arXiv subjects

Abhishek Kulkarni

Publications and source records attributed to Abhishek Kulkarni.

11 recordsLinked to original sources

arXiv2026

Designing AI Tutors for Interest-Based Learning: Insights from Human Instructors

Interest-based learning (IBL) is a paradigm of instruction in which educational content is contextualized using learners' interests to enhance content relevance. IBL has been shown to result in improved learning outcomes. Unfortunately, high effort is needed for instructors to design and deliver IBL content for individual students. LLMs in the form of AI tutors may allow for IBL to scale across many students. Designing an AI tutor for IBL, however, first requires an understanding of how IBL is implemented in teaching scenarios. This paper presents a study that seeks to derive this understanding from an analysis of how human instructors design and deliver IBL content. We studied 14 one-to-one online tutoring sessions (28 participants) in which tutors designed and delivered a lesson tailored to a student's self-identified interest. Using lesson artifacts, tutoring transcripts, interviews, and questionnaires, findings include themes on how tutors integrate interests during instruction and why. Finally, actionable design implications are presented for LLM-powered AI tutors that aim to deliver IBL at scale.

cs.HC↗

arXiv2026

E3VA: Enhancing Emotional Expressiveness in Virtual Conversational Agents

With the advent of generative AI and large language models, embodied conversational agents are becoming synonymous with online interactions. These agents possess vast amounts of knowledge but suffer from exhibiting limited emotional expressiveness. Without adequate expressions, agents might fail to adapt to users' emotions, which may result in a sub-optimal user experience and engagement. Most current systems prioritize content based responses, neglecting the emotional context of conversations. Research in this space is currently limited to specific contexts, like mental health. To bridge this gap, our project proposes the implementation of expressive features in a virtual conversational agent which will utilize sentiment analysis and natural language processing to inform the generation of empathetic, expressive responses. The project delivers a functional conversational agent capable of assessing and responding to user emotions accordingly. We posit this will enhance usability, engagement, and the overall quality of conversations and present results from an exploratory pilot study investigating the same.

cs.HC↗

arXiv2026

Deceptive Sequential Decision-Making via Regularized Policy Optimization

Autonomous systems are increasingly expected to operate in the presence of adversaries, though adversaries may infer sensitive information simply by observing a system. Therefore, present a deceptive sequential decision-making framework that not only conceals sensitive information, but actively misleads adversaries about it. We model autonomous systems as Markov decision processes, with adversaries using inverse reinforcement learning to recover reward functions. To counter them, we present three regularization strategies for policy synthesis problems that actively deceive an adversary about a system's reward. ``Diversionary deception'' leads an adversary to draw any false conclusion about the system's reward function. ``Targeted deception'' leads an adversary to draw a specific false conclusion about the system's reward function. ``Equivocal deception'' leads an adversary to infer that the real reward and a false reward both explain the system's behavior. We show how each form of deception can be implemented in policy optimization problems and analytically bound the loss in total accumulated reward induced by deception. Next, we evaluate these developments in a multi-agent setting. We show that diversionary, targeted, and equivocal deception all steer the adversary to false beliefs while still attaining a total accumulated reward that is at least 98% of its optimal, non-deceptive value.

cs.LG↗

arXiv2025

Privacy-preserving Nash Equilibrium Synthesis with Partially Ordered Temporal Objectives

Nash equilibrium is a central solution concept for reasoning about self-interested agents. We address the problem of synthesizing Nash equilibria in two-player deterministic games on graphs, where players have private, partially-ordered preferences over temporal goals. Unlike prior work, which assumes preferences are common knowledge, we develop a communication protocol for equilibrium synthesis in settings where players' preferences are private information. In the protocol, players communicate to synthesize equilibria by exchanging information about when they can force desirable outcomes. We incorporate privacy by ensuring the protocol stops before enough information is revealed to expose a player's preferences. We prove completeness by showing that, when no player halts communication, the protocol either returns an equilibrium or certifies that none exists. We then prove privacy by showing that, with stopping, the messages a player sends are always consistent with multiple possible preferences and thus do not reveal some given secret regarding a player's true preference ordering. Experiments demonstrate that we can synthesize non-trivial equilibria while preserving privacy of preferences, highlighting the protocol's potential for applications in strategy synthesis with constrained information sharing.

cs.GT↗

arXiv2025

GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization

Generative models have recently demonstrated strong potential in multi-behavior recommendation systems, leveraging the expressive power of transformers and tokenization to generate personalized item sequences. However, their adoption is hindered by (1) the lack of explicit information for token reasoning, (2) high computational costs due to quadratic attention complexity and dense sequence representations after tokenization, and (3) limited multi-scale modeling over user history. In this work, we propose GRACE (Generative Recommendation via journey-aware sparse Attention on Chain-of-thought tokEnization), a novel generative framework for multi-behavior sequential recommendation. GRACE introduces a hybrid Chain-of-Thought (CoT) tokenization method that encodes user-item interactions with explicit attributes from product knowledge graphs (e.g., category, brand, price) over semantic tokenization, enabling interpretable and behavior-aligned generation. To address the inefficiency of standard attention, we design a Journey-Aware Sparse Attention (JSA) mechanism, which selectively attends to compressed, intra-, inter-, and current-context segments in the tokenized sequence. Experiments on two real-world datasets show that GRACE significantly outperforms state-of-the-art baselines, achieving up to +106.9% HR@10 and +106.7% NDCG@10 improvement over the state-of-the-art baseline on the Home domain, and +22.1% HR@10 on the Electronics domain. GRACE also reduces attention computation by up to 48% with long sequences.

cs.CL↗

arXiv2025

Dynamic Coalitions in Games on Graphs with Preferences over Temporal Goals

In multiplayer games with sequential decision-making, self-interested players form dynamic coalitions to achieve most-preferred temporal goals beyond their individual capabilities. We introduce a novel procedure to synthesize strategies that jointly determine which coalitions should form and the actions coalition members should choose to satisfy their preferences in a subclass of deterministic multiplayer games on graphs. In these games, a leader decides the coalition during each round and the players not in the coalition follow their admissible strategies. Our contributions are threefold. First, we extend the concept of admissibility to games on graphs with preferences and characterize it using maximal sure winning, a concept originally defined for adversarial two-player games with preferences. Second, we define a value function that assigns a vector to each state, identifying which player has a maximal sure winning strategy for certain subset of objectives. Finally, we present a polynomial-time algorithm to synthesize admissible strategies for all players based on this value function and prove their existence in all games within the chosen subclass. We illustrate the benefits of dynamic coalitions over fixed ones in a blocks-world domain. Interestingly, our experiment reveals that aligned preferences do not always encourage cooperation, while conflicting preferences do not always lead to adversarial behavior.

cs.GT↗

arXiv2024

Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents

Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large number of environment interactions. To mitigate sample complexity issues, recent approaches have used high-level task specifications, such as Linear Temporal Logic (LTL$_f$) formulas or Reward Machines (RM), to guide the learning progress of the agent. In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions. Unlike previous work, LSTS does not assume information about the environment dynamics or the Reward Machine, and dynamically samples promising tasks that lead to successful goal policies. We evaluate LSTS on a gridworld and show that it achieves improved time-to-threshold performance on complex sequential decision-making problems compared to state-of-the-art RM and Automaton-guided RL baselines, such as Q-Learning for Reward Machines and Compositional RL from logical Specifications (DIRL). Moreover, we demonstrate that our method outperforms RM and Automaton-guided RL baselines in terms of sample-efficiency, both in a partially observable robotic task and in a continuous control robotic manipulation task.

cs.AI↗

arXiv2023

Automaton-Guided Curriculum Generation for Reinforcement Learning Agents

Despite advances in Reinforcement Learning, many sequential decision making tasks remain prohibitively expensive and impractical to learn. Recently, approaches that automatically generate reward functions from logical task specifications have been proposed to mitigate this issue; however, they scale poorly on long-horizon tasks (i.e., tasks where the agent needs to perform a series of correct actions to reach the goal state, considering future transitions while choosing an action). Employing a curriculum (a sequence of increasingly complex tasks) further improves the learning speed of the agent by sequencing intermediate tasks suited to the learning capacity of the agent. However, generating curricula from the logical specification still remains an unsolved problem. To this end, we propose AGCL, Automaton-guided Curriculum Learning, a novel method for automatically generating curricula for the target task in the form of Directed Acyclic Graphs (DAGs). AGCL encodes the specification in the form of a deterministic finite automaton (DFA), and then uses the DFA along with the Object-Oriented MDP (OOMDP) representation to generate a curriculum as a DAG, where the vertices correspond to tasks, and edges correspond to the direction of knowledge transfer. Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e.g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e.g, Q-Learning for Reward Machines). Further, we demonstrate that AGCL performs well even in the presence of noise in the task's OOMDP description, and also when distractor objects are present that are not modeled in the logical specification of the tasks' objectives.

cs.AI↗

arXiv2019

A Comparative Study of Asynchronous Many-Tasking Runtimes: Cilk, Charm++, ParalleX and AM++

We evaluate and compare four contemporary and emerging runtimes for high-performance computing(HPC) applications: Cilk, Charm++, ParalleX and AM++. We compare along three bases: programming model, execution model and the implementation on an underlying machine model. The comparison study includes a survey of each runtime system's programming models, their corresponding execution models, their stated features, and performance and productivity goals. We first qualitatively compare these runtimes with programmability in mind. The differences in expressivity and programmability arising from their syntax and semantics are clearly enunciated through examples common to all runtimes. Then, the execution model of each runtime, which acts as a bridge between the programming model and the underlying machine model, is compared and contrasted to that of the others. We also evaluate four mature implementations of these runtimes, namely: Intel Cilk++, Charm++ 6.5.1, AM++ and HPX, that embody the principles dictated by these models. With the emergence of the next generation of supercomputers, it is imperative for parallel programming models to evolve and address the integral challenges introduced by the increasing scale. Rather than picking a winner out of these four models under consideration, we end with a discussion on lessons learned, and how such a study is instructive in the evolution of parallel programming frameworks to address the said challenges.

cs.DC↗

arXiv2019

Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix

Recent developments in the field of deep learning have motivated many researchers to apply these methods to problems in quantum information. Torlai and Melko first proposed a decoder for surface codes based on neural networks. Since then, many other researchers have applied neural networks to study a variety of problems in the context of decoding. An important development in this regard was due to Varsamopoulos et al. who proposed a two-step decoder using neural networks. Subsequent work of Maskara et al. used the same concept for decoding for various noise models. We propose a similar two-step neural decoder using inverse parity-check matrix for topological color codes. We show that it outperforms the state-of-the-art performance of non-neural decoders for independent Pauli errors noise model on a 2D hexagonal color code. Our final decoder is independent of the noise model and achieves a threshold of $10 \%$. Our result is comparable to the recent work on neural decoder for quantum error correction by Maskara et al.. It appears that our decoder has significant advantages with respect to training cost and complexity of the network for higher lengths when compared to that of Maskara et al.. Our proposed method can also be extended to arbitrary dimension and other stabilizer codes.

quant-ph↗

arXiv2018

Efficiently decoding the 3D toric codes and welded codes on cubic lattices

The recent years have seen a growing interest in quantum codes in three dimensions (3D). One of the earliest proposed 3D quantum codes is the 3D toric code. It has been shown that 3D color codes can be mapped to 3D toric codes. The 3D toric code on cubic lattice is also a building block for the welded code which has highest energy barrier to date. Although well known, the performance of the 3D toric code has not been studied extensively. In this paper, we propose efficient decoding algorithms for the 3D toric code on a cubic lattice with and without boundaries and report their performance for various quantum channels. We observe a threshold of $\gtrsim 12\%$ for the bit flip errors, $\approx 3\%$ for phase flip errors and $ 24.8\%$ for erasure channel. We also study the performance of the welded 3D toric code on the quantum erasure channel. We did not observe a threshold for the welded code over the erasure channel.

quant-ph↗