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Syed Muhammad Aqdas Rizvi

Publications and source records attributed to Syed Muhammad Aqdas Rizvi.

4 recordsLinked to original sources

Single-Connection Mixed-Criticality Transport with CATS: Bounded Guarantees, Three Structural Limits, and a QUIC Escape

Satellite terminals, industrial telemetry-and-control, embedded systems, tactical networks often multiplex a small, latency-critical message class with bulk traffic over one connection. A single FIFO connection can starve the critical class. Parallel connections cost another five-tuple (often blocked by carrier-grade NAT, port budgets, and operator policy), are not always available, and when the critical class is light are bandwidth-fair only in aggregate. We present CATS (Conductor-driven Asymmetric Transport Scheme), a sender-side, receiver-transparent TCP priority scheme in which a Conductor assigns each message a priority class and just-in-time sequence numbers under a credit-based shaper. CATS provides deterministic non-starvation, single-flow fairness, and a provable bounded per-class delay. We then show CATS-over-TCP is not a tail-latency mechanism. Three structural barriers bound single-connection in-band priority: the in-order sequence space, the shared congestion window, and per-flow network QoS. These barriers explain why fair queuing and L4S fail to help a single connection and can worsen it by granting a parallel-connection competitor per-flow isolation while leaving in-band priority unserved, and why two parallel connections reduce tail latency by 4-8 times at the cost of another flow. We give CATS-over-QUIC as the principled escape: independent sequence spaces with per-stream isolation under aggregate-coupled congestion control attain the guarantees on one fair flow. A proof of concept shows unreliable datagrams achieve a flat tail (approx. 27 ms p99.9 vs. approx. 307 ms over TCP) by never retransmitting lost messages, while reliable streams still require an aggregate-coupled congestion controller; we frame this standards gap as a research program. An ns-3 evaluation across loss, contention, and handover regimes supports every claim, including the negative ones.

cs.NI↗

A Case for CATS: A Conductor-driven Asymmetric Transport Scheme for Semantic Prioritization

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.

cs.NI↗

The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus

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

cs.AI↗

A Literature Review of Keyword Spotting Technologies for Urdu

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.

eess.AS↗