TreeDiff: AST-Guided Code Generation with Diffusion LLMs (2026 · 3 citations)
Diffusion-based language models (DLLMs) generate text by iteratively un-masking a corrupted sequence. They do this poorly for code, partly because training masks tokens uniformly at random, ignoring program grammar. As a result, models often produce syntactically broken drafts and miss long-range relations such as…
EdgeFormer: Latency-Aware Collaborative Multi-Head Attention of Transformer Inference in Edge Networks (2026)
Cloud-hosted Transformer models require users to send potentially sensitive data to third parties. Splitting inference across trusted edge devices keeps data local, but earlier systems partition the model statically and treat multi-head attention as one indivisible unit. EdgeFormer addresses both problems. First, it…
OntoGuard: Enforcing Action Admissibility for LLM Agents in Complex Interactive Environments (2026)
LLM agents in interactive simulators often hallucinate. They attempt actions that are impossible in the current state or that break the required order of steps. Methods based on imitation or sparse-reward learning only fit the statistics of the data and never represent these rules explicitly. OntoGuard is a…

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Updated 2 November 2026