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A Two-Tier Perspective on Inference-Time Parallelism in Multi-Agent LLM Systems
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Proposes TIPEX, a two-tier framework separating Replica Parallelism (multiple solution paths with generation and selection) from Structural Parallelism (DAG-based concurrent agent/tool scheduling) on top of Magentic-One, evaluated on GAIA.
Problem
Inference-time parallelism in LLM multi-agent systems is studied only in isolated pieces; the accuracy-latency-cost roles and interactions of task-level and within-trajectory parallelism are unclear.
Approach
Model inference-time parallelism as two decision levels: replica-level exploration with generation (RIG, OHG) and selection (GS, FVES, TKS) strategies, and instance-level dynamic DAG scheduling (SS, BP, AP), unified under one controllable execution framework built on Magentic-One.
Key results
- On GAIA Level 1, OHG+TKS reaches 56.5% accuracy vs 43.2% for Magentic-One, and RIG+GS reaches 56.7%.
- On GAIA Level 2, OHG+GS reaches 38.7% vs 25.8% for Magentic-One, with OHG+TKS at 32.0% and lower latency than GS.
- Level 2 ablation under OHG+TKS shows BP with 5 replicas reaching 37% accuracy vs 29% with one replica, while AP drops accuracy to 25% at 5 replicas.
Surprising findings
- Aggressive structural parallelism (AP) lowers accuracy relative to balanced parallelism while increasing token cost.
- Increasing replicas from 3 to 5 gives marginal or negative accuracy gains in some configurations.
Limitations
- Judge quality is a bottleneck; a persistent gap remains between actual and oracle accuracy.
- Small per-level sample sizes and repeated runs; results come from a single main backbone (Qwen-Plus) plus limited supplementary tests.
- Auto Router is preliminary and does not improve accuracy or token cost.
Experimental setup
Datasets: GAIA GAIA2-mini
Benchmarks: GAIA GAIA2-mini
Metrics: accuracy oracle accuracy wall-clock time token consumption
Baselines: Magentic-One RIG+GS RIG+FVES RIG+TKS OHG+GS OHG+FVES
Builds on: Magentic-One self-consistency LLMCompiler
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