7 citations · 16 across the 11 of their papers we have counts for
11 papers
Routed Graph Handoff: Adaptive Format Selection for Multi-Agent LLM Delegation
Pratyay Banerjee, Ankit Chadha
Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget. Replacing these with structured graphs reduces cost but fails on t…
APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI
Pratyay Banerjee, Masud Moshtaghi, Shivashankar Subramanian +2
Large language models still struggle with reliable long-term conversational memory: simply enlarging context windows or applying naive retrieval often introduces noise and destabil…
APEX-EM: Non-Parametric Online Learning for Autonomous Agents via Structured Procedural-Episodic Experience Replay
Pratyay Banerjee, Masud Moshtaghi, Ankit Chadha
LLM agents rerun full reasoning for every task, even one they solved moments earlier. We introduce \textbf{APEX-EM}, a non-parametric experience memory that stores complete procedu…
Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection
Minh Van Nguyen, Kishan KC, Toan Nguyen +3
Answer sentence selection (AS2) in open-domain question answering finds answer for a question by ranking candidate sentences extracted from web documents. Recent work exploits answ…
Controlled Text Generation with Hidden Representation Transformations
Vaibhav Kumar, Hana Koorehdavoudi, Masud Moshtaghi +3
We propose CHRT (Control Hidden Representation Transformation) - a controlled language generation framework that steers large language models to generate text pertaining to certain…
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages
Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha +1
While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets. In…
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization
Aiswarya Sankar, Ankit Chadha
Abstractive multi document summarization has evolved as a task through the basic sequence to sequence approaches to transformer and graph based techniques. Each of these approaches…
Training Mixed-Domain Translation Models via Federated Learning
Peyman Passban, Tanya Roosta, Rahul Gupta +2
Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques. In this work, we leverage federated learning…
Communication-Efficient Federated Learning for Neural Machine Translation
Tanya Roosta, Peyman Passban, Ankit Chadha
Training neural machine translation (NMT) models in federated learning (FL) settings could be inefficient both computationally and communication-wise, due to the large size of tran…
Deep Reinforced Self-Attention Masks for Abstractive Summarization (DR.SAS)
Ankit Chadha, Mohamed Masoud
We present a novel architectural scheme to tackle the abstractive summarization problem based on the CNN/DMdataset which fuses Reinforcement Learning (RL) withUniLM, which is a pre…
BERTQA -- Attention on Steroids
Ankit Chadha, Rewa Sood
In this work, we extend the Bidirectional Encoder Representations from Transformers (BERT) with an emphasis on directed coattention to obtain an improved F1 performance on the SQUA…