Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2604. 20050v3 Announce Type: replace-cross Abstract: Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements?
arXiv:2607. 19899v1 Announce Type: cross Abstract: Disagreement-triggered escalation can create a structural blind spot in multi-agent arbitration: as base learners improve, they tend to converge, weakening safety monitoring where correlated failures concentrate.
arXiv:2607. 19356v1 Announce Type: new Abstract: Tool-using LLM agents increasingly execute high-impact actions, making runtime safety monitoring essential.
arXiv:2607. 19592v1 Announce Type: new Abstract: Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the agent's own code.
arXiv:2607. 20237v1 Announce Type: new Abstract: Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows.
arXiv:2607. 19794v1 Announce Type: cross Abstract: Production LLM-based financial sentiment analysis faces a structural cost trap: most queries are trivially classifiable, yet expensive cloud reasoners process them all, and the bill scales linearly with user count.
arXiv:2607. 19351v1 Announce Type: new Abstract: LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instructions through inter-agent communication to propagate harmful behaviors.
arXiv:2607. 19913v1 Announce Type: new Abstract: Agent safety is moving from content moderation toward preventing operational failures before tool-using agents act.
arXiv:2607. 20255v1 Announce Type: cross Abstract: LLM-driven autonomous agents are reshaping offensive security.
arXiv:2507. 10142v2 Announce Type: replace Abstract: Multi-Agent Reinforcement Learning (MARL) has achieved strong performance in simulated benchmarks, yet real deployments often violate the assumptions under which algorithms are designed and evaluated.
arXiv:2607. 19661v1 Announce Type: cross Abstract: Several edge computing platforms, such as autonomous vehicles and smart sensing devices, need to adapt to dynamic environments in real time by learning from new data in the field.
arXiv:2607. 20005v1 Announce Type: new Abstract: In modern IT operations (IT-Ops), the cost of an incorrect repair often exceeds the cost of no action at all.
arXiv:2607. 20124v1 Announce Type: new Abstract: Tsetlin Machine (TM) is a rule-based machine-learning algorithm comprising collectives of two-action Tsetlin Automata (TAs) that cooperatively form conjunctive logical clauses from Boolean inputs through stochastic feedback.
arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.
arXiv:2512. 05462v2 Announce Type: replace-cross Abstract: Pharmaceutical drug discovery demands machine learning (ML) infrastructure that goes beyond general-purpose Machine Learning Operations (MLOps): inference-time composition of multiple models for multi-parameter optimization (MPO), version management for physics-based models without serialized ML artifacts, enterprise compound library precomputation, and governance structured around scientific organizational units rather than generic access controls.
arXiv:2607. 19809v1 Announce Type: cross Abstract: In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability.
arXiv:2512. 16300v3 Announce Type: replace Abstract: Existing image forgery detection (IFD) methods either exploit low-level, semantics-agnostic artifacts or rely on multimodal large language models (MLLMs) with high-level semantic knowledge.
arXiv:2607. 19359v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents that interact across sessions, yet current memory benchmarks primarily evaluate single-hop recall, leaving multi-hop association largely unmeasured.
arXiv:2607. 19400v1 Announce Type: new Abstract: Pre-trained foundation models (FMs) have begun transforming single-cell genomics, but scaling them raises privacy concerns.
arXiv:2607. 19406v1 Announce Type: new Abstract: Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology.