8 citations · 49 across the 40 of their papers we have counts for
9 papers · 1 filter
More Memory, Worse Agents: Error Reproduction and Anti-Persistence in LLM Agents
Ruhan Wang, Kishan Panaganti, Dongruo Zhou
LLM agents can improve across tasks without updating model parameters by storing experience from their own executions in external memory and reusing it to guide later decisions. A…
InSight: A Benchmark for Agentic Claim Verification in Interactive Visualizations
Maeve Hutchinson, Syed Mahbubul Huq, Mohammad Albinhassan +3
Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic, requiring the active interr…
First Make It Playable, Then Make It Good: Staged Interaction Learning for Small Dialogue-Game Agents
Syed Mahbubul Huq, Pranava Madhyastha
We present Qwen-GuidePlay-2B, a 2B-parameter language model for dialogue-game interaction. We fine-tune Qwen3.5-2B using three steps: a) SFT on only successful game trajectories fr…
Knowing Before Answering: Decoding Language Models for Reliable RAG
Syed Mahbubul Huq, Christopher Child, Tillman Weyde +1
In Retrieval-Augmented Generation (RAG), retrieval may provide insufficient or conflicting information needed to answer a question. The system should not only know when to answer b…
Neurosymbolic Embodied Agents
Mohammad Albinhassan, Yuming Feng, Alessandra Russo +1
Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dynamics or act on incorrectly gr…
Attention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMs
Maeve Hutchinson, Abderrahmane Wassim Mehdaoui, Pranava Madhyastha
Understanding how vision-language models (VLMs) interpret data visualizations remains an open problem, and is increasingly important as these models are used for analytical tasks w…
A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility
Pranava Madhyastha
In this (work in progress) paper, we present Bounded Pragmatic Listener (or BPL), a cognitively grounded Bayesian framework for modelling susceptibility to information disorder. BP…
Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity
Pranava Madhyastha, Dagmar Adamcova
We investigate the integration of human-like working memory constraints into the Transformer architecture and implement several cognitively inspired attention variants, including f…
Learning and Enforcing Context-Sensitive Control for LLMs
Mohammad Albinhassan, Pranava Madhyastha, Mark Law +1
Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limitations of Context-Free Grammar…