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
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses continual visual anomaly detection, not LLM agent systems.
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on continual learning benchmarks rather than model compression or efficient serving.
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Benchmark is specifically for visual anomaly detection, not language model evaluation.
Belief Embedding Tree Search This paper proposes a tree search method using belief embeddings for planning in partially observable environments. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on POMDP belief representation, not LLM compression or inference efficiency.
Belief Embedding Tree Search This paper proposes a tree search method using belief embeddings for planning in partially observable environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address deployment safety, guardrails, or hallucination mitigation.
Belief Embedding Tree Search This paper proposes a tree search method using belief embeddings for planning in partially observable environments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on classical POMDP tree search rather than language model agents.
Belief Embedding Tree Search This paper proposes a tree search method using belief embeddings for planning in partially observable environments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Deals with reinforcement learning search algorithms rather than LLM benchmarking.
Trace Evolution Bench: Measuring Whether Agents Improve from Their Own Environment Traces This paper introduces a benchmark to measure whether autonomous coding agents truly improve their policies from interaction traces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates self-evolution measurement protocols rather than deployment guardrails or safety attacks.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a fine-tuning strategy rather than LLM benchmarking or evaluation tools.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies data supervision strategies rather than model pruning, distillation, or quantization.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Presents text-based ASR transcript correction, not multimodal vision-language models.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on ASR post-editing supervision, not multi-step autonomous agents.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Covers speech-to-text transcript correction, not software coding or program repair.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses speech recognition transcript rewriting, not document retrieval-augmented generation.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical dental imaging rather than manufacturing or industrial inspection.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies traditional CNN vision segmentation without multimodal language reasoning.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines dental image segmentation, not deployment safety or alignment risks.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Applies standard CNN segmenters to dental images, not LLM evaluation benchmarks.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers medical radiograph segmentation, not autonomous LLM agents.
Exposing AI Inference as a Telco Network API over the 5G User Plane This paper demonstrates an API and network framework to support split AI inference over 5G mobile networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Covers telecommunications networking protocols, not LLM benchmark evaluation.
Exposing AI Inference as a Telco Network API over the 5G User Plane This paper demonstrates an API and network framework to support split AI inference over 5G mobile networks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on 5G network API abstractions, not autonomous planning agents.
Exposing AI Inference as a Telco Network API over the 5G User Plane This paper demonstrates an API and network framework to support split AI inference over 5G mobile networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses edge tensor transport and split inference, not safety or alignment.
Knowledge transfer between simple discrete worlds This paper examines how recurrent models generalize and transfer rule-based reasoning across discrete puzzle environments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Explores abstract puzzle reasoning, not autonomous tool-using agents.
Knowledge transfer between simple discrete worlds This paper examines how recurrent models generalize and transfer rule-based reasoning across discrete puzzle environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on abstract rule generalization rather than deployment safety and reliability.
GAMW: Generation-Aware Mel-Domain Watermarking for TTS Traceability This paper proposes a watermarking method embedded during speech synthesis to trace and verify generated audio. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers audio watermarking for TTS, not autonomous multi-step planning agents.
GAMW: Generation-Aware Mel-Domain Watermarking for TTS Traceability This paper proposes a watermarking method embedded during speech synthesis to trace and verify generated audio. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces watermarking techniques rather than LLM benchmarking or judge evaluations.
Self-Evolving Visual Agent via Margin Reward This paper introduces a self-evolution framework that trains visual agents to invoke tools using reinforcement learning and unlabeled images. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Proposes a training scheme for tool use, not safety or guardrail mechanisms.
Self-Evolving Visual Agent via Margin Reward This paper introduces a self-evolution framework that trains visual agents to invoke tools using reinforcement learning and unlabeled images. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning for tool use rather than benchmark evaluation.
Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Develops a neuro-symbolic argument reasoning system, not an autonomous tool-using agent.
Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies persuasion modeling rather than AI safety, red-teaming, or guardrails.
Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an annotated discussion dataset rather than an LLM evaluation benchmark.
Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model quantization, distillation, or efficient inference serving.
Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses purely on text discourse, not multimodal image, audio, or video processing.
Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Uses retrieval as a minor component for argument extraction, not enterprise RAG.
Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on discourse persuasion analysis, not software engineering or code generation.
VLCQ: Post-training quantization for deep neural networks using variable length coding Introduces a variable-length coding method for post-training neural network quantization that maintains accuracy without retraining. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Discusses general vision model quantization rather than industrial defect detection.
VLCQ: Post-training quantization for deep neural networks using variable length coding Introduces a variable-length coding method for post-training neural network quantization that maintains accuracy without retraining. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Covers post-training compression for vision backbones without multimodal language integration.
VLCQ: Post-training quantization for deep neural networks using variable length coding Introduces a variable-length coding method for post-training neural network quantization that maintains accuracy without retraining. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a neural network quantization method, not autonomous planning agents.
VLCQ: Post-training quantization for deep neural networks using variable length coding Introduces a variable-length coding method for post-training neural network quantization that maintains accuracy without retraining. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses model compression algorithms, not benchmark contamination or LLM evaluation.
VLCQ: Post-training quantization for deep neural networks using variable length coding Introduces a variable-length coding method for post-training neural network quantization that maintains accuracy without retraining. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference bit-width reduction rather than safety guardrails or red-teaming.
TurnCost: Turns, Not Observations: Cache-Aware Loop Engineering for Coding Agents Analyzes coding-agent trajectories to model execution costs under prompt caching, showing turn count matters far more than observation token length. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent runtime efficiency and costs, not safety guardrails or alignment.
TurnCost: Turns, Not Observations: Cache-Aware Loop Engineering for Coding Agents Analyzes coding-agent trajectories to model execution costs under prompt caching, showing turn count matters far more than observation token length. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies trajectory costs and loops rather than presenting benchmark evaluation methodologies.
Structural Chain-of-Thought: Reasoning with Executable Computational Graphs Replaces free-form reasoning text with executable computational graphs trained via reinforcement learning to improve reasoning speed and verifiability. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a structured reasoning technique rather than novel evaluation benchmarks.
scHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data Evaluates a compact bidirectional Mamba state-space autoencoder for classifying heart failure disease states from single-nucleus RNA sequencing data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses biological classification tasks, not general LLM evaluation benchmarks.
scHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data Evaluates a compact bidirectional Mamba state-space autoencoder for classifying heart failure disease states from single-nucleus RNA sequencing data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on heart failure genomics rather than deployment safety and reliability.
scHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data Evaluates a compact bidirectional Mamba state-space autoencoder for classifying heart failure disease states from single-nucleus RNA sequencing data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Describes a compact biological autoencoder rather than efficient LLM serving techniques.
scHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data Evaluates a compact bidirectional Mamba state-space autoencoder for classifying heart failure disease states from single-nucleus RNA sequencing data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a bioinformatics model, not an autonomous multi-step agent.
ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer Introduces an elastic transformer pretraining architecture enabling simultaneous hyperparameter transfer and subnetwork execution within a single model. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses transformer architecture pretraining, not agent workflows or planning.
ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer Introduces an elastic transformer pretraining architecture enabling simultaneous hyperparameter transfer and subnetwork execution within a single model. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Presents pretraining scaling formulations without discussing safety or reliability guardrails.
ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer Introduces an elastic transformer pretraining architecture enabling simultaneous hyperparameter transfer and subnetwork execution within a single model. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on architecture and hyperparameter transfer, not benchmark evaluation.
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition Presents an enhanced convolutional neural network architecture for real-time traffic sign classification in autonomous driving applications. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on standard traffic benchmarks rather than studying evaluation methodology.
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition Presents an enhanced convolutional neural network architecture for real-time traffic sign classification in autonomous driving applications. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a vision architecture, not an autonomous planning LLM agent.
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition Presents an enhanced convolutional neural network architecture for real-time traffic sign classification in autonomous driving applications. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies autonomous driving traffic sign classification, explicitly excluded from industrial vision.
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition Presents an enhanced convolutional neural network architecture for real-time traffic sign classification in autonomous driving applications. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on CNN classification speed and accuracy, not AI deployment safety.
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition Presents an enhanced convolutional neural network architecture for real-time traffic sign classification in autonomous driving applications. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on pure computer vision classification without cross-modal language components.
To Defer or Not to Defer? Online Learning with LLMs and Congested Experts Proposes an online learning algorithm that decides whether language models should answer queries directly or defer to congested human experts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies human-in-the-loop deferral optimization, not language model benchmark testing.
To Defer or Not to Defer? Online Learning with LLMs and Congested Experts Proposes an online learning algorithm that decides whether language models should answer queries directly or defer to congested human experts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses an online query-deferral algorithm rather than autonomous multi-step agents.
To Defer or Not to Defer? Online Learning with LLMs and Congested Experts Proposes an online learning algorithm that decides whether language models should answer queries directly or defer to congested human experts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model distillation, quantization, or efficient model architectures.
GesBench: When and Where Do Multimodal Language Models Perceive Social Meaning? Introduces a benchmark evaluating how and when multimodal language models perceive human social cues from partial audio and video snippets. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates multimodal perception of social cues, not agentic planning and action.
GesBench: When and Where Do Multimodal Language Models Perceive Social Meaning? Introduces a benchmark evaluating how and when multimodal language models perceive human social cues from partial audio and video snippets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on conversational cue perception rather than safety, red-teaming, or guardrails.

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