The paper introduces a multi-objective reinforcement learning framework for planning AI data center build-outs, treating the long-term infrastructure decisions as a tiered Markov decision process. It proposes a preference-conditioned agent that can generate policies balancing cost, carbon, water, and reliability, and validates the approach on real site data across different commitment regimes. The study also presents a lightweight counterfactual rollout model to assess long-horizon dynamics and releases the associated environment, agents, and evaluation tools.
By Satyapragnya Kar, Rajarshi Das, PR Panigrahi
arXiv:2606. 17454v1 Announce Type: new Abstract: AI agent performance is not just a modeling problem, it is fundamentally a systems problem.
By Gaurav Gupta, Vatshank Chaturvedi, Jun Huan, Anoop Deoras
arXiv:2607. 00871v1 Announce Type: new Abstract: Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated.
By Biswa Sengupta
arXiv:2610.15093v1 Announce Type: cross
Abstract: AI coding agents operate in iterative loops: they modify code, run tools, inspect the results, and decide what to do next. We call the design of this...
By Pramod Kumar, Raj Kumar Pandey
arXiv:2607. 10569v1 Announce Type: cross Abstract: Modern coding agents expose multiple tool surfaces -- IDE primitives, bash, and Model Context Protocol (MCP) code-execution -- and the field has shipped three contradictory claims about which one matters.
By Hong Yang, Qi Yu, Travis Desell
Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger replayed contexts -- so tokens per task grow faster than task value. Falling per-token prices mask the pattern; total spend rises anyway.