Pith Number

pith:LAIDF7CG

pith:2026:LAIDF7CG5C6IV22FB3QHWP2B3K

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Multi-Paradigm Agent Interaction in Practice:A Systematic Analysis of Generator-Evaluator, ReAct Loop,and Adversarial Evaluation in the buddyMe Framework

Chao Han, Kai Yu, Liang Wang, Xiaohua Wang, Xiaoliang Xu

Analysis of the buddyMe framework shows Generator-Evaluator pre-review detects requirement omissions in 20 percent of complex tasks, ReAct loops produce around 30 percent redundant tool calls, and adversarial discussions reach consensus in

arxiv:2605.16821 v1 · 2026-05-16 · cs.AI

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\pithnumber{LAIDF7CG5C6IV22FB3QHWP2B3K}

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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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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Through four empirical case studies drawn from real-world deployment logs, Generator-Evaluator pre-review detects requirement omissions in 20 percent of complex tasks with 80 percent passing initial inspection, the ReAct loop ensures stable subtask execution but leads to around 30 percent redundant tool invocations, and adversarial Evaluator-Defender discussions reach consensus within 2-3 rounds for nearly 70 percent of scenarios.

C2weakest assumption

The four case studies drawn from real-world deployment logs are representative of broader agent interaction challenges and the six-dimensional weighted evaluation schema provides an unbiased measure of system performance across paradigms.

C3one line summary

Empirical analysis of multi-paradigm agent interactions in buddyMe framework reports that Generator-Evaluator detects omissions in 20% of complex tasks, ReAct causes 30% redundant tool calls, and adversarial discussions reach consensus in 2-3 rounds for 70% of cases.

References

15 extracted · 15 resolved · 3 Pith anchors

[1] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. In Proceedings of ICLR 2023 2023

[2] AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation 2023 · arXiv:2308.08155

[3] PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing 2026 · arXiv:2604.05018

[4] In Proceedings of EMNLP 2025 2025

[5] In Proceedings of NeurIPS 2025 2025

Receipt and verification

First computed	2026-05-20T00:03:24.392427Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

581032fc46e8bc8aeb450ee07b3f41da9f952d8ac7145b94c85f817838a09470

Aliases

arxiv: 2605.16821 · arxiv_version: 2605.16821v1 · doi: 10.48550/arxiv.2605.16821 · pith_short_12: LAIDF7CG5C6I · pith_short_16: LAIDF7CG5C6IV22F · pith_short_8: LAIDF7CG

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LAIDF7CG5C6IV22FB3QHWP2B3K \
  | 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: 581032fc46e8bc8aeb450ee07b3f41da9f952d8ac7145b94c85f817838a09470

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b5e1809dfc965162f8c659e945c3dc28ca81a4d0f5b101cae100c9e9c487dd85",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-16T05:35:50Z",
    "title_canon_sha256": "8a88a75906fa721d05a5df83bf02f648e16bf2602c5a9faec9703a70c84c57ab"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16821",
    "kind": "arxiv",
    "version": 1
  }
}