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REP-LIE estimates which Transformer weights to remove using gradients from LoRA low-rank matrices, aiming to reduce the resources required for model pruning and subsequent fine-tuning.
A new arXiv preprint argues that neural-network surrogates help optimization only when they assist candidate selection within a bounded neighborhood and leave the underlying acceptance mechanism intact.
A new preprint introduces AFDBench, a benchmark for testing whether language models can turn structured AI weather forecasts into accurate, professionally written National Weather Service discussions.
A new preprint breaks down reinforcement-learning post-training for language models, examining how rewards, prompts, model scale and prior behavior shape the results.
An arXiv preprint describes MacroAgent, a four-stage framework that uses large language models to discover contour algorithms for arranging large circuit components. The authors report improved layout regularity, shorter routed wirelength and better end-to-end results on TILOS, Chipyard and Cadence Innovus evaluations.
Routed Graph Handoff uses a lightweight language-model router to choose between structured dependency graphs and natural-language messages when AI agents delegate tasks.
A new benchmark evaluates whether language models can generate and improve GPU code for database-style queries, with the paper reporting speedups of up to 2.11× on one H100 GPU and 2.54× across four H100 GPUs.
A paper accepted at EMNLP 2026 reports that flipping routing-layer bits in mixture-of-experts language models can sharply inflate output length, potentially creating an availability and inference-cost risk.
An arXiv preprint proposes fixing a report’s evidence, numbers, directions and permitted wording before an LLM generates connective prose. The authors report higher cross-run reproducibility than a hybrid template in fMRI and randomized-trial reporting, alongside lower observed token use and median latency in one…
A newly submitted paper presents ClueWeaver, a reward-trained framework that divides long-narrative question answering between evidence retrieval and interpretation in compact, locally deployable language models.
A new arXiv paper proposes Golden-GRPO Injection, a three-stage self-learning framework intended to help large language models absorb newly injected knowledge and apply it across paraphrases, combined documents and reasoning tasks.
A new preprint reports that BanglaMamba, a state space model for Bangla fake-news detection, uses less GPU memory and processes data faster than tested BERT-based systems, while trailing BanglaBERT on accuracy and external-dataset performance.
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