Pith Number

pith:GGWLA2XC

pith:2026:GGWLA2XCY3BUPSXI2CXPN6CGKP

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Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems

Alvaro Maggiar, Angel Wang, Carson Eisenach, Dean Foster, Dominique Perrault-Joncas

Population-aware learned maps let planners coordinate large multi-agent systems across changing compositions without retraining.

arxiv:2605.13900 v1 · 2026-05-12 · cs.MA · cs.LG

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest claim

In a supply-chain capacity-control case study, population-aware interfaces reduce forecast error by 16--19% and capacity violations by 20--51% relative to population-unaware baselines under composition shift; 20K-agent cohorts support accurate coordination of 500K-agent populations; and simulator-trained primal maps achieve 11.1% MAPE on real observations versus 13--24% for baselines.

C2weakest assumption

That compact population summaries encode sufficient response-relevant structure for the learned primal and dual maps to remain reliable across evolving populations without per-cycle retraining.

C3one line summary

Population-conditioned learned primal and dual maps support reliable coordination of large multi-agent systems under composition shifts without per-cycle retraining, cutting forecast error 16-19% and violations 20-51% in supply-chain tests.

References

45 extracted · 45 resolved · 0 Pith anchors

[1] CACHON, G. P. (2003). Supply chain coordination with contracts. InHandbooks in Operations Research and Management Science, vol. 11. Elsevier, 227–339 2003

[2] FEDERGRUEN, A. and ZIPKIN, P. H. (1999). Coordination mechanisms for a distribution system with one supplier and multiple retailers.Management science451493–1507 1999

[3] BOYD, S., PARIKH, N., CHU, E., PELEATO, B. and ECKSTEIN, J. (2011). Distributed opti- mization and statistical learning via the alternating direction method of multipliers.Foundations and Trends in Ma 2011

[4] FISHER, M. L. (1981). The lagrangian relaxation method for solving integer programming problems.Management science271–18 1981

[5] LOWE, R., WU, Y., TAMAR, A., HARB, J., ABBEEL, P. and MORDATCH, I. (2017). Multi- agent actor-critic for mixed cooperative-competitive environments. InAdvances in Neural Information Processing Systems 2017

Receipt and verification

First computed	2026-05-17T23:39:18.943744Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

31acb06ae2c6c347cae8d0aef6f84653e89a0fb34799d5972545c29a8a898e1f

Aliases

arxiv: 2605.13900 · arxiv_version: 2605.13900v1 · doi: 10.48550/arxiv.2605.13900 · pith_short_12: GGWLA2XCY3BU · pith_short_16: GGWLA2XCY3BUPSXI · pith_short_8: GGWLA2XC

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GGWLA2XCY3BUPSXI2CXPN6CGKP \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 31acb06ae2c6c347cae8d0aef6f84653e89a0fb34799d5972545c29a8a898e1f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b5311d1a7aae2939dba6ab5e28f7696ce983686074c3b0195adfc82df2cafa71",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.MA",
    "submitted_at": "2026-05-12T16:57:24Z",
    "title_canon_sha256": "4a3924843c076074726e4819eb47533a7c9c4136a43a6c550f7ad08390dfa34b"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}