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ARXIV:2604.07821 · MULTI-AGENT SYSTEMS · SUBMITTED 10 APR · 20:18 UTC · FRESHNESS STALE
ARXIV:2604.07821MULTI-AGENT SYSTEMSSUBMITTED 10 APR · 20:18 UTCFRESHNESS STALEAdvait Yadav · Sid Black · Oliver Sourbut · arXiv
LLM agents exhibit cooperation failures in frictionless environments, indicating that scaling intelligence alone is insufficient for multi-agent coordination.
Opportunity summary
Pain LLM agents exhibit cooperation failures in frictionless environments, indicating that scaling intelligence alone is insufficient for multi-agent coordination.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
LLM agents exhibit cooperation failures in frictionless environments, indicating that scaling intelligence alone is insufficient for multi-agent coordination. In many real-world coordination problems, from knowledge sharing in organizations to code documentation, helping others carries…
Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation failures may arise. In many real-world coordination problems, from knowledge sharing in organizations to…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We find that capability does not predict cooperation: OpenAI o3 achieves only 17% of optimal collective performance while OpenAI o3-mini reaches 50%, despite identical…
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 3.0/10. Implementation evidence is present through a linked repository.
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Score3.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
LLM agents exhibit cooperation failures in frictionless environments, indicating that scaling intelligence alone is insufficient for multi-agent coordination.
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Paper Pack
10.48550/arXiv.2604.07821LLM agents exhibit cooperation failures in frictionless environments, indicating that scaling intelligence alone is insufficient for multi-agent coordination.
Abstract
Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation failures may arise. In many real-world coordination problems, from knowledge sharing in organizations to code documentation, helping others carries negligible personal cost while generating substantial collective benefits. However, whether LLM agents cooperate when helping neither benefits nor harms the helper, while being given explicit instructions to do so, remains unknown. We build a multi-agent setup designed to study cooperative behavior in a frictionless environment, removing all strategic complexity from cooperation. We find that capability does not predict cooperation: OpenAI o3 achieves only 17% of optimal collective performance while OpenAI o3-mini reaches 50%, despite identical instructions to maximize group revenue. Through a causal decomposition that automates one side of agent communication, we separate cooperation failures from competence failures, tracing their origins through agent reasoning analysis. Testing targeted interventions, we find that explicit protocols double performance for low-competence models, and tiny sharing incentives improve models with weak cooperation. Our findings suggest that scaling intelligence alone will not solve coordination problems in multi-agent systems and will require deliberate cooperative design, even when helping others costs nothing.
Source availability
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Extraction status
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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 3.0
PROBLEM
LLM agents exhibit cooperation failures in frictionless environments, indicating that scaling intelligence alone is insufficient for multi-agent coordination. In many real-world coordination problems, from knowledge sharing in organizations to code documentation, helping others...
METHOD
Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation failures may arise. In many real-world coordination problems, from knowledge sharing in organizations to code documentation, helping others...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We find that capability does not predict cooperation: OpenAI o3 achieves only 17% of optimal collective performance while OpenAI o3-mini reaches 50%, despite identical instructions to maximize group reven...
WHY NOW
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 3.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
LLM agents exhibit cooperation failures in frictionless environments, indicating that scaling intelligence alone is insufficient for multi-agent coordination. In many real-world coordination problems, from knowledge sharing in organizations to code documentation, helping others carries negligible personal cost while generating substantial collective benefits.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation failures may arise. In many real-world coordination problems, from knowledge sharing in organizations to code documentation, helping others carries negligible personal cost while generating substantial collective benefits.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We find that capability does not predict cooperation: OpenAI o3 achieves only 17% of optimal collective performance while OpenAI o3-mini reaches 50%, despite identical instructions to maximize group revenue. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-Agent Systems moved forward this cycle; last verified April 2026. Public score 3.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Materials
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LLM agents exhibit cooperation failures in frictionless environments, indicating that scaling intelligence alone is insufficient for multi-agent coordination.
Segment
Multi-Agent Systems
Adoption evidence
Public code linked for build inspection
Commercial read
3.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Commercially relevant
Conflicting
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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
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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Derived signals show verified:false until source-backed receipts exist.
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
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, 4 sources, 67% 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
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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
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
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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People
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Gaps
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Regulatory need unclassified.
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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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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
Buzz trend pending.