Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv:2609.37824v1 Announce Type: cross Abstract: Transformer language models build predictions through successive residual updates, but how their representations become specific to an eventual outco...
arXiv:2609.37882v1 Announce Type: cross Abstract: Every text classifier for an African language begins with a budgeting question: how many labelled examples are needed, and can labels from other Afri...
arXiv:2609.37889v1 Announce Type: cross Abstract: Multimodal continual instruction tuning (MCIT) aims to enable multimodal large language models to acquire new capabilities from sequential tasks whil...
arXiv:2609.37891v1 Announce Type: cross Abstract: Current pre-training datasets are derived from web crawls, with all their issues, and were not designed to support mid- and post-training pipelines--...
arXiv:2609.38058v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on...
arXiv:2609.38165v1 Announce Type: cross Abstract: The landscape of satellite imagery time series datasets and boundary-pushing architectures for cropland segmentation has never been richer. However,...
The paper introduces Global Relative Kinetic Utility (Global RKU), a label‑free method for calibrating cross‑layer credit in global structured pruning of large language models. Global RKU estimates channel importance via a final‑hidden‑state activation‑gradient signal and applies block‑relative normalization to remove block‑common scale while preserving within‑block ordering, enabling a single‑stage static pruning topology. Experiments on Qwen‑2.5‑7B show significant performance gains at various sparsity levels, and ablation studies confirm the effectiveness of the relative‑normalization step.
arXiv:2605.12070v3 Announce Type: replace-cross Abstract: Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy o...
The paper investigates the impact of low‑resolution data on machine learning models, especially when high‑resolution data is scarce. It provides a theoretical analysis using Kullback‑Leibler divergence to quantify how data resolution affects a datapoint’s influence and derives bounds on information loss from downsampling. Empirical results with a vision transformer and a convolutional neural network show that incorporating low‑resolution data consistently improves performance in such settings.
The paper investigates how the choice of graph tokenization affects transformer expressivity. It analyzes three tokenization families—spectral, random‑walk, and adjacency—showing that each induces different depth requirements and that some tokenizations are inherently lossy or ill‑conditioned for certain tasks. The authors prove lower bounds and impossibility results for converting between tokenizations and validate these findings with experiments on synthetic and real‑world data.
arXiv:2605.23200v2 Announce Type: replace-cross Abstract: The linear growth of the Key-Value (KV) cache is a critical bottleneck in long-form LLM inference. Existing KV compression methods mitigate t...
The paper introduces RAISE, a diagnostic framework that tests whether a costly large language model (LLM) signal provides enough pre-call information to justify selective use. It identifies the failure mode of acquisition collapse, where an LLM appears useful overall but lacks actionable evidence for individual decisions. The authors demonstrate RAISE with Structured Hypothesis Embeddings (SHE) and evaluate it across multiple study designs, showing that predictable incremental benefit, rather than average lift, indicates recoverable selective value.
The paper presents a Physics‑Conditioned Neural Operator (PCNO), a transformer‑based model that predicts mitral and tricuspid valve leaflet displacement, strain, and stress fields conditioned on systolic blood pressure and tissue properties. Trained on FEBio finite‑element simulations, PCNO achieves a displacement error of 4.48 % and maintains mean errors of unsupervised geometric measures within 3.5 % even when pressure and material parameters lie outside the training set, demonstrating a conditioned solution operator rather than simple interpolation. Compared to graph neural network baselines, PCNO shows superior accuracy, particularly in stress predictions.
arXiv:2609.34426v2 Announce Type: replace Abstract: This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an of...
The paper introduces Alignment‑Guided Flow Transformer (AGFT), a framework for Vision‑Language‑Action (VLA) models that explicitly enforces tri‑modal alignment among vision, language, and action through a dedicated alignment loss. AGFT bridges representational gaps across modalities, improving task adaptation and robustness, and employs a flow‑matching objective to reduce inference steps compared to diffusion‑based policies. Experiments on a large benchmark demonstrate that AGFT achieves higher success rates and lower inference latency than state‑of‑the‑art baselines, highlighting tri‑modal alignment as crucial for scalable VLA manipulation.
arXiv:2501.12632v3 Announce Type: replace-cross Abstract: Weakly supervised object localization (WSOL) models can predict both the object class and the spatial regions corresponding to the object, wi...
The paper introduces AttWarp, a lightweight technique that uses a multimodal large language model’s cross‑modal attention to perform rectilinear warping of input images at test time. By reallocating spatial resolution toward query‑relevant regions without altering model weights or architecture, AttWarp preserves global context while making small objects and subtle relationships easier for the model to read. Experiments on five benchmarks and four MLLMs show consistent accuracy gains, improved compositional reasoning, and reduced hallucinations compared to baseline image‑manipulation methods.
arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...
arXiv:2605.11608v2 Announce Type: replace-cross Abstract: A single base LLM now comes with dozens of post-training variants, quantized, LoRA-adapted, or distilled, and each has to be checked before r...
arXiv:2605.29139v2 Announce Type: replace-cross Abstract: Question-answering services built on retrieval-augmented generation (RAG), in which a language model answers from retrieved documents, are in...