75 citations · 394 across the 149 of their papers we have counts for
170 papers
Authority-Preserving Evaluation of Medical Vision-Language Assistants
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +1
Medical vision-language models can propose how urgently a skin lesion should be reviewed, but the local service retains authority to accept or replace that proposal under referral…
Geometric Iterative Retrieval for Neural Audio Codec Resynthesis
Leo Schmidt-Traub, Frédéric Berdoz, Luca A. Lanzendörfer +1
Neural audio codecs based on Residual Vector Quantization (RVQ) have become the dominant discrete representation for token-based general audio generation, yet resynthesizing high-q…
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…
Post-Training Speech Enhancement Language Models with Perceptual Rewards
Frédéric Berdoz, Luca A. Lanzendörfer, Antonis Asonitis +1
Speech enhancement language models achieve strong results when trained on discrete audio tokens, but their optimization relies on token-level cross-entropy rather than the perceptu…
Data Attribution in Large Language Models via Bidirectional Gradient Optimization
Frédéric Berdoz, Luca A. Lanzendörfer, Kaan Bayraktar +1
Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance. Understanding wh…
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…
TreeFlash: Parallel AR-Approximation for Faster Speculative Decoding
Peer Rheinboldt, Frédéric Berdoz, Roger Wattenhofer
One-shot block drafters for speculative decoding generate the full draft in a single forward pass, achieving strong throughput by eliminating sequential token generation. However,…
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…
N-vium: Mixture-of-Exits Transformer for Accelerated Exact Generation
Aleksander Lorenc, Frédéric Berdoz, Joël Mathys +1
Improving the inference efficiency of autoregressive transformers typically means reducing FLOPs per token, usually through approximations that degrade model quality. We introduce…
From Message-Passing to Linearized Graph Sequence Models
Joël Mathys, Basil Rohner, Saku Peltonen +1
Message-passing based approaches form the default backbone of most learning architectures on graph-structured data. However, the rapid progress of modern deep learning architecture…
WorldSpeech: A Multilingual Speech Corpus from Around the World
Antonis Asonitis, Luca A. Lanzendörfer, Frédéric Berdoz +1
Automatic speech recognition (ASR) performs well for high-resource languages with abundant paired audio-transcript data, but its accuracy degrades sharply for most languages due to…
Reasoning Boosts Opinion Alignment in LLMs
Frédéric Berdoz, Yann Billeter, Yann Vonlanthen +1
Opinion modeling aims to capture individual or group political preferences, enabling applications such as digital democracies, where models could help shape fairer and more popular…
Can AI Agents Agree?
Frédéric Berdoz, Leonardo Rugli, Roger Wattenhofer
Large language models are increasingly deployed as cooperating agents, yet their behavior in adversarial consensus settings has not been systematically studied. We evaluate LLM-bas…
Subliminal Signals in Preference Labels
Isotta Magistrali, Frédéric Berdoz, Sam Dauncey +1
As AI systems approach superhuman capabilities, scalable oversight increasingly relies on LLM-as-a-judge frameworks where models evaluate and guide each other's training. A core as…
Subcubic Coin Tossing in Asynchrony without PKI
Mose Mizrahi, Roger Wattenhofer
We consider an asynchronous network of parties connected to each other via secure channels, up to of which are byzantine. We study common coin tossing, a task where the par…
Breaking the Illusion of Artificial Consensus: Clone-Robust Weighting in Arbitrary Metric Spaces
Damien Berriaud, Roger Wattenhofer, Supanat Wangsutthitham
Social media, pseudonymous identities and generative AI have made online platforms highly vulnerable to Coordinated Inauthentic Behavior: by generating large numbers of similar pos…
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…
On the Expressive Power of GNNs for Boolean Satisfiability
Saku Peltonen, Roger Wattenhofer
Machine learning approaches to solving Boolean Satisfiability (SAT) aim to replace handcrafted heuristics with learning-based models. Graph Neural Networks have emerged as the main…
You Can Learn Tokenization End-to-End with Reinforcement Learning
Sam Dauncey, Roger Wattenhofer
Tokenization is a hardcoded compression step which remains in the training pipeline of Large Language Models (LLMs), despite a general trend towards architectures becoming increasi…
BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks
Simon Bührer, Andreas Plesner, Aczel Till +1
Gradient-based LUT- and logic-gate-based neural networks (LUTNet, LogicNets, DiffLogic, PolyLUT, NeuraLUT, WARP-LUT, DWN, LILogicNet, LightLUT) replace multiply-accumulate arithmet…
GIC-DLC: Differentiable Logic Circuits for Hardware-Friendly Grayscale Image Compression
Till Aczel, David F. Jenny, Simon Bührer +3
Neural image codecs achieve higher compression ratios than traditional hand-crafted methods such as PNG or JPEG-XL, but often incur substantial computational overhead, limiting the…
EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications
Ard Kastrati, Josua Bürki, Jonas Lauer +3
We introduce a unified benchmarking framework focused on evaluating EEG-based foundation models in clinical applications. The benchmark spans 11 well-defined diagnostic tasks acros…
Adapting Neural Audio Codecs to EEG
Ard Kastrati, Luca Lanzendörfer, Riccardo Rigoni +2
EEG and audio are inherently distinct modalities, differing in sampling rate, channel structure, and scale. Yet, we show that pretrained neural audio codecs can serve as effective…
Steering Pretrained Drafters during Speculative Decoding
Frédéric Berdoz, Peer Rheinboldt, Roger Wattenhofer
Speculative decoding accelerates language model inference by separating generation into fast drafting and parallel verification. Its main limitation is drafter-verifier misalignmen…