REVIEW 9 cited by
When One LLM Drools, Multi-LLM Collaboration Rules
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This position paper argues that in many realistic (i.e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output. We challenge the status quo of relying solely on a single general-purpose LLM and argue for multi-LLM collaboration to better represent the extensive diversity of data, skills, and people. We first posit that a single LLM underrepresents real-world data distributions, heterogeneous skills, and pluralistic populations, and that such representation gaps cannot be trivially patched by further training a single LLM. We then organize existing multi-LLM collaboration methods into a hierarchy, based on the level of access and information exchange, ranging from API-level, text-level, logit-level, to weight-level collaboration. Based on these methods, we highlight how multi-LLM collaboration addresses challenges that a single LLM struggles with, such as reliability, democratization, and pluralism. Finally, we identify the limitations of existing multi-LLM methods and motivate future work. We envision multi-LLM collaboration as an essential path toward compositional intelligence and collaborative AI development.
Forward citations
Cited by 9 Pith papers
-
Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning
Per-question database-style plan search over multi-LLM DAGs improves QA quality under budgets by ~58% (MMLU-Pro) and ~41% (SimpleQA) versus reimplemented baselines.
-
MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
MAGPIE is a 158-scenario benchmark showing large language model agents misclassify and leak contextually private information in multi-agent collaboration, even under explicit privacy instructions.
-
Towards Fundamental Language Models: Does Linguistic Competence Scale with Model Size?
Across 23 models from 135M to 32B parameters, internal factual knowledge scales about twice as fast with model size as linguistic competence, supporting modular small-model-plus-retrieval systems.
-
How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs
A leader LLM trained with a GRPO variant that conditions on frozen agent responses improves both collaborative and zero-shot accuracy on BBH, MATH, and MMLU.
-
Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team
A training-free multi-agent framework with episodic and shared memory reports new best results on GSM8K, AIME 2024/2025, Math-500, and LiveCodeBench.
-
Byzantine-Robust Decentralized Coordination of LLM Agents
A leaderless, Byzantine-robust LLM agent coordination protocol selects answers by geometric-median aggregation of evaluator scores instead of leader-based quorum voting.
-
Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate
A leader-follower multi-agent debate protocol improves ambiguity detection for two of three tested LLMs, but the reported results lack error bars, a clear success metric, and contain internal numerical inconsistencies.
-
Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
A survey of multi-LLM systems in edge computing, covering architectures, enabling technologies, trust mechanisms, applications, and open datasets for edge general intelligence.
-
Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead
A literature review organizes LLM development into a six-phase software engineering lifecycle and identifies challenges and research directions for each phase.
Discussion (0). Sign in to comment.