Intelligent assistants based on long-context large language models (LLMs) face a system-level reliability problem: they must preserve useful conversational state while obeying user attempts to switch tasks. This paper studies a recurring failure mode in which a model imports obsolete terms, entities, constraints, affective framing, or answer formats from an earlier topic into a new one. We call the behavior contextual inertia and propose Attention Gravity as a bounded behavioral framework for measuring and mitigating it from input-output traces. The updated evidence package integrates Qwen, DashScope Qwen, SiliconFlow, and OpenRouter experiments across English, Chinese, controlled industrial prompts, and source-grounded industrial scenarios. The package contains 39,569 annotated rows, of which 39,567 have valid success/partial/failure labels; the overall heterogeneous carryover rate is 6.5%, but this pooled number is used only as an audit statistic rather than a primary claim. The strongest controlled English Qwen validation shows that 20-turn weak-switch carryover reaches 20.8%, compared with 0.8% at 0 turns. At 20 turns, interventions reduce carryover from 20.8% to 1.8% when explicit boundary, concrete new-task, and relevance-filter conditions are pooled. The newly added DashScope Qwen Chinese main validation contributes 8640 responses and shows the same direction: 20-turn weak-switch carryover is 15.9%, compared with 1.8% at 0 turns. Its Chinese counterfactual ablation shows original old-topic context at 18.3% versus 6.1% for weak-switch controls; an explicit-boundary robustness slice reduces original-context carryover to 1.9%, with no positive original-over-control effect. A completed 2304-row multi-length, two-seed addendum keeps weak-switch original-context carryover high across 5-, 10-, and 20-turn contexts while showing that explicit boundaries sharply suppress, though do not entirely eliminate, residual carryover. Cross-platform comparison strengthens but also bounds the claim. English 20-turn weak-switch carryover ranges from 2.8% to 20.8% across Qwen, SiliconFlow, and OpenRouter, while Chinese industrial 20-turn weak-switch carryover remains high across platforms, ranging from 18.3% to 28.9%. Real-data-grounded industrial prompts show aggregate carryover but low harmful carryover, indicating that industrial systems require controlled continuity rather than indiscriminate forgetting. A 480-row stratified human validation confirms that the rubric is human-reproducible (four-class agreement 93.3%, kappa = 0.831; binary carryover kappa = 0.926), while the automatic judge should be treated as a high-recall, low-precision conservative screen (binary carryover recall 95.8%, precision 47.1%). We conclude that Attention Gravity is a reproducible behavioral evaluation framework, not a proven universal mechanism of self-attention.