Morocco

When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this paper, we introduce McMining, the task of mining programming misconceptions from samples of code from a student. To enable the training and evaluation of McMining systems, we develop an extensible benchmark dataset of misconceptions together with a large set of code samples where these misconceptions are manifested. We then introduce two LLM-based McMiner approaches and, through extensive evaluations, show that models from the Gemini, Claude, and GPT families are effective at discovering misconceptions in student code.

EACL 2026

McMining: Automated Discovery of Misconceptions in Student Code

programming misconceptions

programming education

benchmarks

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#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Large language models (LLMs) often generate off-domain or harmful responses when deployed in specialized, high-stakes domains, motivating the need for rigorous LLM domain certification. While the VALID algorithm (Emde et al., 2025) achieves formal domain certificate guarantee using a guide model G trained from scratch on in-domain data, it suffers from poor generalization due to limited training. In this work, we propose PRISM, a novel approach that overcomes this key limitation by leveraging pretrained language models as guide models, enhanced via contrastive fine-tuning to sharply distinguish acceptable from refused content. We explore and experiment variants of PRISM with different loss functions to ensure that the model exploits the rich world knowledge of pretrained models while aligned to the target domain. We show that two variants of PRISM, PRISM-BC and PRISM-GA, achieve superior OOD rejection and tighter certification bounds across eight diverse data regimes and perturbations, establishing a more reliable approach to domain-adherent LLM deployment.

Improving LLM Domain Certification with Pretrained Guide Models

We introduce TDFlow, a novel test-driven agentic workflow that frames repository-scale software engineering as a test-resolution task, specifically designed to solve human-written tests. Given a set of tests, TDFlow repeatedly proposes, revises, and debugs repository-scale patches using precisely engineered sub-agents and tightly constrained tools. The workflow decomposes software engineering program repair into four components governed by respective sub-agents. This simple, forced decoupling of patch proposing, debugging, patch revision, and optional test generation (1) reduces long-context burden on any individual sub-agent, (2) focuses each sub-agent on specific, pre-defined sub-tasks, and (3) allows for specialized performance improvement on specific sub-tasks. When provided human-written tests, TDFlow attains 88.8% pass rate on SWE-Bench Lite (an absolute improvement of 27.8% over the next best baseline) and 94.3% on SWE-Bench Verified. In this work, we further show that the primary obstacle to human-level software engineering performance lies within writing successful reproduction tests. Manual inspection of the 800 TDFlow runs within SWE-Bench Lite and Verified uncover only 7 instances of test hacking, which were subsequently counted as failures. We envision a human-LLM interactive system powered by TDFlow where human developers write tests solved by LLM systems. Together, these results show that modern LLMs, when embedded in a narrowly engineered, test-driven workflow, already achieve human-level test resolution -- with the final frontier for fully autonomous repository repair being accurate reproduction test generation.

TDFlow: Agentic Workflows for Test Driven Development

Large language models (LLMs) struggle with ex-ante reasoning - making inferences or predictions without access to future information. Even under explicit temporal cutoffs, they often rely on internalized post-cutoff knowledge. To systematically evaluate this issue, we introduce a benchmark that assesses LLMs' ex-ante inference ability across four tasks: stock prediction, question answering, Wikipedia event generation, and scientific publication generation. We quantify temporal leakage using a leakage rate metric, which measures models' reliance on future information beyond cutoff timestamps, and a quality measure that evaluates task performance. Experimental results show that LLMs frequently violate temporal constraints across tasks, revealing persistent challenges in ex-ante reasoning. Our benchmark serves as a rigorous testbed for studying temporal reasoning in time-sensitive contexts and provides complete datasets, results, and evaluation resources to support future research on improving temporal consistency in modern LLMs.

ExAnte: A Benchmark for Ex-Ante Inference in Large Language Models

The miscalibration of Large Reasoning Models (LRMs) undermines their reliability in high-stakes domains, necessitating methods to accurately estimate the confidence of their long-form, multi-step outputs. To address this gap, we introduce the Reasoning Model Confidence estimation Benchmark (RMCB), a public resource of 347,496 reasoning traces from six popular LRMs across different architectural families. The benchmark is constructed from a diverse suite of datasets spanning high-stakes domains, including clinical, financial, legal, and mathematical reasoning, alongside complex general reasoning benchmarks, with correctness annotations provided for all samples. Using RMCB, we conduct a large-scale empirical evaluation of over ten distinct representation-based methods, spanning sequential, graph-based, and text-based architectures. Our central finding is a persistent trade-off between discrimination (AUROC) and calibration (ECE): text-based encoders achieve the best AUROC (0.672), while structurally-aware models yield the best ECE (0.148), with no single method dominating both. Furthermore, we find that increased architectural complexity does not reliably outperform simpler sequential baselines, suggesting a performance ceiling for methods relying solely on chunk-level hidden states. This work provides the most comprehensive benchmark for this task to date, establishing rigorous baselines and demonstrating the limitations of current representation-based paradigms.

How Reliable are Confidence Estimators for Large Reasoning Models? A Systematic Benchmark on High-Stakes Domains

Large language models (LLMs) have achieved remarkable success across a wide range of tasks, yet fine-tuning them efficiently under black-box or memory-constrained settings remains challenging. Parameter-efficient fine-tuning (PEFT) techniques such as LoRA alleviate memory usage by restricting updates to low-rank adapters, while zeroth-order (ZO) optimization further avoids back-propagation by estimating gradients from function evaluations. Recent work, such as LOZO, leverages random low-rank perturbations to reduce the variance of ZO estimates, but it overlooks the intrinsic geometric structure of LoRA adapters and suffers from unstable convergence and limited integration with adaptive optimizers. To address these limitations, we propose RoZO, a Riemannian zeroth-order optimization framework that constrains updates to the tangent space of the LoRA manifold. By exploiting geometry-aware updates with parallel transport, adaptive preconditioning, and trust-region control, RoZO achieves more stable convergence, tighter variance bounds, and superior performance compared to existing ZO methods.

RoZO: Geometry-Aware Zeroth-Order Fine-Tuning on Low-Rank Adapters for Black-Box Large Language Models

LLMs have recently been explored by psychometrics researchers as proxies for human participants. Although they show considerable potential in automating labor-intensive tasks and bridging AI research with psychological studies, these models often fail to generate heterogeneous data with human-like diversity, which diminishes their value in advancing social science research. To address this gap, we proposed a novel method to incorporate psychological insights into LLM simulation through the Personality Structured Interview (PSI). PSI leveraged psychometric scale-development procedures to capture personality-related linguistic information from a formal psychological perspective. To systematically evaluate the simulation fidelity, we developed a measurement theory grounded evaluation procedure that considers the latent construct nature of personality and evaluates its reliability, structural validity, and external validity. Results from three experiments demonstrate that PSI effectively improves human-like heterogeneity in LLM-simulated personality data and predicts personality-related behavioral outcomes. We further develop a theoretical framework for designing theory-informed structured interviews to enhance the reliability and effectiveness of LLMs in simulating human-like data for broader psychometric research.

Generative Personality Simulation via Theory-Informed Structured Interview

Large Language Models (LLMs) have achieved substantial progress in alignment, ensuring safer and more reliable outputs. However, jailbreak attacks can still bypass these safeguards and provoke harmful responses from well-aligned models. While some studies have achieved defenses against jailbreak attacks by modifying output distributions or detecting harmful content, the exact rationale still remains elusive. In this work, we present a novel neuron-level interpretability method that focuses on the role of safety-related knowledge neurons. Unlike existing approaches, our method projects the model's internal representation into a more consistent and interpretable vocabulary space. We then show that adjusting the activation of safety-related neurons can effectively control the model's behavior with a mean ASR higher than 97%. Building on this insight, we propose SafeTuning, a fine-tuning strategy that reinforces safety-critical neurons to improve model robustness against jailbreaks. SafeTuning consistently reduces attack success rates across multiple LLMs and outperforms all four baseline defenses. These findings offer a new perspective on understanding and defending against jailbreak attacks.

Unraveling LLM Jailbreaks Through Safety Knowledge Neurons

The advent of Large Language Models (LLMs) has significantly advanced web-based Question Answering (QA) systems over semi-structured content, raising questions about the continued utility of knowledge extraction for question answering. This paper investigates the value of triple extraction in this new paradigm by extending an existing benchmark with knowledge extraction annotations and evaluating commercial and open-source LLMs of varying sizes. Our results show that web-scale knowledge extraction remains a challenging task for LLMs. Despite achieving high QA accuracy, LLMs can still benefit from knowledge extraction, through augmentation with extracted triples and multi-task learning. These findings provide insights into the evolving role of knowledge triple extraction in web-based QA and highlight strategies for maximizing LLM effectiveness across different model sizes and resource settings.

Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs?

Visual metonymy is a cognitive mechanism through which a concept is evoked indirectly by presenting associated visual cues rather than explicitly depicting it. These associative links prompt viewers to infer broader concepts. In this work, we present the first computational investigation of visual metonymy. We introduce a novel pipeline grounded in semiotic theory, that leverages large language models and text-to-image models to generate metonymic visual representations. Using this framework, we construct ViMET, the first visual metonymy dataset comprising 2,000 multiple-choice questions to evaluate the cognitive reasoning abilities in multimodal language models. Experimental results on our dataset reveal a significant gap between human performance (86.9%) and state-of-the-art vision-language models (65.9%), highlighting limitations in machines ability to interpret indirect visual references.

Inferring the Unseen: A Computational Approach to Visual Metonymy

Large language model (LLM)-based systems now power chatbots, computer-use agents, and even robots, relying on prompts that often require costly manual tuning. LLM-based prompt optimizers cut that effort by iteratively refining prompts with scored feedback, yet their security has gone largely unexamined. Our work provides the first systematic analysis of poisoning risks in this optimization loop, revealing that optimization itself can become a new vulnerability point. Using HarmBench, we show optimized systems are far more vulnerable to manipulated feedback than to malicious queries, with Attack Success Ratio jumping by up by 48% absolute points. We propose a feedback-poisoning attack that embeds fake reward tokens in prompts, significantly boosting attack success under realistic assumptions, and evaluate tailored defenses that drastically decrease that threat. Our findings establish prompt optimization pipelines as an urgent and underexplored attack surface for LLM safety research, calling for stronger safeguards in future optimization frameworks.

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