Large Language Models (LLMs) are prone to hallucinations, producing fluent but factually incorrect statements. Recent multi-agent debate methods improve hallucination detection by jointly improving reasoning and decision-making. However, existing approaches either collaborate which amplifies shared overconfidence, or adopt adversarial preset stances, that can inject incorrect information complicating decision making. To address this, we propose Contradictory Statement Multi-Agent Debate (CSMAD), a multi-agent framework that creates structured disagreement by generating a contradictory claim for each input claim. CSMAD asks independent agents to evaluate the claim and the contradictory claim, which encourages different lines of reasoning without assigning preset stances. When the outcome is non-discriminative; both the contradictory statements are either accepted or rejected; the agents exchange rationales and update their judgments after considering opposing evidence. A final judge then decides the truth of the original claim, using both arguments as context. To make contradictory statement generation reliable, we add a Natural Language Inference (NLI) based verifier that checks whether the generated statement actually contradicts the original claim; if it does not, the system falls back to an explicit negation-based contradiction. Across public benchmarks for question answering and scientific claim verification, as well as a proprietary e-commerce claims dataset, we show that CSMAD consistently outperforms the strongest baseline for both large (Claude-3.5 Sonnet) and medium-sized (Qwen3-8B) language models, improving F1 by +2.3 and +4.1 points, respectively, while reducing LLM token cost by 28%.

Product recommendations incentivize customers to make multi-unit purchases by surfacing relevant products, leading to lower cost per unit for e-commerce stores and lower prices for their customers. However, the humongous scale of products, implicit co-purchase asymmetry and variation in co-purchase behavior across different categories, are orthogonal problems to solve. To address these problems, we propose MERLIN (Multimodal & Multilingual Embedding for Recommendations at Large-scale via Item associations), a Graph Neural Network that generates product recommendations from a heterogeneous and directed product graph. We mine category associations to remove noisy product co-purchase associations, leading to higher quality recommendations. Leveraging product co-view relationships, we finetune SentenceBERT model for textual representation, and train a self-supervised knowledge distillation model to learn visual representation, which allows us to learn product representations which are multi-lingual and multi-modal in nature. We selectively align node embeddings leveraging co-viewed products. MERLIN model can handle node asymmetry by learning dual embeddings for each product, and can generate recommendations for cold-start products by employing catalog metadata such as title, category and image. Extensive offline experiments on internal and external datasets show that MERLIN model outperforms state-of-the-art baselines for node recommendation and link prediction task. We conduct ablations to quantify the impact of our model components and choices. Further, MERLIN model delivers significant improvement in sales measured through an A/B experiment.

Iris Recognition (IR) is one of the most effective biometric authentication techniques available today. It is an obvious candidate for authentication on most head mounted devices. Networks for IR trained on datasets collected by existing hardware may not generalize to newer hardware due to domain gap induced by changes in sensor configurations noise, resolution, camera placements etc. Coupled with the challenge of acquiring high quality iris samples, domain adaptation in IR is an important topic that remains poorly studied in literature. We introduce the problem of supervised Domain Adaptation (DA) for IR, where we assume access to abundant source training data, but extremely limited labeled target training data. Additionally, we propose a novel mixup strategy called LAMDA that mitigates the domain gap between source and target IR datasets by augmenting samples from these datasets. Unlike existing mixup techniques, LAMDA does not require performing label-mixup, and outperforms existing DA techniques in almost all of our problem settings, irrespective of the availability of target training data, and across various image quality degradations. As a way to facilitate research, we also introduce new dataset splits for the problem.

The Internet of Things (IoT) connects a huge number of IoT devices, including sensors, actuators, computing nodes, etc. Those IoT devices communicate with each other by using different communication techniques. Generally, LTE, 5G, and WiFi are involved in IoT to support communication. However, it needs considerable communication infrastructures to support wireless communication. An obvious issue is how to create a communication network in some regions that have less communication support. Wireless Sensor Network (WSN) is a kind of wireless ad hoc network, which organizes a huge number of wireless sensors to create a self-organized wireless network. Therefore, the WSN is a potential approach to providing communication in those regions. Since WSN is highly dynamic, how to create the wireless network to provide optimal coverage rate is still open. In this article, we propose a dynamic wireless sensor deployment scenarios that provide optimal coverage rate in a certain region. Our model proposes a coverage rate based on possible disaster scenarios for communication between Base Stations and UAVs. We optimize the Particle Swarm Optimization (PSO) algorithm and find the maximum coverage rate.

A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency efficient. However, existing eye-based frameworks a) perform authentication and Presentation Attack Detection (PAD) independently and b) involve significant pre-processing steps to extract the iris region. Here, we introduce a joint framework for EA and PAD using periocular images. While a deep Multitask Learning (MTL) network can perform both the tasks, MTL suffers from the forgetting effect since the training datasets for EA and PAD are disjoint. To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting. To further improve the EA performance, we introduce a novel approach called EyePAD++ that includes training an MTL network on both EA and PAD data, while distilling the ‘versatility’ of the EyePAD network through an additional distillation step. Our proposed methods outperform the SOTA in PAD and obtain near-SOTA performance in eye-to-eye verification, without any pre-processing. We also demonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verification with PAD across network backbones and image quality.

We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being sufficiently grounded in vision to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person real-time chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and consists of$\sim$1.2M dialog question-answer pairs from 10-round, human-human dialogs grounded in$\sim$120k images from the COCO dataset. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders—Late Fusion, Hierarchical Recurrent Encoder and Memory Network (optionally with attention over image features)—and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank and recall$@k$of human response. We quantify the gap between machine and human performance on the Visual Dialog task via human studies. Putting it all together, we demonstrate the first ‘visual chatbot’! Our dataset, code, pretrained models and visual chatbot are available onhttps://visualdialog.org.

We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being grounded in vision enough to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial contains 1 dialog (10 question-answer pairs) on ~140k images from the COCO dataset, with a total of ~1.4M dialog question-answer pairs. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders (Late Fusion, Hierarchical Recurrent Encoder and Memory Network) and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank of human response. We quantify gap between machine and human performance on the Visual Dialog task via human studies. Our dataset, code, and trained models will be released publicly at https://visualdialog.org. Putting it all together, we demonstrate the first visual chatbot!.