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Research2026-08-27 · source-backed
The authors extract a steering direction from the model's existing tool-use preference signal and apply it at inference, producing monotonic control over how often the agent reaches for a tool while keeping invocations valid (arXiv 2608.25198). Open-domain QA accuracy with live tool execution nearly doubled, from 0.29 to 0.56, by tuning the rate rather than the prompt. It generalizes to unseen tools and works across dense, MoE and multimodal architectures. That makes tool-call frequency a deployment knob you set per environment instead of a paragraph you keep rewriting in the system prompt.
Each link below shares sources, entities, or timing with this story.
Shared entity: MoE / Same source domain / Shared topic / Earlier coverage
Both cover MoE; reported by the same outlet (arxiv.org); overlapping topics (agent, dense, deployment).
Both cover MoE; reported by the same outlet (arxiv.org); overlapping topics (agent, author).
Both cover MoE; reported by the same outlet (arxiv.org); overlapping topics (accuracy, dense).
Shared entity: MoE / Same source domain / Earlier coverage / Tension
Both cover MoE; reported by the same outlet (arxiv.org); earlier MoE coverage from 2026-08-12.
Shared entity: MoE / Shared topic / Earlier coverage / Tension
Both cover MoE; overlapping topics (deployment, over); earlier MoE coverage from 2026-07-27.
Both cover MoE; overlapping topics (agent, prompt); earlier MoE coverage from 2026-07-25.
Shared entity: MoE / Same source domain / Earlier coverage / Downstream implication
Both cover MoE; reported by the same outlet (arxiv.org); earlier MoE coverage from 2026-07-20.
Shared entity: MoE / Shared topic / Earlier coverage / Tension
Both cover MoE; overlapping topics (agent, control); earlier MoE coverage from 2026-03-17.