arXiv:2605. 09907v2 Announce Type: replace Abstract: 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 planning, etc.
arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...
arXiv:2608.11596v1 Announce Type: cross Abstract: Modern radar systems require adapting their processing strategies in response to changing interference, clutter, and data availability. This paper in...
arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.
arXiv:2606. 16328v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable potential in dynamic graph reasoning, but suffer from a scaling bottleneck: current models can only handle graphs with tens of nodes, constrained by exponential reasoning overhead and finite context windows.
arXiv:2608.21156v1 Announce Type: cross Abstract: LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms includi...
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities,...
arXiv:2511. 19969v2 Announce Type: replace Abstract: Recent advancements in multi-modal retrieval-augmented generation (mRAG), which enhance multi-modal large language models (MLLMs) with external knowledge, have demonstrated that the collective intelligence of multiple agents can significantly outperform a single model through effective communication.
arXiv:2606. 01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents.
The paper introduces Agentic Semantic Sensing (Agentic SemS), a closed‑loop framework for AI‑enabled radio access networks that dynamically adjusts sensing configurations within a communication‑feasible profile set. A profile‑conditioned causal Transformer updates semantic beliefs from streaming data, while a semantic utility network guides the selection of the next sensing profile and determines when to exit early, balancing task benefit against sensing cost. Experiments on Widar3.0 demonstrate that Agentic SemS reduces cumulative sensing cost by 25.33% compared to a full‑sequence baseline while maintaining 85.79% Macro‑F1, and that semantic early exit yields an additional 12.35% cost savings with minimal performance loss.
arXiv:2607. 01942v1 Announce Type: new Abstract: LLM-based agents have shown strong potential for solving complex multi-step tasks, yet existing performance improvements often rely on either scaling to larger backbone models or task-specific fine-tuning.
The paper examines when multi‑agent collaboration is beneficial versus single‑agent approaches. It finds that collaboration yields systematic advantages mainly in long‑horizon tasks with sparse dependencies, while single agents perform better in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight multi‑agent mechanism that models collaboration as a dynamically evolving graph, and show that it balances context efficiency and task performance without always improving outcomes as more agents are added.
SimCRAFT is a model‑agnostic framework that distills remote sensing orchestration into a compact 7B‑scale model. It creates a large, constraint‑validated workflow planning corpus (SimRS‑14k) using a multi‑agent synthesis engine and a Mock Execution Engine, then fine‑tunes the model with Contextual Retrieval‑Augmented Fine‑Tuning (CRAFT) to reason analogically. Experiments show SimCRAFT‑7B outperforms open‑weight LLMs and rivals advanced closed‑source models, providing a lightweight, efficient baseline for autonomous remote sensing deployment.