Beyond the Autoregressive Horizon: A Comprehensive Survey of Diffusion Models, World Modelling, and State Space Models for Code

2026-06-24T08:51:51Z38898e9562c635a9aa36c8a78011fa615250ca46facfb1f126f8afddd92cf48f
C-languageESAAGDPRLLM-agentsLOLASemChunk-Cautoregressive-modelsbinary-analysiscode-generationcode-intelligenceconversational-memorydiffusion-modelsevent-sourcinggoal-oriented-dialogues','GODR','Jupyter-notebooks','bug-dete c=graph-neural-networksmodel-based-diagnosisprivacy-engineeringprivacy-enhancing-technologiesreverse-engineeringruntime-verificationsemantic-chunkingstate-space-modelstransformerstype-inferenceworld-models

What happened

Collection of newly announced software-engineering papers (arXiv 2026-06-24) covering next-generation code models and architectures (diffusion models, code world models, state-space models) as alternatives to autoregressive LMs; advances in binary type-inference (transformers, GNNs) and reverse engineering; a systematic review of privacy engineering under GDPR and related PETs/metrics; semantic chunking for C-family code (SemChunk-C); unified runtime verification and model-based diagnosis in LOLA; event-sourced conversational memory for heterogeneous LLM coding agents (ESAA-Conversational); a框

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_se
Record identifier
38898e9562c635a9aa36c8a78011fa615250ca46facfb1f126f8afddd92cf48f
Enrichment time
2026-06-24T08:51:51Z
AI-assisted enrichment
Yes

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