Many structured prediction and reasoning tasks can be framed as program synthesis problems, where the goal is to generate a program in a \emph{domain-specific language} (DSL) that transforms input data into the desired output. Unfortunately, purely neural approaches, such as large language models (LLMs), often fail to produce fully correct programs in unfamiliar DSLs, while purely symbolic methods based on combinatorial search scale poorly to complex problems. Motivated by these limitations, we introduce a hybrid approach, where LLM completions for a given task are used to learn a task-specific, context-free surrogate model, which is then used to guide program synthesis. We evaluate this hybrid approach on three domains, and show that it outperforms both unguided search and direct sampling from LLMs, as well as existing program synthesizers.

Are LLMs The W ay Forward? Abstract--Autonomous vehicle navigation in complex environments such as dense and fast-moving highways and merging scenarios remains an active area of research. In the past decade, many planning and control approaches have used reinforcement learning (RL) with notable success. However, a key limitation of RL is its reliance on well-specified reward functions, which often fail to capture the full semantic and social complexity of diverse, out-of-distribution situations. As a result, a rapidly growing line of research explores using Large Language Models (LLMs) to replace or supplement RL for direct planning and control, on account of their ability to reason about rich semantic context. However, LLMs present significant drawbacks: they can be unstable in zero-shot safety-critical settings, produce inconsistent outputs, and often depend on expensive API calls with network latency. This motivates our investigation into whether small, locally deployed LLMs ( 14B parameters) can meaningfully support autonomous highway driving through reward shaping rather than direct control. These models are attractive for practical deployment as they can run on a single GPU and avoid external API dependencies. We present a case study comparing RL-only, LLM-only, and hybrid approaches, where LLMs augment RL rewards by scoring state-action transitions during training, while standard RL policies execute at test time.

General logical reasoning, defined as the ability to reason deductively on domain-agnostic tasks, continues to be a challenge for large language models (LLMs). Current LLMs fail to reason deterministically and are not interpretable. As such, there has been a recent surge in interest in neurosymbolic AI, which attempts to incorporate logic into neural networks. We first identify two main neurosymbolic approaches to improving logical reasoning: (i) the integrative approach comprising models where symbolic reasoning is contained within the neural network, and (ii) the hybrid approach comprising models where a symbolic solver, separate from the neural network, performs symbolic reasoning. Both contain AI systems with promising results on domain-specific logical reasoning benchmarks. However, their performance on domain-agnostic benchmarks is understudied. To the best of our knowledge, there has not been a comparison of the contrasting approaches that answers the following question: Which approach is more promising for developing general logical reasoning? To analyze their potential, the following best-in-class domain-agnostic models are introduced: Logic Neural Network (LNN), which uses the integrative approach, and LLM-Symbolic Solver (LLM-SS), which uses the hybrid approach. Using both models as case studies and representatives of each approach, our analysis demonstrates that the hybrid approach is more promising for developing general logical reasoning because (i) its reasoning chain is more interpretable, and (ii) it retains the capabilities and advantages of existing LLMs. To support future works using the hybrid approach, we propose a generalizable framework based on LLM-SS that is modular by design, model-agnostic, domain-agnostic, and requires little to no human input.

Code review is one of the key processes in the software development lifecycle and is essential to maintain code quality. However, manual code review is subjective and time consuming. Given its rule-based nature, code review is well suited for automation. In recent years, significant efforts have been made to automate this process with the help of artificial intelligence. Recent developments in Large Language Models (LLMs) have also emerged as a promising tool in this area, but these models often lack the logical reasoning capabilities needed to fully understand and evaluate code. To overcome this limitation, this study proposes a hybrid approach that integrates symbolic reasoning techniques with LLMs to automate the code review process. We tested our approach using the CodexGlue dataset, comparing several models, including CodeT5, CodeBERT, and GraphCodeBERT, to assess the effectiveness of combining symbolic reasoning and prompting techniques with LLMs. Our results show that this approach improves the accuracy and efficiency of automated code review.

Software obfuscation, particularly prevalent in JavaScript, hinders code comprehension and analysis, posing significant challenges to software testing, static analysis, and malware detection. This paper introduces CASCADE, a novel hybrid approach that integrates the advanced coding capabilities of Gemini with the deterministic transformation capabilities of a compiler Intermediate Representation (IR), specifically JavaScript IR (JSIR). By employing Gemini to identify critical prelude functions, the foundational components underlying the most prevalent obfuscation techniques, and leveraging JSIR for subsequent code transformations, CASCADE effectively recovers semantic elements like original strings and API names, and reveals original program behaviors. This method overcomes limitations of existing static and dynamic deobfuscation techniques, eliminating hundreds to thousands of hardcoded rules while achieving reliability and flexibility. CASCADE is already deployed in Google's production environment, demonstrating substantial improvements in JavaScript deobfuscation efficiency and reducing reverse engineering efforts.

Building energy modeling is a key tool for optimizing the performance of building energy systems. Historically, a wide spectrum of methods has been explored -- ranging from conventional physics-based models to purely data-driven techniques. Recently, hybrid approaches that combine the strengths of both paradigms have gained attention. These include strategies such as learning surrogates for physics-based models, modeling residuals between simulated and observed data, fine-tuning surrogates with real-world measurements, using physics-based outputs as additional inputs for data-driven models, and integrating the physics-based output into the loss function the data-driven model. Despite this progress, two significant research gaps remain. First, most hybrid methods focus on deterministic modeling, often neglecting the inherent uncertainties caused by factors like weather fluctuations and occupant behavior. Second, there has been little systematic comparison within a probabilistic modeling framework. This study addresses these gaps by evaluating five representative hybrid approaches for probabilistic building energy modeling, focusing on quantile predictions of building thermodynamics in a real-world case study. Our results highlight two main findings. First, the performance of hybrid approaches varies across different building room types, but residual learning with a Feedforward Neural Network performs best on average. Notably, the residual approach is the only model that produces physically intuitive predictions when applied to out-of-distribution test data. Second, Quantile Conformal Prediction is an effective procedure for calibrating quantile predictions in case of indoor temperature modeling.

In this paper, we propose HCVR (Hybrid approach with Correlation-aware Voting Rules), a lightweight rule-based feature selection method that combines Parameter-to-Parameter (P2P) and Parameter-to-Target (P2T) correlations to eliminate redundant features and retain relevant ones. This method is a hybrid of non-iterative and iterative filtering approaches for dimensionality reduction. It is a greedy method, which works by backward elimination, eliminating possibly multiple features at every step. The rules contribute to voting for features, and a decision to keep or discard is made by majority voting. The rules make use of correlation thresholds between every pair of features, and between features and the target. We provide the results from the application of HCVR to the SPAMBASE dataset. The results showed improvement performance as compared to traditional non-iterative (CFS, mRMR and MI) and iterative (RFE, SFS and Genetic Algorithm) techniques. The effectiveness was assessed based on the performance of different classifiers after applying filtering.

Scientific publications, primarily digitized as PDFs, remain static and unstructured, limiting the accessibility and reusability of the contained knowledge. At best, scientific knowledge from publications is provided in tabular formats, which lack semantic context. A more flexible, structured, and semantic representation is needed to make scientific knowledge understandable and processable by both humans and machines. We propose an evolution model of knowledge representation, inspired by the 5-star Linked Open Data (LOD) model, with five stages and defined criteria to guide the stepwise transition from a digital artifact, such as a PDF, to a semantic representation integrated in a knowledge graph (KG). Based on an exemplary workflow implementing the entire model, we developed a hybrid approach, called SciMantify, leveraging tabular formats of scientific knowledge, e.g., results from secondary studies, to support its evolving semantification. In the approach, humans and machines collaborate closely by performing semantic annotation tasks (SATs) and refining the results to progressively improve the semantic representation of scientific knowledge. We implemented the approach in the Open Research Knowledge Graph (ORKG), an established platform for improving the findability, accessibility, interoperability, and reusability of scientific knowledge. A preliminary user experiment showed that the approach simplifies the preprocessing of scientific knowledge, reduces the effort for the evolving semantification, and enhances the knowledge representation through better alignment with the KG structures.

--The advancement of Pre-Trained Language Models (PTLMs) and Large Language Models (LLMs) has led to their widespread adoption across diverse applications. Despite their success, these models remain vulnerable to attacks that exploit their inherent weaknesses to bypass safety measures. Two primary inference-phase threats are token-level and prompt-level jailbreaks. T oken-level attacks embed adversarial sequences that transfer well to black-box models like GPT but leave detectable patterns and rely on gradient-based token optimization, whereas prompt-level attacks use semantically structured inputs to elicit harmful responses yet depend on iterative feedback that can be unreliable. T o address the complementary limitations of these methods, we propose two hybrid approaches that integrate token-and prompt-level techniques to enhance jailbreak effectiveness across diverse PTLMs. GCG + PAIR and the newly explored GCG + WordGame hybrids were evaluated across multiple Vicuna and Llama models. GCG + PAIR consistently raised attack-success rates over its constituent techniques on undefended models; for instance, on Llama-3, its Attack Success Rate (ASR) reached 91.6%, a substantial increase from PAIR's 58.4% baseline. Meanwhile, GCG + WordGame matched the raw performance of WordGame maintaining a high ASR of over 80% even under stricter evaluators like Mistral-Sorry-Bench. Crucially, both hybrids retained transferability and reliably pierced advanced defenses such as Gradient Cuff and JBShield, which fully blocked single-mode attacks. These findings expose previously unreported vulnerabilities in current safety stacks, highlight trade-offs between raw success and defensive robustness, and underscore the need for holistic safeguards against adaptive adversaries. Large Language Models (LLMs)--such as GPT -4, LLaMA, and Claude--have become indispensable in healthcare, finance, education, and other high-stakes domains [1]-[3]. Their ability to understand context, generate human-like responses, and adapt to diverse tasks fuels widespread deployment. Y et these same models remain vulnerable to jailbreak attacks, which exploit weaknesses in alignment mechanisms to induce harmful or disallowed content [4].

This demo paper reports on a new workflow \textit{GhostWriter} that combines the use of Large Language Models and Knowledge Graphs (semantic artifacts) to support navigation through collections. Situated in the research area of Retrieval Augmented Generation, this specific workflow represents the creation of local and adaptable chatbots. Based on the tool-suite \textit{EverythingData} at the backend, \textit{GhostWriter} provides an interface that enables querying and ``chatting'' with a collection. Applied iteratively, the workflow supports the information needs of researchers when interacting with a collection of papers, whether it be to gain an overview, to learn more about a specific concept and its context, and helps the researcher ultimately to refine their research question in a controlled way. We demonstrate the workflow for a collection of articles from the \textit{method data analysis} journal published by GESIS -- Leibniz-Institute for the Social Sciences. We also point to further application areas.