activity
20182026
most citedFinding Experts in Transformer Models

15 citations · 28 across the 31 of their papers we have counts for

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Showing 2026Show all

8 papers · 1 filter

cs.LG2026

Homogenization in Multi-Agent Systems

Prakhar Ganesh, Kyra Wilson, Luca Zappella +4

Multi-agent systems (MAS) leverage interactions between agents to perform complex tasks. Despite their success, we show that these interactions can also lead to homogenization, i.e…

cs.AI2026

The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models

Xavier Suau, Alex Ferrando de las Morenas, Luca Zappella +1

When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How much tree-structured compositi…

cs.CL2026

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study

Iuri Macocco, Pau Rodríguez, Arno Blaas +3

Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved trade-offs remains elusive. C…

cs.LG2026

HyperTransport: Amortized Conditioning of T2I Generative Models

Valentino Maiorca, Eleonora Gualdoni, Xavier Suau +3

As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can be costly, and while promptin…

cs.CL2026

Uncertainty Quantification for LLM Function-Calling

Zihuiwen Ye, Lukas Aichberger, Michael Kirchhof +5

Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM Function-Calling paradigm, a widely used approac…

cs.CL2026

Attention to Mamba: A Recipe for Cross-Architecture Distillation

Abhinav Moudgil, Ningyuan Huang, Eeshan Gunesh Dhekane +3

State Space Models (SSMs) such as Mamba have become a popular alternative to Transformer models, due to their reduced memory consumption and higher throughput at generation compare…

cs.LG2026

The Design Space of Tri-Modal Masked Diffusion Models

Louis Bethune, Victor Turrisi, Bruno Kacper Mlodozeniec +21

Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and fine-tuning a base unimodal model for bimodal gen…

cs.AI2026

GenCtrl -- A Formal Controllability Toolkit for Generative Models

Emily Cheng, Carmen Amo Alonso, Federico Danieli +4

As generative models become ubiquitous, there is a critical need for fine-grained control over the generation process. Yet, while controlled generation methods from prompting to fi…