75 citations · 394 across the 149 of their papers we have counts for
11 papers · 1 filter
FedPref: Federated Preference Learning for Structured Radiology Report Extraction
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +1
Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labe…
Reasoning Structure of Large Language Models
Frédéric Berdoz, Luca A. Lanzendörfer, Fabian Farestam +1
Large reasoning models (LRMs) are often evaluated using metrics such as final-answer accuracy or token count. However, identical scores on these metrics can hide fundamentally diff…
GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
Saku Peltonen, August Bøgh Rønberg, Andreas Plesner +1
Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce GraphARC, a benchmark for abst…
Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol
Flint Xiaofeng Fan, Cheston Tan, Roger Wattenhofer +1
As AI agents powered by large language models (LLMs) increasingly use external tools for high-stakes decisions, a critical reliability question arises: how do errors propagate acro…
Reasoning Effort and Problem Complexity: A Scaling Analysis in LLMs
Benjamin Estermann, Roger Wattenhofer
Large Language Models (LLMs) have demonstrated remarkable text generation capabilities, and recent advances in training paradigms have led to breakthroughs in their reasoning perfo…
Can Large Reasoning Models do Analogical Reasoning under Perceptual Uncertainty?
Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer +2
This work presents a first evaluation of two state-of-the-art Large Reasoning Models (LRMs), OpenAI's o3-mini and DeepSeek R1, on analogical reasoning, focusing on well-established…
Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning
Michael Hersche, Giacomo Camposampiero, Roger Wattenhofer +2
This work compares large language models (LLMs) and neuro-symbolic approaches in solving Raven's progressive matrices (RPM), a visual abstract reasoning test that involves the unde…
Can an AI Agent Safely Run a Government? Existence of Probably Approximately Aligned Policies
Frédéric Berdoz, Roger Wattenhofer
While autonomous agents often surpass humans in their ability to handle vast and complex data, their potential misalignment (i.e., lack of transparency regarding their true objecti…
GraphFSA: A Finite State Automaton Framework for Algorithmic Learning on Graphs
Florian Grötschla, Joël Mathys, Christoffer Raun +1
Many graph algorithms can be viewed as sets of rules that are iteratively applied, with the number of iterations dependent on the size and complexity of the input graph. Existing m…
Cue Point Estimation using Object Detection
Giulia Argüello, Luca A. Lanzendörfer, Roger Wattenhofer
Cue points indicate possible temporal boundaries in a transition between two pieces of music in DJ mixing and constitute a crucial element in autonomous DJ systems as well as for l…
Abstract Visual Reasoning Enabled by Language
Giacomo Camposampiero, Loic Houmard, Benjamin Estermann +2
While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexi…