Pith Number

pith:P2BTFOUP

pith:2024:P2BTFOUP6V4NG72P5C5MKZNZKI

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LLM Multi-Agent Systems: Challenges and Open Problems

Qifan Zhang, Shanshan Han, Weizhao Jin, Zhaozhuo Xu

Multi-agent LLM systems can solve complex tasks through agent collaboration but leave several challenges inadequately addressed.

arxiv:2402.03578 v3 · 2024-02-05 · cs.MA · cs.AI

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Claims

C1strongest claim

By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration, yet several challenges remain inadequately addressed including task allocation, robust reasoning through iterative debates, complex context management, memory management, and blockchain applications.

C2weakest assumption

The assumption that the listed challenges (task allocation, iterative debates, context and memory management, blockchain uses) are currently inadequately addressed and that discussing them will meaningfully guide future development, without providing systematic evidence or citations quantifying the inadequacy.

C3one line summary

The paper identifies inadequately addressed challenges in optimizing task allocation, fostering robust reasoning through debates, managing layered context, enhancing memory, and applying multi-agent systems to blockchain.

References

50 extracted · 50 resolved · 15 Pith anchors

[1] Evil geniuses: Delving into the safety of llm-based agents

[2] Identifying the Risks of LM Agents with an LM-Emulated Sandbox · arXiv:2309.15817

[3] Igniting language intelligence: The hitchhiker’s guide from chain-of-thought reasoning to language agents

[4] R-judge: Benchmarking safety risk awareness for llm agents

[5] Multi-Agent Security Workshop@ NeurIPS'23 , year=

Formal links

2 machine-checked theorem links

Cited by

31 papers in Pith

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Learning from Self-Debate: Preparing Reasoning Models for Multi-Agent Debate

AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

Receipt and verification

First computed	2026-05-17T23:38:14.385231Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7e8332ba8ff578d37f4fe8bac565b9523afd6ccc42521d95318c496811485ead

Aliases

arxiv: 2402.03578 · arxiv_version: 2402.03578v3 · doi: 10.48550/arxiv.2402.03578 · pith_short_12: P2BTFOUP6V4N · pith_short_16: P2BTFOUP6V4NG72P · pith_short_8: P2BTFOUP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/P2BTFOUP6V4NG72P5C5MKZNZKI \
  | 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: 7e8332ba8ff578d37f4fe8bac565b9523afd6ccc42521d95318c496811485ead

Canonical record JSON

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    "abstract_canon_sha256": "1f89747d210256f5f7b10b1391ce7e18b6b2a443581df0aedfc7e133cf6993e5",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.MA",
    "submitted_at": "2024-02-05T23:06:42Z",
    "title_canon_sha256": "9b21548318a341fb23c00b41a527dc7f06c3766df04bf2bd85c271fddf00c853"
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  "source": {
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