arXiv subjects

Ashish Goswami

Publications and source records attributed to Ashish Goswami.

4 recordsLinked to original sources

arXiv2026

Closed-Loop L4S-as-a-Service in 5G-Advanced: NEF-PCF Control with NWDAF-Driven Assurance

Ultra-low latency services in 5G-Advanced demand deterministic delay and high-fidelity congestion signaling beyond peak throughput. While the Low Latency, Low Loss, Scalable Throughput (L4S) architecture enables sub-millisecond queuing through ECN-based feedback and Dual-Queue Coupled AQM, its integration within the 5G Core (5GC) remains functionally siloed. Current 3GPP Release 18/19 specifications provide mechanisms for L4S enablement, but they do not define a unified closed-loop framework that links application intent to verified service outcomes. To address this gap, we propose Closed-Loop L4S-as-a-Service (C-L4SaaS), an architectural framework that orchestrates the Network Exposure Function (NEF), Policy Control Function (PCF), and Network Data Analytics Function (NWDAF) for automated latency assurance. The framework translates high-level intent into enforceable PCC rules and uses NWDAF-driven compliance analytics, derived from User Plane Function (UPF) measurements, to trigger bounded policy adaptations. We model this interaction as a discrete-time feedback system and derive stability conditions and signaling overhead bounds to guide parameter selection under volatile wireless conditions. The proposed core-driven orchestration provides a standards-aligned path to expose, assure, and govern managed low-latency services in 5G-Advanced ecosystems.

cs.NI↗

arXiv2025

GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis

Text-to-image (T2I) generation has seen significant progress with diffusion models, enabling generation of photo-realistic images from text prompts. Despite this progress, existing methods still face challenges in following complex text prompts, especially those requiring compositional and multi-step reasoning. Given such complex instructions, SOTA models often make mistakes in faithfully modeling object attributes, and relationships among them. In this work, we present an alternate paradigm for T2I synthesis, decomposing the task of complex multi-step generation into three steps, (a) Generate: we first generate an image using existing diffusion models (b) Plan: we make use of Multi-Modal LLMs (MLLMs) to identify the mistakes in the generated image expressed in terms of individual objects and their properties, and produce a sequence of corrective steps required in the form of an edit-plan. (c) Edit: we make use of an existing text-guided image editing models to sequentially execute our edit-plan over the generated image to get the desired image which is faithful to the original instruction. Our approach derives its strength from the fact that it is modular in nature, is training free, and can be applied over any combination of image generation and editing models. As an added contribution, we also develop a model capable of compositional editing, which further helps improve the overall accuracy of our proposed approach. Our method flexibly trades inference time compute with performance on compositional text prompts. We perform extensive experimental evaluation across 3 benchmarks and 10 T2I models including DALLE-3 and the latest -- SD-3.5-Large. Our approach not only improves the performance of the SOTA models, by upto 3 points, it also reduces the performance gap between weaker and stronger models. $\href{https://dair-iitd.github.io/GraPE/}{https://dair-iitd.github.io/GraPE/}$

cs.CV↗

arXiv2024

A class of polynomials from enumerating queen paths

We study a class polynomials obtained from an enumeration of the number of queen paths. In particular, we find the generating function for the diagonal sequence of this table and the zero distribution of a sequence of related polynomials.

math.CO↗

arXiv2023

Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach

We are interested in image manipulation via natural language text -- a task that is useful for multiple AI applications but requires complex reasoning over multi-modal spaces. We extend recently proposed Neuro Symbolic Concept Learning (NSCL), which has been quite effective for the task of Visual Question Answering (VQA), for the task of image manipulation. Our system referred to as NeuroSIM can perform complex multi-hop reasoning over multi-object scenes and only requires weak supervision in the form of annotated data for VQA. NeuroSIM parses an instruction into a symbolic program, based on a Domain Specific Language (DSL) comprising of object attributes and manipulation operations, that guides its execution. We create a new dataset for the task, and extensive experiments demonstrate that NeuroSIM is highly competitive with or beats SOTA baselines that make use of supervised data for manipulation.

cs.CV↗