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
Timer-M1: A Multivariate Time Series Foundation Model via Learning Primitives Introduces a multivariate time series foundation model pretrained using synthetic temporal and relational pattern primitives for zero-shot forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a forecasting foundation model rather than an LLM evaluation benchmark.
Onboard Marine Anomaly Detection on $Φ$sat-2: From Simulation-Based Development to In-Orbit Demonstration Describes an onboard satellite pipeline combining segmentation and self-supervised feature encoding for real-time marine anomaly detection. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Covers deployment of an anomaly detection model rather than an evaluation benchmark.
Onboard Marine Anomaly Detection on $Φ$sat-2: From Simulation-Based Development to In-Orbit Demonstration Describes an onboard satellite pipeline combining segmentation and self-supervised feature encoding for real-time marine anomaly detection. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Involves satellite vision processing, not cross-modal vision-language models.
Onboard Marine Anomaly Detection on $Φ$sat-2: From Simulation-Based Development to In-Orbit Demonstration Describes an onboard satellite pipeline combining segmentation and self-supervised feature encoding for real-time marine anomaly detection. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses onboard satellite computer vision, not language model agents.
Onboard Marine Anomaly Detection on $Φ$sat-2: From Simulation-Based Development to In-Orbit Demonstration Describes an onboard satellite pipeline combining segmentation and self-supervised feature encoding for real-time marine anomaly detection. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses remote sensing anomaly detection rather than AI deployment safety risks.
Uncovering and Fixing Collider Bias in Bayesian PINNs Analyzes collider bias in Bayesian physics-informed neural networks and introduces a hierarchical generative chain model to resolve parameter estimation drift. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Deals with physics-informed neural networks, not AI agents.
Uncovering and Fixing Collider Bias in Bayesian PINNs Analyzes collider bias in Bayesian physics-informed neural networks and introduces a hierarchical generative chain model to resolve parameter estimation drift. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies parameter estimation bias in PINNs rather than LLM benchmarks.
Uncovering and Fixing Collider Bias in Bayesian PINNs Analyzes collider bias in Bayesian physics-informed neural networks and introduces a hierarchical generative chain model to resolve parameter estimation drift. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on scientific machine learning dynamics rather than deployment safety guardrails.
Uncovering and Fixing Collider Bias in Bayesian PINNs Analyzes collider bias in Bayesian physics-informed neural networks and introduces a hierarchical generative chain model to resolve parameter estimation drift. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on Bayesian inference formulation, not model compression or efficient serving.
Scalable AI Uncertainty Quantification via Generalized Laplace Active Subspaces Develops a low-rank generalized Laplace approximation over active curvature directions for scalable neural network uncertainty quantification. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a mathematical method for parameter uncertainty rather than evaluating LLM capabilities.
Scalable AI Uncertainty Quantification via Generalized Laplace Active Subspaces Develops a low-rank generalized Laplace approximation over active curvature directions for scalable neural network uncertainty quantification. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Discusses Bayesian uncertainty quantification, not LLM agents or tool use.
DEX: Digit-Level Early Exit for Energy-Efficient MSDF Neural Network Inference Designs a most-significant-digit-first neural network accelerator supporting early exits and digit-level pruning for energy-efficient medical image segmentation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents hardware acceleration mechanisms rather than model evaluation methods.
DEX: Digit-Level Early Exit for Energy-Efficient MSDF Neural Network Inference Designs a most-significant-digit-first neural network accelerator supporting early exits and digit-level pruning for energy-efficient medical image segmentation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on hardware accelerators for U-Net, not AI agents.
DEX: Digit-Level Early Exit for Energy-Efficient MSDF Neural Network Inference Designs a most-significant-digit-first neural network accelerator supporting early exits and digit-level pruning for energy-efficient medical image segmentation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Pertains to hardware acceleration and energy efficiency rather than AI safety.
Where Draft Trees Lose Target Mass: Exit-Guided Speculative Decoding Introduces an exact level-parallel tree verification procedure for speculative decoding derived from a canonical exit law of target-model probabilities. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an inference speedup method, not an evaluation framework or benchmark.
Where Draft Trees Lose Target Mass: Exit-Guided Speculative Decoding Introduces an exact level-parallel tree verification procedure for speculative decoding derived from a canonical exit law of target-model probabilities. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses speculative decoding latency, not safety, hallucination, or alignment.
Where Draft Trees Lose Target Mass: Exit-Guided Speculative Decoding Introduces an exact level-parallel tree verification procedure for speculative decoding derived from a canonical exit law of target-model probabilities. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on speculative decoding verification for LLMs, not multi-step tool-using agents.
Same Outcome, Different Evidence: Intent Recovery in LLM Safety Evaluation Pairs attack success rate with operative understanding rate to assess whether models recognize harmful intent during safety evaluations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Analyzes safety evaluation metrics rather than model compression or inference efficiency.
Same Outcome, Different Evidence: Intent Recovery in LLM Safety Evaluation Pairs attack success rate with operative understanding rate to assess whether models recognize harmful intent during safety evaluations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies safety refusal evaluation rather than retrieval-augmented generation architectures.
Same Outcome, Different Evidence: Intent Recovery in LLM Safety Evaluation Pairs attack success rate with operative understanding rate to assess whether models recognize harmful intent during safety evaluations. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses text-based intent recovery rather than multimodal vision-language architectures.
Same Outcome, Different Evidence: Intent Recovery in LLM Safety Evaluation Pairs attack success rate with operative understanding rate to assess whether models recognize harmful intent during safety evaluations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on safety metrics and prompt recovery rather than autonomous multi-step agents.
Same Outcome, Different Evidence: Intent Recovery in LLM Safety Evaluation Pairs attack success rate with operative understanding rate to assess whether models recognize harmful intent during safety evaluations. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Examines intent recovery in safety evaluation without focusing on coding assistants.
Narrow and Deep: An Ontology Tower as the Knowledge of an LLM Agent for an Industrial Equipment System Proposes a narrow-and-deep ontology tower to structure equipment knowledge for language model agents operating industrial air-handling systems. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Applies models to equipment tasks rather than developing general LLM evaluation benchmarks.
RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing Proposes an interpretability framework using sparse autoencoders to explain mixture of experts routing based on superposed fine-grained features. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Studies sparse MoE routing mechanisms in language models, not multimodal architectures.
RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing Proposes an interpretability framework using sparse autoencoders to explain mixture of experts routing based on superposed fine-grained features. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Provides a model interpretability technique rather than an evaluation benchmark.
RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing Proposes an interpretability framework using sparse autoencoders to explain mixture of experts routing based on superposed fine-grained features. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Interprets MoE routing rather than measuring safety, robustness, or deployment guardrails.
RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing Proposes an interpretability framework using sparse autoencoders to explain mixture of experts routing based on superposed fine-grained features. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents an MoE routing interpretability method, not multi-step tool-using agents.
RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing Proposes an interpretability framework using sparse autoencoders to explain mixture of experts routing based on superposed fine-grained features. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on foundational MoE routing interpretability rather than code LLMs or software tools.
RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing Proposes an interpretability framework using sparse autoencoders to explain mixture of experts routing based on superposed fine-grained features. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Analyzes internal MoE expert routing rather than external retrieval systems.
AuraLuxMuse: Adaptive Fusion Modeling for Aesthetic Stage Lighting Design with Music and Expert Guidance Presents a system combining representation learning and music alignment to generate editable aesthetic stage lighting cues for live performances. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a lighting synthesis system rather than general LLM evaluation benchmarks.
AuraLuxMuse: Adaptive Fusion Modeling for Aesthetic Stage Lighting Design with Music and Expert Guidance Presents a system combining representation learning and music alignment to generate editable aesthetic stage lighting cues for live performances. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses aesthetic lighting design rather than AI safety, guardrails, or model alignment.
AuraLuxMuse: Adaptive Fusion Modeling for Aesthetic Stage Lighting Design with Music and Expert Guidance Presents a system combining representation learning and music alignment to generate editable aesthetic stage lighting cues for live performances. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on stage lighting design generation rather than autonomous LLM agents.
Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Uses an internal rulebook memory rather than standard retrieval-augmented generation systems.
Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an agent self-improvement architecture rather than a benchmarking framework.
Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies self-improving world models rather than safety, guardrails, or alignment in deployment.
Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Compiles rules to code for internal agent simulation rather than assisting software developers.
Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates simulated agent dynamics rather than industrial computer vision inspection.
Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on agentic world modeling rather than model compression or efficient serving.
Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent reasoning and state exploration rather than multimodal model design.
From Pixels to Structure: Lightweight Vision-Language Models for Document OCR and Structured JSON Extraction Evaluates eight lightweight open-source vision-language models on optical character recognition and schema-compliant JSON extraction across heritage document collections. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on extraction accuracy of heritage documents rather than safety or guardrails.
From Pixels to Structure: Lightweight Vision-Language Models for Document OCR and Structured JSON Extraction Evaluates eight lightweight open-source vision-language models on optical character recognition and schema-compliant JSON extraction across heritage document collections. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Comparative study on document OCR without introducing a general LLM benchmark methodology.
From Pixels to Structure: Lightweight Vision-Language Models for Document OCR and Structured JSON Extraction Evaluates eight lightweight open-source vision-language models on optical character recognition and schema-compliant JSON extraction across heritage document collections. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies document OCR and JSON extraction, not multi-step autonomous planning agents.
From Pixels to Structure: Lightweight Vision-Language Models for Document OCR and Structured JSON Extraction Evaluates eight lightweight open-source vision-language models on optical character recognition and schema-compliant JSON extraction across heritage document collections. Peer-reviewed	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates heritage document understanding rather than industrial factory vision or inspection.
From Suppression to Repair: Mitigating Object Hallucination in Large Vision-Language Models via Localized Distribution Alignment Introduces a training-free inference method using Gaussian optimal transport to reduce object hallucination in vision-language models without suppressing valid representations. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses general object hallucination in VLMs, not industrial inspection or defect detection.
From Suppression to Repair: Mitigating Object Hallucination in Large Vision-Language Models via Localized Distribution Alignment Introduces a training-free inference method using Gaussian optimal transport to reduce object hallucination in vision-language models without suppressing valid representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a hallucination repair algorithm rather than a new evaluation suite or benchmark.
From Suppression to Repair: Mitigating Object Hallucination in Large Vision-Language Models via Localized Distribution Alignment Introduces a training-free inference method using Gaussian optimal transport to reduce object hallucination in vision-language models without suppressing valid representations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates internal representation repairs in vision-language models rather than autonomous agents.
Probability-Signature Dynamics: Unpacking Modular Addition Learning Within Two-Layer Networks Analyzes modular addition learning in two-layer neural networks using probability signatures to explain how gradient training selects Fourier-structured representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies learning mechanics of toy networks, not deployment safety, robustness, or alignment.
Probability-Signature Dynamics: Unpacking Modular Addition Learning Within Two-Layer Networks Analyzes modular addition learning in two-layer neural networks using probability signatures to explain how gradient training selects Fourier-structured representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Theoretical analysis of modular arithmetic training rather than LLM benchmarking.
Probability-Signature Dynamics: Unpacking Modular Addition Learning Within Two-Layer Networks Analyzes modular addition learning in two-layer neural networks using probability signatures to explain how gradient training selects Fourier-structured representations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines theoretical learning dynamics in two-layer networks, not LLM agents.
GRPODropout: Less is More for Online Reinforcement Learning Rollouts Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an algorithmic training intervention rather than an evaluation framework or benchmark.
GRPODropout: Less is More for Online Reinforcement Learning Rollouts Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on core reinforcement learning algorithms for reasoning, not multi-step agent architectures.
GRPODropout: Less is More for Online Reinforcement Learning Rollouts Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Pertains to post-training RL optimization, not retrieval-augmented generation systems.
GRPODropout: Less is More for Online Reinforcement Learning Rollouts Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Presents a general RL algorithm without a primary focus on code development or SE tasks.
GRPODropout: Less is More for Online Reinforcement Learning Rollouts Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates language reasoning models rather than multimodal or vision-language models.
GRPODropout: Less is More for Online Reinforcement Learning Rollouts Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses sampling diversity in RL updates, not model quantization, pruning, or small models.
GRPODropout: Less is More for Online Reinforcement Learning Rollouts Presents a rollout selection strategy that drops high-probability positive-advantage trajectories to prevent entropy collapse during language model reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies optimization stability in RL rollouts rather than deployment safety or guardrails.
Trajectory-Guided Fault Localization for Agent Skill Evolution Proposes a method linking execution traces to specific skill segments to guide automated revision of software engineering agent skills. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with execution accuracy in coding skills rather than safety, red teaming, or jailbreaks.
Trajectory-Guided Fault Localization for Agent Skill Evolution Proposes a method linking execution traces to specific skill segments to guide automated revision of software engineering agent skills. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a skill refinement method rather than a general LLM evaluation benchmark study.
LEVER: Adaptive Cost-Aware Proof Search Over AND/OR Graphs Introduces an AND/OR graph proof search algorithm optimizing customizable objectives like length and compute cost during automated formal theorem proving. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Applies a theorem proving algorithm on existing benchmarks rather than proposing evaluation methods.
LEVER: Adaptive Cost-Aware Proof Search Over AND/OR Graphs Introduces an AND/OR graph proof search algorithm optimizing customizable objectives like length and compute cost during automated formal theorem proving. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Investigates cost-aware theorem proving rather than AI safety, guardrails, or alignment risks.

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