Legacy smart HVAC controllers rely on vendor-cloud TLS secured by ECDH and RSA, both broken by Shor's algorithm, and typical 10–15 year lifespans mean today's devices remain in service through the quantum-threat era. Direct on-device post-quantum cryptography is infeasible: an ESP32-S3, representative of capable HVAC hardware, has only 339 KB free heap against the 900 KB ML-KEM-768 requires, and even classical ECDH-P256 keygen (111.93 ms) dwarfs hardware AES-128 (0.032 ms). We propose a non-invasive PQC proxy, requiring no device, firmware, or vendor-cloud changes, performing ML-KEM-768 encapsulation and ML-DSA-65 authentication (NIST FIPS 203/204) with AES-256-GCM session keys via HKDF, implemented with Open Quantum Safe liboqs on a Raspberry Pi 4B gateway. Over 500 runs, the post-quantum handshake (Steps 1–6) completes in 2.48 ms, 0.38 ms slower than classical baseline, with PQC computation around 8% of handshake time at 20 ms simulated round-trip network latency. The gateway sustains 443 sessions/second, 100% success under 32 concurrent connections, extrapolating to 3,546 sessions/second on a 32-core cloud instance. Five side-channel tests, including verified in-place session-key zeroization and a fixed-vs-random TVLA timing analysis, found no exploitable timing leakage or susceptibility to man-in-the-middle attacks. The architecture is vendor-agnostic and becomes unnecessary once vendors adopt NIST PQC natively.

Retrieval-Augmented Generation (RAG) enables large language models to classify network flows and generate human-readable incident reports by retrieving semantically similar historical traffic from a vector knowledge base. However, the retrieval layer introduces vulnerabilities to knowledge poisoning and prompt-injection attacks. We present RAG-IDS, a three-tier multi-agent intrusion detection framework with a retrieval-boundary defense combining soft trust scoring, label-embedding consistency checking (LECC), and prompt sanitization designed to recover classification quality under retrieval-layer attacks. Experiments on CIC-UNSW-NB15 show recovery relative to clean undefended performance ranging from R=1.0 at 1% poisoning to R=0.57 at 30%, with negligible clean-performance overhead. Under prompt injection, multi-document retrieval limits label-flip success to 0.6–2.4%, compared with 35–55% for single-document retrieval. Ablation results show that LECC is the primary contributor to robustness, while soft trust-based demotion outperforms hard filtering. The defended RAG pipeline offers an explainable, attack-resilient foundation for intrusion detection, well suited for hybrid deployment alongside high-throughput classifiers.

We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19. Rather than proposing a new model, we evaluate a fixed scientific RAG pipeline across three corpus scales: 1,034 chunks (1K papers), 5,160 chunks (5K papers), and 15,480 chunks (15K papers). The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation. Across these settings, hybrid retrieval is more robust than either sparse-only or dense-only retrieval in our setting, reaching Recall@10 of 1.000 at 1K and 15K. In contrast, an MS MARCO-trained cross-encoder reranker reduces precision on the scientific corpus, suggesting that domain mismatch can outweigh the benefits of stronger query-passage interaction. Generation faithfulness measured with RAGAS increases with corpus scale in our setup. Retrieval evaluation uses pseudo-relevance labels derived from the hybrid system, so we treat the results as controlled comparative evidence rather than a benchmark claim. We release code, indexes, and evaluation outputs to support replication and follow-up studies.

We present MalariAI, a two-stage decoupled framework for annotation-agnostic cell segmentation and explainable stage classification in dense malaria blood smears. Stage 1 applies distance-transform watershed to recover 75.95% of ground-truth cells without any annotation input; end-to-end binary parasitized AP@0.5 reaches 29.10% (multi-class mAP@0.5: 8.67%). Stage 2 fine-tunes EfficientNet-B0 with Focal Loss to 98.36% crop classification accuracy, with 87.5% and 75.0% on the rare schizont and gametocyte stages. Grad-CAM++ heatmaps provide per-cell spatial evidence for clinical audit, confirmed by energy-in-box analysis (+0.0485, paired p = 1.4×10⁻³³). Evaluated on NIH BBBC041 with zero-shot transfer to MP-IDB.