1 citations · 1 across the 8 of their papers we have counts for
11 papers · 1 filter
Self-Reports Do Not Identify Self-Models: An Identifiability Test for Counterfactual Reports
Phongsakon Mark Konrad, Toygar Tanyel, Serkan Ayvaz
Language-model self-reports are evidence about behavior in a prompt environment, not by themselves evidence of a self-model. We investigate counterfactual reports about affect-like…
Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
Riccardo Terrenzi, Serkan Ayvaz
Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems. We study six metadata-generation settings…
Routing Subspaces: Auditing Evaluation-to-Deployment Mismatch in Fine-Tuned Language Models
Phongsakon Mark Konrad, Toygar Tanyel, Serkan Ayvaz
Safety evaluations often assume that behavior observed during testing reflects behavior in ordinary use, but fine-tuning can break this assumption. A checkpoint can appear fixed un…
PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries
Riccardo Terrenzi, Matteo Falconi, Serkan Ayvaz +1
The rapid growth of tabular datasets in data lakes, data spaces, and open data portals makes effective dataset search essential for reuse and analysis. Existing search systems rely…
Why Neighborhoods Matter: Traversal Context and Provenance in Agentic GraphRAG
Riccardo Terrenzi, Maximilian von Zastrow, Serkan Ayvaz
Retrieval-Augmented Generation can improve factuality by grounding answers in external evidence, but Agentic GraphRAG complicates what it means for citations to be faithful. In the…
The Open-Box Fallacy: Why AI Deployment Needs a Calibrated Verification Regime
Phongsakon Mark Konrad, Tim Lukas Adam, Ane Cathrine Holst Merrild +4
AI deployment in sensitive domains such as health care, credit, employment, and criminal justice is often treated as unsafe to authorize until model internals can be explained. Thi…
Acceptance Cards:A Four-Diagnostic Standard for Safe Fine-Tuning Defense Claims
Phongsakon Mark Konrad, Toygar Tanyel, Serkan Ayvaz
Safe fine-tuning defenses are often endorsed on the basis of a held-out gap reduction, but the same reduction can come from sampling noise, subject artifacts, capability loss, or a…
CAKE: Cloud Architecture Knowledge Evaluation of Large Language Models
Tim Lukas Adam, Phongsakon Mark Konrad, Riccardo Terrenzi +4
In today's software architecture, large language models (LLMs) serve as software architecture co-pilots. However, no benchmark currently exists to evaluate large language models' a…
Non-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models
Phongsakon Mark Konrad, Casper Kunstmann-Olsen, Jacek Fiutowski +1
Post-harvest fruit quality assessment is essential for reducing food waste, yet reliable non-destructive methods typically depend on expensive hyperspectral cameras and computation…
Architecture Without Architects: How AI Coding Agents Shape Software Architecture
Phongsakon Mark Konrad, Tim Lukas Adam, Riccardo Terrenzi +1
AI coding agents select frameworks, scaffold infrastructure, and wire integrations, often in seconds. These are architectural decisions, yet almost no one reviews them as such. We…
A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search
Riccardo Terrenzi, Phongsakon Mark Konrad, Tim Lukas Adam +1
Ad hoc dataset search requires matching underspecified natural-language queries against sparse, heterogeneous metadata records, a task where typical lexical or dense retrieval alon…