Can LLMs Perform Synthesis?
2026-03-24T08:52:06Z•540322082fd23d7d8ef39ca6b3b15afcf1f964a9fad211a51542dc35d680934d
AI hardwareErlangLLM agentsLLMsMINISASyGuSabstract machinesaccess controlchoreographic programmingdistributed protocolsdistributed transactionsfault toleranceinstruction set architecturepermissionsprogram synthesisprogram verificationreactive synthesisreconfigurable acceleratorsrepresentationruntime systemsset-theoretic typesstatic typessymbolic regressionsymbolic synthesistype checking
What happened
Collection of recent programming-languages and systems research from arXiv covering: (1) an empirical comparison of LLMs (Qwen-32B coupled with a verifier, GPT-5) against state-of-the-art symbolic program synthesis tools across domains (LTL/reactive synthesis, SyGuS, distributed protocol and recursive-function synthesis) showing symbolic tools solve more benchmarks and are faster; (2) Accompanist, a resilient runtime for choreographic programming that enables decentralised saga-style transactions via sidecars and provides formal correctness proofs under modest assumptions (determinism, idempot
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_pl
- Record identifier
- 540322082fd23d7d8ef39ca6b3b15afcf1f964a9fad211a51542dc35d680934d
- Enrichment time
- 2026-03-24T08:52:06Z
- AI-assisted enrichment
- Yes
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