ARXIV:2602.16928 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Discovering Multiagent Learning Algorithms with Large Language Models

Q: What is the startup potential of "Discovering Multiagent Learning Algorithms with Large Langua"?

Automate the discovery of multiagent learning algorithms using AlphaEvolve, powered by LLMs for semantic evolution of code.

Q: What products could be built from this research?

To productize, create an API service where users submit their machine learning code, and AlphaEvolve evolves it to find optimized algorithm variants, focusing on performance benchmarks.

Q: What are the practical use cases?

Develop a SaaS platform where businesses can input existing machine learning algorithms to receive optimized versions through semantic code evolution, targeting improved performance and efficiency.

Q: What industries could this research disrupt?

This approach could replace traditional methods of algorithm development and optimization, reducing reliance on iterative manual tuning and enhancing the speed of innovation in strategic AI development.

arXiv

AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models. While foundational families like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO) rest on solid theoretical ground, the design of their…

METHOD

Full abstract

Much of the advancement of Multi-Agent Reinforcement Learning (MARL) in imperfect-information games has historically depended on manual iterative refinement of baselines. While foundational families like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO) rest on solid theoretical ground, the design of their most effective variants often relies on human intuition to navigate a vast algorithmic design space. In this work, we propose the use of AlphaEvolve, an evolutionary coding agent powered by large language models, to automatically discover new multiagent learning algorithms. We demonstrate the generality of this framework by evolving novel variants for two distinct paradigms of game-theoretic learning. First, in the domain of iterative regret minimization, we evolve the logic governing regret accumulation and policy derivation, discovering a new algorithm, Volatility-Adaptive Discounted (VAD-)CFR. VAD-CFR employs novel, non-intuitive mechanisms-including volatility-sensitive discounting, consistency-enforced optimism, and a hard warm-start policy accumulation schedule-to outperform state-of-the-art baselines like Discounted Predictive CFR+. Second, in the regime of population based training algorithms, we evolve training-time and evaluation-time meta strategy solvers for PSRO, discovering a new variant, Smoothed Hybrid Optimistic Regret (SHOR-)PSRO. SHOR-PSRO introduces a hybrid meta-solver that linearly blends Optimistic Regret Matching with a smoothed, temperature-controlled distribution over best pure strategies. By dynamically annealing this blending factor and diversity bonuses during training, the algorithm automates the transition from population diversity to rigorous equilibrium finding, yielding superior empirical convergence compared to standard static meta-solvers.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We demonstrate the generality of this framework by evolving novel variants for two distinct paradigms of game-theoretic learning.

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainAlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.16928 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Discovering Multiagent Learning Algorithms with Large Language Models

arXiv

AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainAlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.16928

Discovering Multiagent Learning Algorithms with Large Language Models

AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback31 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models. While foundational families like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO) rest on solid theoretical ground, the design of their most effective variants often relies on human intuition to navigate a vast algorithmic design space.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Much of the advancement of Multi-Agent Reinforcement Learning (MARL) in imperfect-information games has historically depended on manual iterative refinement of baselines. While foundational families like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO) rest on solid theoretical ground, the design of their most effective variants often relies on human intuition to navigate a vast algorithmic design space.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We demonstrate the generality of this framework by evolving novel variants for two distinct paradigms of game-theoretic learning.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Agents

Competitors

not indexed

Competitive landscape

AlphaEvolve automatically discovers superior multiagent learning algorithms using large language models.

Segment

Agents

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.16928 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(31)

Mathematical exploration and discovery at scale

2025Bogdan Georgiev, Javier G'omez-Serrano et al.

Discovering state-of-the-art reinforcement learning algorithms

2025Junhyuk Oh, Gregory Farquhar et al.

Reinforced Generation of Combinatorial Structures: Hardness of Approximation

2025Ansh Nagda, Prabhakar Raghavan et al.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

2025Gheorghe Comanici, Eric Bieber et al.

AlphaEvolve: A coding agent for scientific and algorithmic discovery

2025Alexander Novikov, Ngân V˜u et al.

Exponential Lower Bounds on the Double Oracle Algorithm in Zero-Sum Games

2024B. Zhang, T. Sandholm

Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent

2024Hang Xu, Kai Li et al.

Faster Game Solving via Hyperparameter Schedules

2024Naifeng Zhang, S. McAleer et al.

Empirical Game Theoretic Analysis: A Survey

2024Michael P. Wellman, Karl Tuyls et al.

Policy Space Response Oracles: A Survey

2024Ariyan Bighashdel, Yongzhao Wang et al.

Dynamic Discounted Counterfactual Regret Minimization

2024Hang Xu, Kai Li et al.

Symbolic Discovery of Optimization Algorithms

2023Xiangning Chen, Chen Liang et al.

AutoCFR: Learning to Design Counterfactual Regret Minimization Algorithms

2022Hang Xu, Kai Li et al.

Evaluating Strategy Exploration in Empirical Game-Theoretic Analysis

2021Yongzhao Wang, Gary Qiurui Ma et al.

Evolving Reinforcement Learning Algorithms

2021John D. Co-Reyes, Yingjie Miao et al.

Neural Auto-Curricula in Two-Player Zero-Sum Games

2021Xidong Feng, Oliver Slumbers et al.

Faster Game Solving via Predictive Blackwell Approachability: Connecting Regret Matching and Mirror Descent

2020Gabriele Farina, Christian Kroer et al.

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

2020Esteban Real, Chen Liang et al.

Grandmaster level in StarCraft II using multi-agent reinforcement learning

2019O. Vinyals, Igor Babuschkin et al.

A Generalized Training Approach for Multiagent Learning

2019Paul Muller, Shayegan Omidshafiei et al.

Showing 20 of 31 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisMLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery

0.0

Builds On ThisEvolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat

0.0

Commercially relevant

Higher ViabilityEvolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models

7.0

Higher ViabilityRetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback

8.0

Higher ViabilityBreakthrough the Suboptimal Stable Point in Value-Factorization-Based Multi-Agent Reinforcement Learning

7.0

Higher ViabilityCORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

8.0

Higher ViabilityAdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection

8.0

Higher ViabilityCOvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum Game

7.0

Higher ViabilityAdaptive Robust Estimator for Multi-Agent Reinforcement Learning

7.0

Conflicting

Competing ApproachEvolving-RL: End-to-End Optimization of Experience-Driven Self-Evolving Capability within Agents

4.0

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Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.16928
Route: /paper/discovering-multiagent-learning-algorithms-with-large-language-models
Active tab: read
Artifact: discovering-multiagent-learning-algorithms-with-large-language-models

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/discovering-multiagent-learning-algorithms-with-large-language-models/paper-pack
REST build passport API/api/v1/paper/discovering-multiagent-learning-algorithms-with-large-language-models/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "ad69bd01-05e9-47f1-b9c1-8eb0941ded19",
  "arxiv_id": "2602.16928",
  "canonical_route": "/paper/discovering-multiagent-learning-algorithms-with-large-language-models",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "discovering-multiagent-learning-algorithms-with-large-language-models",
  "endpoints": {
    "paper_pack": "/api/v1/paper/discovering-multiagent-learning-algorithms-with-large-language-models/paper-pack",
    "build_passport": "/api/v1/paper/discovering-multiagent-learning-algorithms-with-large-language-models/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/discovering-multiagent-learning-algorithms-with-large-language-models

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Discovering Multiagent Learning Algorithms with Large Language Models

Canonical ID discovering-multiagent-learning-algorithms-with-large-language-models | Route /paper/discovering-multiagent-learning-algorithms-with-large-language-models

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/discovering-multiagent-learning-algorithms-with-large-language-models

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.16928"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Discovering Multiagent Learning Algorithms with Large Language Models",
  "normalized_query": "2602.16928",
  "route": "/paper/discovering-multiagent-learning-algorithms-with-large-language-models",
  "paper_ref": "discovering-multiagent-learning-algorithms-with-large-language-models",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Discovering Multiagent Learning Algorithms with Large Language Models

/buildability/discovering-multiagent-learning-algorithms-with-large-language-models

Watchwatch

Subject: Discovering Multiagent Learning Algorithms with Large Language Models

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/discovering-multiagent-learning-algorithms-with-large-language-models

Paper ref

discovering-multiagent-learning-algorithms-with-large-language-models

arXiv id

2602.16928

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

daaf9505ff5dbcd1fda192d9a170db2ff348ae5c99141f6650479d552fe4a2a1

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Discovering Multiagent Learning Algorithms with Large Language Models

Discovering Multiagent Learning Algorithms with Large Language Models

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