Public articles linked to the same research event.
arXiv JOVE is an online framework that decomposes complex reasoning queries into directed acyclic task graphs and distributes them across heterogeneous LLMs while jointly deciding which intermediate outputs to send for paid verification; it makes execution and verification decisions by solving a sequence of per-query mixed-integer linear programs, updates task-dependent LLM quality estimates online from verification feedback, and adds an information-gain bonus that folds the value of learning into allocation, achieving sublinear quality-learning regret under a long-term budget and a per-query latency constraint, with competitive accuracy against standard inference baselines and at least 3.17x lower average cost and latency across four reasoning benchmarks.
JOVE is an online framework that decomposes complex reasoning queries into directed acyclic task graphs and distributes them across heterogeneous LLMs while jointly deciding which intermediate outputs to send for paid verification; it makes execution and verification decisions by solving a sequence of per-query mixed-integer linear programs, updates task-dependent LLM quality estimates online from verification feedback, and adds an information-gain bonus that folds the value of learning into allocation, achieving sublinear quality-learning regret under a long-term budget and a per-query latency constraint, with competitive accuracy against standard inference baselines and at least 3.17x lower average cost and latency across four reasoning benchmarks.
JOVE is an online framework that decomposes complex reasoning queries into directed acyclic task graphs and distributes them across heterogeneous LLMs while jointly deciding which intermediate outputs to send for paid verification; it makes execution and verification decisions by solving a sequence of per-query mixed-integer linear programs, updates task-dependent LLM quality estimates online from verification feedback, and adds an information-gain bonus that folds the value of learning into allocation, achieving sublinear quality-learning regret under a long-term budget and a per-query latency constraint, with competitive accuracy against standard inference baselines and at least 3.17x lower average cost and latency across four reasoning benchmarks.
JOVE is an online framework that decomposes complex reasoning queries into directed acyclic task graphs and distributes them across heterogeneous LLMs while jointly deciding which intermediate outputs to send for paid verification; it makes execution and verification decisions by solving a sequence of per-query mixed-integer linear programs, updates task-dependent LLM quality estimates online from verification feedback, and adds an information-gain bonus that folds the value of learning into allocation, achieving sublinear quality-learning regret under a long-term budget and a per-query latency constraint, with competitive accuracy against standard inference baselines and at least 3.17x lower average cost and latency across four reasoning benchmarks.