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
VoTSpeech: Decoupling Voice Design from Speech Generation in Instruction-Following TTS This paper presents an instruction-following text-to-speech model that separates voice design from speech generation using an intermediate voice latent. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Addresses speech generation, not retrieval-augmented generation.
Amharic-English Cross-Language Question Answering Using Statistical Machine Translation, Named Entity Recognition, and Semantic Indexing This paper presents a bidirectional Amharic-English cross-language question answering system using machine translation, entity recognition, and semantic indexing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Traditional statistical machine translation and indexing, not modern RAG.
Amharic-English Cross-Language Question Answering Using Statistical Machine Translation, Named Entity Recognition, and Semantic Indexing This paper presents a bidirectional Amharic-English cross-language question answering system using machine translation, entity recognition, and semantic indexing. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Traditional cross-lingual QA pipeline, not autonomous LLM agents.
Amharic-English Cross-Language Question Answering Using Statistical Machine Translation, Named Entity Recognition, and Semantic Indexing This paper presents a bidirectional Amharic-English cross-language question answering system using machine translation, entity recognition, and semantic indexing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an information retrieval system, not benchmark methodology.
Amharic-English Cross-Language Question Answering Using Statistical Machine Translation, Named Entity Recognition, and Semantic Indexing This paper presents a bidirectional Amharic-English cross-language question answering system using machine translation, entity recognition, and semantic indexing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not investigate model compression or efficient inference.
Amharic-English Cross-Language Question Answering Using Statistical Machine Translation, Named Entity Recognition, and Semantic Indexing This paper presents a bidirectional Amharic-English cross-language question answering system using machine translation, entity recognition, and semantic indexing. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses cross-lingual QA, not software engineering.
Amharic-English Cross-Language Question Answering Using Statistical Machine Translation, Named Entity Recognition, and Semantic Indexing This paper presents a bidirectional Amharic-English cross-language question answering system using machine translation, entity recognition, and semantic indexing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on cross-lingual retrieval effectiveness, not safety.
Amharic-English Cross-Language Question Answering Using Statistical Machine Translation, Named Entity Recognition, and Semantic Indexing This paper presents a bidirectional Amharic-English cross-language question answering system using machine translation, entity recognition, and semantic indexing. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Processes text documents without multimodal features.
The Constitution Speaks Nheengatu: An Open MT System and Reproducible Corpus Pipeline for the Amazonian Língua Geral This paper releases an open parallel corpus, speech evaluation set, and translation models for Portuguese and the Amazonian language Nheengatu. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses low-resource natural language translation, not code.
The Constitution Speaks Nheengatu: An Open MT System and Reproducible Corpus Pipeline for the Amazonian Língua Geral This paper releases an open parallel corpus, speech evaluation set, and translation models for Portuguese and the Amazonian language Nheengatu. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on machine translation pipelines, not autonomous agents.
The Constitution Speaks Nheengatu: An Open MT System and Reproducible Corpus Pipeline for the Amazonian Língua Geral This paper releases an open parallel corpus, speech evaluation set, and translation models for Portuguese and the Amazonian language Nheengatu. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Creates a translation corpus and system, not LLM evaluation.
The Constitution Speaks Nheengatu: An Open MT System and Reproducible Corpus Pipeline for the Amazonian Língua Geral This paper releases an open parallel corpus, speech evaluation set, and translation models for Portuguese and the Amazonian language Nheengatu. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Text and speech translation corpus, not multimodal model reasoning.
The Constitution Speaks Nheengatu: An Open MT System and Reproducible Corpus Pipeline for the Amazonian Língua Geral This paper releases an open parallel corpus, speech evaluation set, and translation models for Portuguese and the Amazonian language Nheengatu. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on translation accuracy rather than deployment safety.
The Constitution Speaks Nheengatu: An Open MT System and Reproducible Corpus Pipeline for the Amazonian Língua Geral This paper releases an open parallel corpus, speech evaluation set, and translation models for Portuguese and the Amazonian language Nheengatu. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not investigate model compression or efficient serving.
The Constitution Speaks Nheengatu: An Open MT System and Reproducible Corpus Pipeline for the Amazonian Língua Geral This paper releases an open parallel corpus, speech evaluation set, and translation models for Portuguese and the Amazonian language Nheengatu. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Deals with parallel corpus translation, not retrieval generation.
Who Receives Respect? A Counterfactual Benchmark for Honorific Allocation Bias in Bengali This paper introduces a counterfactual benchmark to evaluate honorific register allocation bias across gender and occupations in English-to-Bengali translation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on translation bias without retrieval augmentation.
Who Receives Respect? A Counterfactual Benchmark for Honorific Allocation Bias in Bengali This paper introduces a counterfactual benchmark to evaluate honorific register allocation bias across gender and occupations in English-to-Bengali translation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Text translation analysis without multimodal data.
Who Receives Respect? A Counterfactual Benchmark for Honorific Allocation Bias in Bengali This paper introduces a counterfactual benchmark to evaluate honorific register allocation bias across gender and occupations in English-to-Bengali translation. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Examines machine translation bias, not software engineering.
Who Receives Respect? A Counterfactual Benchmark for Honorific Allocation Bias in Bengali This paper introduces a counterfactual benchmark to evaluate honorific register allocation bias across gender and occupations in English-to-Bengali translation. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies honorific allocation in translation, not AI agents.
Who Receives Respect? A Counterfactual Benchmark for Honorific Allocation Bias in Bengali This paper introduces a counterfactual benchmark to evaluate honorific register allocation bias across gender and occupations in English-to-Bengali translation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not explore model compression or efficiency.
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model This paper introduces a training-free retrieval-augmented generation framework for biomedical named entity linking to improve cross-domain adaptability. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on entity linking, not autonomous multi-step agents.
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model This paper introduces a training-free retrieval-augmented generation framework for biomedical named entity linking to improve cross-domain adaptability. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an entity-linking framework, not an evaluation benchmark.
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model This paper introduces a training-free retrieval-augmented generation framework for biomedical named entity linking to improve cross-domain adaptability. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on entity linking accuracy rather than deployment safety.
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model This paper introduces a training-free retrieval-augmented generation framework for biomedical named entity linking to improve cross-domain adaptability. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses biomedical entity linking, not code generation.
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model This paper introduces a training-free retrieval-augmented generation framework for biomedical named entity linking to improve cross-domain adaptability. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not investigate model compression or efficient inference.
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model This paper introduces a training-free retrieval-augmented generation framework for biomedical named entity linking to improve cross-domain adaptability. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with biomedical text without multimodal components.
Dependency Reasoning for Aspect Sentiment Quadruple Prediction This paper injects linearized dependency parse trees into language model prompts to improve aspect sentiment quadruple prediction. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Addresses structured sentiment prediction rather than autonomous agents.
Dependency Reasoning for Aspect Sentiment Quadruple Prediction This paper injects linearized dependency parse trees into language model prompts to improve aspect sentiment quadruple prediction. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies aspect-sentiment extraction rather than AI safety.
Dependency Reasoning for Aspect Sentiment Quadruple Prediction This paper injects linearized dependency parse trees into language model prompts to improve aspect sentiment quadruple prediction. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on sentiment quadruple extraction, not software code.
Dependency Reasoning for Aspect Sentiment Quadruple Prediction This paper injects linearized dependency parse trees into language model prompts to improve aspect sentiment quadruple prediction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on a task-specific extraction method, not benchmarks.
Dependency Reasoning for Aspect Sentiment Quadruple Prediction This paper injects linearized dependency parse trees into language model prompts to improve aspect sentiment quadruple prediction. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Examines text sentiment prediction without multimodal elements.
Dependency Reasoning for Aspect Sentiment Quadruple Prediction This paper injects linearized dependency parse trees into language model prompts to improve aspect sentiment quadruple prediction. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not explore model compression or efficient serving.
Dependency Reasoning for Aspect Sentiment Quadruple Prediction This paper injects linearized dependency parse trees into language model prompts to improve aspect sentiment quadruple prediction. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Uses demonstration retrieval for prompting, not a RAG pipeline.
Social Bias in Chinese Large Language Models under Social Media Expressions This paper evaluates social bias in Chinese language models across social media expressions, internet slang, and sarcastic packaging. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model efficiency, quantization, or serving.
Social Bias in Chinese Large Language Models under Social Media Expressions This paper evaluates social bias in Chinese language models across social media expressions, internet slang, and sarcastic packaging. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses solely on text-based Chinese social media expressions.
Social Bias in Chinese Large Language Models under Social Media Expressions This paper evaluates social bias in Chinese language models across social media expressions, internet slang, and sarcastic packaging. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Investigates social bias, not software code generation.
Social Bias in Chinese Large Language Models under Social Media Expressions This paper evaluates social bias in Chinese language models across social media expressions, internet slang, and sarcastic packaging. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not involve external retrieval or document augmentation.
Social Bias in Chinese Large Language Models under Social Media Expressions This paper evaluates social bias in Chinese language models across social media expressions, internet slang, and sarcastic packaging. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:18	Not relevant Measures social bias in models, not autonomous tool use.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on extraction accuracy rather than AI safety.
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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Text extraction task without visual, audio, or multimodal components.
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	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	AI agents via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of function approximation, not AI agents.
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	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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Pure mathematical function approximation with no multimodal aspects.
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	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	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	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	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	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	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.

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