Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems Describes an industrial framework for evaluating, ablating, and deprecating features in production recommender systems without performance regressions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Deals with feature deprecation in recommender systems rather than LLM benchmarking.
Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing Measures output variability across repeated runs at temperature zero in semantic query engines backed by language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines database query execution engines powered by LLMs rather than autonomous agent systems.
MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents Introduces a controlled Minecraft benchmark to evaluate whether embodied agents acquire persistent, transferrable skills across environment resets. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Tests game-environment agents in Minecraft rather than real-world industrial computer vision.
MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents Introduces a controlled Minecraft benchmark to evaluate whether embodied agents acquire persistent, transferrable skills across environment resets. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies self-improvement capabilities rather than safety risks, hallucination, or guardrails.
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a training framework for LVLMs rather than a foundational evaluation methodology.
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on internalizing spatial reasoning in vision-language models rather than tool-using agents.
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on spatial reasoning capabilities rather than deployment safety, alignment, or guardrails.
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general spatial reasoning in vision-language models, not industrial inspection.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, or efficient inference serving.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Applies models to historical archives rather than proposing RAG retrieval techniques.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Analyzes comic books and manga, unrelated to software code generation.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on digital humanities document analysis rather than autonomous multi-step agents.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Applies existing models to historical analysis rather than evaluating LLM capabilities.
Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on cultural analytics rather than AI safety, red-teaming, or reliability.
X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems Presents an automated red-teaming framework that searches for harmful compositional tool-use paths across multi-service environments. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on red-teaming tool trajectories rather than general LLM capability benchmarking.
RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics Combines dual-encoder semantic retrieval with a contextual bandit to adaptively select agent tools using execution reliability feedback. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses specifically on tool/API retrieval for agents rather than knowledge base RAG.
RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics Combines dual-encoder semantic retrieval with a contextual bandit to adaptively select agent tools using execution reliability feedback. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant The primary focus is an agent tool retrieval system, not general LLM benchmarking.
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	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not investigate code generation, software engineering, or code assistants.
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	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	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	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses solely on text-based language models without multimodal inputs or outputs.
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	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.
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	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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates regression objectives in tabular neural networks, not language model evaluation.
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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