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: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.
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.
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.
arXiv:2608.29971v1 Announce Type: new Abstract: As agentic systems evolve into complex multi agent orchestration workflows, there is a growing and critical need for systematic frameworks that measure...
The unprecedented surge in Earth observation data volume and diversity has exposed a critical bottleneck for traditional manual workflows, catalyzing the emergence of Remote Sensing (RS) Agents. Howev...
The paper introduces Gated-Memory Routing, a method for efficient collaboration in multi‑agent large language model systems. It uses a learned execution memory with write and retrieval gates to keep only non‑redundant reasoning steps, and an adaptive halting controller to stop execution when enough evidence is gathered. Experiments on five reasoning and code‑generation benchmarks show the approach achieves higher accuracy and reduces inference cost by 31.9% compared to the strongest baseline.