| --- |
| license: cc-by-4.0 |
| configs: |
| - config_name: nl2rule |
| data_files: |
| - split: K-Standard-NumRule5-RandomSampleFalse |
| path: nl2rule/K-Standard-NumRule5-RandomSampleFalse-* |
| - split: K-Standard-NumRule5-RandomSampleTrue |
| path: nl2rule/K-Standard-NumRule5-RandomSampleTrue-* |
| - split: K-NonStandard-NumRule5-RandomSampleFalse |
| path: nl2rule/K-NonStandard-NumRule5-RandomSampleFalse-* |
| - split: K-NonStandard-NumRule5-RandomSampleTrue |
| path: nl2rule/K-NonStandard-NumRule5-RandomSampleTrue-* |
| - split: S-Standard-NumRule5-RandomSampleFalse |
| path: nl2rule/S-Standard-NumRule5-RandomSampleFalse-* |
| - split: S-Standard-NumRule5-RandomSampleTrue |
| path: nl2rule/S-Standard-NumRule5-RandomSampleTrue-* |
| - split: S-NonStandard-NumRule5-RandomSampleFalse |
| path: nl2rule/S-NonStandard-NumRule5-RandomSampleFalse-* |
| - split: S-NonStandard-NumRule5-RandomSampleTrue |
| path: nl2rule/S-NonStandard-NumRule5-RandomSampleTrue-* |
| - config_name: rule2nl |
| data_files: |
| - split: K-Standard-NumDescription5-RandomSampleFalse |
| path: rule2nl/K-Standard-NumDescription5-RandomSampleFalse-* |
| - split: K-Standard-NumDescription5-RandomSampleTrue |
| path: rule2nl/K-Standard-NumDescription5-RandomSampleTrue-* |
| - split: K-NonStandard-NumDescription5-RandomSampleFalse |
| path: rule2nl/K-NonStandard-NumDescription5-RandomSampleFalse-* |
| - split: K-NonStandard-NumDescription5-RandomSampleTrue |
| path: rule2nl/K-NonStandard-NumDescription5-RandomSampleTrue-* |
| - split: S-Standard-NumDescription5-RandomSampleFalse |
| path: rule2nl/S-Standard-NumDescription5-RandomSampleFalse-* |
| - split: S-Standard-NumDescription5-RandomSampleTrue |
| path: rule2nl/S-Standard-NumDescription5-RandomSampleTrue-* |
| - split: S-NonStandard-NumDescription5-RandomSampleFalse |
| path: rule2nl/S-NonStandard-NumDescription5-RandomSampleFalse-* |
| - split: S-NonStandard-NumDescription5-RandomSampleTrue |
| path: rule2nl/S-NonStandard-NumDescription5-RandomSampleTrue-* |
| - config_name: predrule |
| data_files: |
| - split: K-Standard-human-written |
| path: predrule/K-Standard-human-written-* |
| - split: K-NonStandard-human-written |
| path: predrule/K-NonStandard-human-written-* |
| - split: S-Standard-human-written |
| path: predrule/S-Standard-human-written-* |
| - split: S-NonStandard-human-written |
| path: predrule/S-NonStandard-human-written-* |
| - config_name: predstate |
| data_files: |
| - split: K-Standard-human-written |
| path: predstate/K-Standard-human-written-* |
| - split: K-NonStandard-human-written |
| path: predstate/K-NonStandard-human-written-* |
| - split: S-Standard-human-written |
| path: predstate/S-Standard-human-written-* |
| - split: S-NonStandard-human-written |
| path: predstate/S-NonStandard-human-written-* |
| - split: K-Standard-llm-translated |
| path: predstate/K-Standard-llm-translated-* |
| - split: K-NonStandard-llm-translated |
| path: predstate/K-NonStandard-llm-translated-* |
| - split: S-Standard-llm-translated |
| path: predstate/S-Standard-llm-translated-* |
| - split: S-NonStandard-llm-translated |
| path: predstate/S-NonStandard-llm-translated-* |
| - split: K-Standard-fuzzer-generated |
| path: predstate/K-Standard-fuzzer-generated-* |
| - split: K-NonStandard-fuzzer-generated |
| path: predstate/K-NonStandard-fuzzer-generated-* |
| - split: S-Standard-fuzzer-generated |
| path: predstate/S-Standard-fuzzer-generated-* |
| - split: S-NonStandard-fuzzer-generated |
| path: predstate/S-NonStandard-fuzzer-generated-* |
| - config_name: predtrace |
| data_files: |
| - split: K-Standard-human-written |
| path: predtrace/K-Standard-human-written-* |
| - split: K-NonStandard-human-written |
| path: predtrace/K-NonStandard-human-written-* |
| - split: S-Standard-human-written |
| path: predtrace/S-Standard-human-written-* |
| - split: S-NonStandard-human-written |
| path: predtrace/S-NonStandard-human-written-* |
| --- |
| |
| <div align="center"> |
| <p>The 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea</p> |
| <h1> |
| LLMs Lean on Priors, Not Programming Language |
| <span style="white-space: nowrap;"> |
| Semantics |
| <img |
| src="https://raw.githubusercontent.com/EngineeringSoftware/PLSemanticsBench/main/docs/icons/logo.png" |
| alt="PLSemanticsBench logo" |
| width="60" |
| style="display: inline-block !important; |
| vertical-align: -1.0em; |
| margin-left: 10px; |
| margin-bottom: 0;"> |
| </span> |
| </h1> |
| |
| <p style="font-size: 20px;"> |
| by |
| <a href="https://www.adityathimmaiah.com">Aditya Thimmaiah</a><sup>1</sup>, |
| <a href="https://jiyangzhang.github.io/">Jiyang Zhang</a><sup>1</sup>, |
| <a href="https://scholar.google.com/citations?user=HtNfeKYAAAAJ&hl=en">Jayanth Srinivasa</a><sup>2</sup>, |
| <a href="https://www.jessyli.com">Junyi Jessy Li</a><sup>1</sup>, |
| <a href="https://users.ece.utexas.edu/~gligoric/">Milos Gligoric</a><sup>1</sup> |
| </p> |
| |
| <p> |
| <sup>1</sup>The University of Texas at Austin |
| <sup>2</sup>Cisco Research |
| </p> |
| </div> |
| |
| <div align="center"> |
|
|
| [](https://engineeringsoftware.github.io/PLSemanticsBench/) |
| [](https://arxiv.org/pdf/2510.03415v3) |
| [](https://github.com/EngineeringSoftware/PLSemanticsBench) |
| [](https://huggingface.co/datasets/EngineeringSoftware/PLSemanticsBench) |
|
|
| </div> |
|
|
| --- |
|
|
| **TLDR:** Frontier LLMs execute programs with up to 90–100% accuracy when symbols retain their usual meanings (e.g., + means addition). Under counterfactual semantic shifts (e.g., redefining + to mean subtraction) accuracy collapses by 40–70 percentage points. Despite handing the complete formal rules, the models keep answering as if the rules were never changed. |
| LLMs don't faithfully interpret the semantics they are given—they retrieve what symbols usually mean from pretraining. |
|
|
| ## Abstract |
| Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pre-training. |
| Program execution provides a canonical instance: formal semantics define behavior through symbolic transition rules that can be systematically altered |
| under distribution shift. We investigate whether LLMs can condition their reasoning on formal semantics through program execution and introduce PLSEMANTICSBENCH, |
| pairing featherweight C programs with two semantic systems—small-step operational semantics and K semantics—and probing four capabilities: composing rules for |
| final states, selecting rules when state is unmutated, sustaining such conditioning over long traces, and following supplied rules under novel semantics. To |
| decouple semantic reasoning from syntactic familiarity, we redefine familiar operators to induce symbol-meaning conflict and introduce novel symbols defined only |
| through the supplied rules, and stress-test models on Human-Written, LLM-Translated, and Fuzzer-Generated splits with increasing structural complexity. Across |
| 11 frontier LLMs, strong final-state accuracy under standard semantics (up to 90%) drops sharply—by as much as 40–60% points—under semantic mutations and increasing |
| structural complexity. Only a handful of models achieve non-zero long-horizon conditioning accuracy, and even the best systems reach just 35%. Together, these results |
| suggest that contemporary LLMs often rely on pretrained lexical associations rather than systematically conditioning on supplied formal rules. |
|
|
| ## Table of Contents |
| - [About](#about) |
| - [Installation](#installation) |
| - [Quick Start](#quick-start) |
| - [Detailed Usage](#detailed-usage) |
| - [Benchmark](#benchmark) |
| - [Citation](#citation) |
|
|
| ## About |
| PLSemanticsBench is the first counterfactual PL semantics dataset for rule-conditioned reasoning of LLMs. We introduce three tasks to evaluate this: |
|
|
| | Task | Description | |
| |------|-------------| |
| | ✨ **PredState**| Predicts the final program state | |
| | ✨ **PredRule** | Predicts the ordered sequence of semantic rules needed to evaluate a program| |
| | ✨ **PredTrace**| Predicts the step-by-step execution of a program | |
|
|
| You must implement [BaseRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/base_experiment.py)(`_query` method) to evaluate your models. We provide two example implementations for OpenAI models ([GPTRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/gpt_experiment.py)) and Ollama models ([OllamaRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/ollama_experiment.py)). |
|
|
| ## Installation |
|
|
| ### System Requirements |
| - Python 3.11 or higher |
| - OpenAI API key (for running experiments with OpenAI models) |
|
|
|
|
| ### Step-by-Step Installation |
| 1. Create and activate the conda environment: |
| ```bash |
| conda env create -f env.yaml |
| conda activate plsemanticsbench |
| ``` |
|
|
| 2. Set up your OpenAI API key (only for OpenAI models): |
| ```bash |
| export OPENAI_API_KEY='your-api-key-here' |
| ``` |
|
|
| ## Quick Start |
|
|
| We provide a bash script `quick` that: |
| 1. Sets up the `plsemanticsbench` conda environment. |
| 2. Pulls the `DeepSeek-R1 1.5B` model. |
| 3. Evaluates the `DeepSeek-R1 1.5B` model on the `PredState` task with `no-semantics` and `chain-of-thought` prompting on the `Human-Written` dataset. |
| 4. Prints the `accuracy` and `malformed-count` to screen. |
| 5. Creates `metrics-predstate-deepseek-r1:1.5b.json` that contains the evaluation result. |
| |
| ```bash |
| bash quick |
| ``` |
|
|
| ## Detailed Usage |
|
|
| ### Basic Example |
| Here's a minimal example to get started: |
|
|
| ```python |
| from plsemanticsbench import GPTRunner |
| from plsemanticsbench import ExperimentArgs, LLMEvaluator |
| from plsemanticsbench import ( |
| PROMPT_STRATEGY, |
| Task, |
| Formalization, |
| Semantics_Type, |
| Language, |
| PLDataset |
| ) |
| |
| # Model name |
| model_name = "o3-mini" |
| |
| # Experiment args: Run the PredState task on the IMP language with |
| # standard semantics formalized using SOS and with direct prompting |
| exp_args = ExperimentArgs( |
| dataset=PLDataset.Human_Written, |
| task=Task.PredState, |
| language=Language.IMP, |
| formalization=Formalization.SOS, |
| semantics_type=Semantics_Type.Standard, |
| model_name=model_name, |
| prompt_strategy=PROMPT_STRATEGY.DA, |
| num_datapoints_to_run=2, # Run just 2 datapoints (omit to run entire dataset) |
| ) |
| |
| # Run inference using the OpenAI API |
| gpt_runner = GPTRunner(args=exp_args) |
| |
| # Generation (generate LLM prediction on the predstate task) |
| predictions = gpt_runner.do_experiment() # path to dump results can be provided |
| |
| # Evaluation (evaluate LLM prediction against ground-truth) |
| llm_eval = LLMEvaluator(task=exp_args.task, semantics_type=exp_args.semantics_type) |
| evaluation_result = llm_eval.evaluate_from_list(results=predictions, model_name=model_name) |
| print(evaluation_result) |
| ``` |
|
|
| ### Expected Output |
|
|
| ```python |
| { |
| 'accuracy': 1, |
| 'malformed-count': 0, |
| } |
| ``` |
|
|
| ## Benchmark |
|
|
| You can load the dataset using the `datasets` library. Here is an example: |
| ```python |
| from datasets import load_dataset |
| |
| # Load PredState task with standard semantics (uk) and K-semantics formalization (K) and with the Human Written (human-written) dataset |
| predstate_IMP_K_uk_human_written = load_dataset("EngineeringSoftware/PLSemanticsBench", name="predstate-IMP-K-uk-human-written") |
| |
| # Load PredRule task with nonstandard semantics (mk) ans SOS formalization (SOS) and with the LLM Translated (llm-translated) dataset |
| predrule_IMP_SOS_mk_llm_translated = load_dataset("EngineeringSoftware/PLSemanticsBench", name="predrule-IMP-SOS-mk-llm-translated") |
| |
| # Load PredState task with no-semantics (nk) and with the Fuzzer Generated (fuzzer-generated) dataset |
| predstate_IMP_nk_fuzzer_generated = load_dataset("EngineeringSoftware/PLSemanticsBench", name="predstate-IMP-nk-fuzzer-generated") |
| ``` |
|
|
| ### Dataset Split |
|
|
| <table> |
| <tr> |
| <th>Task</th> |
| <th>Split</th> |
| <th>Description</th> |
| </tr> |
| <tr> |
| <td rowspan="5">✨ <strong>PredState</strong><br>(Final State Prediction)</td> |
| <td> predstate-IMP-nk-{dataset-name} </td> |
| <td> No semantics </td> |
| </tr> |
| <tr> |
| <td> predstate-IMP-K-uk-{dataset-name} </td> |
| <td>Standard semantics with K-semantics formalization</td> |
| </tr> |
| <tr> |
| <td> predstate-IMP-K-mk-{dataset-name} </td> |
| <td>Nonstandard semantics with K-semantics formalization</td> |
| </tr> |
| <tr> |
| <td> predstate-IMP-SOS-uk-{dataset-name} </td> |
| <td>Standard semantics with SOS formalization</td> |
| </tr> |
| <tr> |
| <td> predstate-IMP-SOS-mk-{dataset-name} </td> |
| <td>Nonstandard semantics with SOS formalization</td> |
| </tr> |
| <tr> |
| <td rowspan="4">✨ <strong>PredRule</strong><br>(Semantic Rule Prediction)</td> |
| <td> predrule-IMP-K-uk-human-written </td> |
| <td>Standard semantics with K-semantics formalization</td> |
| </tr> |
| <tr> |
| <td> predrule-IMP-K-mk-human-written </td> |
| <td>Nonstandard semantics with K-semantics formalization</td> |
| </tr> |
| <tr> |
| <td> predrule-IMP-SOS-uk-human-written </td> |
| <td>Standard semantics with SOS formalization</td> |
| </tr> |
| <tr> |
| <td> predrule-IMP-SOS-mk-human-written </td> |
| <td>Nonstandard semantics with SOS formalization</td> |
| </tr> |
| <tr> |
| <td rowspan="4">✨ <strong>PredTrace</strong><br>(Execution Trace Prediction)</td> |
| <td> predtrace-IMP-K-uk-human-written </td> |
| <td>Standard semantics with K-semantics formalization</td> |
| </tr> |
| <tr> |
| <td> predtrace-IMP-K-mk-human-written </td> |
| <td>Nonstandard semantics with K-semantics formalization</td> |
| </tr> |
| <tr> |
| <td> predtrace-IMP-SOS-uk-human-written </td> |
| <td>Standard semantics with SOS formalization</td> |
| </tr> |
| <tr> |
| <td> predtrace-IMP-SOS-mk-human-written </td> |
| <td>Nonstandard semantics with SOS formalization</td> |
| </tr> |
| </table> |
| |
|
|
| ### Data Example |
|
|
| One example of the dataset is as follows: |
| ```json |
| { |
| "program": "int ans; ans = 1; ...", |
| "syntax": "<program> :: ...", |
| "semantics": "ℤ := Set of integers ...", |
| "mutated-program": "int ans; ans = 1; ...", |
| "mutation-pattern": "KeyWordSwap", |
| "exec-trace": [ |
| { |
| "linenumber": 1, |
| "rule": ["Rule 38", "Rule 39"], |
| "state": {"ans": 1} |
| } |
| ], |
| "ground-truth": "<answer>...</answer>" |
| } |
| ``` |
|
|
| ## Citation |
| ```bibtex |
| @inproceedings{ThimmaiahETAL25PLSemanticsBench, |
| title = {LLMs Lean on Priors, Not Programming Language Semantics}, |
| author = {Thimmaiah, Aditya and Zhang, Jiyang and Srinivasa, Jayanth |
| and Li, Junyi Jessy and Gligoric, Milos}, |
| booktitle = {ICML}, |
| year = {2026} |
| } |
| ``` |
|
|
|
|
| ## License |
| This project is licensed under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/). |
|
|
