Year: 2026
Type: conference-paper
Abstract: The AKBC 2026 shared task (Kalo et al., 2026) asks a system to complete the object set of a subject and a relation out of a language model's parameters, with no retrieval, no training, and no more than 32B parameters.Our system drives one 29.6Bbackbone through three stages.Several elicitation channels per relation build a candidate pool, exhaustive onetoken Yes/No probes verify whatever part of the answer space can be enumerated, and a perrelation decision layer emits the object set.The backbone writes a reasoning channel before its user-facing answer, so a probe prompt has to append the answer-channel header before its first generated token is a verdict.Forcing that channel also makes auxiliary generations cheap, which is what puts roughly 26k border probes inside the budget.Most decision constants are fitted on validation gold under a rule that displaces an incumbent only on a strict improvement of more than one row, and the fitting procedure itself is cross-validated, which cut one city gain from +0.070 to +0.020 before it was adopted.The system scores 0.6553 official macro-F1 against 0.6415 for a prompting baseline on the same backbone, and the gap is uneven: the machinery is worth +0.1662 on awards and costs 0.0204 on hasCapacity, where plain prompting wins.We also report the ceiling we could not move.Twenty-one method families have been fitted against hasCapacity and none of them beats a mass vote over the pool.Harvesting the model's own reasoning traces raises pool coverage from 0.582 to 0.724 and converts none of it.
Authors: Yiqun Sun, Pengfei Wei, Lawrence B. Hsieh
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7224672236