, and cost-aware routing.
The abstract clearly outlines the design principles of MemBoost, distinguishing it from standard RAG.
partial
MemBoost substantially reduces expensive large-model invocations and overall inference cost
This is a primary result reported in the abstract and supported by experimental figures showing reduced memory-use rate (indicating fewer oracle calls).
partial
while maintaining high answer quality comparable to the strong model baseline.
This is a key performance claim made in the abstract, indicating that cost reduction does not come at the expense of quality.
partial
Under our cost model, this implies that MemBoost achieves lower total cost than an oracle-only baseline.
The experimental results section explicitly states this, supported by figures showing reduced memory-use rate and latency.
partial
MemBoost reduces latency relative to the oracle-only baseline as an increasing fraction of queries are served from AME.
This is a direct result presented in Figure 3 and discussed in the text.
partial
MemBoost, a memory-boosted LLM serving framework that enables a lightweight model to reuse previously generated answers and retrieve relevant supporting information for cheap inference
This is a core claim stated directly in the abstract and elaborated on in the method section.
partial
while selectively escalating difficult or uncertain queries to a stronger model.
This is a key component of the MemBoost architecture, explicitly mentioned in the abstract and detailed in the method section.
partial
MemBoost is designed for interactive settings by supporting answer reuse, continual memory growth, and cost-aware routing.
The abstract clearly outlines the unique features of MemBoost compared to standard RAG, highlighting its suitability for interactive use cases.
partial
MemBoost substantially reduces expensive large-model invocations and overall inference cost
The abstract states this as a key result, and the figures and discussion in the experiment section provide supporting evidence.
partial
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Concepts
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A framework that reduces LLM inference costs by intelligently reusing answers and escalating complex queries to a stronger model.
Segment
LLM Inference Optimization
Adoption evidence
No public code link in the paper record yet
Commercial read
5.0/10 public viability
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
25 refs / 3 sources / 50% 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
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
25 references, 3 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
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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
Next test
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
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Gaps
Next test
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.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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