We present SENTINELEDGE, an open-source streaming detector for English-language scam phone calls that scores speech sentence-by- sentence and raises alerts while a conversa- tion is in progress, protecting callers rather than explaining losses afterward. It targets multi-minute scripted fraud calls, where sys- tems are closed and un-auditable and aca- demic alternatives use neural backbones un- suited to on-device budgets. SENTINELEDGE uses a streaming framework with per-sentence scoring, exponential moving average smooth- ing, and threshold-triggered alerts. It com- pares classifier heads using hand-crafted fea- tures, TF–IDF, SVM, gradient-boosted trees, and fine-tuned transformer on identical inputs and robustness conditions, revealing storage– accuracy trade-offs. We report bootstrap con- fidence intervals, stress-test ASR-style noise, adversarial scam phrasings and retraining, and zero-shot transfer to SMS and phishing URLs. Code, models, evaluation harness, and demon- stration are released under the MIT license

Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most systems act as keyword filters, failing to handle skill synonyms and nonlinear careers, resulting in missed candidates and opaque match scores. We introduce JobMatchAI, a production-ready system integrating Transformer embeddings, skill knowledge graphs, and interpretable reranking. Our system optimizes utility across skill fit, experience, location, salary, and company preferences, providing factor-wise explanations through resume-driven search workflows. We release JobSearch-XS benchmark and a hybrid retrieval stack combining BM25, knowledge graph and semantic components to evaluate skill generalization. We assess system performance on JobSearch-XS across retrieval tasks, provide a demo video, a hosted website and installable package.

Temporal Table Reasoning poses a significant challenge for Large Language Models (LLMs), requiring effective reasoning to extract relevant insights. Despite existence of multiple prompting methods, their impact on table reasoning remains largely unexplored. Furthermore, model performance varies drastically across different table and context structures, making it difficult to determine an optimal approach. This work investigates multiple prompting technique on diverse table types to determine that performance depends on factors such as entity type, table structure, requirement of additional context and question complexity, with “NO” single method consistently outperforming others. To address this, we introduce SEAR, an adaptive prompting framework inspired by human reasoning that dynamically adjusts to context and integrates structured reasoning. SEAR_Unified, its cost-efficient variant. We also demonstrate that optional table refactoring (preprocessing) enhances both approaches when tables lack structural consistency. Our results demonstrate that SEAR prompts achieve superior performance across all table types compared to baseline prompting techniques