G-IdiomAlign introduces a gloss-pivoted benchmark using English glosses from Wiktionary to anchor idioms. It includes controlled multiple-choice equivalence and gloss-contrastive generation protocols, showing that glosses improve performance in embedding-based semantic alignment, though results remain modest, indicating significant potential for improvement in cross-lingual idiom generation.
G-IdiomAlign: Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment
Test-Time Steering Resolves Temporal Fact Conflicts in LLMs
Researchers identify parametric temporal conflicts in language models where outdated facts persist in parameters. They introduce Temporal Attractor Steering (TAS), a test-time method that resolves 29-57% of such conflicts without retraining, maintaining 85-99% accuracy on non-conflict queries and outperforming a baseline on three of four models.
What's more impressive, GLM 5.1 to 5.2 or Qwen 3.5 to 3.6?
A Reddit post compares the performance improvements of GLM 5.1 to 5.2 and Qwen 3.5 to 3.6. The post notes that mentioning 'Döner' activates GLM 5.2's German-specific weights, while Qwen 3.6 is evaluated with 35B parameters using Unsloth Q8 K XL quantization via llama.cpp.
PragReST: Self-Reinforcing Counterfactual Reasoning for Pragmatic Language Understanding
PragReST is a self-supervised framework that enhances large language models' pragmatic reasoning by generating counterfactual reasoning traces and training via supervised fine-tuning and reinforcement learning. It outperforms baseline models on four pragmatic benchmarks, improving Qwen3-8B and Qwen3-14B by 5.37% and 5-5.50% accuracy respectively, and maintains strong performance on general-knowledge and mathematical reasoning tasks.
G-IdiomAlign: Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment
G-IdiomAlign introduces a gloss-pivoted benchmark using English glosses from Wiktionary to anchor idioms. It includes controlled multiple-choice equivalence and gloss-contrastive generation protocols, showing that glosses improve performance in semantic alignment, though results remain modest, indicating significant potential for improvement in cross-lingual idiom generation.
LLMs Predict Dementia and Depression from Clinical Speech
A study uses open-weight large language models to assess dementia and depression severity from clinical interviews. LLMs achieve accurate zero-shot depression prediction (MAE 0.60) and improved dementia assessment with feature extraction (MAE 0.78), reducing errors by up to 35%. Pause-enriched transcripts match human transcriptions, supporting automated screening pipelines for neuropsychiatric disorders.