Every paper checked — nothing hidden

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

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
PEDBench: Can Speech Models Detect Phonetic Errors? Introduces a benchmark combining natural, scripted, and synthetic audio to evaluate phonetic error detection in speech models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not evaluate model compression, distillation, or serving efficiency.
PEDBench: Can Speech Models Detect Phonetic Errors? Introduces a benchmark combining natural, scripted, and synthetic audio to evaluate phonetic error detection in speech models. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not about code generation or software engineering tasks.
PEDBench: Can Speech Models Detect Phonetic Errors? Introduces a benchmark combining natural, scripted, and synthetic audio to evaluate phonetic error detection in speech models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Does not evaluate autonomous tool-using agents or multi-agent planning.
Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalog Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses strictly on text tokenization without multimodal data.
Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalog Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not address AI safety, robustness, or reliability guardrails.
Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalog Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not about code generation, coding assistants, or program repair.
Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalog Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on linguistic tokenization, not LLM benchmark evaluation.
Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalog Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies morphological tokenization rather than model compression or inference efficiency.
Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalog Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not examine retrieval-augmented generation systems.
Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalog Tests whether linguistically accurate morpheme boundaries in tokenizers improve translation performance compared to scrambled control segmentations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Not about autonomous agents or multi-step agent planning.
CogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection Proposes a hierarchical multimodal transformer to model long-term behavioral changes across multi-session interactions for cognitive impairment detection. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not about code generation, repair, or programming assistants.
CogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection Proposes a hierarchical multimodal transformer to model long-term behavioral changes across multi-session interactions for cognitive impairment detection. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Does not focus on general LLM evaluation or benchmarks.
CogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection Proposes a hierarchical multimodal transformer to model long-term behavioral changes across multi-session interactions for cognitive impairment detection. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation or document search.
CogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection Proposes a hierarchical multimodal transformer to model long-term behavioral changes across multi-session interactions for cognitive impairment detection. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Not about autonomous multi-step planning or tool-using agents.
CogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection Proposes a hierarchical multimodal transformer to model long-term behavioral changes across multi-session interactions for cognitive impairment detection. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
CogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection Proposes a hierarchical multimodal transformer to model long-term behavioral changes across multi-session interactions for cognitive impairment detection. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not investigate model compression, distillation, or efficient serving.
Task-Centric Multimodal Learning for Cognitive Impairment Detection Evaluates a multimodal screening method using voice, video, and text from voice-assistant interactions to detect cognitive impairment. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not address code generation, repair, or software engineering.
Task-Centric Multimodal Learning for Cognitive Impairment Detection Evaluates a multimodal screening method using voice, video, and text from voice-assistant interactions to detect cognitive impairment. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Not focused on autonomous multi-step planning or tool use agents.
Task-Centric Multimodal Learning for Cognitive Impairment Detection Evaluates a multimodal screening method using voice, video, and text from voice-assistant interactions to detect cognitive impairment. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Not focused on general LLM evaluation benchmarks or methodologies.
Task-Centric Multimodal Learning for Cognitive Impairment Detection Evaluates a multimodal screening method using voice, video, and text from voice-assistant interactions to detect cognitive impairment. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not study external document retrieval or grounding architectures.
Task-Centric Multimodal Learning for Cognitive Impairment Detection Evaluates a multimodal screening method using voice, video, and text from voice-assistant interactions to detect cognitive impairment. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not focus on AI safety, alignment, or guardrails.
Task-Centric Multimodal Learning for Cognitive Impairment Detection Evaluates a multimodal screening method using voice, video, and text from voice-assistant interactions to detect cognitive impairment. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model efficiency, quantization, or serving compression.
Prune the Summary, Not the History: Source-Judged Compaction for Long-Horizon Agents Introduces a method to filter unsupported compressor inferences from long-horizon agent summaries to avoid compounding execution errors. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Operates on textual agent interaction history without multimodal components.
Prune the Summary, Not the History: Source-Judged Compaction for Long-Horizon Agents Introduces a method to filter unsupported compressor inferences from long-horizon agent summaries to avoid compounding execution errors. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on agent history compaction rather than external knowledge retrieval.
Prune the Summary, Not the History: Source-Judged Compaction for Long-Horizon Agents Introduces a method to filter unsupported compressor inferences from long-horizon agent summaries to avoid compounding execution errors. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not about software development, programming tools, or code repair.
Prune the Summary, Not the History: Source-Judged Compaction for Long-Horizon Agents Introduces a method to filter unsupported compressor inferences from long-horizon agent summaries to avoid compounding execution errors. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a compaction technique rather than an LLM evaluation benchmark.
VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents Presents a benchmark with human-annotated visual evidence to evaluate retrieval and question answering across long documents. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not address deployment safety, jailbreaks, or red-teaming.
VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents Presents a benchmark with human-annotated visual evidence to evaluate retrieval and question answering across long documents. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not cover model compression, distillation, or efficient inference serving.
VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents Presents a benchmark with human-annotated visual evidence to evaluate retrieval and question answering across long documents. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents a QA dataset rather than multi-step autonomous agent architectures.
VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents Presents a benchmark with human-annotated visual evidence to evaluate retrieval and question answering across long documents. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not related to code generation, software repair, or programming tasks.
VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents Presents a benchmark with human-annotated visual evidence to evaluate retrieval and question answering across long documents. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Analyzes documents and PDFs rather than industrial defect inspection.
One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation Deconstructs multiple-choice option bias in language models into two factors tracking answer flips and positional skew. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on multiple-choice evaluation metrics, not autonomous planning agents.
One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation Deconstructs multiple-choice option bias in language models into two factors tracking answer flips and positional skew. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not concern retrieval-augmented generation or grounding.
One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation Deconstructs multiple-choice option bias in language models into two factors tracking answer flips and positional skew. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not about code generation, software development, or code evaluation.
One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation Deconstructs multiple-choice option bias in language models into two factors tracking answer flips and positional skew. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Examines text multiple-choice questions without multimodal data.
One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation Deconstructs multiple-choice option bias in language models into two factors tracking answer flips and positional skew. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on benchmark evaluation artifacts rather than deployment safety guardrails.
One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation Deconstructs multiple-choice option bias in language models into two factors tracking answer flips and positional skew. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Not about model compression, distillation, or serving efficiency.
Can LLMs Anticipate Their Cruxes? Explores methods to extract the underlying decisive sub-questions that drive language model decisions across diverse generated outputs. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Analyzes decision rationale distributions rather than autonomous agent systems.
Can LLMs Anticipate Their Cruxes? Explores methods to extract the underlying decisive sub-questions that drive language model decisions across diverse generated outputs. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not related to code generation, program repair, or software engineering.
Can LLMs Anticipate Their Cruxes? Explores methods to extract the underlying decisive sub-questions that drive language model decisions across diverse generated outputs. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not explore model quantization, pruning, or small models.
Can LLMs Anticipate Their Cruxes? Explores methods to extract the underlying decisive sub-questions that drive language model decisions across diverse generated outputs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve external document retrieval or RAG architectures.
Can LLMs Anticipate Their Cruxes? Explores methods to extract the underlying decisive sub-questions that drive language model decisions across diverse generated outputs. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Studies text reasoning distributions without multimodal elements.
Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval Proposes an adaptive reasoning framework that supervises intermediate retrieval states and stops reasoning steps dynamically to cut latency. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not address safety alignment, red teaming, or guardrails.
Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval Proposes an adaptive reasoning framework that supervises intermediate retrieval states and stops reasoning steps dynamically to cut latency. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a retrieval method rather than a general evaluation benchmark.
Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval Proposes an adaptive reasoning framework that supervises intermediate retrieval states and stops reasoning steps dynamically to cut latency. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not about software development, coding tools, or code generation.
Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval Proposes an adaptive reasoning framework that supervises intermediate retrieval states and stops reasoning steps dynamically to cut latency. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on retriever reasoning rather than autonomous agent tool use.
Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval Proposes an adaptive reasoning framework that supervises intermediate retrieval states and stops reasoning steps dynamically to cut latency. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Applies strictly to text retrieval without multimodal data.
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Covers tabular data prediction rather than multimodal vision-language models.
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or efficient deployment.
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Introduces a distribution shift benchmark, not agent planning or tool use.
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not cover retrieval-augmented generation or grounding mechanisms.
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on tabular prediction benchmarks rather than code generation.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text and structured table data, not vision or audio modalities.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not address retrieval-augmented generation architectures or workflows.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not related to software engineering or code synthesis.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a data synthesis method rather than an LLM evaluation benchmark.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model pruning, distillation, or efficient inference.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on privacy-preserving representation synthesis, not autonomous agents.
Multimodal AI Detection for Math-Heavy Scientific Text The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Investigates AI-generated content detectors rather than broad LLM capability benchmarks.
Multimodal AI Detection for Math-Heavy Scientific Text The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on AI text detection rather than autonomous planning agents.

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