Proof pending. Core topic summary fields are still materializing.
Recent developments in multi-agent systems are increasingly focused on enhancing collaboration and communication among agents to tackle complex tasks more effectively. Research is exploring innovative frameworks like Symphony, which emulates human cognitive patterns to improve long-video understanding through task decomposition and collaborative reasoning. Meanwhile, frameworks such as AceMAD address limitations in multi-agent debate by introducing mechanisms that break consensus traps, allowing agents to converge toward truth rather than erroneous majority beliefs. Additionally, the DiffMAS framework optimizes inter-agent communication, enabling agents to learn how to encode and interpret information dynamically, which enhances reasoning accuracy across various benchmarks. These advancements have significant implications for commercial applications, including legal decision-making, innovation analysis, and robotics, where improved coordination and communication can lead to more efficient and transparent outcomes. Overall, the field is shifting toward systems that not only perform tasks but also learn and adapt their collaborative strategies over time, making them more suitable for real-world applications.
Topic-specific paper and score movement from the daily diff ledger.
Despite rapid developments and widespread applications of MLLM agents, they still struggle with long-form video understanding (LVU) tasks, which are characterized by high information density and exten...
Multi-agent systems built on large language models have shown strong performance on complex reasoning tasks, yet most work focuses on agent roles and orchestration while treating inter-agent communica...
As artificial intelligence engineering paradigms shift from single-agent Prompt and Context Engineering toward multi-agent \textbf{Coordination Engineering}, the ability to codify and systematically i...
Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning. We extend such scaling princi...
We revisit multi-agent delegation under a stronger and more realistic assumption: an agent's capability is not fixed at the skill level, but depends on task context. A coding agent may excel at short ...
Current AI-assisted innovation systems typically apply a single ideation methodology (such as TRIZ or Design Thinking) using sequential prompt-based workflows that do not preserve intermediate reasoni...
Multi-Agent Debate (MAD) has emerged as a promising paradigm for enhancing large language model reasoning. However, recent work reveals a limitation:standard MAD cannot improve belief correctness beyo...
Most existing Large Language Model (LLM)-based Multi-Agent Systems (MAS) rely on predefined workflows, where human engineers enumerate task states in advance and specify routing rules and contextual i...
Compared with individual agents, large language model based multi-agent systems have shown great capabilities consistently across diverse tasks, including code generation, mathematical reasoning, and ...
Multi-agent reinforcement learning systems deployed in real-world robotics applications face severe communication constraints that significantly impact coordination effectiveness. We present a framewo...
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Canonical route: /topics
Agent Handoff
Canonical ID multi-agent-systems | Route /topic/multi-agent-systems
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/multi-agent-systemsMCP example
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}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.