Florian von Wangenheim

Edward Gaere, Florian Wangenheim

This paper introduces PRIMETIME, a synthetic generator that supports both benchmarking and fine-tuning of two primitive operations underlying temporal reasoning in Large Language Models (LLMs): parsing and arithmetic on datetimes. Existing temporal benchmarks assume simplified canonical datetime forms, conflate arithmetic, composition, and world knowledge into a single aggregate score, and offer no direct path to remediation. The first contribution is methodological: the PRIMETIME synthetic generator delivers non-conflated, uncontaminated, and unlimited datetime exemplars that enable a decompositional evaluation strategy for each primitive in isolation. The generator is extensible to support complex datetime tasks and is publicly released, alongside generated benchmarks. The second contribution is diagnostic: under this evaluation strategy, the primitives themselves prove individually unreliable, with per-primitive accuracy ranging from near-zero to perfect across models and prompting conditions. The third contribution is constructive: the same generator used for diagnosis also produces new training exemplars for fine-tuning, and the resulting models show that the primitives are fully learnable and the composed Event Planning task reaches frontier-level accuracy using small quantized LoRA transformers. The broader takeaway is that a single synthetic generator can serve both diagnosis and production-ready deployment. This methodological pattern may apply beyond temporal reasoning.

Hannes Kunstmann, Joseph Ollier, Joel Persson et al.

Large language models (LLMs) present an enormous evolution in the strategic potential of conversational recommender systems (CRS). Yet to date, research has predominantly focused upon technical frameworks to implement LLM-driven CRS, rather than end-user evaluations or strategic implications for firms, particularly from the perspective of a small to medium enterprises (SME) that makeup the bedrock of the global economy. In the current paper, we detail the design of an LLM-driven CRS in an SME setting, and its subsequent performance in the field using both objective system metrics and subjective user evaluations. While doing so, we additionally outline a short-form revised ResQue model for evaluating LLM-driven CRS, enabling replicability in a rapidly evolving field. Our results reveal good system performance from a user experience perspective (85.5% recommendation accuracy) but underscore latency, cost, and quality issues challenging business viability. Notably, with a median cost of $0.04 per interaction and a latency of 5.7s, cost-effectiveness and response time emerge as crucial areas for achieving a more user-friendly and economically viable LLM-driven CRS for SME settings. One major driver of these costs is the use of an advanced LLM as a ranker within the retrieval-augmented generation (RAG) technique. Our results additionally indicate that relying solely on approaches such as Prompt-based learning with ChatGPT as the underlying LLM makes it challenging to achieve satisfying quality in a production environment. Strategic considerations for SMEs deploying an LLM-driven CRS are outlined, particularly considering trade-offs in the current technical landscape.

Aydin Javadov, Samir Garibov, Tobias Hoesli et al.

Medical time series analysis is challenging due to data sparsity, noise, and highly variable recording lengths. Prior work has shown that stochastic sparse sampling effectively handles variable-length signals, while retrieval-augmented approaches improve explainability and robustness to noise and weak temporal correlations. In this study, we generalize the stochastic sparse sampling framework for retrieval-informed classification. Specifically, we weight window predictions by within-channel similarity and aggregate them in probability space, yielding convex series-level scores and an explicit evidence trail for explainability. Our method achieves competitive iEEG classification performance and provides practitioners with greater transparency and explainability. We evaluate our method in iEEG recordings collected in four medical centers, demonstrating its potential for reliable and explainable clinical variable-length time series classification.