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A preprint reports that language models can appear to forget targeted information under ordinary tests while still recovering it under strategic adversarial prompts.
An arXiv study reports that six language models contained linearly decodable signals associated with being evaluated, while those internal signals only partly matched what the models said. The authors say steering along probe-derived directions changed verbalization scores, raising questions about how reliably…
A paper accepted to EMNLP 2026 proposes FCPRAG, a controller that selectively combines passage-specific LoRA adapters in parametric retrieval-augmented generation. The authors report higher F1 scores than standard and parametric RAG baselines across four question-answering datasets, with gains of up to 7.55%.
An analysis of 13,921 papers from major NLP conferences found that the papers reporting the most GPU capability captured most reported resources but only a minority of citations and awards. The study found a statistical association between compute and scholarly impact, but little standalone explanatory power.
chemeurope.com reports that Microsoft Research’s Skala AI model has been integrated into the open-source CP2K simulation ecosystem, giving researchers access to an AI-based exchange-correlation functional for quantum-mechanical simulations. The outlet says initial testing showed improved accuracy in a specific test…
An arXiv preprint introduces PUMA, a 900-task benchmark for evaluating multimodal AI in Polish cultural and linguistic contexts, reporting strong visual question-answering performance but substantial weaknesses in complex audio and document understanding.
A preprint introduces HiDiffTIR, a reinforcement-learning method that gives different weights to tool-use trajectories and reasoning steps based on their difficulty.
A new arXiv paper presents LURE, a zero-data self-play method in which one language model sets task difficulty while another solves verifiable reasoning challenges. The authors report stronger out-of-distribution zero-shot accuracy than trained baselines across nine held-out benchmarks, but the abstract does not…
A paper accepted to EMNLP 2026 introduces EDGE, a training framework that helps language-model agents reuse useful experience instead of relying on external retrieval at inference time. The authors report higher success rates on ALFWorld and WebShop and say the method retained most of its gains after removing the…
An arXiv preprint introduces NC-GRPO, a reinforcement-learning method that perturbs a vision-language model’s hidden representation rather than its input image. The authors report improved out-of-domain mathematical reasoning and hallucination robustness on Qwen2.5-VL-7B, while noting tradeoffs between…
A new arXiv preprint describes DamageScope, a retrieval-augmented system that combines satellite imagery with vision-language and language models to support natural-language queries about property damage. The authors report up to 14-fold faster indexing and roughly threefold reductions in operational cost and…
An arXiv preprint presents LiteEvent-AE, a compact autoencoder for event-based vision that the authors say cuts model size and energy use while maintaining recognition performance on two constrained devices.
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