Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2607. 04508v1 Announce Type: new Abstract: Agentic AI-for-Science can automate ideation, planning, and analysis, but final validation still depends on real experiments.
arXiv:2607. 02520v1 Announce Type: cross Abstract: Automated research agents increasingly generate code, retrieve literature, and draft scientific artifacts, but they often fail to verify whether generated experiments execute correctly or whether cited sources support generated claims.
arXiv:2607. 02864v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction.
arXiv:2607. 05391v1 Announce Type: new Abstract: Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs.
arXiv:2607. 05155v1 Announce Type: cross Abstract: Pretraining scaling laws reveal that model capability improves predictably with data and compute.
arXiv:2607. 05378v1 Announce Type: new Abstract: Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed.
arXiv:2607. 05277v1 Announce Type: cross Abstract: Defenses that provide security guarantees against prompt injection attacks rely on strict isolation between trusted instructions and untrusted data.
arXiv:2606. 23327v2 Announce Type: replace-cross Abstract: Video editing has become essential in digital media creation, yet existing automated systems are restricted to short segment processing and domain-specific tasks.
arXiv:2606. 30111v2 Announce Type: replace-cross Abstract: Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules.
arXiv:2504. 09662v4 Announce Type: replace-cross Abstract: Multi-agent large language model simulations have the potential to model complex human behaviors and interactions.
arXiv:2607. 04631v1 Announce Type: new Abstract: The cost of producing code is rapidly diminishing with increasingly capable AI agents, while quality assurance of generated programs has not kept pace.
arXiv:2607. 03215v1 Announce Type: cross Abstract: Artificial intelligence has spread across the whole of the security lifecycle.
arXiv:2607. 03181v1 Announce Type: cross Abstract: Successful diffusion of AI in the workforce hinges on the economic value that AI brings to human endeavors.
arXiv:2607. 04708v1 Announce Type: cross Abstract: Agentic AI is shifting online shopping from search toward delegated purchasing, where autonomous buying agents monitor markets and decide when to buy on a consumer's behalf.
arXiv:2601. 21523v2 Announce Type: replace Abstract: To promote cooperation in Multi-Agent Reinforcement Learning, the reward signals of all agents can be aggregated together, forming global rewards that are commonly known as the fully cooperative setting.
arXiv:2601. 11049v2 Announce Type: replace-cross Abstract: We examine whether large language models (LLMs) can predict biased decision-making in conversational settings, and whether their predictions capture not only human cognitive biases but also how those effects change under cognitive load.
arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.
arXiv:2607. 03723v1 Announce Type: cross Abstract: Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry.
arXiv:2408. 09112v2 Announce Type: replace Abstract: Reinforcement learning policies parametrized by deep neural networks have achieved strong performance for continuous control, yet even small input perturbations may lead to unpredictable behavior.
arXiv:2510. 01764v3 Announce Type: replace Abstract: Reinforcement learning (RL) research requires diverse, challenging environments that are both tractable and scalable.