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ARXIV:2603.15255 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15255AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
SAGE is a self-evolving multi-agent framework that enhances reasoning in LLMs through closed-loop training with minimal human input.
Opportunity summary
Pain SAGE is a self-evolving multi-agent framework that enhances reasoning in LLMs through closed-loop training with minimal human input.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
SAGE is a self-evolving multi-agent framework that enhances reasoning in LLMs through closed-loop training with minimal human input. While self-play reduces this dependency, it often lacks explicit planning and strong quality control, limiting stability…
Reinforcement learning with verifiable rewards improves reasoning in large language models (LLMs), but many methods still rely on large human-labeled datasets. While self-play reduces this dependency, it often lacks explicit planning and strong quality…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Reinforcement learning with verifiable rewards improves reasoning in large language models (LLMs), but many methods still rely on large human-labeled datasets.
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10.
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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
SAGE is a self-evolving multi-agent framework that enhances reasoning in LLMs through closed-loop training with minimal human input.
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Paper Pack
10.48550/arXiv.2603.15255SAGE is a self-evolving multi-agent framework that enhances reasoning in LLMs through closed-loop training with minimal human input.
Abstract
Reinforcement learning with verifiable rewards improves reasoning in large language models (LLMs), but many methods still rely on large human-labeled datasets. While self-play reduces this dependency, it often lacks explicit planning and strong quality control, limiting stability in long-horizon multi-step reasoning. We present SAGE (Self-evolving Agents for Generalized reasoning Evolution), a closed-loop framework where four agents: Challenger, Planner, Solver, and Critic, co-evolve from a shared LLM backbone using only a small seed set. The Challenger continuously generates increasingly difficult tasks; the Planner converts each task into a structured multi-step plan; and the Solver follows the plan to produce an answer, whose correctness is determined by external verifiers. The Critic scores and filters both generated questions and plans to prevent curriculum drift and maintain training signal quality, enabling stable self-training. Across mathematics and code-generation benchmarks, SAGE delivers consistent gains across model scales, improving the Qwen-2.5-7B model by 8.9% on LiveCodeBench and 10.7% on OlympiadBench.
Source availability
PDF linkedThe paper record includes a public PDF URL.
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
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
SAGE is a self-evolving multi-agent framework that enhances reasoning in LLMs through closed-loop training with minimal human input. While self-play reduces this dependency, it often lacks explicit planning and strong quality control, limiting stability in long-horizon multi-ste...
METHOD
Reinforcement learning with verifiable rewards improves reasoning in large language models (LLMs), but many methods still rely on large human-labeled datasets. While self-play reduces this dependency, it often lacks explicit planning and strong quality control, limiting stabilit...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Reinforcement learning with verifiable rewards improves reasoning in large language models (LLMs), but many methods still rely on large human-labeled datasets.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 8.0/10.
improving the Qwen-2.5-7B model by 8.9% on LiveCodeBench
Explicitly stated in the abstract with specific numeric results.
partial
and 10.7% on OlympiadBench.
Explicitly stated in the abstract with specific numeric results.
partial
a closed-loop framework where four agents: Challenger, Planner, Solver, and Critic, co-evolve from a shared LLM backbone
Directly stated in the abstract describing the framework components.
partial
using only a small seed set.
Strongly supported by abstract statement about using 'only a small seed set' and context about reducing human dependency.
partial
The Critic scores and filters both generated questions and plans to prevent curriculum drift
Directly stated function of the Critic agent in the abstract.
partial
Across mathematics and code-generation benchmarks, SAGE delivers consistent gains across model scales
Directly stated in abstract but without specific numeric evidence for all model scales.
partial
whose correctness is determined by external verifiers.
Explicitly stated in the abstract and reinforced in caveats.
partial
Risk of curriculum drift if the Critic agent fails to maintain quality control
Explicitly stated in the caveats section of the analysis.
partial
Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.
Concepts
Methods
Materials
Markets
Competitors
SAGE is a self-evolving multi-agent framework that enhances reasoning in LLMs through closed-loop training with minimal human input.
Segment
Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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Bluesky
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CITED BY
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Foundation
Extension
Commercially relevant
Conflicting
Owned Distribution
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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.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
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
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.
Gaps
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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
Cost passport has no observed_usd value.
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.
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.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.