arXiv:2604. 02863v2 Announce Type: replace Abstract: Majority voting is the standard for aggregating multi-agent responses into a final decision.
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The paper introduces Revision‑Aware Independent Agent Graphs (RIAG) to address dynamic task routing, where an event stream continually revises task bindings and a system must select the correct document version at query time. By repurposing six benchmarks into over 31,000 dynamic episodes, the authors demonstrate that RIAG balances recomputation and reuse, achieving 54.24 % joint routing‑and‑answer accuracy with only 0.62 calls per query—substantially better than the strongest baseline. The study highlights the trade‑off between stale conclusions and wasted work in dynamic reasoning settings.
arXiv:2602. 12276v2 Announce Type: replace Abstract: Test-time scaling has become a standard way to improve performance and boost reliability of neural network models.
arXiv:2604. 09679v2 Announce Type: replace-cross Abstract: Multi-Agent Debate (MAD) is a collaborative framework in which multiple agents iteratively refine solutions through the generation of reasoning and alternating critique cycles.
arXiv:2609.32754v2 Announce Type: replace Abstract: Large language model agents can often make reasonable local decisions on short tasks, yet their performance degrades when success requires long seq...
arXiv:2601. 05746v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of Large Language Model-based Multi-Agent Systems (MAS), which excel at collaborative decision-making and complex problem-solving.
arXiv:2608.25937v2 Announce Type: replace Abstract: Multi-agent systems (MAS) sometimes already have the potential to answer correctly, but still report a wrong answer. Explaining this outcome is dif...