arXiv AI
1d ago
Agentic Test-Time Scaling for WebAgents
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.
EMS: Multi-Agent Voting via Efficient Majority-then-Stopping
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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arXiv AI
Jun 16
HCP-MAD:Heterogeneous Consensus-Progressive Reasoning for Efficient Multi-Agent Debate
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 AI
Jun 16
DynaDebate: Breaking Homogeneity in Multi-Agent Debate with Dynamic Path Generation
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 AI
Jun 8
When Does Multi-Agent Collaboration Help? An Entropy Perspective
arXiv:2602. 04234v6 Announce Type: cross Abstract: Multi-agent systems (MAS) have emerged as a prominent paradigm for leveraging large language models (LLMs) to tackle complex tasks.
arXiv AI
Jun 30
Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
arXiv:2606. 29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision.
arXiv AI
Jul 7
Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias
arXiv:2604. 02923v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases.
arXiv AI
Jun 10
$\tau$-Rec: A Verifiable Benchmark for Agentic Recommender Systems
arXiv:2606. 10156v1 Announce Type: cross Abstract: As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation paradigms have struggled to keep pace.
arXiv AI
Jun 10
Decentralized Multi-Agent Systems with Shared Context
arXiv:2606. 10662v1 Announce Type: cross Abstract: Multi-agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks.
arXiv Machine Learning
Jul 9
TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination
arXiv:2605. 15207v2 Announce Type: replace Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines.
arXiv AI
Jul 24
DynamicMCPBench: A Trace-Grounded, Effect-Scored Benchmark for LLM Agents over Live MCP Servers
arXiv:2607. 20531v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the benchmarks used to evaluate them score the final answer or a fixed "ground-truth" list of tools, both of which are fragile once the underlying data is live and stateful.
arXiv AI
Jun 2
Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus
arXiv:2606. 01828v1 Announce Type: cross Abstract: Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specialization, and cross-validation.
arXiv AI
Jul 14
AgentAbstain: Do LLM Agents Know When Not to Act?
arXiv:2607. 10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain.