A Hacker News discussion raised the question of how much of the F-Droid Android app repository, including its code and app listings, was produced with large language models. Commenters debated methods for estimating AI-written content and what such provenance questions mean for open-source projects. No verified measurement was reached in the thread.
A research paper posted to arXiv examines how large language models struggle when asked to write code against APIs from private, non-public libraries. The authors note that most existing methods focus on public code and propose teaching models to work with private library documentation. The work is a replacement submission on the cs.AI listing.
A new arXiv paper introduces TyPatch, a method that converts historical Linux kernel patches into typestate rules usable by static analysis. Building on prior work where large language models generate checkers from past patches, the approach aims to extend the reach of defect knowledge beyond the sites where bugs were originally fixed. The technique targets automated kernel bug detection.
A new arXiv paper investigates whether large language models can help identify security flaws in JavaScript, which underpins the vast majority of websites. The authors note that conventional static analysis tools frequently miss real-world vulnerabilities, motivating a learning-based approach. The work is a research preprint and has not yet been peer reviewed.
A new arXiv paper examines why large language models produce incorrect answers even when the correct knowledge is present in their training data. The authors frame inference as competition between latent associations, where the wrong "key" can win out during answer selection, and use this view to identify and flag hallucinated outputs.
A research paper examines how fairness and bias behave in large language models when several sensitive attributes, such as gender and ethnicity, are considered together rather than one at a time. The authors run a systematic evaluation across six LLMs to assess intersectional fairness, a setting relevant to socially sensitive deployments. The work highlights gaps in how current models handle overlapping demographic characteristics.
A new arXiv paper proposes combining large language models with multi-agent reinforcement learning to jointly optimize electric vehicle charging scheduling in public charging systems. The approach targets three competing goals at once: driver charging satisfaction, charging station profitability, and stability of the smart grid. It is positioned as a unified optimization method for connected EV infrastructure in IoT settings.
A new arXiv paper evaluates rule-based systems produced by large language models against conventional machine learning classifiers for predicting heart disease. Using the UCI Heart Disease dataset, the authors benchmark models including logistic regression and k-nearest neighbors. The work examines whether LLM-derived decision rules can match or complement established clinical prediction methods.
A arXiv paper examines where the concept of animacy is encoded inside large language models. The authors trace internal circuits tied to the animate/inanimate distinction, which involves verb-argument constraints and contextual cues beyond simple word-level features. The work is a revised version of a preprint in the cs.CL category.
A new arXiv preprint examines whether supportive empathy in large language models can be controlled through activation steering, as has been done for traits like honesty and refusal. Using the EPITOME dataset, the authors analyze the geometry of the underlying mechanisms and find that empathy does not map cleanly onto a single controllable direction, indicating a multi-axial structure. The work also looks at how persona settings influence these empathy-related representations.
A new arXiv position paper contends that attributing human-like traits to AI systems is an automatic habit that persists even among technical experts, and that it skews how researchers frame and evaluate language models. The authors review a large body of published work to show how anthropomorphic language shapes experimental design, interpretation of results, and safety claims. They call for alternative conceptual frameworks that describe model behavior without implying human-like minds.
A new arXiv study looks at how factual mistakes appear in text written by people, aiming to inform automatic detection of incorrect spans. The authors argue that factual error detection has long been a key research problem, but interest has shifted with the rise of large language models. The work connects analysis of human-written errors to building systems that can flag factual inaccuracies.
A new arXiv preprint examines using abstract meaning representation to generate hospital discharge summaries, which are long documents recapping inpatient visits. The authors argue that automating this documentation could ease clinician workload and free up time for patient care, with large language models as a possible component.
A new arXiv preprint in computational linguistics studies how arguments are structured when a direct proof of a claim is difficult, and how an alternative but related statement can be used instead. The authors apply large language models to compare argumentation and proof patterns across different text genres. The work falls within NLP research on reasoning and argument mining.
A new arXiv preprint examines mechanistic interpretability from a formal, theoretical angle, questioning how much this approach can reveal about large language models. The work focuses on interpretable replacement networks, which are trained as stand-ins for frontier models so researchers can study their behavior. It argues that structural constraints may cap what such analyses can uncover about model internals.
A new arXiv paper introduces FLoKD, a method for fine-tuning large language models across wireless networks without centralizing user data. It combines federated learning with low-rank adaptation and adaptive knowledge distillation to reduce communication and computation costs. The approach targets privacy-preserving deployment of LLMs in distributed, bandwidth-limited settings.
A new arXiv paper examines how large language models can be used to make complex medical information easier for patients to understand, using diabetes as a case study. The authors argue that clearer simplification supports patient comprehension, informed decision-making, and better health outcomes. The work focuses on the challenges of translating clinical knowledge into patient-friendly language.
A revised arXiv preprint (2601.04260v2) investigates the internal computations LLMs use when solving propositional logic tasks. The authors argue that earlier mechanistic interpretability work focused on task-specific circuits, leaving broader questions about the underlying computational structure unanswered. The paper appears in both cs.AI and cs.LG listings as a replacement submission.
A new preprint examines what happens to batch decomposition and downstream model performance when training sets mix human data with text or images produced by existing large language models. Using random datasets containing anomalies, the authors study criticality in dissimilar decomposition and undersampling techniques. The work aims to clarify the statistical behavior of datasets that are increasingly populated with synthetic samples.
A new arXiv paper examines how entrepreneurial cognition research can be extended to large language models, which are increasingly used in entrepreneurial tasks. The authors identify an internal representation tied to opportunity recognition and show they can causally steer it, effectively turning the behavior up or down. The work sits at the intersection of entrepreneurship theory and interpretability research on model internals.
A new arXiv paper investigates why large language models can give wrong medical answers when the context they receive is misleading, even though their standalone medical accuracy is high. The authors analyze the internal mechanisms behind this susceptibility, aiming to explain how flawed context overrides a model's medical knowledge. The work is a revised cross-listing on arXiv and falls under AI and machine learning research.
A new arXiv paper reviews how large language models used in clinical and medical settings should be assessed. It argues that evaluating these systems is harder than conventional machine learning evaluation for a variety of reasons. The work is framed as an introductory guide to evaluation approaches for healthcare LLMs.
A new arXiv paper proposes a schema-aware split learning approach that uses LLMs to predict mental distress from survey data while keeping sensitive records private. The method is designed to work across surveys with differing structures and questions, which is a common obstacle when pooling mental health data from schools, employers, and clinics. The work targets privacy-preserving collaboration, so data stays local rather than being centralized.
A new arXiv paper argues that the debate over synthetic data in marketing research has been stuck between two extremes: treating large language models as a replacement for human survey respondents, or rejecting them outright. The authors say the more useful question is when synthetic respondents can be trusted, and they outline how that reliability should be evaluated. The work focuses on marketing research but touches on broader issues of validating model-generated data.
A new arXiv preprint introduces a modular framework aimed at reducing harmful outputs from large language models in a more targeted way. The authors note that current alignment approaches work but are expensive and tightly coupled, motivating a cheaper, more flexible alternative. The abstract frames the work around mitigating bias, toxicity, and other outputs that diverge from human preferences.
A new arXiv preprint examines how large language models are used as stand-ins for survey respondents, a practice that can cut costs dramatically compared with traditional polling. The authors argue that aggregate validation scores hide systematic problems such as compressed variance and flipped coefficient signs, and they offer diagnostic and correction methods to address them.
A newly revised arXiv preprint analyzes how quantization affects large language models deployed for machine translation in server settings. The work weighs reduced memory use and faster inference against the quality loss that compression can introduce. It aims to help practitioners pick quantization settings that balance latency and translation accuracy.
A new arXiv preprint examines few-shot in-context learning, the ability of a model to pick up abstract patterns from examples in its prompt and apply them to new inputs. The authors note this behavior has been studied mainly in large language models trained on next-token prediction, and report that it arises in a convergent way across different modalities. The announcement provides only the abstract, so methodological details are not yet available.
This arXiv paper investigates how large language models are used to interpret computational results and draft scientific manuscripts. Holding the underlying computational evidence fixed, the authors tested whether spreading comparisons across different modeling choices changes how findings are described and qualified. The results indicate that editorial routing decisions influence the hedging and qualification of reported results in AI-assisted writing.
Researchers propose a cloud-edge framework for collaborative large language model training and deployment. The approach accelerates personalized bilevel optimization to reconcile globally consistent models with locally adapted ones. It targets the difficulty existing cloud-edge systems have in balancing those two goals.
Researchers present LLM ORDER BY as a semantic operator for ordering data with large language models. The paper studies physical implementations and proposes improvements to existing semantic sorting algorithms.
A new arXiv paper addresses a limitation in context parameterization, a technique that lets language models absorb context into reusable parameters instead of reprocessing it for every query. The authors note that current approaches treat context as static and are therefore ill-suited to settings where information changes over time. Their work introduces a method for keeping those internalized parameters up to date as contexts evolve.
A new arXiv paper introduces ECAS, an agentic system that uses large language models to turn a scientist's high-level goal into correct, target-scale runs on high-performance computing resources. The approach places control at the edge and gates execution behind validation checks, aiming to address how brittle and labor-intensive it currently is to translate research intent into working HPC workflows. The authors position the work as a step toward more reliable LLM-driven scientific computing.
Researchers present CoTAL, a human-in-the-loop prompt engineering method for using large language models to score formative assessments and generate feedback for students. The work examines how well such prompting approaches generalize across educational contexts, with teachers involved in refining the prompts. It is published as an arXiv preprint in the computation and language category.
A new arXiv paper describes an approach for building and steering causal virtual worlds using large language and video model agents. It relies on feedback fuzzy cognitive maps to capture the detailed causal structure of the simulated environment. The technique converts sequenced FCMs into worlds that video generators can render.
A new arXiv paper addresses a known failure mode in reinforcement learning with verifiable rewards (RLVR), where training problems that are too hard for a model produce uniformly failed attempts and yield no learning signal. The authors propose a teacher-guided curriculum that sequences training data so the model encounters problems it can actually solve, making the process more data-efficient. The work targets mathematical reasoning in large language models and falls within the cs.CL area.
A new arXiv preprint in the cs.CL category describes work on combining large language models with automatic speech recognition systems. The paper focuses on knowledge-merging techniques related to established LM fusion approaches such as shallow fusion and density ratio methods. It appears to be a research contribution rather than a product or model release.
A new arXiv preprint describes a deterministic, training-free pipeline for shortening the prompts given to large language models by compressing their lexical content. The authors report a Pareto analysis of the trade-offs between compression and task performance across eleven task categories. The work targets the growing cost and context limits caused by long prompts in techniques like chain-of-thought and in-context learning.
A new arXiv paper examines how well established design recommendations apply to AI companions, conversational agents built on large language models that aim to foster emotional bonds rather than complete practical tasks. Using several research methods, the authors assess where conventional design guidance holds up and where it falls short for this emotionally oriented class of systems. The work is posted as a cross-listing on arXiv cs.AI.
A new arXiv paper proposes Bypass Observation, an architecture that adds read-only observation heads to selected Transformer layers so internal hidden states can be inspected without altering the model's behavior. The approach aims to bridge the gap between the high-dimensional reasoning space of large language models and the text-only outputs users normally see. It is presented as a conceptual design for non-intrusive semantic extraction.