Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,313In brief24More (on-topic)3,333Not relevant13,754Not assessed87Older352Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| Task Context Shapes Component Roles in Language Models Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models. Preprint | Small and efficient models via arxiv · 12 Oct, 16:28 | Not relevant Studies internal representations rather than model compression or inference efficiency. | |
| Task Context Shapes Component Roles in Language Models Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Presents mechanistic interpretability analysis rather than standard LLM evaluation benchmarks. | |
| Task Context Shapes Component Roles in Language Models Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models. Preprint | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Studies internal attention head mechanics in language models, not autonomous agents. | |
| Task Context Shapes Component Roles in Language Models Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Investigates internal model activations rather than retrieval-augmented generation architectures. | |
| Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping Evaluates regression objective functions and global early stopping limits across neural network backbones and established tabular datasets. Preprint | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Focuses on tabular deep regression, not LLM agents or multi-step planning. | |
| Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping Evaluates regression objective functions and global early stopping limits across neural network backbones and established tabular datasets. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Investigates regression objectives in tabular neural networks, not language model evaluation. | |
| Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping Evaluates regression objective functions and global early stopping limits across neural network backbones and established tabular datasets. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:28 | Not relevant Addresses training objectives for tabular data rather than AI safety or deployment guardrails. | |
| Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping Evaluates regression objective functions and global early stopping limits across neural network backbones and established tabular datasets. Preprint | Small and efficient models via arxiv · 12 Oct, 16:28 | Not relevant Examines tabular regression loss functions rather than model compression or efficient LLM inference. | |
| Hide and Seek: HIDE Concepts to SEEK explanations for black box models Introduces a model-agnostic, concept-level attribution method for black-box vision models by masking concepts located with vision-language models. Preprint | Computer vision for industrial applications via arxiv · 12 Oct, 16:28 | Not relevant Focuses on general vision interpretability on standard benchmarks, not industrial tasks. | |
| Hide and Seek: HIDE Concepts to SEEK explanations for black box models Introduces a model-agnostic, concept-level attribution method for black-box vision models by masking concepts located with vision-language models. Preprint | Small and efficient models via arxiv · 12 Oct, 16:28 | Not relevant Does not address model compression, efficient serving, or small language models. | |
| Hide and Seek: HIDE Concepts to SEEK explanations for black box models Introduces a model-agnostic, concept-level attribution method for black-box vision models by masking concepts located with vision-language models. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Proposes an interpretability attribution method rather than LLM benchmarking or evaluation frameworks. | |
| Hide and Seek: HIDE Concepts to SEEK explanations for black box models Introduces a model-agnostic, concept-level attribution method for black-box vision models by masking concepts located with vision-language models. Preprint | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Focuses on vision model interpretability rather than autonomous multi-step agents. | |
| Can Language Models Play the Wikipedia Game? Tests language models on graph-based information retrieval and navigation using the Wikipedia Game across multiple languages and citation datasets. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:28 | Not relevant Does not address safety, hallucination, guardrails, or deployment reliability issues. | |
| Can Language Models Play the Wikipedia Game? Tests language models on graph-based information retrieval and navigation using the Wikipedia Game across multiple languages and citation datasets. Preprint | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Studies link-based navigation in Wikipedia rather than document retrieval or RAG systems. |
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