Long-video agents face a fundamental constraint: they must process hours or days of multimodal evidence while keeping context within token and latency budgets. In the arXiv preprint LVMH: A Meta-Optimization Harness for Long-Video Agent Memory (arXiv:2610.23904), the authors frame this as a context-management challenge rather than a storage problem, arguing that memory design is what determines whether an agent can navigate extended video evidence effectively.
The paper's title points to a meta-optimization harness as the proposed solution, but the abstract as provided to us is truncated after noting that existing memory systems largely rely on... unspecified approaches. No further details about the method or experimental results are available in the source text.
Because only a single source was supplied, there are no independent claims to compare. The key takeaway from the available text is the problem framing: long-video agent memory must be optimized under strict resource budgets, and the authors position LVMH as a harness for that optimization.