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This repository contains various tools to support interactive theorem proving in Isabelle/HOL using artificial intelligence. This repository contains the implementation of proof strategy language (PSL) and its default strategy, try_hard, for Isabelle2025. Past versions of Isabelle, such as Isabelle2022-1, are no longer supported.
We opened a YouTube channel to introduce aspects of this project.
The basic steps are the same as MacOS and Linux. However, instead of using the binary file directly, use Isabelle2025-2\Cygwin-Terminal in Command Prompt. Once you start Isabelle2025-2\Cygwin-Terminal, you can install our tools by typing isabelle jedit -d (path to the directory that contains this README file) -l Smart_Isabelle. Note that once you started Isabelle2025-2\Cygwin-Terminal, you should not specify the path to the Isabelle binary file. Therefore, the command you need after starting Isabelle2025-2\Cygwin-Terminal is something like isabelle jedit -d . -l Smart_Isabelle, assuming that your current directory is this one with this README.md/
If you find it difficult to install our tool, please refer to the Isabelle System Manual. Alternatively, you can just send an email to Yutaka at united.reasoning+gmail.com (reaplace + with @).
prove/prove_by_abduction only work at the very start of a proof attempt: they parse a fresh top-level goal statement, so they cannot be used inside a structured Isar proof (after proof (induct x), at a show/case) or partway through a chain of apply steps. suggest/solve lift this restriction: like sledgehammer/find_proof/try_hard, they read the goal from whichever proof is currently open, wherever that is - inside a structured proof, or mid an apply chain - and only ever attack subgoal 1, matching apply's own convention.
suggest prints a suggested proof script rather than closing the goal itself, for you to copy in by hand:
lemma foo: "..." proof (induct x) case (Suc x) suggest (* suggests a script for this subgoal, using Suc.IH etc. as available *) ...
Any auxiliary lemmas it needed are suggested as their own have name: "..." for ... blocks nested inside the current proof (a top-level lemma would be illegal syntax while a proof is already open); the goal itself is suggested as a bare tactic script with no such header, ready to paste in directly as the continuation of the current proof.
solve runs the same search but, if successful, applies the found proof directly and closes the goal itself - no copying required:
lemma foo: "..." proof (induct x) case (Suc x) solve (* closes this subgoal automatically, if AbductionProver finds a proof *) ...
It works equally well directly after proof - (with no show yet) and mid an apply chain at the bottom of a proof, closing exactly as done would there. If AbductionProver cannot find a proof, solve raises an error rather than silently leaving the goal open - try suggest at that point to see whatever progress it made.
PSL's runtime tactic generation can result in a large number of messages in Isabelle/jEdit's output panel. This might cause Isabelle/jEdit to pause PSL's proof search after reaching its default upper limit for tracing messages.

Isabelle's default threads = 0 ("guess from hardware") relies on Poly/ML's physical-core detection (Thread.Thread.numPhysicalProcessors), which can badly under-count CPU cores on cloud/KVM virtual machines whose /proc/cpuinfo reports each vCPU as its own single-core physical id (a common vCPU topology on many cloud providers). When this happens, isabelle build/isabelle jedit can end up running with effectively 1 worker thread, even on an 8-, 16-, or 32-core VM. PSL, TBC, and AbductionProver rely heavily on Par_List/POrs-style parallelism (e.g. Par_List.get_some, which does run alternative tactics in parallel and cancel the losers once one succeeds, exactly as documented) — but with only 1 thread available, those "parallel" alternatives are effectively tried one at a time, so even a trivial one-step-induction goal can take minutes instead of seconds.
Symptom: a goal that should take a few seconds instead takes minutes, and isabelle build -v reports (1 threads, ...) in its timing summary even though the machine has many more cores (check with nproc).
Fix: always pass the real core count explicitly, e.g.:
isabelle build -o threads=$(nproc) -d . <session> isabelle jedit -o threads=$(nproc) -d . -l Smart_Isabelle
Note: a threads value baked into a session's ROOT file options [...] clause takes priority over -o threads=... on the command line (confirmed empirically), so we deliberately do not hardcode a threads default in this repository's ROOT files — always pass -o threads=$(nproc) per invocation instead, so it stays correct across machines of any size.
Isabelle bounds an external prover's time but not its memory — veriT, for instance, is invoked with only --max-time. A prover that starts diverging therefore allocates freely until it finishes, its time budget expires, or the machine runs out of memory. Measured here: a single diverging veriT reached 6.35GB while the Isabelle ML process held only 1.9GB, so capping the ML heap alone does not help — it merely leaves more room for the provers to take.
Symptom: a run dies with the OS OOM killer, with Poly/ML's Run out of store, or with AbductionProver's own stopping early: running low on memory. In the recorded position matrix this was 12 of 37 runs.
There are two limits, at two levels, and they share one number.
1. How many Sledgehammer invocations run at once (on by default). The budget used to be derived from processors alone — cores / 2, so six concurrent invocations on a 12-core machine, each launching every prover in sledgehammer_provers, against a machine one prover can fill by itself. It now answers to memory as well:
slots = max(1, min(cores / 2, free_memory_MB / hammer_memory_quota_mb))
Read against free memory rather than total, so the budget narrows as a search grows. Override either part from a theory:
declare [[max_parallel_hammers = 4]] (* explicit slot count, wins outright *)
declare [[hammer_memory_quota_mb = 3072]] (* memory assumed per invocation; default 2048 *)2. A ceiling on each external prover process (off by default, opt-in). Isabelle locates every prover through a settings variable, so the only place a per-process limit can be imposed is between that variable and the real binary — not from ML, which never sees the launch. Register this repository as an Isabelle component and its etc/settings points those variables at wrappers in contrib/prover_wrappers, keeping the originals in PSL_REAL_<VARIABLE>:
echo /path/to/PSL >> "$(isabelle getenv -b ISABELLE_HOME_USER)/etc/components"
With that done, nothing changes until you ask for a limit:
PSL_PROVER_MEMORY_MB=2048 isabelle build -o threads=$(nproc) <session>
Each prover then runs in its own transient cgroup with that resident-memory ceiling (systemd-run --user --scope -p MemoryMax=..., plus MemorySwapMax=0, because a ceiling a solver can page around is not a ceiling). A prover that exceeds it is killed; Sledgehammer records that prover as failed and carries on with the others, and the session survives. Keep this number and hammer_memory_quota_mb in step: one decides how many provers are admitted, the other what each is allowed, and if they disagree the search admits more provers than the machine has ceilings for.
Three things to know before enabling it:
The validated fallback, if you would rather not enable the wrappers, remains an ML heap ceiling plus the hammer budget (see Eval/run_eval.sh): those converted every SIGKILL tested into a completed run and left 6/6 previously-succeeding targets still succeeding.
We published academic papers describing the ideas implemented in this project.
We presented the final goal of this project at AITP2017. Our position paper "Towards Smart Proof Search for Isabelle" is available at arXiv.
We also plan to improve the proof automation using evolutionary computation. We presented our plan during the poster session at GECCO2019. Our poster-only paper is available at ACM digital library and arXiv.
PSL: Nagashima, Y., Kumar, R. (2017). A Proof Strategy Language and Proof Script Generation for Isabelle/HOL. In: de Moura, L. (eds) Automated Deduction – CADE 26. CADE 2017. Lecture Notes in Computer Science(), vol 10395. Springer, Cham. https://doi.org/10.1007/978-3-319-63046-5_32
PGT: Nagashima, Y., Parsert, J. (2018). Goal-Oriented Conjecturing for Isabelle/HOL. In: Rabe, F., Farmer, W., Passmore, G., Youssef, A. (eds) Intelligent Computer Mathematics. CICM 2018. Lecture Notes in Computer Science(), vol 11006. Springer, Cham. https://doi.org/10.1007/978-3-319-96812-4_19
PaMpeR: Yutaka Nagashima and Yilun He. 2018. PaMpeR: proof method recommendation system for Isabelle/HOL. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE 2018). Association for Computing Machinery, New York, NY, USA, 362–372. DOI:https://doi.org/10.1145/3238147.3238210
Towards Evolutionary Theorem Proving for Isabelle/HOL: Yutaka Nagashima. 2019. Towards evolutionary theorem proving for Isabelle/HOL. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ’19). Association for Computing Machinery, New York, NY, USA, 419–420. DOI:https://doi.org/10.1145/3319619.3321921
LiFtEr: Nagashima, Y. (2019). LiFtEr: Language to Encode Induction Heuristics for Isabelle/HOL. In: Lin, A. (eds) Programming Languages and Systems. APLAS 2019. Lecture Notes in Computer Science(), vol 11893. Springer, Cham. https://doi.org/10.1007/978-3-030-34175-6_14
Simple Dataset Nagashima Y. (2020) Simple Dataset for Proof Method Recommendation in Isabelle/HOL. In: Benzmüller C., Miller B. (eds) Intelligent Computer Mathematics. CICM 2020. Lecture Notes in Computer Science, vol 12236. Springer, Cham. https://doi.org/10.1007/978-3-030-53518-6_21
Smart Induction Yutaka Nagashima. Smart Induction for Isabelle/HOL (Tool Paper). In: Ivrii A., Strichman O. (eds) Proceedings of the 20th Conference on Formal Methods in Computer-Aided Design – FMCAD 2020 DOI:https://doi.org/10.34727/2020/isbn.978-3-85448-042-6_32
sem_ind Yutaka Nagashima. Faster Smarter Proof by Induction in Isabelle/HOL. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence Main Track. Pages 1981-1988 DOI:https://doi.org/10.24963/ijcai.2021/273
Definitional Quantifier and SeLFiE Nagashima, Y. (2022). Definitional Quantifiers Realise Semantic Reasoning for Proof by Induction. In: Kovács, L., Meinke, K. (eds) Tests and Proofs. TAP 2022. Lecture Notes in Computer Science, vol 13361. Springer, Cham. https://doi.org/10.1007/978-3-031-09827-7_4
Template-Based Conjecturing Nagashima, Y., Xu, Z., Wang, N., Goc, D.S., Bang, J. (2023). Template-Based Conjecturing for Automated Induction in Isabelle/HOL. In: Hojjat, H., Ábrahám, E. (eds) Fundamentals of Software Engineering. FSEN 2023. Lecture Notes in Computer Science, vol 14155 . Springer, Cham. https://doi.org/10.1007/978-3-031-42441-0_9
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