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.

All17,313 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
Agent-Guided Evidence Acquisition for Emotion-Cause Pair Extraction This paper investigates an agent framework that selectively queries expert judgments for borderline emotion-cause pairs to improve extraction accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies extraction strategies rather than LLM benchmarking methods.
Agent-Guided Evidence Acquisition for Emotion-Cause Pair Extraction This paper investigates an agent framework that selectively queries expert judgments for borderline emotion-cause pairs to improve extraction accuracy. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies agent query decisions rather than RAG architectures.
Agent-Guided Evidence Acquisition for Emotion-Cause Pair Extraction This paper investigates an agent framework that selectively queries expert judgments for borderline emotion-cause pairs to improve extraction accuracy. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on evidence acquisition strategies, not model compression.
Agent-Guided Evidence Acquisition for Emotion-Cause Pair Extraction This paper investigates an agent framework that selectively queries expert judgments for borderline emotion-cause pairs to improve extraction accuracy. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses emotion-cause pair extraction, not software development.
Description Complexity Bounds for Analytic Approximations of Lipschitz Functions This paper derives theoretical description complexity bounds for approximating one-dimensional Lipschitz functions using elementary analytic expression trees. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Mathematical approximation theory, not software engineering.
Description Complexity Bounds for Analytic Approximations of Lipschitz Functions This paper derives theoretical description complexity bounds for approximating one-dimensional Lipschitz functions using elementary analytic expression trees. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Mathematical function complexity theory, not efficient model deployment.
Description Complexity Bounds for Analytic Approximations of Lipschitz Functions This paper derives theoretical description complexity bounds for approximating one-dimensional Lipschitz functions using elementary analytic expression trees. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Theoretical mathematics with no retrieval-augmented generation aspects.
Description Complexity Bounds for Analytic Approximations of Lipschitz Functions This paper derives theoretical description complexity bounds for approximating one-dimensional Lipschitz functions using elementary analytic expression trees. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical mathematical proofs, not AI safety or guardrails.
Description Complexity Bounds for Analytic Approximations of Lipschitz Functions This paper derives theoretical description complexity bounds for approximating one-dimensional Lipschitz functions using elementary analytic expression trees. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical math analysis, not LLM benchmarking or evaluation.
Description Complexity Bounds for Analytic Approximations of Lipschitz Functions This paper derives theoretical description complexity bounds for approximating one-dimensional Lipschitz functions using elementary analytic expression trees. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Pure mathematical function approximation with no multimodal aspects.
Description Complexity Bounds for Analytic Approximations of Lipschitz Functions This paper derives theoretical description complexity bounds for approximating one-dimensional Lipschitz functions using elementary analytic expression trees. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of function approximation, not AI agents.
TeachGrade: Training-Free Short Answer Scoring with Teacher Evidence This paper proposes a training-free framework that uses teacher-graded examples and dual memories to improve automated short answer grading. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies grading frameworks rather than model compression techniques.
TeachGrade: Training-Free Short Answer Scoring with Teacher Evidence This paper proposes a training-free framework that uses teacher-graded examples and dual memories to improve automated short answer grading. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on textual short-answer scoring without multimodal input.
TeachGrade: Training-Free Short Answer Scoring with Teacher Evidence This paper proposes a training-free framework that uses teacher-graded examples and dual memories to improve automated short answer grading. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on automated grading scoring, not autonomous agentic workflows.
TeachGrade: Training-Free Short Answer Scoring with Teacher Evidence This paper proposes a training-free framework that uses teacher-graded examples and dual memories to improve automated short answer grading. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Applies teacher-graded case retrieval specifically for automated grading scoring.
TeachGrade: Training-Free Short Answer Scoring with Teacher Evidence This paper proposes a training-free framework that uses teacher-graded examples and dual memories to improve automated short answer grading. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on evaluation accuracy, not safety or guardrails.
TeachGrade: Training-Free Short Answer Scoring with Teacher Evidence This paper proposes a training-free framework that uses teacher-graded examples and dual memories to improve automated short answer grading. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Assesses short answer scoring, not software engineering.
FidelityQ: Preserving Predictive Fidelity in Ultra-Low-Bit Language Model Quantization This paper introduces a scalar quantization method using analytic coding to preserve language model predictive fidelity at precisions down to two bits. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on weight quantization, not coding tasks.
FidelityQ: Preserving Predictive Fidelity in Ultra-Low-Bit Language Model Quantization This paper introduces a scalar quantization method using analytic coding to preserve language model predictive fidelity at precisions down to two bits. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on post-quantization predictive accuracy, not safety guardrails.
FidelityQ: Preserving Predictive Fidelity in Ultra-Low-Bit Language Model Quantization This paper introduces a scalar quantization method using analytic coding to preserve language model predictive fidelity at precisions down to two bits. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Addresses model weight quantization, not autonomous planning agents.
FidelityQ: Preserving Predictive Fidelity in Ultra-Low-Bit Language Model Quantization This paper introduces a scalar quantization method using analytic coding to preserve language model predictive fidelity at precisions down to two bits. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not involve retrieval mechanisms or external databases.
FidelityQ: Preserving Predictive Fidelity in Ultra-Low-Bit Language Model Quantization This paper introduces a scalar quantization method using analytic coding to preserve language model predictive fidelity at precisions down to two bits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a quantization method rather than benchmark evaluation.
FidelityQ: Preserving Predictive Fidelity in Ultra-Low-Bit Language Model Quantization This paper introduces a scalar quantization method using analytic coding to preserve language model predictive fidelity at precisions down to two bits. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on language model quantization without multimodal components.
Do Personalized LLMs Need the Whole Conversation? Matching the Full History with Only 5% of Its Text via Relevance-Filtered Context This paper demonstrates that sentence-level relevance retrieval can match full conversation history accuracy in personalized language models using only five percent context. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies history compression for personalization, not autonomous agents.
Do Personalized LLMs Need the Whole Conversation? Matching the Full History with Only 5% of Its Text via Relevance-Filtered Context This paper demonstrates that sentence-level relevance retrieval can match full conversation history accuracy in personalized language models using only five percent context. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates context filtering techniques, not general LLM benchmarks.
Do Personalized LLMs Need the Whole Conversation? Matching the Full History with Only 5% of Its Text via Relevance-Filtered Context This paper demonstrates that sentence-level relevance retrieval can match full conversation history accuracy in personalized language models using only five percent context. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on context selection efficiency rather than safety risks.
Do Personalized LLMs Need the Whole Conversation? Matching the Full History with Only 5% of Its Text via Relevance-Filtered Context This paper demonstrates that sentence-level relevance retrieval can match full conversation history accuracy in personalized language models using only five percent context. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals strictly with text conversation histories.
Do Personalized LLMs Need the Whole Conversation? Matching the Full History with Only 5% of Its Text via Relevance-Filtered Context This paper demonstrates that sentence-level relevance retrieval can match full conversation history accuracy in personalized language models using only five percent context. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Examines conversation history retrieval, not code generation.
HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on depression detection, not software engineering.
HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies multimodal depression classification rather than deployment safety.
HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a challenge submission network rather than evaluation methodology.
HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on multimodal classification, not agentic planning.
HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not involve external document retrieval or grounding.
HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not investigate model compression or efficient serving.
HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses clinical depression detection, not industrial manufacturing inspection.
HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention This paper introduces a discrete Hodge decomposition method to separate semantic flow transitions within transformer attention mechanisms. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not cover external retrieval or grounding techniques.
HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention This paper introduces a discrete Hodge decomposition method to separate semantic flow transitions within transformer attention mechanisms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes theoretical attention decomposition without focusing on efficiency.
HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention This paper introduces a discrete Hodge decomposition method to separate semantic flow transitions within transformer attention mechanisms. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical formulation of attention, not code generation.
HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention This paper introduces a discrete Hodge decomposition method to separate semantic flow transitions within transformer attention mechanisms. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Theoretical attention paper without cross-modal multimodal integration.
HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention This paper introduces a discrete Hodge decomposition method to separate semantic flow transitions within transformer attention mechanisms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an attention architecture, not benchmark evaluation methodology.
HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention This paper introduces a discrete Hodge decomposition method to separate semantic flow transitions within transformer attention mechanisms. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of Transformer attention rather than agent systems.
HodgePhaseNet: Discrete Hodge Decomposition of Semantic Flows in Transformer Attention This paper introduces a discrete Hodge decomposition method to separate semantic flow transitions within transformer attention mechanisms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical model of attention, not AI safety or alignment.
What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO) This paper examines how content matching, retrieval mechanics, and model knowledge affect web page citations in generative search engines. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Examines search engine citation mechanisms rather than autonomous agents.
What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO) This paper examines how content matching, retrieval mechanics, and model knowledge affect web page citations in generative search engines. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Analyzes generative search citations, not code generation.
What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO) This paper examines how content matching, retrieval mechanics, and model knowledge affect web page citations in generative search engines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not explore model efficiency, pruning, or quantization.
What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO) This paper examines how content matching, retrieval mechanics, and model knowledge affect web page citations in generative search engines. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Empirical analysis of search engines rather than evaluation methodology.
What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO) This paper examines how content matching, retrieval mechanics, and model knowledge affect web page citations in generative search engines. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies search engine citations rather than AI safety guardrails.
What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO) This paper examines how content matching, retrieval mechanics, and model knowledge affect web page citations in generative search engines. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text-based web search citation dynamics.
PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media This paper presents a personality-driven augmentation and contrastive learning framework to improve depression detection from social media posts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on transfer learning rather than model compression.
PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media This paper presents a personality-driven augmentation and contrastive learning framework to improve depression detection from social media posts. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses social media mental health detection, not software engineering.
PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media This paper presents a personality-driven augmentation and contrastive learning framework to improve depression detection from social media posts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not investigate retrieval-augmented generation architectures.
PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media This paper presents a personality-driven augmentation and contrastive learning framework to improve depression detection from social media posts. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Processes text posts without multimodal data integration.
PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media This paper presents a personality-driven augmentation and contrastive learning framework to improve depression detection from social media posts. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on post classification, not autonomous multi-step agents.
PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media This paper presents a personality-driven augmentation and contrastive learning framework to improve depression detection from social media posts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses clinical text classification rather than AI safety risks.
PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Media This paper presents a personality-driven augmentation and contrastive learning framework to improve depression detection from social media posts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a detection framework rather than a general evaluation benchmark.
RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMs The paper introduces an information extraction benchmark for extracting structured regulatory fields and source provenance from dense environmental policy documents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Addresses structured extraction from documents, not RAG pipelines.
RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMs The paper introduces an information extraction benchmark for extracting structured regulatory fields and source provenance from dense environmental policy documents. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals purely with regulatory text extraction without visual modality.
RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMs The paper introduces an information extraction benchmark for extracting structured regulatory fields and source provenance from dense environmental policy documents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies extraction accuracy rather than AI safety or reliability.
RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMs The paper introduces an information extraction benchmark for extracting structured regulatory fields and source provenance from dense environmental policy documents. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on regulatory information extraction rather than multi-step agents.
RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMs The paper introduces an information extraction benchmark for extracting structured regulatory fields and source provenance from dense environmental policy documents. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies regulatory legal extraction, not software code tasks.

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