This program is tentative and subject to change.

Fri 19 Jun 2026 15:50 - 16:10 at Flatirons 3 - Potpourri

We propose an incremental approach for safety proofs that decomposes a proof with a complex inductive invariant into a sequence of simpler proof steps. Our proof system combines rules for (i) forward reasoning using inductive invariants, (ii) backward reasoning using inductive invariants of a time-reversed system, and (iii) prophecy steps that add witnesses for existentially quantified properties. We prove each rule sound and give a construction that recovers a single safe inductive invariant from an incremental proof. The construction of the invariant demonstrates the increased complexity of a single inductive invariant compared to the invariant formulas used in an incremental proof, which may have simpler Boolean structures and fewer quantifiers and quantifier alternations. Under natural restrictions on the available invariant formulas, each proof rule strictly increases proof power. That is, each rule allows to prove more safety problems with the same set of formulas. Thus, the incremental approach is able to reduce the search space of invariant formulas needed to prove safety of a given system. A case study on Paxos, several of its variants, and Raft demonstrates that forward-backward steps can remove complex Boolean structure while prophecy eliminates quantifiers and quantifier alternations.

This program is tentative and subject to change.

Fri 19 Jun

Displayed time zone: Mountain Time (US & Canada) change

15:50 - 17:10
15:50
20m
Talk
Simplifying Safety Proofs with Forward-Backward Reasoning and Prophecy
PLDI Research Papers
Eden Frenkel Tel Aviv University, Kenneth L. McMillan University of Texas at Austin, Oded Padon Weizmann Institute of Science, Sharon Shoham Tel Aviv University
DOI
16:10
20m
Talk
TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation
PLDI Research Papers
Henrijs Princis University of Bristol, Arindam Sharma University of Bristol, Cristina David University of Bristol
DOI
16:30
20m
Talk
[TOPLAS] Guiding LLM-based Loop Invariant Synthesis via Feedback on Local Reasoning Errors
PLDI Research Papers
Tianchi Li Peking University, China, Zhenyu Yan Peking University, Junhao Liu Peking University, Peng Di Ant Group & UNSW Sydney, Xin Zhang Peking University
16:50
20m
Talk
[SIGPLAN] Active Learning for Neurosymbolic Program Synthesis
PLDI Research Papers
Celeste Barnaby University of Texas at Austin, Jocelyn Qiaochu Chen New York University, University of Alberta, Ramya Ramalingam , Osbert Bastani University of Pennsylvania, Işıl Dillig University of Texas at Austin