arXiv subjects

Tahir Ahmad

Publications and source records attributed to Tahir Ahmad.

3 recordsLinked to original sources

arXiv2026

Risk-Constrained Freshness-Aware Semantic Caching for Open-Web Retrieval-Augmented LLMs

Semantic caching reduces the latency and cost of retrieval-augmented generation (RAG) by serving cached answers to semantically similar queries, but most existing methods do not model the time-varying freshness of open-web evidence. We present FreshCache, a three-tier semantic cache that treats cache reuse as a risk-constrained temporal inference problem: before approving a cache hit, FreshCache estimates the probability that the cached result is stale using a fitted exponential decay model enhanced by a learned MLP, and approves reuse only when that probability falls below a per-tier error budget across answers (epsilon = 0.10), URL lists (epsilon = 0.20), and page content (epsilon = 0.35). This allows the system to degrade gracefully as entries age rather than forcing a binary choice between a stale hit and a full pipeline execution. We introduce FreshCache-Bench, a benchmark of 8,072 base queries across five freshness classes with ground truth staleness labels drawn from real web snapshots at 1, 12, 24 hours, and 7 days after a baseline crawl, expanded to 31,201 queries via paraphrase generation. At the 24-hour evaluation window, FreshCache_MLP achieves 97% search API savings at 0.1% hash-based stale error, and an LLM-judge evaluation on 396 confirmed change pairs shows that only 34.3% of detected content changes actually affect answer correctness, placing true answer-affecting stale error at approximately 0.034%. The rule-based FreshCache achieves 98% search savings at 3.3% stale error under a temporal holdout calibration, outperforming SemanticTTL (14.9% stale, 72% saved), vCache (7.2% stale, 47% saved), and SCALM (5.2% stale, 96% saved). Ablations show the temporal risk gate accounts for an 11.6 point reduction in stale error over similarity-only reuse, and the learned MLP reduces stale error a further 3.2 points over the rule-based model.

cs.CL↗

arXiv2024

Specialized Change Detection using Segment Anything

Change detection (CD) is a fundamental task in Earth observation. While most change detection methods detect all changes, there is a growing need for specialized methods targeting specific changes relevant to particular applications while discarding the other changes. For instance, urban management might prioritize detecting the disappearance of buildings due to natural disasters or other reasons. Furthermore, while most supervised change detection methods require large-scale training datasets, in many applications only one or two training examples might be available instead of large datasets. Addressing such needs, we propose a focused CD approach using the Segment Anything Model (SAM), a versatile vision foundation model. Our method leverages a binary mask of the object of interest in pre-change images to detect their disappearance in post-change images. By using SAM's robust segmentation capabilities, we create prompts from the pre-change mask, use those prompts to segment the post-change image, and identify missing objects. This unsupervised approach demonstrated for building disappearance detection, is adaptable to various domains requiring specialized CD. Our contributions include defining a novel CD problem, proposing a method using SAM, and demonstrating its effectiveness. The proposed method also has benefits related to privacy preservation.

eess.IV↗

arXiv2021

Federated Transfer Learning: concept and applications

Development of Artificial Intelligence (AI) is inherently tied to the development of data. However, in most industries data exists in form of isolated islands, with limited scope of sharing between different organizations. This is an hindrance to the further development of AI. Federated learning has emerged as a possible solution to this problem in the last few years without compromising user privacy. Among different variants of the federated learning, noteworthy is federated transfer learning (FTL) that allows knowledge to be transferred across domains that do not have many overlapping features and users. In this work we provide a comprehensive survey of the existing works on this topic. In more details, we study the background of FTL and its different existing applications. We further analyze FTL from privacy and machine learning perspective.

cs.LG↗