Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework The paper presents a teacher-student framework using language models for weak annotation to classify cybersecurity tweets. Peer-reviewed	AI agents via arxiv · 17 Oct, 06:17	Not relevant Covers weak supervision for classification, not LLM planning or agent systems.
LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework The paper presents a teacher-student framework using language models for weak annotation to classify cybersecurity tweets. Peer-reviewed	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses cybersecurity text classification, not model safety, guardrails, or jailbreaks.
Isolating a Language Model's Judgment with a Label-Free Subspace The paper proposes isolating internal model judgments from instruction formatting variations using a label-free hidden state subspace. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates language models on text judgments without multimodal components.
Isolating a Language Model's Judgment with a Label-Free Subspace The paper proposes isolating internal model judgments from instruction formatting variations using a label-free hidden state subspace. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Not focused on model compression, distillation, or efficient serving.
Isolating a Language Model's Judgment with a Label-Free Subspace The paper proposes isolating internal model judgments from instruction formatting variations using a label-free hidden state subspace. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Analyzes internal representations, not autonomous agentic workflows or tool use.
Isolating a Language Model's Judgment with a Label-Free Subspace The paper proposes isolating internal model judgments from instruction formatting variations using a label-free hidden state subspace. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not cover code synthesis or software engineering tasks.
Isolating a Language Model's Judgment with a Label-Free Subspace The paper proposes isolating internal model judgments from instruction formatting variations using a label-free hidden state subspace. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Not about external document retrieval or grounding enterprise systems.
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data The paper introduces TimeCap, an open deterministic pipeline for generating signal-grounded captions from wearable health time-series data. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not about code generation, software repair, or developer tools.
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data The paper introduces TimeCap, an open deterministic pipeline for generating signal-grounded captions from wearable health time-series data. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on wearable time-series captioning, not autonomous multi-step LLM planning.
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data The paper introduces TimeCap, an open deterministic pipeline for generating signal-grounded captions from wearable health time-series data. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not address safety alignment, red teaming, jailbreaks, or guardrails.
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data The paper introduces TimeCap, an open deterministic pipeline for generating signal-grounded captions from wearable health time-series data. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a dataset creation pipeline rather than an LLM evaluation benchmark.
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data The paper introduces TimeCap, an open deterministic pipeline for generating signal-grounded captions from wearable health time-series data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or enterprise document retrieval systems.
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data The paper introduces TimeCap, an open deterministic pipeline for generating signal-grounded captions from wearable health time-series data. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Not about model quantization, distillation, or efficient model serving.
Making every stage of medical device document assessment with large language models deterministic The paper evaluates output non-determinism in commercial language model deployments during medical device regulatory compliance assessments. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text extraction and retrieval rather than multimodal vision-language models.
Making every stage of medical device document assessment with large language models deterministic The paper evaluates output non-determinism in commercial language model deployments during medical device regulatory compliance assessments. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Analyzes medical regulatory document compliance, not software programming.
Making every stage of medical device document assessment with large language models deterministic The paper evaluates output non-determinism in commercial language model deployments during medical device regulatory compliance assessments. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Addresses production pipeline determinism rather than standard LLM benchmark evaluations.
Making every stage of medical device document assessment with large language models deterministic The paper evaluates output non-determinism in commercial language model deployments during medical device regulatory compliance assessments. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on determinism and reproducibility rather than model compression or distillation.
Making every stage of medical device document assessment with large language models deterministic The paper evaluates output non-determinism in commercial language model deployments during medical device regulatory compliance assessments. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on deterministic document processing, not multi-step agent tool use.
VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation The paper introduces VALCON to evaluate and improve value consistency when language models alternate between generator and evaluator roles. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on value consistency across roles rather than external document retrieval.
VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation The paper introduces VALCON to evaluate and improve value consistency when language models alternate between generator and evaluator roles. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Deals with value alignment and evaluation, not software engineering or coding.
VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation The paper introduces VALCON to evaluate and improve value consistency when language models alternate between generator and evaluator roles. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies dual-role value alignment consistency, not multi-step agent planning or tools.
VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation The paper introduces VALCON to evaluate and improve value consistency when language models alternate between generator and evaluator roles. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates text-based value judgments, not multimodal vision-language architectures.
VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation The paper introduces VALCON to evaluate and improve value consistency when language models alternate between generator and evaluator roles. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Addresses behavioral alignment consistency rather than model compression or efficient serving.
PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning The paper presents PocketTSLM, deploying an on-device soft-prompt time-series language model on smartphones for personal health data. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Deals with on-device sensor reasoning, not multi-step tool-using agents.
PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning The paper presents PocketTSLM, deploying an on-device soft-prompt time-series language model on smartphones for personal health data. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on deployment efficiency and privacy architecture rather than safety guardrails or jailbreaks.
PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning The paper presents PocketTSLM, deploying an on-device soft-prompt time-series language model on smartphones for personal health data. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Applies language models to physiological time series, not code generation.
PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning The paper presents PocketTSLM, deploying an on-device soft-prompt time-series language model on smartphones for personal health data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on time-series soft prompting rather than document retrieval pipelines.
PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning The paper presents PocketTSLM, deploying an on-device soft-prompt time-series language model on smartphones for personal health data. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents an on-device deployment system rather than an LLM evaluation benchmark.
ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer The paper introduces ZoomCap, an adaptive visual zooming and evidence-routing framework to improve fine-grained image captioning supervision. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Uses internal image evidence routing rather than external document retrieval for RAG.
ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer The paper introduces ZoomCap, an adaptive visual zooming and evidence-routing framework to improve fine-grained image captioning supervision. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant General image captioning research, not industrial inspection or defect detection on assembly lines.
ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer The paper introduces ZoomCap, an adaptive visual zooming and evidence-routing framework to improve fine-grained image captioning supervision. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces FactCycle for caption scoring, but primary focus is multimodal caption generation.
ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer The paper introduces ZoomCap, an adaptive visual zooming and evidence-routing framework to improve fine-grained image captioning supervision. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Deals with image captioning and visual verification, not coding or program repair.
ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer The paper introduces ZoomCap, an adaptive visual zooming and evidence-routing framework to improve fine-grained image captioning supervision. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on visual captioning quality rather than model compression or inference efficiency.
ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer The paper introduces ZoomCap, an adaptive visual zooming and evidence-routing framework to improve fine-grained image captioning supervision. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on visual captioning and zooming, not autonomous agent workflows.
Hybrid Generative-Tagging Decoding for Efficient Product Attribute-Value Extraction The paper introduces a hybrid generative-tagging architecture to improve efficiency and coverage in product attribute and value extraction. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on attribute extraction decoding rather than retrieval-augmented generation systems.
Hybrid Generative-Tagging Decoding for Efficient Product Attribute-Value Extraction The paper introduces a hybrid generative-tagging architecture to improve efficiency and coverage in product attribute and value extraction. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on product attribute extraction, not software engineering or code generation.
Hybrid Generative-Tagging Decoding for Efficient Product Attribute-Value Extraction The paper introduces a hybrid generative-tagging architecture to improve efficiency and coverage in product attribute and value extraction. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on extraction throughput and accuracy rather than safety risks or guardrails.
Hybrid Generative-Tagging Decoding for Efficient Product Attribute-Value Extraction The paper introduces a hybrid generative-tagging architecture to improve efficiency and coverage in product attribute and value extraction. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates extraction accuracy rather than general LLM evaluation methodologies.
Hybrid Generative-Tagging Decoding for Efficient Product Attribute-Value Extraction The paper introduces a hybrid generative-tagging architecture to improve efficiency and coverage in product attribute and value extraction. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Operates on text-based product descriptions, not multimodal models.
Hybrid Generative-Tagging Decoding for Efficient Product Attribute-Value Extraction The paper introduces a hybrid generative-tagging architecture to improve efficiency and coverage in product attribute and value extraction. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Develops a decoding architecture for catalog extraction, not agent planning.
VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility The paper presents VoteGEO-Bench to measure how rewriting web content influences information visibility in generative search engine election queries. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on generative engine optimization rather than model compression or efficient serving.
VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility The paper presents VoteGEO-Bench to measure how rewriting web content influences information visibility in generative search engine election queries. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on web page text and election information, not code generation.
VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility The paper presents VoteGEO-Bench to measure how rewriting web content influences information visibility in generative search engine election queries. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies search engine visibility manipulation, not LLM agent systems.
VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility The paper presents VoteGEO-Bench to measure how rewriting web content influences information visibility in generative search engine election queries. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Analyzes textual web queries and generative engine outputs, not multimodal models.
QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery The paper introduces QuasiLeak-Bench to evaluate whether privacy detectors can identify distributed quasi-identifiers governed by disclosure policies. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on privacy attribute inference rather than retrieval-augmented generation.
QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery The paper introduces QuasiLeak-Bench to evaluate whether privacy detectors can identify distributed quasi-identifiers governed by disclosure policies. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents a benchmark for privacy detection, not autonomous agent planning or tools.
QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery The paper introduces QuasiLeak-Bench to evaluate whether privacy detectors can identify distributed quasi-identifiers governed by disclosure policies. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Examines privacy detection benchmark performance, not model compression or efficient serving.
QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery The paper introduces QuasiLeak-Bench to evaluate whether privacy detectors can identify distributed quasi-identifiers governed by disclosure policies. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Operates strictly on natural language messages, not multimodal inputs.
QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery The paper introduces QuasiLeak-Bench to evaluate whether privacy detectors can identify distributed quasi-identifiers governed by disclosure policies. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Deals with privacy detection in text, not software engineering or code LLMs.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Tests general fine-tuning across tasks without a primary focus on code assistance.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses catastrophic forgetting in training rather than safety guardrails or jailbreaks.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies continual RL training methods rather than LLM benchmark designs.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Addresses text-based reinforcement learning fine-tuning, not multimodal architectures.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on parameter-efficient RL fine-tuning, not multi-step agent planning.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Investigates attention head fine-tuning rather than retrieval-augmented generation.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates question answering methods rather than proposing new evaluation benchmarks.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on reinforcement learning credit assignment rather than model compression or inference efficiency.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on knowledge graph queries, not code generation or software repair.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Applies to text and structured knowledge graphs, not multimodal vision models.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on question-answering accuracy rather than deployment safety, jailbreaks, or guardrails.

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