8 citations · 49 across the 40 of their papers we have counts for
53 papers
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…
DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning
Lachlan McPheat, Navdeep Kaur, Robert Blackwell +3
We introduce DecompSR, decomposed spatial reasoning, a large benchmark dataset (over 5m datapoints) and generation framework designed to analyse compositional spatial reasoning abi…
Noise or Nuance: An Investigation Into Useful Information and Filtering For LLM Driven AKBC
Alex Clay, Ernesto Jiménez-Ruiz, Pranava Madhyastha
RAG and fine-tuning are prevalent strategies for improving the quality of LLM outputs. However, in constrained situations, such as that of the 2025 LM-KBC challenge, such technique…
Chart Question Answering from Real-World Analytical Narratives
Maeve Hutchinson, Radu Jianu, Aidan Slingsby +2
We present a new dataset for chart question answering (CQA) constructed from visualization notebooks. The dataset features real-world, multi-view charts paired with natural languag…
Capturing Visualization Design Rationale
Maeve Hutchinson, Radu Jianu, Aidan Slingsby +2
Prior natural language datasets for data visualization have focused on tasks such as visualization literacy assessment, insight generation, and visualization generation from natura…
IYKYK: Using language models to decode extremist cryptolects
Christine de Kock, Arij Riabi, Zeerak Talat +3
Extremist groups develop complex in-group language, also referred to as cryptolects, to exclude or mislead outsiders. We investigate the ability of current language technologies to…
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning
Navdeep Kaur, Lachlan McPheat, Alessandra Russo +2
In this paper, we examine the use of Conformal Language Modelling (CLM) alongside Answer Set Programming (ASP) to enhance the performance of standard open-weight LLMs on complex mu…
: Semantically Controlled Decoding
Mohammad Albinhassan, Pranava Madhyastha, Alessandra Russo
Ensuring both syntactic and semantic correctness in Large Language Model (LLM) outputs remains a significant challenge, despite being critical for real-world deployment. In this pa…
LLM-Assisted Visual Analytics: Opportunities and Challenges
Maeve Hutchinson, Radu Jianu, Aidan Slingsby +1
We explore the integration of large language models (LLMs) into visual analytics (VA) systems to transform their capabilities through intuitive natural language interactions. We su…
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
Xinyu Liu, Yan Ding, Kaikai An +4
While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robus…
Are words equally surprising in audio and audio-visual comprehension?
Pranava Madhyastha, Ye Zhang, Gabriella Vigliocco
We report a controlled study investigating the effect of visual information (i.e., seeing the speaker) on spoken language comprehension. We compare the ERP signature (N400) associa…
Towards preserving word order importance through Forced Invalidation
Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo
Large pre-trained language models such as BERT have been widely used as a framework for natural language understanding (NLU) tasks. However, recent findings have revealed that pre-…
Theoretical Conditions and Empirical Failure of Bracket Counting on Long Sequences with Linear Recurrent Networks
Nadine El-Naggar, Pranava Madhyastha, Tillman Weyde
Previous work has established that RNNs with an unbounded activation function have the capacity to count exactly. However, it has also been shown that RNNs are challenging to train…
Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering
Chenxi Whitehouse, Tillman Weyde, Pranava Madhyastha
The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. However, current systems mostly rely on…
Exploring the Long-Term Generalization of Counting Behavior in RNNs
Nadine El-Naggar, Pranava Madhyastha, Tillman Weyde
In this study, we investigate the generalization of LSTM, ReLU and GRU models on counting tasks over long sequences. Previous theoretical work has established that RNNs with ReLU a…
Belief Revision based Caption Re-ranker with Visual Semantic Information
Ahmed Sabir, Francesc Moreno-Noguer, Pranava Madhyastha +1
In this work, we focus on improving the captions generated by image-caption generation systems. We propose a novel re-ranking approach that leverages visual-semantic measures to id…