100 citations · 342 across the 39 of their papers we have counts for
40 papers
Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents
Richmond Alake, Cesare Bernardis, Paul Cayet +10
Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery of user-specific facts and…
Open Agent Specification (Agent Spec): A Unified Representation for AI Agents
Soufiane Amini, Yassine Benajiba, Cesare Bernardis +16
The proliferation of agent frameworks has led to fragmentation in how agents are defined, executed, and evaluated. Existing systems differ in their abstractions, data flow semantic…
Concise Reasoning via Reinforcement Learning
Mehdi Fatemi, Banafsheh Rafiee, Mingjie Tang +1
A major drawback of reasoning models is their excessive token usage, inflating computational cost, resource demand, and latency. We show this verbosity stems not from deeper reason…
"Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer?
Benjamin Reichman, Kartik Talamadupula
Human communication is a multifaceted and multimodal skill. Communication requires an understanding of both the surface-level textual content and the connotative intent of a piece…
Emotional RAG LLMs: Reading Comprehension for the Open Internet
Benjamin Reichman, Adar Avsian, Kartik Talamadupula +2
Queries to large language models (LLMs) can be divided into two parts: the instruction/question and the accompanying context. The context for retrieval-augmented generation (RAG) s…
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury +3
Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning.…
Are Human Conversations Special? A Large Language Model Perspective
Toshish Jawale, Chaitanya Animesh, Sekhar Vallath +2
This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human). We analyze three…
Knowledge-augmented Deep Learning and Its Applications: A Survey
Zijun Cui, Tian Gao, Kartik Talamadupula +1
Deep learning models, though having achieved great success in many different fields over the past years, are usually data hungry, fail to perform well on unseen samples, and lack o…
Better Together? An Evaluation of AI-Supported Code Translation
Justin D. Weisz, Michael Muller, Steven I. Ross +5
Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from co…
Investigating Explainability of Generative AI for Code through Scenario-based Design
Jiao Sun, Q. Vera Liao, Michael Muller +4
What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative mo…
When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data
Edmond Awad, Sydney Levine, Andrea Loreggia +6
One of the most remarkable things about the human moral mind is its flexibility. We can make moral judgments about cases we have never seen before. We can decide that pre-establish…
Using Document Similarity Methods to create Parallel Datasets for Code Translation
Mayank Agarwal, Kartik Talamadupula, Fernando Martinez +5
Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has…
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents
Keerthiram Murugesan, Subhajit Chaudhury, Kartik Talamadupula
Text-based games (TBGs) have become a popular proving ground for the demonstration of learning-based agents that make decisions in quasi real-world settings. The crux of the proble…
Perfection Not Required? Human-AI Partnerships in Code Translation
Justin D. Weisz, Michael Muller, Stephanie Houde +5
Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised ne…
NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
Mayank Agarwal, Tathagata Chakraborti, Quchen Fu +6
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line. Participants were tasked with building models that can t…
Quality Estimation & Interpretability for Code Translation
Mayank Agarwal, Kartik Talamadupula, Stephanie Houde +5
Recently, the automated translation of source code from one programming language to another by using automatic approaches inspired by Neural Machine Translation (NMT) methods for n…
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning
Thomas Carta, Subhajit Chaudhury, Kartik Talamadupula +1
We present VisualHints, a novel environment for multimodal reinforcement learning (RL) involving text-based interactions along with visual hints (obtained from the environment). Re…
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi +6
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential deci…
Reading Comprehension as Natural Language Inference: A Semantic Analysis
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar +3
In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited…
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games
Subhajit Chaudhury, Daiki Kimura, Kartik Talamadupula +3
We show that Reinforcement Learning (RL) methods for solving Text-Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes. To address this iss…
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar +3
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it. However, t…
Type-augmented Relation Prediction in Knowledge Graphs
Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula +2
Knowledge graphs (KGs) are of great importance to many real world applications, but they generally suffer from incomplete information in the form of missing relations between entit…
Towards an Atlas of Cultural Commonsense for Machine Reasoning
Anurag Acharya, Kartik Talamadupula, Mark A Finlayson
Existing commonsense reasoning datasets for AI and NLP tasks fail to address an important aspect of human life: cultural differences. We introduce an approach that extends prior wo…
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge
Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla +3
In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments. This…