arXiv:2512.03704v3 Announce Type: replace Abstract: Long-context dialogue systems suffer from state inertia, where models over-attend to history and fail to adapt to evolving intents. We demonstrate that standard alignment methods like DPO and even recent long-context optimization techniques struggle to resolve this without incurring a severe contextual alignment tax--a substantial perplexity surge caused by disrupting pre-trained priors. To address this, we propose DZ-TiDPO, a minimally invasive framework that synergizes conflict-aware optimization (during training) with a structural temporal
The increasing complexity of AI models, particularly in long-context dialogue systems, is pushing the boundaries of current alignment and optimization techniques.
This research addresses a critical limitation in AI's ability to maintain coherent and adaptive understanding over extended interactions, impacting the reliability and utility of advanced AI systems.
New methods for overcoming 'state inertia' could lead to more robust and adaptable AI agents, especially in dynamic environments where context frequently evolves.
- · AI developers
- · Companies deploying long-context AI
- · AI research community
- · Developers relying solely on traditional alignment methods
AI systems will become more adept at handling evolving user intents and complex, multi-turn conversations.
This improved contextual understanding could accelerate the development and adoption of AI agents for more sophisticated tasks.
More reliable autonomous AI agents could further drive the automation of white-collar workflows, transforming certain industries.
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Read at arXiv cs.CL