Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2606. 05080v1 Announce Type: new Abstract: Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts.
arXiv:2606. 04903v1 Announce Type: cross Abstract: We introduce the LLM agent architecture Agentic Redux, intended for use with nontrivial problem domains that require linear auditability.
arXiv:2606. 04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents.
arXiv:2606. 05158v1 Announce Type: cross Abstract: Multi-agent reasoning systems adopt a "generate-then-transfer" paradigm that forces end-to-end latency to scale linearly with pipeline depth.
arXiv:2603. 03482v2 Announce Type: replace-cross Abstract: Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities.
arXiv:2606. 04779v1 Announce Type: new Abstract: Complementarity is the case in which a human--AI interaction (HAI) outperforms the best prediction benchmark available among its members.
arXiv:2605. 07032v2 Announce Type: replace-cross Abstract: The evolution of generative models from next-token predictors to autonomous engines of complex systems necessitates rigorous safety hardening.
arXiv:2512. 16919v2 Announce Type: replace-cross Abstract: Perceiving and reconstructing 3D scene geometry from visual inputs is crucial for autonomous driving.
arXiv:2606. 04223v1 Announce Type: new Abstract: Multi-agent systems are commonly designed to reduce disagreement through voting, consensus protocols, debate, or fault-tolerant aggregation.
arXiv:2606. 04812v1 Announce Type: cross Abstract: Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibility to transition perturbations that result in unknown or unsafe behaviour.
arXiv:2606. 04656v1 Announce Type: cross Abstract: Object detection is a safety-critical component of autonomous driving.
arXiv:2602. 16966v2 Announce Type: replace-cross Abstract: Scalable methods for networked multi-agent reinforcement learning let each agent plan using only a small neighborhood of the agent graph.
arXiv:2606. 04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server.
arXiv:2606. 04425v1 Announce Type: cross Abstract: Modern agentic systems transform LLMs from session-bounded assistants into stateful systems that persist and evolve shared world state across sessions through memories, filesystems, tools, and other long-lived contextual artifacts.
arXiv:2606. 04465v1 Announce Type: cross Abstract: System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions.
arXiv:2605. 10246v2 Announce Type: replace Abstract: AI scientist systems are increasingly deployed for autonomous research, yet their academic integrity has never been systematically evaluated.
arXiv:2606. 04971v1 Announce Type: new Abstract: Machine learning engineering (MLE) agents promise to automate end-to-end ML pipeline development from raw data and natural language instructions, potentially making ML accessible to non-technical domain experts.
arXiv:2602. 14117v2 Announce Type: replace-cross Abstract: Open Radio Access Networks (O-RAN) promise flexible 6G network access through disaggregated, software-driven components and open interfaces, but this programmability also increases operational complexity.
arXiv:2603. 10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question.
arXiv:2510. 27191v5 Announce Type: replace-cross Abstract: Planning under partial observability is an essential capability of autonomous robots.