Syed A. Rizvi

Syed Muhammad Aqdas Rizvi

Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentinel-Bench, an 840-inference empirical framework executing a strict intra-model ablation on Qwen-3.5-9B. By toggling latent reasoning across frozen weights, we isolate the impact of inference-time compute against an adversarial Optimism DAO dataset. Our findings reveal a severe compute-accuracy inversion. The autoregressive baseline (System 1) achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds. Conversely, System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non-Convergence (cognitive collapse) rate. This collapse degraded trial-to-trial consensus stability to 72.6% and imposed a 17x latency overhead, introducing critical vulnerabilities to Governance Extractable Value (GEV) and hardware centralization. While rare (1.5% of adversarial trials), we empirically captured "Reasoning-Induced Sycophancy," where the model generated significantly longer internal monologues (averaging 25,750 characters) to rationalize failing the adversarial trap. We conclude that for edge-native SLMs operating under Byzantine Fault Tolerance (BFT) constraints, System 1 parameterized intuition is structurally and economically superior to System 2 iterative deliberation for decentralized consensus. Code and Dataset: https://github.com/smarizvi110/sentinel-bench

Syed Muhammad Aqdas Rizvi

Standard transport protocols like TCP operate as a blind, FIFO conveyor belt for data, a model that is increasingly suboptimal for latency-sensitive and interactive applications. This paper challenges this model by introducing CATS (Conductor-driven Asymmetric Transport Scheme), a framework that provides TCP with the semantic awareness necessary to prioritize critical content. By centralizing scheduling intelligence in a transport-native "Conductor", CATS significantly improves user-perceived performance by delivering essential data first. This architecture directly confronts a cascade of historical performance workarounds and their limitations, including the high overhead of parallel connections in HTTP/1.1, the transport-layer Head-of-Line blocking in HTTP/2, and the observed implementation heterogeneity of prioritization in HTTP/3 over QUIC. Built upon TCP BBR, our ns-3 implementation demonstrates this principle by reducing the First Contentful Paint by over 78% in a representative webpage download configured as a deliberate worst-case scenario, with no penalty to total page load time compared to the baseline.

Emanuele Zappala, Daniel Levine, Sizhuang He et al.

Deep neural networks, despite their success in numerous applications, often function without established theoretical foundations. In this paper, we bridge this gap by drawing parallels between deep learning and classical numerical analysis. By framing neural networks as operators with fixed points representing desired solutions, we develop a theoretical framework grounded in iterative methods for operator equations. Under defined conditions, we present convergence proofs based on fixed point theory. We demonstrate that popular architectures, such as diffusion models and AlphaFold, inherently employ iterative operator learning. Empirical assessments highlight that performing iterations through network operators improves performance. We also introduce an iterative graph neural network, PIGN, that further demonstrates benefits of iterations. Our work aims to enhance the understanding of deep learning by merging insights from numerical analysis, potentially guiding the design of future networks with clearer theoretical underpinnings and improved performance.

Syed Muhammad Aqdas Rizvi

Mixed-criticality applications, such as satellite terminals, industrial telemetry, embedded systems, tactical, and other constrained links, often multiplex a small, latency-critical message class and bulk traffic over a single commodity transport connection. A single FIFO connection can starve the critical class under load. The obvious alternative, opening parallel connections, costs an additional five-tuple (often blocked by carrier-grade NAT, port budgets, and operator policy) and is not always available; when the critical class is light, two connections can also be bandwidth-fair only in aggregate rather than single-flow fair. We present CATS (Conductor-driven Asymmetric Transport Scheme), a sender-side, receiver-transparent transport-layer priority scheme over TCP: a Conductor assigns each message a priority class and just-in-time sequence numbers, using a credit-based shaper. CATS provides the one combination its alternatives cannot: deterministic non-starvation together with single-flow fairness, plus a provable bounded per-class delay. We then show that, crucially, CATS-over-TCP is not a tail-latency mechanism, and why. Three structural barriers bound in-band priority: the in-order sequence space (head-of-line blocking), the shared congestion window (cross-class coupling), and the per-flow granularity of network QoS (in-band priority is invisible to it). These barriers explain why fair-queuing and even the modern low-latency standard L4S cannot help a single connection, and why two parallel connections reduce the latency tail at the cost of an additional flow. We give CATS-over-QUIC as the principled escape: independent streams with per-stream isolation under aggregate-coupled congestion control self-isolate at the endpoint, attaining the guarantees on one fair flow. An ns-3 evaluation and QUIC proof-of-concept support the findings.

Syed Muhammad Aqdas Rizvi

This literature review surveys the advancements of keyword spotting (KWS) technologies, specifically focusing on Urdu, Pakistan's low-resource language (LRL), which has complex phonetics. Despite the global strides in speech technology, Urdu presents unique challenges requiring more tailored solutions. The review traces the evolution from foundational Gaussian Mixture Models to sophisticated neural architectures like deep neural networks and transformers, highlighting significant milestones such as integrating multi-task learning and self-supervised approaches that leverage unlabeled data. It examines emerging technologies' role in enhancing KWS systems' performance within multilingual and resource-constrained settings, emphasizing the need for innovations that cater to languages like Urdu. Thus, this review underscores the need for context-specific research addressing the inherent complexities of Urdu and similar URLs and the means of regions communicating through such languages for a more inclusive approach to speech technology.