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
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation or software tooling.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Covers speech-text representation alignment, not autonomous agents.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces modality repair layers, not benchmark methodology.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address external document retrieval or RAG pipelines.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates spoken language ID models, not broad LLM benchmarks.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve retrieval-augmented generation systems.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on speech classification accuracy, not safety or guardrails.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on audio language identification, not multi-step agents.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to programming, software engineering, or code.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on dedicated conformer LID, not model compression methods.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers basic NLP classification, not safety, guardrails, or hallucination.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on low-resource NLP labeling rather than LLM benchmarks.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses text-only sequence tagging in Saraiki script.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to software development, repair, or coding.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses part-of-speech sequence tagging, not planning agents.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Evaluates existing transformer encoders without introducing compression methods.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or knowledge grounding.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval triggering rather than autonomous multi-step agent planning.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates medical reasoning benchmarks, not code generation.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a retrieval mechanism, not benchmark validation or scoring.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Operates on text-based medical QA without vision or audio.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval timing rather than parameter reduction or quantization.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on parameter efficiency or model compression.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not address vision or cross-modal modalities.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Covers agent internal memory updates, not document-based RAG.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not focused on coding assistants or software engineering tasks.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes agent architectures, not benchmark analysis or judge models.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent adaptation rather than guardrails or safety.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address document retrieval or knowledge augmentation.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not explore model distillation, quantization, or efficient architectures.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Targets speech diarization benchmarking, not general LLM evaluation.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses audio diarization, not safety, guardrails, or hallucination.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation or software maintenance.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on speech speaker diarization, not autonomous multi-step agents.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate code generation or software engineering.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Investigates text-based forum agents, not multimodal models.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on social behavior in agents, not retrieval systems.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers conversational behavior, not deployment safety or jailbreak risks.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes agent social discourse, not standard LLM evaluation benchmarks.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient serving.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies sociological and linguistic drift rather than AI safety or guardrails.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Analyzes linguistic diffusion in agent communities, not retrieval-augmented generation.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on linguistic diffusion and community dynamics, not model compression.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Studies forum text and lexical persistence, with no multimodal modalities.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates linguistic dynamics across platforms rather than proposing an LLM benchmark.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Examines multi-agent language drift, not programming tools or code models.
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual document search, not coding assistants or software engineering.
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual document retrieval architectures, not autonomous decision agents.
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Optimizes multimodal retrieval efficiency rather than safety, guardrails, or alignment.
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a retrieval framework evaluated on existing benchmarks, not a benchmark itself.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Introduces a classification/reasoning benchmark, not retrieval-augmented generation systems.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Analyzes text-based social media hate speech, with no multimodal input.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates hate speech detection tasks, not autonomous agentic systems.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on natural language hate speech analysis, not coding assistants.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmarking hate speech detection, not model compression or inference efficiency.
FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding This paper introduces FOLD, a training-free speculative decoding approach treating distant tokens as position-free far memory for long-context generation. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Deals with inference acceleration techniques rather than code synthesis or repair.
FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding This paper introduces FOLD, a training-free speculative decoding approach treating distant tokens as position-free far memory for long-context generation. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents a speculative decoding strategy, not autonomous multi-step planning agents.
FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding This paper introduces FOLD, a training-free speculative decoding approach treating distant tokens as position-free far memory for long-context generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an inference speedup technique rather than an evaluation benchmark.
FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding This paper introduces FOLD, a training-free speculative decoding approach treating distant tokens as position-free far memory for long-context generation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates long-context text decoding, not multimodal image/audio systems.
FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding This paper introduces FOLD, a training-free speculative decoding approach treating distant tokens as position-free far memory for long-context generation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on draft-model attention mechanisms, not document retrieval or knowledge grounding.

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