-
Characterizing Web Search in The Age of Generative AI
Authors:
Elisabeth Kirsten,
Jost Grosse Perdekamp,
Mihir Upadhyay,
Krishna P. Gummadi,
Muhammad Bilal Zafar
Abstract:
The advent of LLMs has given rise to a new type of web search: Generative search, where LLMs retrieve web pages related to a query and generate a single, coherent text as a response. This output modality stands in stark contrast to traditional web search, where results are returned as a ranked list of independent web pages. In this paper, we ask: Along what dimensions do generative search outputs…
▽ More
The advent of LLMs has given rise to a new type of web search: Generative search, where LLMs retrieve web pages related to a query and generate a single, coherent text as a response. This output modality stands in stark contrast to traditional web search, where results are returned as a ranked list of independent web pages. In this paper, we ask: Along what dimensions do generative search outputs differ from traditional web search? We compare Google, a traditional web search engine, with four generative search engines from two providers (Google and OpenAI) across queries from four domains. Our analysis reveals intriguing differences. Most generative search engines cover a wider range of sources compared to web search. Generative search engines vary in the degree to which they rely on internal knowledge contained within the model parameters v.s. external knowledge retrieved from the web. Generative search engines surface varying sets of concepts, creating new opportunities for enhancing search diversity and serendipity. Our results also highlight the need for revisiting evaluation criteria for web search in the age of Generative AI.
△ Less
Submitted 13 October, 2025;
originally announced October 2025.
-
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Authors:
Gheorghe Comanici,
Eric Bieber,
Mike Schaekermann,
Ice Pasupat,
Noveen Sachdeva,
Inderjit Dhillon,
Marcel Blistein,
Ori Ram,
Dan Zhang,
Evan Rosen,
Luke Marris,
Sam Petulla,
Colin Gaffney,
Asaf Aharoni,
Nathan Lintz,
Tiago Cardal Pais,
Henrik Jacobsson,
Idan Szpektor,
Nan-Jiang Jiang,
Krishna Haridasan,
Ahmed Omran,
Nikunj Saunshi,
Dara Bahri,
Gaurav Mishra,
Eric Chu
, et al. (3410 additional authors not shown)
Abstract:
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal unde…
▽ More
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.
△ Less
Submitted 19 December, 2025; v1 submitted 7 July, 2025;
originally announced July 2025.
-
When Should We Orchestrate Multiple Agents?
Authors:
Umang Bhatt,
Sanyam Kapoor,
Mihir Upadhyay,
Ilia Sucholutsky,
Francesco Quinzan,
Katherine M. Collins,
Adrian Weller,
Andrew Gordon Wilson,
Muhammad Bilal Zafar
Abstract:
Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. We design a framework to orchestrate agents under realistic conditions, such as inference costs or availability constraints. We show theoretically that orchestration is only effective if there are performance or cost diff…
▽ More
Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. We design a framework to orchestrate agents under realistic conditions, such as inference costs or availability constraints. We show theoretically that orchestration is only effective if there are performance or cost differentials between agents. We then empirically demonstrate how orchestration between multiple agents can be helpful for selecting agents in a simulated environment, picking a learning strategy in the infamous Rogers' Paradox from social science, and outsourcing tasks to other agents during a question-answer task in a user study.
△ Less
Submitted 17 March, 2025;
originally announced March 2025.
-
NOVA: NoC-based Vector Unit for Mapping Attention Layers on a CNN Accelerator
Authors:
Mohit Upadhyay,
Rohan Juneja,
Weng-Fai Wong,
Li-Shiuan Peh
Abstract:
Attention mechanisms are becoming increasingly popular, being used in neural network models in multiple domains such as natural language processing (NLP) and vision applications, especially at the edge. However, attention layers are difficult to map onto existing neuro accelerators since they have a much higher density of non-linear operations, which lead to inefficient utilization of today's vect…
▽ More
Attention mechanisms are becoming increasingly popular, being used in neural network models in multiple domains such as natural language processing (NLP) and vision applications, especially at the edge. However, attention layers are difficult to map onto existing neuro accelerators since they have a much higher density of non-linear operations, which lead to inefficient utilization of today's vector units. This work introduces NOVA, a NoC-based Vector Unit that can perform non-linear operations within the NoC of the accelerators, and can be overlaid onto existing neuro accelerators to map attention layers at the edge. Our results show that the NOVA architecture is up to 37.8x more power-efficient than state-of-the-art hardware approximators when running existing attention-based neural networks.
△ Less
Submitted 7 May, 2024;
originally announced May 2024.
-
Underwater Image Enhancement Using Convolutional Neural Network
Authors:
Anushka Yadav,
Mayank Upadhyay,
Ghanapriya Singh
Abstract:
This work proposes a method for underwater image enhancement using the principle of histogram equalization. Since underwater images have a global strong dominant colour, their colourfulness and contrast are often degraded. Before applying the histogram equalisation technique on the image, the image is converted from coloured image to a gray scale image for further operations. Histogram equalizatio…
▽ More
This work proposes a method for underwater image enhancement using the principle of histogram equalization. Since underwater images have a global strong dominant colour, their colourfulness and contrast are often degraded. Before applying the histogram equalisation technique on the image, the image is converted from coloured image to a gray scale image for further operations. Histogram equalization is a technique for adjusting image intensities to enhance contrast. The colours of the image are retained using a convolutional neural network model which is trained by the datasets of underwater images to give better results.
△ Less
Submitted 18 September, 2021;
originally announced September 2021.
-
Blockchain Machine: A Network-Attached Hardware Accelerator for Hyperledger Fabric
Authors:
Haris Javaid,
Ji Yang,
Nathania Santoso,
Mohit Upadhyay,
Sundararajarao Mohan,
Chengchen Hu,
Gordon Brebner
Abstract:
In this paper, we demonstrate how Hyperledger Fabric, one of the most popular permissioned blockchains, can benefit from network-attached acceleration. The scalability and peak performance of Fabric is primarily limited by the bottlenecks present in its block validation/commit phase. We propose Blockchain Machine, a hardware accelerator coupled with a hardware-friendly communication protocol, to a…
▽ More
In this paper, we demonstrate how Hyperledger Fabric, one of the most popular permissioned blockchains, can benefit from network-attached acceleration. The scalability and peak performance of Fabric is primarily limited by the bottlenecks present in its block validation/commit phase. We propose Blockchain Machine, a hardware accelerator coupled with a hardware-friendly communication protocol, to act as the validator peer. It can be adapted to applications and their smart contracts, and is targeted for a server with network-attached FPGA acceleration card. The Blockchain Machine retrieves blocks and their transactions in hardware directly from the network interface, which are then validated through a configurable and efficient block-level and transaction-level pipeline. The validation results are then transferred to the host CPU where non-bottleneck operations are executed. From our implementation integrated with Fabric v1.4 LTS, we observed up to 12x speedup in block validation when compared to software-only validator peer, with commit throughput of up to 68,900 tps. Our work provides an acceleration platform that will foster further research on hardware acceleration of permissioned blockchains.
△ Less
Submitted 20 September, 2021; v1 submitted 14 April, 2021;
originally announced April 2021.