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
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on abstract rule generalization rather than deployment safety and reliability.
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.
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.
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.
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	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	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	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	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.
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	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.
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 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	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 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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies trajectory costs and loops rather than presenting benchmark evaluation methodologies.
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.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a bioinformatics model, not an autonomous multi-step agent.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on architecture and hyperparameter transfer, not benchmark evaluation.
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.
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	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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on pure computer vision classification without cross-modal language components.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a vision architecture, not an autonomous planning LLM agent.
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	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	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 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.
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.
Trust Hinders Bidirectional Human-AI Alignment Critiques trust calibration in human-AI decision making for open-ended problems and outlines dimensions for higher-order system alignment. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Discusses human-AI decision-making philosophy rather than multi-step tool-using agents.
Trust Hinders Bidirectional Human-AI Alignment Critiques trust calibration in human-AI decision making for open-ended problems and outlines dimensions for higher-order system alignment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents conceptual perspectives on trust rather than benchmark creation or validation.
Single-step Retrosynthesis via Reinforcement Learning from Forward Feedback Trains chemical retrosynthesis models via reinforcement learning using forward reaction model consistency to credit plausible alternative pathways. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on chemical synthesis accuracy, not deployment safety or alignment.
Single-step Retrosynthesis via Reinforcement Learning from Forward Feedback Trains chemical retrosynthesis models via reinforcement learning using forward reaction model consistency to credit plausible alternative pathways. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers chemical retrosynthesis modeling, not autonomous tool-using agents.
Single-step Retrosynthesis via Reinforcement Learning from Forward Feedback Trains chemical retrosynthesis models via reinforcement learning using forward reaction model consistency to credit plausible alternative pathways. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a domain-specific model rather than general LLM evaluation frameworks.
Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses Uses multi-objective evolutionary algorithms to optimize agent execution harnesses across competing metrics like accuracy and operating cost. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on optimizing agent harnesses rather than benchmarking methodology or dataset evaluation.
Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses Uses multi-objective evolutionary algorithms to optimize agent execution harnesses across competing metrics like accuracy and operating cost. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address safety, guardrails, jailbreaks, or deployment reliability risks.
Can agents whose sole duty is to keep humans alive be hijacked to kill? Delayed multi-turn derived-action trap attacks in simulated high-stakes environments Tests delayed prompt injection attacks on life-support control agents in a simulated lunar habitat to see if safety duties persist. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on safety and injection attacks rather than evaluating benchmark methodologies.
UDHRBench: Benchmarking Human Rights in Large Language Models Evaluates commercial language models against Universal Declaration of Human Rights principles, revealing misalignments driven by efficiency arguments. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses entirely on textual queries, not multimodal vision-language tasks.
UDHRBench: Benchmarking Human Rights in Large Language Models Evaluates commercial language models against Universal Declaration of Human Rights principles, revealing misalignments driven by efficiency arguments. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates model ethics, not inference optimization or model compression.
UDHRBench: Benchmarking Human Rights in Large Language Models Evaluates commercial language models against Universal Declaration of Human Rights principles, revealing misalignments driven by efficiency arguments. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Assesses legal and ethical norms, not code synthesis or repair.
UDHRBench: Benchmarking Human Rights in Large Language Models Evaluates commercial language models against Universal Declaration of Human Rights principles, revealing misalignments driven by efficiency arguments. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies human rights alignment, not retrieval-augmented generation.
UDHRBench: Benchmarking Human Rights in Large Language Models Evaluates commercial language models against Universal Declaration of Human Rights principles, revealing misalignments driven by efficiency arguments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on alignment benchmarking rather than autonomous agent planning.
Continuous Risk-Based Scheduling for Food Safety Inspection: Integrating Historical Decay with Event-Driven Consumer Review Signals Combines historical violation decay with language-model triage of online consumer reviews to dynamically schedule restaurant health inspections. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Applies review classification to municipal inspections, not AI model safety.
Continuous Risk-Based Scheduling for Food Safety Inspection: Integrating Historical Decay with Event-Driven Consumer Review Signals Combines historical violation decay with language-model triage of online consumer reviews to dynamically schedule restaurant health inspections. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a scheduling pipeline using classifiers, not autonomous planning agents.
Continuous Risk-Based Scheduling for Food Safety Inspection: Integrating Historical Decay with Event-Driven Consumer Review Signals Combines historical violation decay with language-model triage of online consumer reviews to dynamically schedule restaurant health inspections. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on risk scoring algorithms rather than LLM evaluation benchmarks.
SocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair Fine-tunes a coding model using synthetic tutoring dialogues to provide step-by-step guidance without revealing program fixes prematurely. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with Socratic tutoring strategies, not model compression or efficiency.
SocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair Fine-tunes a coding model using synthetic tutoring dialogues to provide step-by-step guidance without revealing program fixes prematurely. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an educational tutoring system rather than an evaluation benchmark.
SocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair Fine-tunes a coding model using synthetic tutoring dialogues to provide step-by-step guidance without revealing program fixes prematurely. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses tutoring and debugging pedagogy, not deployment safety or attacks.
SocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair Fine-tunes a coding model using synthetic tutoring dialogues to provide step-by-step guidance without revealing program fixes prematurely. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on conversational tutoring dialogue rather than autonomous multi-step agents.

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