arXiv subjects

Vinay

Publications and source records attributed to Vinay.

4 recordsLinked to original sources

arXiv2026

FAME: QoS-aware Async Service Orchestration for Agentic Workflows

Agentic workflows built on Large Language Models (LLMs) increasingly rely on external tools, with Model Context Protocol (MCP) services emerging as a common interface for tool discovery and invocation. These workflows are stateful, bursty and can block for minutes to hours while external tools execute. Always-on virtual machines and managed agent runtimes simplify deployment, but can over-provision capacity, bill idle wait time or encounter execution limits for long-running tool calls. We present FAME, a service-oriented middleware that optimizes MCP-enabled agentic workflows through modular service orchestration, explicit state management, MCP service input/output reduction and timeout-safe async invocation for long-running external services. FAME realizes these composite services using FaaS-based agent roles, persists workflow state through agent memory injection, and reduces MCP service overhead using S3 handle passing and tool-output caching. For long-running external MCP tools, FAME checkpoints agent and orchestrator state, suspends execution and resumes from callbacks without billing idle waits. Across 26 short-running service tasks over 10 MCP servers, FAME improves service-level QoS by reducing infrastructure cost by 8-12x relative to virtual machines and 43-106x relative to managed runtimes, while reducing latency by up to 17x, input tokens by up to 88% and total cost by up to 66%. On two long-running MLZero workloads from MLE-Bench, FAME completes all iterations while synchronous AWS Step Functions time out, and is 3.64-4.24x cheaper than AWS Durable Functions at the agent level and 2.67-8.23x cheaper at the orchestrator level.

cs.DC↗

arXiv2024

IntentionNet: Map-Lite Visual Navigation at the Kilometre Scale

This work explores the challenges of creating a scalable and robust robot navigation system that can traverse both indoor and outdoor environments to reach distant goals. We propose a navigation system architecture called IntentionNet that employs a monolithic neural network as the low-level planner/controller, and uses a general interface that we call intentions to steer the controller. The paper proposes two types of intentions, Local Path and Environment (LPE) and Discretised Local Move (DLM), and shows that DLM is robust to significant metric positioning and mapping errors. The paper also presents Kilo-IntentionNet, an instance of the IntentionNet system using the DLM intention that is deployed on a Boston Dynamics Spot robot, and which successfully navigates through complex indoor and outdoor environments over distances of up to a kilometre with only noisy odometry.

cs.RO↗

arXiv2021

Deep Visual Navigation under Partial Observability

How can a robot navigate successfully in rich and diverse environments, indoors or outdoors, along office corridors or trails on the grassland, on the flat ground or the staircase? To this end, this work aims to address three challenges: (i) complex visual observations, (ii) partial observability of local visual sensing, and (iii) multimodal robot behaviors conditioned on both the local environment and the global navigation objective. We propose to train a neural network (NN) controller for local navigation via imitation learning. To tackle complex visual observations, we extract multi-scale spatial representations through CNNs. To tackle partial observability, we aggregate multi-scale spatial information over time and encode it in LSTMs. To learn multimodal behaviors, we use a separate memory module for each behavior mode. Importantly, we integrate the multiple neural network modules into a unified controller that achieves robust performance for visual navigation in complex, partially observable environments. We implemented the controller on the quadrupedal Spot robot and evaluated it on three challenging tasks: adversarial pedestrian avoidance, blind-spot obstacle avoidance, and elevator riding. The experiments show that the proposed NN architecture significantly improves navigation performance.

cs.RO↗

arXiv2020

Roommate Compatibility Detection Through Machine Learning Techniques

Our objective is to develop an artificially intelligent system which aims at checking the compatibility between the roommates of same or different sex sharing a common area of residence. There are a few key factors determining one's compatibility with the other person. Interpersonal behaviour , situational awareness, communication skills. Here we are trying to build a system that evaluates user on these key factors not via pen paper test but through a highly engaging set of questions and answers. Hence using these scores as an input to our machine learning algorithm which is based on previous trends to come up with percentage probability of user being compatible with another user. With the growing population there is always a challenge for organisation and educational institutions to make the students and their employees more and more productive and in such cases a person's social environment comes into play. A person may be a genius but as long as he is not able to work well with his peers there will always be a chance of more productive performance. It is a well-established fact that human are and have always been a social animal and this has helped in creating communities of like-minded people. Many times, even when there are a large no of people employed to do a particular task the result may not be as expected as people may not compatible in working with one another. This at the end creates performance gaps, hinders organisation success and in many cases loss of precious resources. Our intent is not to remove the non-compatible people from the picture but to find out the perfect compatible match for the person elsewhere that will not only save the resources will also enable effective use of resources. Through the use of various machine learning classification techniques, we intent to do this.

cs.SI↗