Large Language Models (LLMs) are widely used in industry but remain prone to hallucinations, limiting their reliability in critical applications. This work addresses hallucination reduction in consumer grievance chatbots built using LLaMA 3.1 8B Instruct, a compact model frequently used in industry. We develop HalluDetect, an LLM-based hallucination detection system that achieves an F1 score of 68.92% outperforming baseline detectors by 22.47%. Benchmarking five hallucination mitigation architectures, we find that out of them, AgentBot minimizes hallucinations to 0.4159 per turn while maintaining the highest token accuracy (96.13%), making it the most effective mitigation strategy. Our findings provide a scalable framework for hallucination mitigation, demonstrating that optimized inference strategies can significantly improve factual accuracy.

Meeting the increasing global demand for food security and sustainable farming requires intelligent crop recommendation systems that operate in real time. Traditional soil analysis techniques are often slow, labor-intensive, and not suitable for on-field decision-making. To address these limitations, we introduce AgroSense, a deep-learning framework that integrates soil image classification and nutrient profiling to produce accurate and contextually relevant crop recommendations. AgroSense comprises two main components: a Soil Classification Module, which leverages ResNet-18, EfficientNet-B0, and Vision Transformer architectures to categorize soil types from images; and a Crop Recommendation Module, which employs a Multi-Layer Perceptron, XGBoost, LightGBM, and TabNet to analyze structured soil data, including nutrient levels, pH, and rainfall. We curated a multimodal dataset of 10,000 paired samples drawn from publicly available Kaggle repositories, approximately 50,000 soil images across seven classes, and 25,000 nutrient profiles for experimental evaluation. The fused model achieves 98.0% accuracy, with a precision of 97.8%, a recall of 97.7%, and an F1-score of 96.75%, while RMSE and MAE drop to 0.32 and 0.27, respectively. Ablation studies underscore the critical role of multimodal coupling, and statistical validation via t-tests and ANOVA confirms the significance of our improvements. AgroSense offers a practical, scalable solution for real-time decision support in precision agriculture and paves the way for future lightweight multimodal AI systems in resource-constrained environments.

-- Speculative decoding has emerged as an effective approach for accelerating autoregressive inference by parallelizing token generation through a draft - then - verify paradigm. However, existing methods rely on static drafting lengths and rigid verification cri teria, limiting their adaptability across varying model uncertainties and input complexities. This paper proposes an information - theoretic framework for speculative decoding based on confidence - modulated drafting. By leveraging entropy and margin - based uncertainty measures over the drafter's output distribution, the proposed method dynamically adjusts the number of speculatively generated tokens at each iteration. This adaptive mechanism reduces rollback frequency, improves resource utilization, an d maintains output fidelity. Additionally, the verification process is modulated using the same confidence signals, enabling more flexible acceptance of drafted tokens without sacrificing generation quality. Experiments on machine translation and summariza tion tasks demonstrate significant speedups over standard speculative decoding while preserving or improving BLEU and ROUGE scores. The proposed approach offers a principled, plug - in method for efficient and robust decoding in large language models under v arying conditions of uncertainty. Keywords -- Speculative Decoding, Autoregressive Models, Confidence Estimation, Adaptive Inference, Entropy - Based Drafting, Sequence Generation, Large Language Models, Large Language Models (LLMs), Information - Theoretic Decoding. The task of sequence generation lies at the heart of numerous applications in natural language processing, including machine translation, text summarization, dialogue generation, and code synthesis. In the overwhelming majority of these applications, autor egressive (AR) decoding remains the dominant paradigm for generating sequences from a probabilistic language model [1 - 2] . Autoregressive models, particularly those based on the Transformer architecture, operate by predicting each token conditioned on the e ntire history of previously generated tokens. This left - to - right decoding strategy, though optimal in terms of likelihood estimation, suffers from a fundamental limitation: the inherently sequential nature of generation prohibits efficient parallelization, severely hindering inference throughput, especially in latency - sensitive deployment scenarios.

Contrastive Decoding (CD) has emerged as an effective inference-time strategy for enhancing open-ended text generation by exploiting the divergence in output probabilities between a large expert language model and a smaller amateur model. Although CD improves coherence and fluency, its dependence on a single amateur restricts its capacity to capture the diverse and multifaceted failure modes of language generation, such as repetition, hallucination, and stylistic drift. This paper proposes Multi-Amateur Contrastive Decoding (MACD), a generalization of the CD framework that employs an ensemble of amateur models to more comprehensively characterize undesirable generation patterns. MACD integrates contrastive signals through both averaging and consensus penalization mechanisms and extends the plausibility constraint to operate effectively in the multi-amateur setting. Furthermore, the framework enables controllable generation by incorporating amateurs with targeted stylistic or content biases. Experimental results across multiple domains, such as news, encyclopedic, and narrative, demonstrate that MACD consistently surpasses conventional decoding methods and the original CD approach in terms of fluency, coherence, diversity, and adaptability, all without requiring additional training or fine-tuning.

Following the pandemic, customers, preference for using e-commerce has accelerated. Since much information is available in multiple reviews (sometimes running in thousands) for a single product, it can create decision paralysis for the buyer. This scenario disempowers the consumer, who cannot be expected to go over so many reviews since its time consuming and can confuse them. Various commercial tools are available, that use a scoring mechanism to arrive at an adjusted score. It can alert the user to potential review manipulations. This paper proposes a framework that fine-tunes a generative pre-trained transformer to understand these reviews better. Furthermore, using "common-sense" to make better decisions. These models have more than 13 billion parameters. To fine-tune the model for our requirement, we use the curie engine from generative pre-trained transformer (GPT3). By using generative models, we are introducing abstractive summarization. Instead of using a simple extractive method of summarizing the reviews. This brings out the true relationship between the reviews and not simply copy-paste. This introduces an element of "common sense" for the user and helps them to quickly make the right decisions. The user is provided the pros and cons of the processed reviews. Thus the user/customer can take their own decisions.

This paper describes the system submitted by Team A to SemEval 2025 Task 11, "Bridging the Gap in Text-Based Emotion Detection. " The task involved identifying the perceived emotion of a speaker from text snippets, with each instance annotated with one of six emotions: joy, sadness, fear, anger, surprise, or disgust. A dataset provided by the task organizers served as the foundation for training and evaluating our models. Among the various approaches explored, the best performance was achieved using multilingual em-beddings combined with a fully connected layer. This paper details the system architecture, discusses experimental results, and highlights the advantages of leveraging multilingual representations for robust emotion detection in text.

"How many tennis balls can fit in a passenger plane?" Neeraj, a young economics graduate from the premier Indian Institute of Technology (IIT), was given 15 minutes to solve this question during his interview rounds at Nation With Namo (NwN), one of the in-house political consultancies of India's governing Bharatiya Janata Party (BJP). He got the calculation right and joined a small team of graduates from India's top universities who were dispatched to the eastern state of Tripura to conduct surveys, collect and analyse voter data for elections that were due in February last year. Their job was to identify who was not voting for the BJP, separate them into demographic cohorts – age, gender, caste, tribe, religion – find a common concern, issue or fear and strategise how to exploit that in the BJP's favour. And they were to do all this while staying under the radar.

The troubling rise of hallucination presents perhaps the most significant impediment to the advancement of responsible AI. In recent times, considerable research has focused on detecting and mitigating hallucination in Large Language Models (LLMs). However, it's worth noting that hallucination is also quite prevalent in Vision-Language models (VLMs). In this paper, we offer a fine-grained discourse on profiling VLM hallucination based on two tasks: i) image captioning, and ii) Visual Question Answering (VQA). We delineate eight fine-grained orientations of visual hallucination: i) Contextual Guessing, ii) Identity Incongruity, iii) Geographical Erratum, iv) Visual Illusion, v) Gender Anomaly, vi) VLM as Classifier, vii) Wrong Reading, and viii) Numeric Discrepancy. We curate Visual HallucInation eLiciTation (VHILT), a publicly available dataset comprising 2,000 samples generated using eight VLMs across two tasks of captioning and VQA along with human annotations for the categories as mentioned earlier.

Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and scalability. This article combines Distributed Artificial Intelligence (DAI) with Zero-touch Provisioning (ZTP) for edge networks. This combination helps to manage network devices seamlessly and intelligently by minimizing human intervention. In addition, several advantages are also highlighted that come with incorporating Distributed AI into ZTP in the context of edge networks. Further, we draw potential research directions to foster novel studies in this field and overcome the current limitations.

Medical image translation is an ill-posed problem. Unlike existing paired unbounded unidirectional translation networks, in this paper, we consider unpaired medical images and provide a strictly bounded network that yields a stable bidirectional translation. We propose a patch-level concatenated cyclic conditional generative adversarial network (pCCGAN) embedded with adaptive dictionary learning. It consists of two cyclically connected CGANs of 47 layers each; where both generators (each of 32 layers) are conditioned with concatenation of alternate unpaired patches from input and target modality images (not ground truth) of the same organ. The key idea is to exploit cross-neighborhood contextual feature information that bounds the translation space and boosts generalization. The generators are further equipped with adaptive dictionaries learned from the contextual patches to reduce possible degradation. Discriminators are 15-layer deep networks that employ minimax function to validate the translated imagery. A combined loss function is formulated with adversarial, non-adversarial, forward-backward cyclic, and identity losses that further minimize the variance of the proposed learning machine. Qualitative, quantitative, and ablation analysis show superior results on real CT and MRI.