Seyed Moein Ayyoubzadeh

Reinforcement learning is increasingly deployed in domains where reward feedback is sparse, delayed, and entangled with long-horizon constraints, making reliable credit assignment difficult. A central development in recent work is the insertion of large language model modules directly into the reinforcement learning loop, not as peripheral interfaces but as components that alter trajectory generation and supervision. In these systems, language modules provide planning priors, structured reward shaping, process verification, synthetic world traces, and tool-memory context that reconfigure optimization at trajectory level. This survey develops a mechanism-first synthesis of that shift. We formalize intervention operators for planning, reward and verifier channels, world construction, and tool-memory mediation; analyze how each operator changes update targets, bias pathways, and stability conditions; and organize the field into a unified taxonomy grounded in optimization effects rather than model branding. We then examine evaluation practice across embodied control, web interaction, games, continuous control, and multi-agent settings, highlighting reproducibility gaps and protocol confounds. Finally, we synthesize recurring failure modes and propose a concrete research agenda on calibration, module authority arbitration, uncertainty-aware simulation, and benchmark design. The resulting perspective positions LLM-in-the-loop reinforcement learning as a systems and optimization discipline centered on trustworthy credit assignment under heterogeneous supervision.

Biomedical AI is increasingly shaped by policy-bound, multi-step clinical workflows and non-stationary, multimodal data and tools. In this setting, the field is moving beyond static predictors toward agentic systems, enabled by foundation models that maintain task-relevant state and operate through a closed perceive$\rightarrow$plan$\rightarrow$act$\rightarrow$observe loop under explicit oversight. However, the field lacks a coherent account that defines biomedical agency, relates foundational model capabilities to agent behaviors, and traces the pathway from pretraining to domain-adapted, deployable systems. This survey offers such an account by synthesizing operational boundaries of agency and framing six core components (memory, planning, reflection, tool use, dialogue, and collaboration) as foundational agent-enabling capabilities that drive the transition from isolated pipelines to fully realized agents. This survey situates these perspectives along the model-building pathway, from pretraining through post-training adaptation to the orchestration mechanisms that operationalize agents. We highlight safety and governance considerations for high-stakes settings, emphasizing the fidelity of process and reasoning, uncertainty and abstention, privacy and provenance, and human oversight. Taken together, this survey provides a structured synthesis of how recent work connects foundation models to governable biomedical agentic systems and distills the recurring challenges and directions identified in the literature for reliable, accountable deployment.

This research presents a computational model designed to analyze influence dynamics between classical Persian poets in producing a multi-dimensional similarity network. Using a hand-curated dataset derived from Ganjoor’s corpus, we integrate semantic, lexical, stylistic, thematic, and metrical features generating weighted similarity matrices subsequently integrated into an influence graph portraying relationships between poets. A network analysis identifies both famous poets such as Hafez, Saʿdi, and Rumi holding central positions alongside minor poets such as Khāqānī and Salmān Sāvajī whose brokerage roles become structurally informative. By using community detection via the Louvain algorithm, we further connect discovered groups to known literary schools, notably Sabk-e Khorasani, Sabk-e Hindi, and the Bazgasht-e Adabi move ment. The findings exemplify how data-driven methods can differentiate between canonical height and structural influence and hence yield new insights into the evolution of Persian poetic lineages. By integrating computational linguistics and literary analysis, our proposed outline presents a scalable and interpretable instrument suited to both historical literary studies and future digital humanities work. Throughout, we use multi-dimensional similarity as a proxy for potential influence. Centrality therefore reflects structural integration within the network rather than cultural magnitude or causal primacy; establishing directionality requires temporal and historical evidence.