Production Retrieval-Augmented Generation (RAG) systems serve repeated queries over evolving document corpora under user-specific access constraints. Standard pipelines perform full retrieval and generation for every request, wasting computation on recurring queries. Naive caching reduces latency but introduces safety failures: stale evidence from updated documents, unauthorized content crossing access boundaries, and superseded passages no longer reflecting source truth. These violations are invisible on academic benchmarks yet unacceptable in enterprise deployment. We introduce SSC (Safe and Sufficient Caching), a validated evidence reuse framework for production RAG. SSC caches evidence chunks (not generated answers) with perchunk provenance metadata, and enforces hard validity constraints before reuse: access-control gating ensures user authorization, and freshness verification confirms source-document currency. Only evidence passing both checks enters a utility-based ranking stage balancing relevance, source trust, historical utility, and serving cost. This separation of safety from efficiency is the central contribution. On WebQuestions and HotpotQA, SSC maintains full quality parity with unrestricted retrieval baselines, confirming zero degradation from evidence reuse. On a proprietary InternalQA workload with role based access control and document versioning, it reduces end-to-end latency by 22% and retrieval cost by 34% while achieving zero invalid reuse, compared to 3.4-9.8% violation rates for standard caching and 0.3-8.2% for TTL-based invalidation. The method is training-free, retriever-agnostic, and deploys as a thin validation layer in existing RAG pipelines.

Seller-authored product titles often contradict their own bullet points (a title reading “128GB” against a bullet claiming “512GB”), which misleads shoppers and drives returns. At catalog scale, detection is precision-binding: even a small false-positive rate flags a huge absolute volume of good listings. We deploy a two-stage cascade on one national storefront of a large e-commerce marketplace: a compact 4B detector flags candidates and a strong reasoning model (DeepSeek R1) verifies them, reaching >99% precision while scoring all 36M listings. Verification is the expensive stage, so our goal is to fold it into the cheap model and run on the 4B detector alone; until then, every gain in the detector’s recall catches more defects per verifier call and lifts coverage. We improve that first stage with Contrastive Token Weighting (CTW), a per-token credit-assignment method for GRPO. GRPO spreads one sequence-level advantage uniformly across a completion, even though only a few pivotal tokens decide correctness; CTW instead reads credit from a signal the reward already computes, the split of each sampled group into correct and incorrect completions, and upweights positions where a frozen scorer tells the two apart. The scorer can be a separate stronger teacher (CTW-T) or, when none is available, a frozen copy of the student itself (CTW-S, teacher-free); the teacher-free variant is the one we deploy, and it lifts the detector to 85.9 F1 (+2.2 over SFT, with the gain concentrated in recall, +3.4). A controlled math study shows the reward-induced partition, not the reweighting itself, is what drives the gain.

Multi-Agent Debate (MAD) frameworks improve factual reliability in large language models (LLMs) by allowing agents to critiqueand refine one another’s reasoning. Yet, existing MAD systems are computationally expensive and prone to degradation under pro-longed debates due to redundant exchanges and unstable judging. We propose a lightweight,industry-deployable alternative that unifies Selective Debate Initiation (SDI) with Evidence Weighted Self-Consistency (EWSC) for adaptive, debate-on-demand reasoning. SDI dynamically predicts when debate is necessary by detecting confidence-likelihood misalignment and semantic disagreement, skippingwell-aligned queries to conserve computation. EWSC replaces a single-judge verdict with a variance-aware, evidence-weighted aggregation across paraphrased evaluations, yielding more stable factual judgments. Combined, SDI and EWSC reduce token consumption by nearly 50% while improving both accuracy and calibration. Evaluated on BoolQ, CosmosQA, and an internal QnA benchmark, our framework achieves higher factual robustness and efficiency, demonstrating that scalable, epistemically reliable multi-agent reasoning is practical for real-world LLM deployments.

Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise,where irrelevant or redundant passages dominate. Current methods-fixed top-k retrieval, cross-encoder reranking, or policy based iteration-depend on static heuristics orcostly reinforcement learning, failing to assess evidence sufficiency, detect subtle mismatches, or reduce redundancy, leading to hallucinations and poor grounding. We introduce ReflectiveRAG, a lightweight yet reasoning-driven architecture that enhances factual grounding through two complementary mechanisms: Self-Reflective Retrieval (SRR) and Contrastive Noise Removal (NR). SRR employs small language model as a decision controller that iteratively evaluates evidence sufficiency, enabling adaptive query reformulation withoutfixed schedules or policy training. NR further refines retrieved content via embedding-based contrastive filtering, enforcing semanticsparsity and removing redundant or tangential passages. Evaluated on WebQuestions, HotpotQA (distractor setting) and InternalQAwith 50M Common Crawl distractors, ReflectiveRAG achieves substantial gains over strong baselines-including DeepRAG-improving EMby +2.7 pp and F1 by +2.5 pp, while reducing evidence redundancy by 30.88% with only 18 ms additional latency. Ablation studies con-firm that SRR and NR jointly drive both factual accuracy and efficiency, validating our central claim that retrieval reasoning and contrastivefiltering can outperform large-scale policy optimization in RAG.

Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise, where relevant evidence is sparse and easily pruned by static retrieval decisions. Existing approaches fixed top-k retrieval, hierarchical chunking, cross-encoder reranking, or policy-based iterative control- either rely on rigid heuristics or incur substantial computational overhead, and often fail to recover context-dependent evidence without introducing redundancy or latency. We introduce NEST (Nested Evidence Survival for Retrieval), a lightweight, training-free RAG framework that improves factual grounding by explicitly separating recall amplification from precision selection. NEST first maximizes recall through Nested Evidence Survival, evaluating candidates under nested retrieval contexts to rescue evidence that would otherwise be pruned by static chunking. It then applies a survival-consistent Mean Reciprocal Rank (MRR) selection mechanism to retain evidence that remains salient across retrieval scopes, removing redundancy without harming recall. Evaluated on WebQuestions, HotpotQA (distractor setting), and a proprietary InternalQA benchmark with 50M Common Crawl distractors, NEST consistently outperforms strong adaptive RAG baselines, including DeepRAG, improving EM by up to +2.4 pp and F1 by +2.1 pp, while increasing retrieval recall by +6.8 pp. These gains are achieved with only 12–18 ms additional latency. Ablation studies confirm that Nested Evidence Survival drives recall improvements, while MRR-based selection converts these gains into precision, demonstrating that recall-first retrieval with principled pruning can outperform iterative control and model scaling in retrieval-augmented generation.

Question Answering (QA) and Visual Question Answering (VQA) are well-studied problems in the language and vision domain. One challenging scenario involves multiple sources of information, each of a different modality, where the answer to the question may exist in one or more sources. This scenario contains richer information but is highly complex to handle. In this work, we formulate a novel question-answer generation (QAG) framework in an environment containing multi-source, multimodal information. The answer may belong to any or all sources; therefore, selecting the most prominent answer source or an optimal combination of all sources for a given question is challenging. To address this issue, we propose a question-guided attention mechanism that learns attention across multiple sources and decodes this information for robust and unbiased answer generation. To learn attention within each source, we introduce an explicit alignment between questions and various information sources, which facilitates identifying the most pertinent parts of the source information relative to the question. Scalability in handling diverse questions poses a challenge. We address this by extending our model to a sparse mixture-of-experts (sparse-MoE) framework, enabling it to handle thousands of question types. Experiments on T5 and Flan-T5 using three datasets demonstrate the model’s efficacy, supported by ablation studies.

Language localization is the adaptation of written content to different linguistic and cultural contexts. Ability to localize written content is crucial for global businesses to provide consistent and reliable customer experience across diverse markets. Traditional methods have approached localization as an application of machine translation (MT), but localization requires more than linguistic conversion – content needs to align with the target audience’s cultural norms, linguistic nuances, and technical requirements. This difference is prominent for long-form text, where multiple facts are present in a creative choice of language. We propose a novel prompt approach for Large Languages Models (LLMs), called Break-Ideate-Generate (BrIdGe), for language localization. BrIdGe ‘breaks’ the source content into granular facts, ‘ideates’ an action plan for content creation in the target language by organizing the granular facts, and finally executes the plan to ‘generate’ localized content. This approach emulates the cognitive processes humans employ in writing that begin with identifying important points, followed by brainstorming on how to structure and organize the output. We evaluated the BrIdGe methodology from multiple perspectives, including impact of BrIdGe prompt on different LLMs and performance comparisons with traditional MT models and direct translation through LLMs on public benchmark and proprietary e-commerce datasets. Through human and LLM-based automated evaluations across content in multiple languages, we demonstrate effectiveness of BrIdGe in generating fluent localized content while preserving factual consistency between source and target languages.

E-commerce websites (e.g. Amazon, Alibaba) have a plethora of structured and unstructured information (text and images) present on the product pages. Sellers often don’t label or mislabel values of the attributes (e.g. color, size etc.) for their products. Automatically identifying these attribute values from an eCommerce product page that contains both text and images is a challenging task, especially when the attribute value is not explicitly mentioned in the catalog. In this paper, we present a scalable solution for this problem where we pose attribute extraction problem as a question-answering task, which we solve using MXT, that consists of three key components: (i) MAG (Multimodal Adaptation Gate), (ii) Xception network, and (iii) T5 encoder-decoder. Our system consists of a generative model that generates attribute-values for a given product by using both textual and visual characteristics (e.g. images) of the product. We show that our system is capable of handling zero-shot attribute prediction (when attribute value is not seen in training data) and value-absent prediction (when attribute value is not mentioned in the text) which are missing in traditional classification-based and NER-based models respectively. We have trained our models using distant supervision, removing dependency on human labeling, thus making them practical for real-world applications. With this framework, we are able to train a single model for 1000s of (product-type, attribute) pairs, thus reducing the overhead of training and maintaining separate models. Extensive experiments on two real world datasets (total 57 attributes) show that our framework improves the absolute recall@90P by 10.16% and 6.9 from the existing state of the art models. In a popular e-commerce store, we have productionized our models that cater to 12K (product-type, attribute) pairs, and have extracted 150MM attribute values.

Product aspect extraction from reviews is a critical task for e-commerce services to understand customer preferences and pain points. While aspect phrases extraction and sentiment analysis have received a lot of attention, clustering of aspect phrases and assigning human readable names to clusters in e-commerce reviews is an extremely important and challenging problem due to the scale of the reviews that makes human review infeasible. In this paper, we propose fully automated methods for clustering aspect words and generating human readable names for the clusters without any manually labeled data. We train transformer based sentence embeddings that are aware of unique e-commerce language characteristics (eg. incomplete sentences, spelling and grammar errors, vernacular etc.). We also train transformer based sequence to sequence models to generate human readable aspect names from clusters. Both the models are trained using heuristic based distant supervision. Additionally, the models are used to improve each other. Extensive empirical testing showed that the clustering model improves the Silhouette Score by 64% when compared to the state-of-the-art baseline and the aspect naming model achieves a high ROUGE-L score of 0.79.