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arXiv:2603.15723v1 Announce Type: new Abstract: Large language models are increasingly deployed in settings where relevant information is embedded within long and noisy contexts. Despite this, …
arXiv:2603.15670v1 Announce Type: new Abstract: Real-world decision-making, from tax compliance assessment to medical diagnosis, requires aggregating multiple noisy and potentially contradictory evidence sources. Existing approaches …
arXiv:2603.15668v1 Announce Type: new Abstract: As agentic artificial intelligence systems scale across globally distributed and long lived infrastructures, secure and policy compliant communication becomes a …
arXiv:2603.16210v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is commonly implemented as a single static policy embedded in model parameters. However, …
arXiv:2603.15633v1 Announce Type: new Abstract: Answering complex first-order logic (FOL) queries on knowledge graphs is essential for reasoning. Symbolic methods offer interpretability but struggle with …
arXiv:2603.15968v1 Announce Type: new Abstract: Constitutional AI is a method to oversee and control LLMs based on a set of rules written in natural language. …
arXiv:2603.15965v1 Announce Type: new Abstract: Multi-adapter serving systems route entire sequences to a single adapter, forcing a choice when requests span multiple domains. This assumption …
arXiv:2603.15666v1 Announce Type: new Abstract: Existing memory systems for language agents address memory management: how to retrieve and page more information within a context budget. …
arXiv:2603.15903v1 Announce Type: new Abstract: Natural languages have been argued to evolve under pressure to efficiently compress meanings into words by optimizing the Information Bottleneck …
arXiv:2603.16264v1 Announce Type: new Abstract: Theory of Mind (ToM) refers to the ability to reason about others' mental states, and higher-order ToM involves considering that …
arXiv:2603.16313v1 Announce Type: new Abstract: Electronic control units (ECUs) embedded within modern vehicles generate a large number of asynchronous events known as diagnostic trouble codes …
arXiv:2603.15994v1 Announce Type: new Abstract: Retrieval-augmented generation stores all content indiscriminately, degrading accuracy as noise accumulates. Parametric approaches compress knowledge into weights, precluding selective updates. …