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ARXIV:2602.02139 · AI CO-SCIENTIST · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.02139AI CO-SCIENTISTSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
EvoMU automates the discovery of optimal data-specific unlearning loss functions, optimizing model privacy and utility without human intervention.
Opportunity summary
Pain EvoMU automates the discovery of optimal data-specific unlearning loss functions, optimizing model privacy and utility without human intervention.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
EvoMU automates the discovery of optimal data-specific unlearning loss functions, optimizing model privacy and utility without human intervention. sensitive or copyrighted material).
Machine unlearning aims to unlearn specified training data (e.g. sensitive or copyrighted material).
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In contrast to previous AI co-scientist works, we do so on a budget: We achieve SotA results using a small 4B parameter model (Qwen3-4B-Thinking),…
AI Co-Scientist moved forward this cycle; last verified April 2026. Public score 7.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
EvoMU automates the discovery of optimal data-specific unlearning loss functions, optimizing model privacy and utility without human intervention.
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Paper Pack
10.48550/arXiv.2602.02139EvoMU automates the discovery of optimal data-specific unlearning loss functions, optimizing model privacy and utility without human intervention.
Abstract
Machine unlearning aims to unlearn specified training data (e.g. sensitive or copyrighted material). A prominent approach is to fine-tune an existing model with an unlearning loss that retains overall utility. The space of suitable unlearning loss functions is vast, making the search for an optimal loss function daunting. Additionally, there might not even exist a universally optimal loss function: differences in the structure and overlap of the forget and retain data can cause a loss to work well in one setting but over-unlearn or under-unlearn in another. Our approach EvoMU tackles these two challenges simultaneously. An evolutionary search procedure automatically finds task-specific losses in the vast space of possible unlearning loss functions. This allows us to find dataset-specific losses that match or outperform existing losses from the literature, without the need for a human-in-the-loop. This work is therefore an instance of automatic scientific discovery, a.k.a. an AI co-scientist. In contrast to previous AI co-scientist works, we do so on a budget: We achieve SotA results using a small 4B parameter model (Qwen3-4B-Thinking), showing the potential of AI co-scientists with limited computational resources. Our experimental evaluation shows that we surpass previous loss-based unlearning formulations on TOFU-5%, TOFU-10%, MUSE and WMDP by synthesizing novel unlearning losses. Our code is available at https://github.com/Batorskq/EvoMU.
Source availability
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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
EvoMU automates the discovery of optimal data-specific unlearning loss functions, optimizing model privacy and utility without human intervention. sensitive or copyrighted material).
METHOD
Machine unlearning aims to unlearn specified training data (e.g. sensitive or copyrighted material).
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In contrast to previous AI co-scientist works, we do so on a budget: We achieve SotA results using a small 4B parameter model (Qwen3-4B-Thinking), showing the potential of AI co-scientists with limited co...
WHY NOW
AI Co-Scientist moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
EvoMU automates the discovery of optimal data-specific unlearning loss functions, optimizing model privacy and utility without human intervention. sensitive or copyrighted material).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Machine unlearning aims to unlearn specified training data (e.g. sensitive or copyrighted material).
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. In contrast to previous AI co-scientist works, we do so on a budget: We achieve SotA results using a small 4B parameter model (Qwen3-4B-Thinking), showing the potential of AI co-scientists with limited computational resources.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Co-Scientist moved forward this cycle; last verified April 2026. Public score 7.0/10.
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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EvoMU automates the discovery of optimal data-specific unlearning loss functions, optimizing model privacy and utility without human intervention.
Segment
AI Co-Scientist
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
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CITED BY
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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
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 / 0 sources / 17% 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, 0 sources, 17% 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
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
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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
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Gaps
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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.
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
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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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
Buzz trend pending.