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
Can LLMs Access their World Knowledge for Event Prediction? The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on formatting event descriptions, not retrieval-augmented generation systems.
Joint Subspace Sparse Attention for Efficient Long-Context Decoding The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Optimizes attention mechanisms, not external knowledge retrieval or RAG.
Joint Subspace Sparse Attention for Efficient Long-Context Decoding The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses attention computation efficiency, not autonomous agentic workflows.
Joint Subspace Sparse Attention for Efficient Long-Context Decoding The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on attention kernel efficiency, not software engineering or coding.
Joint Subspace Sparse Attention for Efficient Long-Context Decoding The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses inference acceleration, not safety, alignment, or guardrail deployment.
Joint Subspace Sparse Attention for Efficient Long-Context Decoding The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text long-context decoding, not multimodal vision-language tasks.
Joint Subspace Sparse Attention for Efficient Long-Context Decoding The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an attention mechanism, not a general benchmark or evaluation methodology.
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies literary fiction corpora, not software engineering or code.
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation or grounding mechanisms.
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on computational literary analysis rather than general LLM evaluation benchmarks.
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates gendered reference modeling, not planning or autonomous agents.
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Analyzes literary gender bias, not AI safety or deployment guardrails.
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Analyzes written fiction text corpora, not multimodal vision-language data.
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Uses RoBERTa for masking without proposing model efficiency methods.
Tracing Morphology, Semantics, and Divergence in Language Acquisition The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates linguistic representation learning, not multi-step autonomous agents.
Tracing Morphology, Semantics, and Divergence in Language Acquisition The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes linguistic acquisition in LLMs, not programming or code synthesis.
Tracing Morphology, Semantics, and Divergence in Language Acquisition The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses exclusively on text-based language acquisition, not multimodal models.
Tracing Morphology, Semantics, and Divergence in Language Acquisition The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external document retrieval or grounding architectures.
Tracing Morphology, Semantics, and Divergence in Language Acquisition The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates linguistic acquisition dynamics, not safety, jailbreaks, or deployment risks.
Tracing Morphology, Semantics, and Divergence in Language Acquisition The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies language acquisition dynamics during training, not evaluation benchmarks or protocols.
Tracing Morphology, Semantics, and Divergence in Language Acquisition The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies checkpoints across scales without proposing efficiency or compression techniques.
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies models to record linkage and demographic inference, not programming.
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Mentions open-weight models in passing without focusing on efficiency techniques.
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates zero-shot social science tasks, not multi-step agentic systems.
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Operates exclusively on tabular and text data, not multimodal inputs.
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies pre-trained models to empirical social science tasks rather than benchmarking LLMs.
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on demographic inference accuracy, not deployment safety or guardrails.
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Introduces record linkage matching, not retrieval-augmented generation pipelines.
Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies ranking models for arena votes, not autonomous agent workflows.
Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines benchmark methodology rather than deployment safety, jailbreaks, or hallucinations.
Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not investigate model compression, distillation, or efficient serving.
Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Examines Chatbot Arena text rankings, not multimodal model architectures.
Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not touch on retrieval augmentation or external knowledge retrieval.
Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on general chatbot evaluation, not software engineering or code.
DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision The paper introduces a benchmark evaluating the robustness of typed decision models against text, image, and prompt injection attacks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on decision model robustness, not autonomous planning agents.
DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision The paper introduces a benchmark evaluating the robustness of typed decision models against text, image, and prompt injection attacks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model efficiency, distillation, or quantization techniques.
DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision The paper introduces a benchmark evaluating the robustness of typed decision models against text, image, and prompt injection attacks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates decision models on text and vision, not software code generation.
DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision The paper introduces a benchmark evaluating the robustness of typed decision models against text, image, and prompt injection attacks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not cover retrieval mechanisms or external database grounding.
DivAlgo: A systematic evaluation of algorithmic diversity for code generation models The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on algorithmic diversity in code, not safety or guardrails.
DivAlgo: A systematic evaluation of algorithmic diversity for code generation models The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not investigate retrieval-augmented generation or external grounding.
DivAlgo: A systematic evaluation of algorithmic diversity for code generation models The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Examines source code and text, not multimodal image or video understanding.
DivAlgo: A systematic evaluation of algorithmic diversity for code generation models The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient serving.
DivAlgo: A systematic evaluation of algorithmic diversity for code generation models The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates code diversity without investigating multi-step planning or tool use.
From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies to qualitative interview coding, not code generation or software tools.
From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses qualitative research methodology, not AI safety or deployment reliability.
From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve retrieval-augmented generation or external document grounding.
From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals strictly with qualitative text representations, not multimodal data.
From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents a qualitative research methodology, not autonomous agentic systems.
From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not explore small models, pruning, distillation, or efficient inference.
From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a mixed-methods qualitative integration procedure, not an LLM benchmark.
Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient inference.
Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate or implement retrieval-augmented generation systems.
Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies to text generation, not software code or programming tasks.
Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on style metrics rather than AI safety, guardrails, or alignment.
Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on text evaluation metrics, not autonomous planning or agents.
Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates only multilingual text generation, not multimodal vision-language models.
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation The paper proposes a method combining offline execution-verified prompt updates and test-time refinement to improve program generation in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external document retrieval or grounding.
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation The paper proposes a method combining offline execution-verified prompt updates and test-time refinement to improve program generation in language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies to programming languages and text, not multimodal inputs.
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation The paper proposes a method combining offline execution-verified prompt updates and test-time refinement to improve program generation in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or small models.
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation The paper proposes a method combining offline execution-verified prompt updates and test-time refinement to improve program generation in language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on program synthesis accuracy rather than AI safety.

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