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
Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Develops preference optimization without involving retrieval or external knowledge.
Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses general alignment, not coding assistants or software repair.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on embedding bias in candidate matching, not autonomous agents.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Audits text embedding models, not multimodal systems.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Evaluates bias impacts, not model compression or efficient serving.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies HR embeddings, not code generation or software engineering.
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval This paper audits request independence in open Jev-style retrieval models across multi-passage query states. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Audits retrieval scoring models, not autonomous planning agents.
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval This paper audits request independence in open Jev-style retrieval models across multi-passage query states. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies information retrieval models, not software development.
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval This paper audits request independence in open Jev-style retrieval models across multi-passage query states. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text passage retrieval without multimodal inputs.
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval This paper audits request independence in open Jev-style retrieval models across multi-passage query states. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates retrieval scoring consistency, not model compression.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant General visual hallucination benchmark, not industrial defect inspection.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on visual reasoning errors rather than document retrieval.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not investigate model quantization or efficient inference.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies vision-language hallucinations, not software engineering.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Analyzes visual hallucinations, not multi-step planning agents.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a TTS model architecture, not evaluation methodology.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on controllable speech synthesis, not deployment safety.
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	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on text-to-speech synthesis, not autonomous agentic systems.
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	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Deals with acoustic modeling, not software development.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not investigate model compression or inference efficiency.
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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Processes text documents without multimodal features.
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.
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	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	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	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	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	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	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	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.
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.
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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with biomedical text without multimodal components.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an entity-linking framework, not an evaluation benchmark.
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	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	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	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	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	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	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	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	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	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	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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Text extraction task without visual, audio, or multimodal components.

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