PATHSEEK: MODEL- AND HARNESS-AGNOSTIC AGENTIC TRAJECTORY OPTIMIZATION

Continued advances in models and agent execution frameworks (harness) have enhanced the capabilities of research agents, yet early-stage trajectory deviations and unsupported intermediate claims can still undermine research quality. We propose PathSeek, which introduces trajectory optimization as a capability-enhancement mechanism that is orthogonal to, and can be combined with, model enhancement and harness improvement, further extending the boundaries of agent capabilities. PathSeek encapsulates one or more consecutive rounds of Reasoning–Action–Observation interaction into a bounded Agent Event, uses executed trajectory prefixes as tree nodes and event-level continuations as search edges, and connects different model–harness combinations through a unified interface. To evaluate unfinished research trajectories, Claim–Evidence Auditing examines the evidence supporting key claims and the dependencies among them, while Middle-Candidate Verification assesses task relevance and overall quality. Together, they provide process-value assessments and corrective feedback at event boundaries to guide branch selection, evidence supplementation, and trajectory adjustment. Across four sets of Deep Research and scientific reasoning benchmarks and multiple combinations of five representative models and three harnesses, PathSeek achieves broad performance improvements and a score of 71.4 on SGI-Bench DeepResearch, demonstrating that our method can more effectively translate existing model capabilities and harnesses into high-quality agent research trajectories.