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
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	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies cultural and geopolitical representations in text, not programming code.
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	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	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	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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates human data annotation methods, not language model agent systems.
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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore model compression, pruning, or efficient inference.
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.
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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Uses standard fine-tuning (QLoRA) as an implementation detail rather than advancing model efficiency.
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	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses purely on relation extraction accuracy without safety, jailbreak, or guardrail analyses.
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	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Processes text inputs exclusively without multimodal elements.
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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents relation extraction classifiers, not autonomous agents or planning 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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Describes a competition submission system rather than proposing a new evaluation methodology or benchmark.
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	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on relation extraction classification rather than retrieval-augmented generation pipelines.
MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents Generates synthetic multi-agent dialogues to benchmark conversational agent robustness against non-cooperative and adversarial user behaviors. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate distillation, pruning, quantization, or hardware efficiency.
MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents Generates synthetic multi-agent dialogues to benchmark conversational agent robustness against non-cooperative and adversarial user behaviors. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on dialogue policy and function calling under adversarial user inputs, not RAG.
MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents Generates synthetic multi-agent dialogues to benchmark conversational agent robustness against non-cooperative and adversarial user behaviors. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses purely on text dialogue trajectories without multimodal capabilities.
MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents Generates synthetic multi-agent dialogues to benchmark conversational agent robustness against non-cooperative and adversarial user behaviors. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies task-oriented dialogue agents rather than software engineering or code generation.
DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models Introduces an evaluation benchmark measuring how effectively language models provide supportive responses during self-harm and crisis disclosures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, efficient architectures, or hardware optimization.
DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models Introduces an evaluation benchmark measuring how effectively language models provide supportive responses during self-harm and crisis disclosures. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text dialogue exclusively, with no vision, audio, or multimodal components.
DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models Introduces an evaluation benchmark measuring how effectively language models provide supportive responses during self-harm and crisis disclosures. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not investigate retrieval systems or document-augmented generation.
DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models Introduces an evaluation benchmark measuring how effectively language models provide supportive responses during self-harm and crisis disclosures. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates conversational responses to crisis disclosure rather than multi-step tool-use agents.
DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models Introduces an evaluation benchmark measuring how effectively language models provide supportive responses during self-harm and crisis disclosures. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates mental health crisis interactions, completely unrelated to code generation.
Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn Examines methodological and conceptual implications of using large language models and agentic workflows for machine-assisted cultural analytics. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Presents qualitative commentary on text analysis without multimodal model development.
Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn Examines methodological and conceptual implications of using large language models and agentic workflows for machine-assisted cultural analytics. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on cultural analytics rather than code generation or software development.
Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn Examines methodological and conceptual implications of using large language models and agentic workflows for machine-assisted cultural analytics. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a conceptual humanities chapter discussing workflow delegation, not technical agent architectures.
Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn Examines methodological and conceptual implications of using large language models and agentic workflows for machine-assisted cultural analytics. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not study retrieval-augmented generation or factual grounding systems.
Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn Examines methodological and conceptual implications of using large language models and agentic workflows for machine-assisted cultural analytics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model efficiency, quantization, or on-device inference.
Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn Examines methodological and conceptual implications of using large language models and agentic workflows for machine-assisted cultural analytics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Offers broad cultural perspectives rather than technical AI safety, jailbreak, or guardrail research.
Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn Examines methodological and conceptual implications of using large language models and agentic workflows for machine-assisted cultural analytics. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Discusses humanities methodology rather than proposing technical LLM evaluation benchmarks.
People Making Conversation: Evaluating How Naturally Speech Models Converse Evaluates whether real-time speech models decide to speak appropriately during natural pauses and turn ends in unscripted conversations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates real-time conversational speech turn-taking rather than goal-directed autonomous agents.
People Making Conversation: Evaluating How Naturally Speech Models Converse Evaluates whether real-time speech models decide to speak appropriately during natural pauses and turn ends in unscripted conversations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational cadence and naturalness rather than safety, red-teaming, or guardrails.
People Making Conversation: Evaluating How Naturally Speech Models Converse Evaluates whether real-time speech models decide to speak appropriately during natural pauses and turn ends in unscripted conversations. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Assesses speech-to-speech audio conversation without evaluating cross-modal visual-text understanding.
People Making Conversation: Evaluating How Naturally Speech Models Converse Evaluates whether real-time speech models decide to speak appropriately during natural pauses and turn ends in unscripted conversations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate external document retrieval or grounding architectures.
People Making Conversation: Evaluating How Naturally Speech Models Converse Evaluates whether real-time speech models decide to speak appropriately during natural pauses and turn ends in unscripted conversations. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on human-model speech interaction rather than code generation or software tools.
People Making Conversation: Evaluating How Naturally Speech Models Converse Evaluates whether real-time speech models decide to speak appropriately during natural pauses and turn ends in unscripted conversations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not propose architectural compression, pruning, or efficient inference methods.
When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks Introduces a benchmark evaluating how misleading visualizations and charts affect the multi-step execution and decisions of web agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not introduce techniques for model compression, distillation, or efficient serving.
When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks Introduces a benchmark evaluating how misleading visualizations and charts affect the multi-step execution and decisions of web agents. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates web-browsing agents rather than code generation or software engineering agents.
When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks Introduces a benchmark evaluating how misleading visualizations and charts affect the multi-step execution and decisions of web agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies multimodal agent web actions rather than retrieval-augmented generation pipelines.
Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets Tests cross-corpus emotion classification transfer across three datasets, finding significant loss in discrimination and conceptual disagreement on labels. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies standard text classifiers, not autonomous or multi-step LLM agents.
Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets Tests cross-corpus emotion classification transfer across three datasets, finding significant loss in discrimination and conceptual disagreement on labels. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation or document grounding.
Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets Tests cross-corpus emotion classification transfer across three datasets, finding significant loss in discrimination and conceptual disagreement on labels. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates unimodal text classifiers rather than vision, audio, or multimodal models.
Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets Tests cross-corpus emotion classification transfer across three datasets, finding significant loss in discrimination and conceptual disagreement on labels. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate model compression, distillation, quantization, or inference acceleration.
Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets Tests cross-corpus emotion classification transfer across three datasets, finding significant loss in discrimination and conceptual disagreement on labels. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Analyzes emotion classification datasets, with no focus on programming or software development.
Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets Tests cross-corpus emotion classification transfer across three datasets, finding significant loss in discrimination and conceptual disagreement on labels. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on label variance in emotion datasets, not safety guardrails or security risks.
Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies cultural satire understanding rather than safety guardrails, red-teaming, or jailbreak defenses.
Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on single-prompt explanation generation, not multi-step tool use or autonomous agents.
Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not explore document retrieval, indexing, or retrieval-augmented generation architectures.
Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Operates exclusively on text articles and does not evaluate multimodal inputs.

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