3 accepted papers
Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation, modifying model inputs with instructions, strategies, or evidence, rather than weight updates. While prior methods improve usability, they often suffer from a brevity bias,…
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 1.5: a carefully…
Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of t…