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Existing multimodal systems falter in repository-level localization, with the best performance still falling short of reliable accuracy thresholds.
SemantiCache achieves up to 2.61x faster decoding and reduces memory footprint without sacrificing model performance by compressing KV caches along semantic boundaries.
VLMs can't count blocks because they lack a view-consistent spatial interface, but decomposing scenes into orthographic projections fixes it.
Even reward models that get the right answer can be dangerously wrong in their reasoning, leading to worse RLHF outcomes, but R-Align fixes this by explicitly aligning rationales with gold standard judgments.
