• Bicriteria FPT-approximation algorithms for vertex deletion to bounded degeneracy graphs 

  Inamdar, Tanmay; Kanesh, Lawqueen; Krithika, R.; Mittal, Harshil; Saurabh, Saket (Springer, 2025-07-21)
• Improved 2-approximate shortest paths for close vertex pairs 

  Gupta, Manoj (Institute of Electrical and Electronics Engineers (IEEE), 2025-12-14)
• Evaluating pre-trained large language models on zero shot prompts for parallelization of source code 

  Yadav, Devansh; Mondal, Shouvick (Elsevier, 2025-12)

  Large Language Models (LLMs) have become prominent in the software development life cycle, yet the generation of performant source code, particularly through automatic parallelization, remains underexplored. This study ...
• m-Eternal domination and variants on some classes of finite and infinite graphs 

  Calamoneri, Tiziana; Corò, Federico; Misra, Neeldhara; Nanoti, Saraswati Girish; Paesani, Giacomo (Cornell University Library, 2025-07)

  We study the m-Eternal Domination problem, which is the following two-player game between a defender and an attacker on a graph: initially, the defender positions k guards on vertices of the graph; the game then proceeds ...
• Sampling-based estimation of Jaccard containment and similarity 

  Joshi, Pranav Ajay (Cornell University Library, 2025-07)

  This paper addresses the problem of estimating the containment and similarity between two sets using only random samples from each set, without relying on sketches of full sets. The study introduces a binomial model for ...
• Coordinating monetary contributions in participatory budgeting 

  Aziz, Haris; Gujar, Sujit; Padala, Manisha; Suzuki, Mashbat; Vollen, Jeremy (Springer, 2025-12)

  We formalize a framework for coordinating funding and selecting projects, the costs of which are shared among agents with quasi-linear utility functions and individual budgets. Our model contains the discrete participatory ...
• Constraining the 3HDM parameter space using active learning 

  Batra, Nipun; Coleppa, Baradhwaj; Khanna, Akshat; Rai, Santosh Kumar; Sarkar, Agnivo (American Physical Society, 2025-07)

  One of the standard ways to study scenarios beyond the Standard Model involves extending the Higgs sector. This work examines the three Higgs doublet model (3HDM) in a type-Z or democratic setup, where each Higgs doublet ...
• On the complexity of problems on graphs defined on groups 

  Das, Bireswar; Dey, Dipan; Ghosh, Jinia (Cornell University Library, 2025-07)

  We study the complexity of graph problems on graphs defined on groups, especially power graphs. We observe that an isomorphism invariant problem, such as Hamiltonian Path, Partition into Cliques, Feedback Vertex Set, ...
• Evaluating pre-trained large language models using simulated human inputs for python code generation 

  Potta, Mukul Paras; Mondal, Shouvick; Meena, Yogesh Kumar (Elsevier, 2025-06)

  Large language models (LLMs) have transformed human-AI interactions, yet research on making their use more accessible is still limited. While some studies address the inclusivity of generated language, less focus has been ...
• Eka-Eval : a comprehensive evaluation framework for large language models in Indian languages 

  Sinha, Samridhi Raj; Sheth, Rajvee; Upperwal, Abhishek; Singh, Mayank (Cornell University Library, 2025-07)

  The rapid advancement of Large Language Models (LLMs) has intensified the need for evaluation frameworks that address the requirements of linguistically diverse regions, such as India, and go beyond English-centric benchmarks. ...
• LoRA-FL: a low-rank adversarial attack for compromising group fairness in federated learning 

  Damle, Sankarshan; Rokvic, Ljubomir; Bhamidi, Venugopal; Padala, Manisha; Faltings, Boi (University of Massachusetts, 2025-06)

  Federated Learning (FL) enables collaborative model training without sharing raw data, but agent distributions can induce unfair outcomes across sensitive groups. Existing fairness attacks often degrade accuracy or are ...
• Sensitivity and query complexity under uncertainty 

  Benson, Deepu; Komarath, Balagopal; Mande, Nikhil; Nalli, Sai Soumya; Sarma, Jayalal; Sreenivasaiah, Karteek (Cornell University Library, 2025-06)

  In this paper, we study the query complexity of Boolean functions in the presence of uncertainty, motivated by parallel computation with an unlimited number of processors where inputs are allowed to be unknown. We allow ...
• A unified platform to evaluate STDP learning rule and synapse model using pattern recognition in a spiking neural network 

  Maskeen, Jaskirat Singh; Lashkare, Sandip (Cornell University Library, 2025-06)

  We develop a unified platform to evaluate Ideal, Linear, and Non-linear \text{Pr}_{0.7}\text{Ca}_{0.3}\text{MnO}_{3} memristor-based synapse models, each getting progressively closer to hardware realism, alongside four ...
• The cost and complexity of minimizing envy in house allocation 

  Madathil, Jayakrishnan; Misra, Neeldhara; Sethia, Aditi (Springer, 2025-12)

  We study almost envy-freeness in house allocation, where m houses are to be allocated among n agents so that every agent receives exactly one house. An envy-free allocation need not exist, and therefore we may have to ...
• Model hubs and beyond: analyzing model popularity, performance, and documentation 

  Kadasi, Pritam; Reddy, Sriman; Chaturvedula, Srivathsa Vamsi; Sen, Rudranshu; Saha, Agnish; Sikdar, Soumavo; Sarkar, Sayani; Mittal, Suhani; Jindal, Rohit; Singh, Mayank (2025-06-23)

  With the massive surge in ML models on platforms like Hugging Face, users often lose track and struggle to choose the best model for their downstream tasks, frequently relying on model popularity indicated by download ...
• A Unified browser-based consent management framework 

  Priyadarsini, Gayatri; Bichhawat, Abhishek (Institute of Electrical and Electronics Engineers (IEEE), 2025-04-27)

  Today’s users are concerned about the privacy of their personal or sensitive information on the Web because of the different techniques employed to track their online activities and behavior. Privacy laws like the GDPR, ...
• Intelligent data dissemination in vehicular networks: leveraging reinforcement learning 

  Bhatia, Jitendra; Shah, Maanit; Prajapati, Rushi; Shah, Khush; Shah, Premal; Trivedi, Harshal; Joshi, Dhaval (Springer, 2025-06)

  Data dissemination in Vehicular Ad Hoc Networks (VANETs) is vital for the development and operation of intelligent transportation systems, as it enables the rapid and reliable exchange of critical information among vehicles ...
• EWGN: Elastic weight generation and context switching in deep learning 

  Sawant, Shriraj P.; Miyapuram, Krishna Prasad (Cornell University Library, 2025-06)

  The ability to learn and retain a wide variety of tasks is a hallmark of human intelligence that has inspired research in artificial general intelligence. Continual learning approaches provide a significant step towards ...
• Dynamic vision from EEG brain recordings: how much does EEG know? 

  Singh, Prajwal; Sharma, Anupam; Pandey, Pankaj; Miyapuram, Krishna; Raman, Shanmuganathan (Cornell University Library, 2025-05)

  Reconstructing and understanding dynamic visual information (video) from brain EEG recordings is challenging due to the non-stationary nature of EEG signals, their low signal-to-noise ratio (SNR), and the limited availability ...
• Breaking mBad! supervised fine-tuning for cross-lingual detoxification 

  Beniwal, Himanshu; Kim, Youngwoo; Sap, Maarten; Dan, Soham; Hartvigsen, Thomas (Cornell University Library, 2025-05)

  As large language models (LLMs) become increasingly prevalent in global applications, ensuring that they are toxicity-free across diverse linguistic contexts remains a critical challenge. We explore "Cross-lingual ...

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