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ARXIV:2604.10923 · AGENTS · SUBMITTED 14 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.10923AGENTSSUBMITTED 14 APR · 20:32 UTCFRESHNESS STALEZihao Cheng · Zeming Liu · Yingyu Shan · Xinyi Wang · Xiangrong Zhu · Yunpu Ma · +4 at arXiv
A co-evolutionary framework for self-evolving agents that expands capabilities by integrating experience memory with asset creation.
Opportunity summary
Pain A co-evolutionary framework for self-evolving agents that expands capabilities by integrating experience memory with asset creation.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
A co-evolutionary framework for self-evolving agents that expands capabilities by integrating experience memory with asset creation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while…
While large language model--powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typically treat these two evolutionary processes in isolation. This separation overlooks…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Specifically, Mem$^{2}$Evolve leverages accumulated experience to guide the dynamic creation of assets, thereby expanding the agent's capability space while simultaneously acquiring new experience to…
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A co-evolutionary framework for self-evolving agents that expands capabilities by integrating experience memory with asset creation.
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Paper Pack
10.48550/arXiv.2604.10923A co-evolutionary framework for self-evolving agents that expands capabilities by integrating experience memory with asset creation.
Abstract
While large language model--powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typically treat these two evolutionary processes in isolation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while the latter generates new assets from scratch without experiential guidance, leading to limited capability growth and unstable evolution. To address this limitation, we introduce a novel paradigm of co-evolutionary Capability Expansion and Experience Distillation. Guided by this paradigm, we propose the \textbf{Mem$^{\textbf{2}}$Evolve}, which integrates two core components: \textbf{Experience Memory} and \textbf{Asset Memory}. Specifically, Mem$^{2}$Evolve leverages accumulated experience to guide the dynamic creation of assets, thereby expanding the agent's capability space while simultaneously acquiring new experience to achieve co-evolution. Extensive experiments across 6 task categories and 8 benchmarks demonstrate that Mem$^{2}$Evolve achieves improvement of 18.53\% over standard LLMs, 11.80\% over agents evolving solely through experience, and 6.46\% over those evolving solely through asset creation, establishing it as a substantially more effective and stable self-evolving agent framework. Code is available at: https://buaa-irip-llm.github.io/Mem2Evolve.
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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
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Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
A co-evolutionary framework for self-evolving agents that expands capabilities by integrating experience memory with asset creation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while the lat...
METHOD
While large language model--powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typically treat these two evolutionary processes in isolation. This separation overlooks their intrinsi...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Specifically, Mem$^{2}$Evolve leverages accumulated experience to guide the dynamic creation of assets, thereby expanding the agent's capability space while simultaneously acquiring new experience to achi...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A co-evolutionary framework for self-evolving agents that expands capabilities by integrating experience memory with asset creation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while the latter generates new assets from scratch without experiential guidance, leading to limited capability growth and unstable evolution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
While large language model--powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typically treat these two evolutionary processes in isolation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while the latter generates new assets from scratch without experiential guidance, leading to limited capability growth and unstable evolution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Specifically, Mem$^{2}$Evolve leverages accumulated experience to guide the dynamic creation of assets, thereby expanding the agent's capability space while simultaneously acquiring new experience to achieve co-evolution. 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
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A co-evolutionary framework for self-evolving agents that expands capabilities by integrating experience memory with asset creation.
Segment
Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
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Adjacent
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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
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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
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Gaps
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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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Buyer clarity
missing
Current read
No budget owner is verified for this paper.
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Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
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Gaps
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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
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Evidence
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Regulatory load
missing
Current read
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
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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.
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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TIMELINE
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BUZZ
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