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Earlier sources: Language models
Language models learn patterns in text and other representations to predict or generate sequences. Follow model releases, training methods, reasoning, evaluation, and deployment research; a benchmark result is specific to its test conditions.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗448 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
LLM-Assisted Unsatisfiability Proofs for Satisfiability Modulo Theories, Oracles, and Natural Language ↗
Modern software systems routinely invoke components whose source code is unavailable, such as proprietary libraries or cloud-based APIs. Such closed-box functions provide only oracle-style access: they can be executed on concrete inputs, but their internal logic cannot be inspected. Prior work has explored augmenting SMT solvers—the foundational engines behind contemporary automated reasoning—to handle satisfiability queries over first-order formulas containing calls to such closed-box functions. However, these approaches primarily rely on testing-based techniques to search for satisfying mode…
- OpenAlex · Published · First archived
Quantum-compatible error correction using a Bayesian Dual-Gated ProbSparse transformer in discrete-variable quantum key distribution ↗
Abstract Quantum Key Distribution (QKD) enables secure key generation with information-theoretic security guaranteed by the principles of quantum mechanics. However, achieving high reconciliation efficiency, high throughput, and low frame error rates under noisy channel conditions remains a significant challenge for practical and cost-effective Discrete-Variable Quantum Key Distribution (DV-QKD) systems. To address this issue, this research proposes a Quantum-based Bayesian Dual-Gated ProbSparse Transformer (Q-BDPT) framework for quantum-compatible error correction during the reconciliation st…
- OpenAlex · Published · First archived
OptiMine: Scalable and Precise Code Optimization for Android Apps via LLM-Driven Semantic Analysis ↗
As Android applications grow in scale, latent performance issues increasingly degrade user experience and business outcomes, yet systematically identifying optimization opportunities in large codebases remains challenging. We present OptiMine, a hybrid knowledge-to-code framework that integrates large language models (LLMs) with static program analysis to automatically uncover actionable performance optimizations. OptiMine systematically transforms unstructured expert knowledge from documents, commit diffs, and reports into structured Optimization Signatures, enabling reproducible and context-…
- OpenAlex · Published · First archived
Monocular depth estimation for endoscopic surgery using swin transformer and conditional random fields ↗
Abstract Monocular depth estimation in endoscopic surgery is challenging due to weak tissue textures, uneven illumination, specular reflections, and frequent smoke or instrument interference. This paper proposes CRFDepth, a pseudo‑supervised framework for endoscopic monocular depth estimation. A Swin Transformer encoder extracts hierarchical global features under weak‑texture conditions. The proposed CRFDepth framework effectively addresses key challenges in endoscopic monocular depth estimation, achieving superior accuracy and robustness. A Pyramid Scene Parsing module aggregates multi‑scale…
- OpenAlex · Published · First archived
Novel encoder-only transformer-convolutional neural network hybrid model to estimate battery state-of-charge in dynamic load and temperature variation ↗
Precise state of charge (SoC) estimation is critical for optimizing lithium-ion battery performance in energy storage systems. This study proposes an innovative Lightweight Transformer using Encoder only with 1D CNN (LTEC) model to significantly improve estimation accuracy while maintaining low computational complexity. The model integrates a 1D CNN layer in place of the standard Feed-Forward Network (FFN) within the Transformer Encoder. By applying weight-shared kernels across localized time windows, the 1D CNN captures short-term dynamics and dampens high-frequency fluctuations in sensor inp…
- OpenAlex · Published · First archived
Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-Based Code Generation ↗
LLM-based systems increasingly incorporate long-term memory to improve cross-session continuity. However, once insecure coding preferences are stored, they may silently influence security-critical decisions in subsequent generations. In this study, we conduct the first systematic empirical study on the impact of insecure coding preferences stored in long-term memory on the security of LLM-based code generation. We evaluate four LLMs (ChatGPT, Gemini, Qwen, and Grok) across five programming languages (Python, C, C++, Go, and JavaScript). Our results show that insecure memories significantly inc…
- OpenAlex · Published · First archived
Spiking neural transformer based confidentiality preserving framework for breast cancer detection in histopathology images ↗
Breast cancer diagnosis by medical imaging requires accuracy and high sensitivity to patient information. Even though artificial intelligence has made major progress in the field of histopathological image analysis, there are still limitations such as noise in the image, structural variability, and class imbalance, low interpretability of deep learning models, high computational complexity, and privacy risks. To overcome these limitations, this paper suggests a Confidentiality-Preserving Spiking Neural Transformer for Breast Cancer Detection (CP-SNT) to secure and robustly detect breast cancer…
- OpenAlex · Published · First archived
Heterogeneous multi-AUV cooperative area search with an LLM-assisted hierarchical planning framework ↗
In heterogeneous multi-AUV cooperative area search, existing methods often decompose the task region before assigning sub-regions to individual AUVs. However, coverage costs associated with platform capability differences, time-energy coupling, and temporal coordination constraints are often insufficiently considered during region decomposition, which may lead to suboptimal partitions and capability-task mismatches. To better account for these factors, a Voronoi-based region parameterization method is developed to formulate region decomposition and task allocation as a continuous optimization…
- OpenAlex · Published · First archived
STARS: Static Analysis-Guided Assertion Synthesis using Large Language Models ↗
Automated unit test generation promises to reduce the cost of software quality assurance, and hence, is attracting attention from both academia and industry. Yet, generating assertions that are executable, meaningful to developers, and able to catch faults remains an unsolved challenge. Existing approaches either randomly enumerate assertions that are plausible based on static program analysis without considering whether they naturally fit the test prefix or query an LLM to generate assertions based on local context only, such as the test prefix and the focal method. However, we observe that l…
- OpenAlex · Published · First archived
Silence of Commit Messages: An Empirical Study for Vulnerability Commit Message Generation using Large Language Models ↗
The vulnerability commit message serves as crucial metadata for maintaining software within version control systems. Nonetheless, manually crafted vulnerability commit messages often lack detail or exhibit inconsistent formatting. Recently, the growing use of Large Language Models (LLMs) for code and natural language comprehension has opened avenues to automate the crafting of these messages. This paper systematically and thoroughly explores the generation of security patch commit messages in the context of LLMs, delving into topics such as dataset construction, evaluation method design, and t…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Language models
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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