Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collab- orate through LLMs
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Personal agents require edge deployment to preserve high-fidelity local context and zero-latency loops, as claimed through three structural shifts away from cloud-centric designs.
PEACE decouples single-pass LLM planning from PX4 execution via ROS 2 and a constraint layer, with modular 3D perception, and shows feasibility in Gazebo SITL with improved explainability and fewer LLM calls.
CommandSwarm uses LoRA-adapted LLMs with safety filtering and deterministic parsing to generate valid behavior trees from text or speech, raising zero-shot BLEU to 0.663 and syntactic validity to 72% on synthetic swarm scenarios.
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.
citing papers explorer
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
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Beyond Scaling: Agents Are Heading to the Edge
Personal agents require edge deployment to preserve high-fidelity local context and zero-latency loops, as claimed through three structural shifts away from cloud-centric designs.
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PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs
PEACE decouples single-pass LLM planning from PX4 execution via ROS 2 and a constraint layer, with modular 3D perception, and shows feasibility in Gazebo SITL with improved explainability and fewer LLM calls.
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CommandSwarm: Safety-Aware Natural Language-to-Behavior-Tree Generation for Robotic Swarms
CommandSwarm uses LoRA-adapted LLMs with safety filtering and deterministic parsing to generate valid behavior trees from text or speech, raising zero-shot BLEU to 0.663 and syntactic validity to 72% on synthetic swarm scenarios.
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Large Language Models for Multi-Robot Systems: A Survey
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.