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
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on visual reasoning models rather than multi-step tool-using agents.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies multimodal physical and spatial reasoning, not software engineering or code.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Develops general visual world modeling rather than industrial quality control or inspection.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not study retrieval pipelines or retrieval-augmented generation.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate model pruning, quantization, or efficient deployment serving.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address document retrieval or knowledge-augmented generation.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates general video streaming rather than industrial manufacturing or defect inspection.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Investigates frame-rate trade-offs rather than model compression, quantization, or distillation.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on perception accuracy rather than safety risks or alignment.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on video perception models, not autonomous planning or tool-using agents.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates dynamic video perception, entirely unrelated to software or coding.
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on code repair and bug injection, not retrieval-augmented generation.
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent coding competence rather than AI safety or guardrails.
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Operates on source code repositories and text rather than multimodal inputs.
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, distillation, or inference optimization.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies to autoregressive text generation without multimodal inputs.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents an inference decoding algorithm, not an autonomous agent framework.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not explore retrieval systems or knowledge-augmented architectures.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on speculative decoding speedup rather than novel evaluation methodologies.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Develops a general speculative decoding mechanism rather than code-specific tools.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on inference speed rather than safety, security, or robustness guardrails.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on event extraction and ontology induction, not retrieval-augmented generation.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not focus on AI safety, alignment, robustness, or deployment guardrails.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Processes text inputs exclusively for event induction.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an unsupervised learning method, not a benchmark or evaluation framework.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore model compression, quantization, or efficient inference serving.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an unsupervised clustering framework, not an LLM agent with tools or planning.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses natural language event extraction rather than programming or software code.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an architecture rather than a benchmarking or evaluation methodology.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Proposes a general language modeling architecture unrelated to coding or software engineering.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals strictly with language modeling architectures without multimodal components.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on computational efficiency and architecture rather than safety or alignment.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval mechanisms or augmented external knowledge.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a recurrent neural network architecture, not an autonomous agent system.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on prompt evolution for research agents rather than retrieval pipelines or indexing.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates deep-research citation agents, not coding or software development agents.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Explores prompt evolution rather than model distillation, pruning, or efficient inference.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses generalizability and overfitting rather than safety, alignment, or guardrails.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Works entirely with text prompts and text-based research queries.
Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities Proposes a diagnostic framework to study how language models compress fluid trans-border ethnic identities into rigid nation-state categories. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not investigate retrieval-augmented architectures or external knowledge grounding.
Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities Proposes a diagnostic framework to study how language models compress fluid trans-border ethnic identities into rigid nation-state categories. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies cultural and geopolitical representations in text, not programming code.
Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities Proposes a diagnostic framework to study how language models compress fluid trans-border ethnic identities into rigid nation-state categories. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates LLM question answering representation, not autonomous agentic systems.
Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities Proposes a diagnostic framework to study how language models compress fluid trans-border ethnic identities into rigid nation-state categories. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Examines multilingual text generation rather than multimodal architectures.
Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities Proposes a diagnostic framework to study how language models compress fluid trans-border ethnic identities into rigid nation-state categories. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, or efficient deployment.
GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports Introduces a benchmark of 5,525 QA pairs from intelligence reports to evaluate RAG document- and chunk-level retrieval. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses exclusively on textual reports and question-answering.
GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports Introduces a benchmark of 5,525 QA pairs from intelligence reports to evaluate RAG document- and chunk-level retrieval. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on question-answering accuracy over reports rather than alignment or guardrails.
GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports Introduces a benchmark of 5,525 QA pairs from intelligence reports to evaluate RAG document- and chunk-level retrieval. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Compares embedding sizes as a finding, but does not contribute model compression methods.
GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports Introduces a benchmark of 5,525 QA pairs from intelligence reports to evaluate RAG document- and chunk-level retrieval. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on geopolitical QA, having no connection to code generation or software tasks.
GeopoliticalQA: Benchmarking Retrieval and Generation over Intelligence Reports Introduces a benchmark of 5,525 QA pairs from intelligence reports to evaluate RAG document- and chunk-level retrieval. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates RAG retrieval and answer generation, not autonomous multi-step planning agents.
perturber: A Stateless Service for Semantics-Preserving Text Perturbations Presents a stateless service and library providing isolated, semantics-preserving text perturbations for language model robustness testing. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a text perturbation library, not an autonomous agent or multi-step system.
perturber: A Stateless Service for Semantics-Preserving Text Perturbations Presents a stateless service and library providing isolated, semantics-preserving text perturbations for language model robustness testing. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not develop efficient architectures, distillation, or quantization techniques.
perturber: A Stateless Service for Semantics-Preserving Text Perturbations Presents a stateless service and library providing isolated, semantics-preserving text perturbations for language model robustness testing. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies perturbations exclusively to text data.
perturber: A Stateless Service for Semantics-Preserving Text Perturbations Presents a stateless service and library providing isolated, semantics-preserving text perturbations for language model robustness testing. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies text perturbations to general NLP benchmarks, not software engineering or code.
perturber: A Stateless Service for Semantics-Preserving Text Perturbations Presents a stateless service and library providing isolated, semantics-preserving text perturbations for language model robustness testing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on synthetic text perturbations rather than retrieval or document augmentation.
Activating the Annotator: Operationalising Lived Experience in Data Annotation Tests whether priming annotators with scenario vignettes and micro-narratives improves the operationalization of lived experience in subjective annotation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address information retrieval or retrieval-augmented generation.
Activating the Annotator: Operationalising Lived Experience in Data Annotation Tests whether priming annotators with scenario vignettes and micro-narratives improves the operationalization of lived experience in subjective annotation. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on annotating subjective social text, unrelated to software code.
Activating the Annotator: Operationalising Lived Experience in Data Annotation Tests whether priming annotators with scenario vignettes and micro-narratives improves the operationalization of lived experience in subjective annotation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses entirely on text annotation tasks without multimodal data.
Activating the Annotator: Operationalising Lived Experience in Data Annotation Tests whether priming annotators with scenario vignettes and micro-narratives improves the operationalization of lived experience in subjective annotation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore model compression, pruning, or efficient inference.
Activating the Annotator: Operationalising Lived Experience in Data Annotation Tests whether priming annotators with scenario vignettes and micro-narratives improves the operationalization of lived experience in subjective annotation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates human data annotation methods, not language model agent systems.
U4RASD at KnowledgeGraphEval-2026: Ontology-Constrained Encoder and LLM Systems for Arabic Relation Extraction Describes an ontology-constrained ensemble system of encoders and fine-tuned language models for directed Arabic relation extraction. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses Arabic relation extraction rather than code generation or software engineering.

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