arXiv:2606. 09672v1 Announce Type: new Abstract: Ask a pretrained biomedical language model whether "cortisol 28 ug/dL" and "stock-market volatility" are related, and it returns a cosine similarity of 0.
By Suraj Biswas, Saurabh Gupta, Pritam Mukherjee
The paper evaluates nine on‑device named‑entity recognition models ranging from classical taggers to large language models, measuring not only accuracy but also latency and output validity. Using a silver‑gold benchmark derived from an LLM judge panel and a human‑validated corpus, the study shows that encoder‑based models achieve comparable accuracy to a 4 B instruct LLM while being much smaller, faster, and producing no malformed output. Confidence calibration of GLiNER is analyzed, revealing over‑confidence but improved reliability after temperature scaling and thresholding.
By Vinay Kumar Chaganti
The study investigates whether particular attention heads and individual neurons within those heads in language models are responsible for detecting network infrastructure information—specifically hostnames paired with IP addresses. Using causal ablation and selective testing across five models from three architecture families, the authors find that a small subset of heads reliably identifies such information with near-perfect accuracy. However, the extent to which this responsibility is concentrated in a single neuron varies by model; in some cases a single neuron suffices, while in others the signal is distributed across the head. The findings generalize to an independent reverse‑DNS dataset, though single‑neuron detectors are less robust.
By Abdul Kadir (University of Oldenburg, Oldenburg, Germany, German Research Center for Artificial Intelligence), Md Mohasin Hossain (German Research Center for Artificial Intelligence, Saarland University, Saarbrucken, Germany), Daniel Sonntag (University of Oldenburg, Oldenburg, Germany, German Research Center for Artificial Intelligence)
arXiv:2608. 12365v1 Announce Type: cross Abstract: For fifty years, data systems have answered two questions.
By Ganesh S
The paper reports the ABAI submission to COLIEE 2026 Task 1, a case law retrieval challenge that suppresses cited passages, and details a four‑stage retrieval pipeline: multi‑view BM25 with reciprocal rank fusion, neural reranking, graph‑based features via a graph attention network, and a LightGBM meta‑learner over 34 features. The best run achieved an F1 score of 0.177 on the official test set, compared to a cross‑validated 0.311, and the authors attribute the gap to a recall ceiling, temporal distribution shift, and threshold miscalibration. A controlled post‑hoc study examined the impact of threshold transfer, decision quality across time, and query similarity, and identified specific remedies—such as BM25 length‑normalisation tuning, event‑triple views, and dense fusion—that improved recall, while other interventions had no effect.
By Minhan Cho, Soyoung Park, Daejin Choi, Jinyoung Han
arXiv:2608. 02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive models.
By Nitish Nagesh, Elahe Khatibi, Thomas Dean Hughes, Mahdi Bagheri, Pratik Gajane, Amir M. Rahmani