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ARXIV:2603.21520 · LLM PROMPT OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.21520LLM PROMPT OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEGuanbao Liang · Yuanchen Bei · Sheng Zhou · Yuheng Qin · Huan Zhou · Bingxin Jia · +2 at arXiv
A self-evolving memory system that automatically optimizes LLM prompts for better generalization and reduced cost.
Opportunity summary
Pain A self-evolving memory system that automatically optimizes LLM prompts for better generalization and reduced cost.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A self-evolving memory system that automatically optimizes LLM prompts for better generalization and reduced cost. This paradigm limits generalization across heterogeneous queries and prevents models from accumulating reusable prompting knowledge over time.
Automatic prompt optimization is a promising approach for adapting large language models (LLMs) to downstream tasks, yet existing methods typically search for a specific prompt specialized to a fixed task. This paradigm limits generalization…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through iterative self-reflection and memory editing, MemAPO continuously updates its memory, enabling prompt optimization to improve over time rather than restarting from scratch for…
LLM Prompt Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A self-evolving memory system that automatically optimizes LLM prompts for better generalization and reduced cost.
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Paper Pack
10.48550/arXiv.2603.21520A self-evolving memory system that automatically optimizes LLM prompts for better generalization and reduced cost.
Abstract
Automatic prompt optimization is a promising approach for adapting large language models (LLMs) to downstream tasks, yet existing methods typically search for a specific prompt specialized to a fixed task. This paradigm limits generalization across heterogeneous queries and prevents models from accumulating reusable prompting knowledge over time. In this paper, we propose MemAPO, a memory-driven framework that reconceptualizes prompt optimization as generalizable and self-evolving experience accumulation. MemAPO maintains a dual-memory mechanism that distills successful reasoning trajectories into reusable strategy templates while organizing incorrect generations into structured error patterns that capture recurrent failure modes. Given a new prompt, the framework retrieves both relevant strategies and failure patterns to compose prompts that promote effective reasoning while discouraging known mistakes. Through iterative self-reflection and memory editing, MemAPO continuously updates its memory, enabling prompt optimization to improve over time rather than restarting from scratch for each task. Experiments on diverse benchmarks show that MemAPO consistently outperforms representative prompt optimization baselines while substantially reducing optimization cost.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
A self-evolving memory system that automatically optimizes LLM prompts for better generalization and reduced cost. This paradigm limits generalization across heterogeneous queries and prevents models from accumulating reusable prompting knowledge over time.
METHOD
Automatic prompt optimization is a promising approach for adapting large language models (LLMs) to downstream tasks, yet existing methods typically search for a specific prompt specialized to a fixed task. This paradigm limits generalization across heterogeneous queries and prev...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through iterative self-reflection and memory editing, MemAPO continuously updates its memory, enabling prompt optimization to improve over time rather than restarting from scratch for each task. Code avai...
WHY NOW
LLM Prompt Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A self-evolving memory system that automatically optimizes LLM prompts for better generalization and reduced cost. This paradigm limits generalization across heterogeneous queries and prevents models from accumulating reusable prompting knowledge over time.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Automatic prompt optimization is a promising approach for adapting large language models (LLMs) to downstream tasks, yet existing methods typically search for a specific prompt specialized to a fixed task. This paradigm limits generalization across heterogeneous queries and prevents models from accumulating reusable prompting knowledge over time.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through iterative self-reflection and memory editing, MemAPO continuously updates its memory, enabling prompt optimization to improve over time rather than restarting from scratch for each task. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Prompt Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A self-evolving memory system that automatically optimizes LLM prompts for better generalization and reduced cost.
Segment
LLM Prompt Optimization
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
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status
missing
reason
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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
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stale
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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.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Integration burden
missing
Current read
No public implementation surface observed.
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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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ARTIFACTS
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DEFENSIBILITY
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