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
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on post-quantization predictive accuracy, not safety guardrails.
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 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	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	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Examines conversation history retrieval, not code generation.
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.
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	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	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses clinical depression detection, not industrial manufacturing inspection.
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	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.
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	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	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	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	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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text-based web search citation dynamics.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies search engine citations rather than AI safety guardrails.
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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a detection framework rather than a general evaluation benchmark.
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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Processes text posts without multimodal data integration.
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	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	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	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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies prompting and extraction methods, not model efficiency.
Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models The paper introduces a multilingual benchmark evaluating safety and discrepancies between verbal refusals and tool actions in tool-calling language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Evaluates tool use and refusal, not software engineering.
Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models The paper introduces a multilingual benchmark evaluating safety and discrepancies between verbal refusals and tool actions in tool-calling language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, quantization, or distillation.
Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models The paper introduces a multilingual benchmark evaluating safety and discrepancies between verbal refusals and tool actions in tool-calling language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Text and tool-calling benchmark without multimodal components.
Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models The paper introduces a multilingual benchmark evaluating safety and discrepancies between verbal refusals and tool actions in tool-calling language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on tool-calling safety rather than retrieval-augmented grounding.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes compute scaling laws rather than LLM evaluation benchmarks.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not investigate retrieval-augmented generation architectures.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies spiking neural network scaling, not LLM agents.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines compute scaling tradeoffs rather than deployment safety guardrails.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Examines general language modeling scaling laws, not code generation.
Compute-Optimal Scaling Laws for Spiking Language Models The paper presents an empirical scaling law study measuring parameter and compute trade-offs in spiking neural network language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text language modeling without multimodal components.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not cover model quantization, pruning, or efficient inference.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Deals with conversational assistant state repair, not code generation.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not address external document retrieval or augmented generation.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an error-recovery framework rather than an evaluation benchmark.
Where Did We Go Wrong? Localizing Errors to Repair Multi-Turn Conversations The paper presents a framework to probabilistically localize user-intent misinterpretations and repair conversational states in multi-turn assistant systems. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses text dialogue interactions without multimodal processing.
Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI The paper proposes a trainable proposition linker that learns atom-level semantic links from sentence-level supervision for neuro-symbolic inference. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies propositional text linking without multimodal inputs.
Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI The paper proposes a trainable proposition linker that learns atom-level semantic links from sentence-level supervision for neuro-symbolic inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a linking architecture rather than an evaluation benchmark.
Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI The paper proposes a trainable proposition linker that learns atom-level semantic links from sentence-level supervision for neuro-symbolic inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Deals with natural language inference, not RAG architectures.
Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI The paper proposes a trainable proposition linker that learns atom-level semantic links from sentence-level supervision for neuro-symbolic inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on symbolic inference accuracy rather than deployment safety.

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