Constant bit-size Transformers are known to be Turing complete, but existing constructions require $Ω(s(n))$ chain-of-thought (CoT) steps per simulated Turing machine (TM) step, leading to impractical reasoning lengths. In this paper, we significantly reduce this efficiency gap by proving that any $(t(n),s(n))$-bounded multi-tape TM can be simulated by a constant bit-size Transformer with an optimal $O(s(n))$-long context window and only $O(s(n)^c)$ CoT steps per TM step, where $c>0$ can be made arbitrarily small by letting the Transformers' head-layer product sufficiently large. In addition, our construction shows that sparse attention with fixed geometric offsets suffices for efficient universal computation. Our proof leverages multi-queue TMs as a bridge. The main technical novelty is a more efficient simulation of multi-tape TMs by synchronous multi-queue TMs, improving both time and space complexity under stricter model assumptions.

We consider a variant of the multiarmed bandit problem where jobs queue for service, and service rates of different servers may be unknown.

Similar to the proof of Proposition 12, given the stability constraint in Eq. Eq. (4), we have C 0, l Theorem 14. Multi-agent load balancing is MPG with the VBF Solid and dashed arrows represent deterministic and non-deterministic procedures respectively. Real-world network applications can be CPU-bound or IO-bound [47, 48]. The simulator allows configuring applications that require multi-stage processes switching between CPU/IO queues (Figure 1b). Two different processing models are used for CPU and IO queues, respectively.

E-14 F Reward Evaluation and Customization F-19 F.1 Load Shifting Penalty ( LS F-19 F.2 Default Reward Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-19 F.3 Customization of Reward Formulations . . . . . . . . . . . . . . . . . . . . . . . Current Workload - The current workload level, which includes both flexible and non-flexible components. The data center modeled is illustrated in Figure 1. The hot air exits the cabinets and returns to the CRAH via the ceiling.

Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.

These profiles are fed back to the LLM, which then revises the code to reduce overhead.

Security operations centers (SOCs) face a persistent challenge: efficiently triaging a high volume of user-reported phishing emails while maintaining robust protection against threats. This paper presents the first randomized controlled trial (RCT) evaluating the impact of a domain-specific AI agent - the Microsoft Security Copilot Phishing Triage Agent - on analyst productivity and accuracy. Our results demonstrate that agent-augmented analysts achieved up to 6.5 times as many true positives per analyst minute and a 77% improvement in verdict accuracy compared to a control group. The agent's queue prioritization and verdict explanations were both significant drivers of efficiency. Behavioral analysis revealed that agent-augmented analysts reallocated their attention, spending 53% more time on malicious emails, and were not prone to rubber-stamping the agent's malicious verdicts. These findings offer actionable insights for SOC leaders considering AI adoption, including the potential for agents to fundamentally change the optimal allocation of SOC resources.

We study how two information feeds, a closed-form Markov estimator of residual sojourn and an online trained actor-critic, affect reneging and jockeying in a dual M/M/1 system. Analytically, for unequal service rates and total-time patience, we show that total wait grows linearly so abandonment is inevitable and the probability of a successful jockey vanishes as the backlog approaches towards infinity. Furthermore, under a mild sub-linear error condition both information models yield the same asymptotic limits (robustness). We empirically validate these limits and quantify finite backlog differences. Our findings show that learned and analytic feeds produce different delays, reneging rates and transient jockeying behavior at practical sizes, but converge to the same asymptotic outcome implied by our theory. The results characterize when value-of-information matters (finite regimes) and when it does not (asymptotics), informing lightweight telemetry and decision-logic design for low-cost, jockeying-aware systems.

However, FHE-based private training is significantly hindered by its substantial execution time.