Update README.md

README.md

@@ -1446,7 +1446,62 @@ configs:
     path: predtrace-IMP-SOS-uk-human-written/train-*
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-# LLMs Lean on Priors, Not Programming Language Semantics (ICML 2026)
+<div align="center">
+  <h1>
+    LLMs Lean on Priors, Not Programming Language
+    <span style="white-space: nowrap;">
+      Semantics
+      <img
+        src="https://raw.githubusercontent.com/EngineeringSoftware/PLSemanticsBench/main/docs/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 &nbsp;&nbsp;&nbsp;
+    <sup>2</sup>Cisco Research
+  </p>
+</div>
+
+<div align="center">
+
+[![Website](https://img.shields.io/badge/Project_Page-PLSemanticsBench-blueviolet)](https://engineeringsoftware.github.io/PLSemanticsBench/)
+[![arXiv](https://img.shields.io/badge/arXiv-2510.03415v3-b31b1b.svg)](https://arxiv.org/pdf/2510.03415v3)
+[![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/EngineeringSoftware/PLSemanticsBench)
+[![Dataset](https://img.shields.io/badge/🤗-Dataset-yellow)](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)