arXiv AI
Jun 16
LLM4RTL: Tool-Assisted LLM for RTL Generation
arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv Machine Learning
Jun 16
Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning
arXiv:2606. 14970v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specific data.
arXiv AI
Jun 16
Hybrid NARX-LLM for Greenland Iceberg Discharge: Prompt-Driven Residual Correction
arXiv:2606. 15288v1 Announce Type: cross Abstract: Greenland iceberg discharge exhibits complex nonlinear dynamics with limited observability, challenging traditional predictive models.
arXiv AI
Jun 16
Sub-Semantic Image Segmentation
arXiv:2606. 14754v1 Announce Type: cross Abstract: Images can be segmented based on visual cues (i.
arXiv AI
Jun 16
PrologMCP: A Standardized Prolog Tool Interface for LLM Agents
arXiv:2606. 14935v1 Announce Type: new Abstract: Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly.
arXiv AI
Jun 16
Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts
arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.
arXiv AI
Jun 16
HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting
arXiv:2606. 17028v1 Announce Type: cross Abstract: Simple linear and frequency-domain models remain surprisingly competitive in long-horizon time-series forecasting, and recent mechanistic evidence suggests that standard forecasting benchmarks may not require the dense superposed representations that make transformers powerful in other domains.
arXiv AI
Jun 16
Tool-IQA: Augmenting Image Quality Assessment with Simple Tools
arXiv:2606. 16082v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA).
arXiv AI
Jun 16
PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization
arXiv:2606. 16074v1 Announce Type: cross Abstract: Motivation: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its use in patient-centered outcomes research.
arXiv AI
Jun 16
LLM-Powered Virtual Population for Demand Simulation and Pricing
arXiv:2606. 16183v1 Announce Type: cross Abstract: We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices.
arXiv AI
Jun 16
Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
arXiv:2605. 21629v2 Announce Type: replace-cross Abstract: How much have students' ordinary learning processes shifted in response to generative AI, and how does that affect their durable learning outcomes?
arXiv AI
Jun 16
Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention
arXiv:2510. 04212v4 Announce Type: replace-cross Abstract: The pursuit of computational efficiency has driven the adoption of low-precision formats for training transformer models.
arXiv AI
Jun 16
Data Augmentations for Data-Constrained Language Model Pretraining
arXiv:2606. 16246v1 Announce Type: cross Abstract: As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shifting toward a data-constrained, compute-abundant regime that demands productive multi-epoch training on fixed corpora.
arXiv AI
Jun 16
CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies
arXiv:2606. 16613v1 Announce Type: new Abstract: As LLM agents become capable of increasingly long-horizon tasks, evaluating their performance in economic systems is becoming increasingly important.
arXiv AI
Jun 16
Gen-VCoT: Generative Visual Chain-of-Thought Reasoning via Diffusion-Based RGB Intermediate Representations
arXiv:2606. 16783v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) excel at visual reasoning but rely on text-based chain-of-thought (CoT), lacking interpretable visual intermediates.
arXiv AI
Jun 16
SPARK: Security Knowledge Priming and Representation-Guided Knowledge Activation for LLM-based Secure Code Generation
arXiv:2606. 16244v1 Announce Type: cross Abstract: Large language models routinely generate code with exploitable security flaws.
arXiv AI
Jun 16
Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering
arXiv:2606. 16890v1 Announce Type: cross Abstract: Aggregate accuracy benchmarks conceal a systematic structure in how large language models fail at electronic health record (EHR) question answering: questions requiring more inferential steps produce disproportionately more errors.
arXiv Machine Learning
Jun 16
If These Walls Could Talk: Critical Play with Large Language Models in Museums
arXiv:2606. 15565v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly being used in museums to as role playing chatbots which let visitors talk to simulated versions of people and artefacts from the past.
arXiv AI
Jun 16
JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory
arXiv:2509. 22888v2 Announce Type: replace Abstract: Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature.
arXiv AI
Jun 16
Frame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: A Mechanistic Interpretability Audit of Ethical Reasoning
arXiv:2606. 15507v1 Announce Type: new Abstract: Behavioral audits of Large Language Models on moral prompts measure what the model says, not the internal computation producing it.