Abstract

Large Language Models (LLMs) are increasingly deployed in highstakes settings where errors are costly and decisions must be made
under time pressure, ambiguity, and strict constraints. However,
most existing benchmarks evaluate model performance under relaxed conditions and fail to capture how LLM behavior degrades
under stress. This paper presents a structured framework for stresstesting LLM robustness across five real-world scenarios with increasing difficulty. We evaluate three widely used models--ChatGPT
(GPT-5.1), Gemini 3, and Claude 4.5 Sonnet--using a 45-item rubric
assessing accuracy, safety, logical consistency, and constraint adherence. To quantify robustness, we introduce a Stress Sensitivity
Index (SSI) that measures performance degradation as task difficulty
increases. Our results show that ChatGPT remains largely stable
under stress, Gemini exhibits moderate degradation, and Claude
displays substantial sensitivity, particularly in constraint-following
tasks. These findings highlight the importance of stress-oriented
evaluation for understanding LLM reliability prior to their deployment in real-world, high-stress human environments.

Abstract

High-dimensional datasets present a fundamental challenge:
understanding how data points relate to each other across different feature combinations. Existing visualization methods typically show global structure through dimensionality reduction or highlight relationships in the full feature space, but they fail to reveal which specific feature subspaces drive point-to-point connections. We present CSP-HEIDI, a visualization framework that resolves this ambiguity by analyzing knearest neighbor (k-NN) relationships across the complete feature subspaces. Central to our approach is the Interpretable Expression Matrix (IEM), which replaces scalar distances with minimal Boolean expressions (minterms). By employing a logic minimization strategy, CSPHEIDI isolates the "root cause" subspaces that are sufficient to maintain neighborhood connections, discarding redundant supersets. Through a novel Concentric Spiral Arrangement, these expressions are organized to reveal structural topology. We demonstrate on biological and synthetic datasets that CSP-HEIDI successfully disentangles intra-cluster cohesion from inter-cluster bridging. The visualization explicitly reveals how high-discriminability features act as "identity anchors" within clusters, while low-discriminability dimensions function as specific "semantic bridges" between them. This offers a qualitative, "white-box" alternative to projection methods, enabling researchers to diagnose the precise logic governing cluster cohesion and overlap.

Abstract

The rapid advancement of Large Language Models (LLMs) has en-
abled them to generate complex, multi-step plans and itineraries.

However, these generated plans often lack temporal and spatial

consistency, particularly in scenarios involving physical travel con-
straints. This research aims to study the temporal performance

of di!erent LLMs and presents a validation framework that eval-
uates and improves the temporal consistency of LLM-generated

travel itineraries. The system employs multiple state-of-the-art
LLMs to generate travel plans and validates them against real-world
"ight duration constraints using the AeroDataBox API. This work
contributes to the understanding of LLM capabilities in handling
complex temporal reasoning tasks like itinerary generation and
provides a framework to rectify any temporal inconsistencies like
overlapping journeys or unrealistic transit times in the itineraries
generated by LLMs before the itinerary is given to the user. Our
experiments reveal that while current LLMs frequently produce
temporally inconsistent itineraries, these can be systematically and
reliably corrected using our framework, enabling their practical
deployment in large-scale travel planning.

Abstract

Bharatanatyam is one of the most celebrated classical dance
forms in India. It is performed and enjoyed by art enthusiasts all over the
world. It comprises expressions, hand gestures, footwork, rhythmic patterns,
eye, neck and head movements, lyrics, and music. These basic components
are integrated and woven into a beautifully crafted visual story.
However, beneath this creative visual craft lies a structured compilation
of technical, mathematical, computational, and modular aspects. In this
paper, we provide a database schema to store the artistic components in
a structured relational database model. By providing a database schema,
we provide a layered approach to store Bharatanatyam choreographies.
This layered approach helps in building choreographies by working individually
on different modules of the art form like the foot work, hand
movement, etc, and consolidating them to form and store sequences.
Bharatanatyam choreographies are a collection of sequences. In this paper,
we discuss how complete choreographies are modelled through the
logical sequencing of these components. Our paper lays the foundation
for bridging traditional knowledge of the performing arts with technologies
to help in digital archiving, cultural analytics, and choreography
suggestions.

Abstract

Millions of people in India who are deaf or hard of hearing rely on Indian Sign Language (ISL) as their main form of communication. Nonetheless, there is still a big communication gap between people who speak ISL and those who don't. For the deaf community, social integration and information access are hampered by the absence of real-time translation tools. The language barrier that separates ISL from commonly spoken languages like Hindi and English also makes communication more difficult and restricts the participation of deaf people in public services, education, and mainstream conversations. This research aims to develop an AI-powered system that can effectively detect and translate Indian Sign Language (ISL) into English, and vice versa.

Abstract

No Results Found

Abstract

Autonomous Underwater Vehicles (AUVs) play a vital role in explor- ing and mapping underwater environments. However, the presence of nadir gaps, or blind zones, in commercial AUVs can lead to unex- plored areas during mission execution, limiting their effectiveness. Our work addresses the challenges of path planning in the presence of nadir gaps and presents scalable coverage strategies for AUVs minimizing either the mission completion time or the total number of turns performed while ensuring complete exploration, eliminating the risk of leaving critical areas unexplored. We provide provably complete strategies and perform extensive simulations on diverse input configurations based on real-world instances to demonstrate the efficacy of our strategies.

Abstract

A system to visualise large graphs

Abstract

A system and method for visualizing large graph data using spirals are provided. The method includes (i) generating communities from an input graph data, (ii) configuring nodes in the communities in an ascending order based on a centrality measure, (iii) determining a spiral for the communities by configuring (a) a node with a highest centrality measure at a spiral center and (b) the nodes in a spiral shape based on the ascending order of the centrality measure along the spiral shape, (iv) determining a first super edge for the spiral of the communities, (v) generating a coarse graph using super nodes, the first super edge, and a second super edge, (vi) configuring, using a standard force-directed layout algorithm, corresponding spirals of the communities on the generated coarse graph based on a weight of the second super edge and attractive forces to generate the visualization of the large graph data.

Abstract

The number of individuals having identical names on the internet is increasing. Thus making the task of searching for a specific individual tedious. The user must vet through many profiles with identical names to get to the actual individual of interest. The online presence of an individual forms the profile of the individual. We need a solution that helps users by consolidating the profiles of such individuals by retrieving factual information available on the web and providing the same as a single result. We present a novel solution that retrieves web profiles belonging to those bearing identical Full Names through an end-to-end pipeline. Our solution involves information retrieval from the web (extraction), LLM-driven Named Entity Extraction (retrieval), and standardization of facts using Wikipedia, which returns profiles with fourteen multi-valued attributes. After that, profiles that correspond to the same real-world individuals are determined. We accomplish this by identifying similarities among profiles based on the extracted facts using a Prefix Tree inspired data structure (validation) and utilizing ChatGPT's contextual comprehension (revalidation). The system offers varied levels of strictness while consolidating these profiles, namely strict, relaxed, and loose matching. The novelty of our solution lies in the innovative use of GPT -- a highly powerful yet an unpredictable tool, for such a nuanced task. A study involving twenty participants, along with other results, found that one could effectively retrieve information for a specific individual.