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
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	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	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on relation extraction classification rather than retrieval-augmented generation pipelines.
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	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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents relation extraction classifiers, not autonomous agents or planning systems.
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	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	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	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	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates mental health crisis interactions, completely unrelated to 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	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	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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, efficient architectures, or hardware optimization.
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	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	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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Discusses humanities methodology rather than proposing technical LLM evaluation benchmarks.
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	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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model efficiency, quantization, or on-device inference.
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	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	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	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	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	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	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies multimodal agent web actions rather than retrieval-augmented generation pipelines.
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.
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	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	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	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.
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.
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	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on satirical news explanation, unrelated to code synthesis, repair, or software engineering.
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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Only uses open-weights models as a baseline; does not propose compression, quantization, or serving efficiencies.
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	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Operates exclusively on text articles and does not evaluate multimodal inputs.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with sequence modeling architectures, not retrieval-augmented generation.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a sequence modeling method rather than an evaluation benchmark.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on general sequence modeling, not software engineering or code.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an alternative sequence modeling architecture, not an autonomous agent system.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with architecture design rather than deployment safety or guardrails.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses unimodal sequence modeling without multimodal data.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a memory framework rather than an evaluation benchmark.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses multi-party conversation memory, not code generation.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Pertains to text dialogue memory without multimodal input.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on memory representations rather than model compression or inference efficiency.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational recall rather than safety or jailbreaks.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on generative decoding speed and modeling, not AI safety or red-teaming.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Introduces an architecture for parallel generation, not agent workflows.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a new generation model architecture, not an evaluation benchmark.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies diffusion to discrete text generation, not multimodal data.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on generic text generation, not software code synthesis.

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