SemVerBench is a benchmark that evaluates how well large language models (LLMs) understand and apply version-constraint resolution semantics, such as determining whether a version satisfies constraints like ^1.2.3 or >=2.0. The study finds that many models struggle with certain corner cases, with GPT‑5.1 performing poorly while Claude and Opus perform much better. The authors suggest that the failures stem from an activation/application gap rather than a lack of knowledge, and recommend that coding agents delegate version resolution to a dedicated resolver tool.
By Qibai Chen, Zeming Liu
arXiv:2607. 06636v1 Announce Type: cross Abstract: Large language models frequently generate code that appears correct on typical inputs yet fails on edge cases, invalid inputs, and other specification-defined corner conditions.
By Amin Haeri, Mahdi Ghelichi
The paper introduces SWE-Flux, a repository‑level benchmark designed to test large language models’ ability to reason about runtime behavior. It contains 480 execution‑grounded instances from 12 real Python repositories, with gold answers automatically harvested from instrumented test executions. Evaluation of five LLMs shows the task remains difficult, with the best model achieving only 37% accuracy, and the benchmark can generate challenging variants through input perturbation.
By Hamed Taherkhani, Mohammad Abdollahi, Melika Sepidband, Hridya Dhulipala, Tien N. Nguyen, Hadi Hemmati
The paper investigates whether large language models (LLMs) can automatically verify no-code bug fixes—solutions that instruct users to change settings, upgrade versions, or alter workflows—by executing them in a real browser environment. Using 322 fixes from a benchmark, the study tests three executor agents (OpenCUA‑72B, Claude Sonnet 5, and Meta’s Muse Glimmer) and finds that only 17.6% of issues could be set up and passed sanity checks, with resolution rates ranging from 14.6% to 49.7% depending on the executor. Even the best configuration resolved fewer than half of the fixes, and executor choice significantly affected outcomes, highlighting the need for execution-based verification before such fixes reach users.
By Utku Boran Torun, Veli Karakaya, Eray T\"uz\"un
arXiv:2609.21190v1 Announce Type: cross
Abstract: Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check corr...
By George Ma, Benjamin Mikek, Haoyu Li, Ferhat Erata, Yuhao Zhang, Zeren Shui, Behrooz Omidvar Tehrani, Jun Huan, Murali Krishna Ramanathan, Somayeh Sojoudi, Hao Zhou, Anoop Deoras
arXiv:2609.10397v1 Announce Type: cross
Abstract: Exception Related Code (ERC), which includes throw statements, conditions (if statements) that guard those throw statements, and try/catch blocks, is...
By Linghan Zhong, Jiyang Zhang, Jayanth Srinivasa, Junyi Jessy Li, Milos Gligoric
The paper examines how large‑language‑model (LLM) decompilers, which produce clean, idiomatic C code, are currently evaluated mainly on recompilability and passing shipped tests. It shows that these metrics can mask significant behavioral differences: a decompiled function may recompile and pass all tests yet diverge on other inputs or lose disclosed vulnerabilities. To address this, the authors propose Decompile‑Diverge, a behavioral oracle that synthesizes drivers, fuzzes inputs, and compares the decompiled code’s behavior to the original, revealing divergences in up to 13% of cases and exposing gaps in current evaluation suites.
By Chang Liu, Edward Raff, Kristopher Micinski
The paper identifies a "specification-oracle gap" in coding‑agent benchmarks, where tests rely on details (constants, module paths, exact messages) that are not stated in the natural‑language task description. The authors formalise this gap, develop a static audit tool that classifies such dependencies across five languages, and demonstrate that many benchmarks (e.g., SWE‑Milestone) contain tasks whose tests depend on unstated information, leading to higher failure rates for agents that do not reproduce upstream implementation details. The study also shows that the prevalence of these gaps varies with benchmark construction and that removing affected tests does not alter leaderboard rankings but shifts scores within repositories, favoring agents that replicate upstream names.
whyItMatters":"The findings reveal that benchmark tests can inadvertently measure an agent’s ability to mimic upstream implementation details rather than its true problem‑solving skills, potentially skewing evaluations and comparisons across agents."
By Fei Ding
arXiv:2608. 19009v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly paired with verifiers (step checkers, self-consistency filters, tool-based fact checkers, formal proof assistants) that claim to detect the model's errors.
By Yajie Yin
arXiv:2607. 13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises.
By Hironao Nakamura
arXiv:2608.30581v1 Announce Type: new
Abstract: Large language models (LLMs) are used as post hoc explainers of sequential decision-making policies, producing natural-language explanations of why an...
By Dennis Gross, Helge Spieker
The paper introduces the first benchmark suite for formal verification of PLC programs, covering both Structured Text and Ladder Diagram encodings of IEC 61131‑3. It contains 50 programs in 83 variants across ten industrial domains, each paired with a formal property, a machine‑checkable expected verdict, and a violation witness in SV‑COMP format. The suite’s ground‑truth methodology uses construction, fault injection, and cross‑tool consensus to ensure reliable verdicts, and the authors validate it with ESBMC and nuXmv, revealing format and semantics fragmentation in existing tools.
By Pierre Dantas, Lucas Cordeiro, Waldir Junior