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
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses representation drift in world models, not deployment safety guardrails.
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluated on visual pendulum physics simulations, not industrial quality control.
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on symplectic dynamics preservation, not model compression or quantization.
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a world model architecture, not an evaluation framework.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses purely on computer vision topologies, not multimodal vision-language models.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Investigates general depth estimation and reconstruction, not industrial defect detection.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with neural architecture search for vision, not agent workflows.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an architectural search framework, not an LLM benchmark.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies geometric vision architectures, not AI safety or reliability.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text-based question answering, not multimodal vision-language models.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Assesses sociolinguistic and regional knowledge, not code generation or repair.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents an evaluation benchmark rather than methods for model efficiency.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates parametric knowledge rather than retrieval-augmented generation systems.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Benchmarks linguistic and cultural understanding rather than safety guardrails or attacks.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on evaluating static LLM QA, not agentic planning.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on microscopy image segmentation, not autonomous planning or agents.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies biological crystal detection, not AI safety or deployment reliability.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on biology microscopy images, not manufacturing or industrial inspection.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Proposes color channel segmentation, not model compression or efficient serving.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses microscopy image segmentation rather than LLM benchmarking or evaluation.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with standard computer vision on microscopy, not multimodal models.
Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation The paper presents Co-DSL, enabling users and language models to co-design reusable spatial concepts to guide layout generation solvers. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on domain-specific layout programs rather than multimodal vision-language models.
Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation The paper presents Co-DSL, enabling users and language models to co-design reusable spatial concepts to guide layout generation solvers. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Generates graphics spatial layouts, not industrial quality control or inspection.
Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation The paper presents Co-DSL, enabling users and language models to co-design reusable spatial concepts to guide layout generation solvers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses spatial layout synthesis, not safety guardrails or reliability risks.
Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation The paper presents Co-DSL, enabling users and language models to co-design reusable spatial concepts to guide layout generation solvers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates spatial layout generation, not LLM benchmark methodologies.
Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation The paper presents Co-DSL, enabling users and language models to co-design reusable spatial concepts to guide layout generation solvers. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies human-in-the-loop DSL design, not autonomous multi-step agents.
OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition The paper presents an ontology-guided retrieval framework combining population and personalized memory for wearable sensor human activity recognition. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates activity recognition accuracy rather than benchmarking methodology.
OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition The paper presents an ontology-guided retrieval framework combining population and personalized memory for wearable sensor human activity recognition. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on personalized sensor classification, not AI safety or jailbreaks.
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models The paper introduces a post-training quantization framework for autoregressive visual generation models to retain image quality under low precision. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Quantizes generative image synthesis models, not industrial inspection systems.
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models The paper introduces a post-training quantization framework for autoregressive visual generation models to retain image quality under low precision. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates quantization performance, not general evaluation methodologies.
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models The paper introduces a post-training quantization framework for autoregressive visual generation models to retain image quality under low precision. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on image generation quantization rather than multimodal perception or reasoning.
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models The paper introduces a post-training quantization framework for autoregressive visual generation models to retain image quality under low precision. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on post-training quantization rather than safety, guardrails, or alignment.
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models The paper introduces a post-training quantization framework for autoregressive visual generation models to retain image quality under low precision. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on post-training quantization, not agent planning or tool use.
CHEAR: Confusion-Hierarchy Evidence-Aware Reasoning for Cost-Efficient Human Activity Recognition The paper introduces CHEAR, an activity recognition framework that pre-organizes confusion hierarchies offline to reduce inference costs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses activity recognition efficiency, not AI safety or alignment.
CHEAR: Confusion-Hierarchy Evidence-Aware Reasoning for Cost-Efficient Human Activity Recognition The paper introduces CHEAR, an activity recognition framework that pre-organizes confusion hierarchies offline to reduce inference costs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a reasoning framework for sensor data, not an LLM benchmark.
CHEAR: Confusion-Hierarchy Evidence-Aware Reasoning for Cost-Efficient Human Activity Recognition The paper introduces CHEAR, an activity recognition framework that pre-organizes confusion hierarchies offline to reduce inference costs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on sensor activity recognition workflows rather than multi-step tool agents.
WisdomNet: trustable machine learning toward error-free classification The paper proposes an artificial neural network framework designed to reject uncertain predictions to prevent misclassification errors. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on standard classification rejection, not language model agents.
WisdomNet: trustable machine learning toward error-free classification The paper proposes an artificial neural network framework designed to reject uncertain predictions to prevent misclassification errors. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies selective classification, not model compression or efficient serving.
WisdomNet: trustable machine learning toward error-free classification The paper proposes an artificial neural network framework designed to reject uncertain predictions to prevent misclassification errors. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an abstention mechanism for neural classifiers, not an LLM benchmark.
Accuracy and Uncertainty in Machine Deception Detection (Research White Paper) The paper proposes a multi-layer detector ensemble to identify deceptive model outputs while calibrating score uncertainty and false-positive rates. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Detects deceptive outputs from activations, not agentic planning.
Accuracy and Uncertainty in Machine Deception Detection (Research White Paper) The paper proposes a multi-layer detector ensemble to identify deceptive model outputs while calibrating score uncertainty and false-positive rates. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies activation-based deception detection, not model efficiency or quantization.
Accuracy and Uncertainty in Machine Deception Detection (Research White Paper) The paper proposes a multi-layer detector ensemble to identify deceptive model outputs while calibrating score uncertainty and false-positive rates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Develops a detector for deception rather than a broader LLM evaluation framework.
SuperAI: Merging Capabilities Across Heterogeneous Foundation Models The paper introduces SuperAI, a framework to merge complementary capabilities across heterogeneous foundation models using low-dimensional subspace alignment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes model fusion rather than LLM benchmark analysis.
SuperAI: Merging Capabilities Across Heterogeneous Foundation Models The paper introduces SuperAI, a framework to merge complementary capabilities across heterogeneous foundation models using low-dimensional subspace alignment. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Merges heterogeneous model representations rather than compressing models for serving.
SuperAI: Merging Capabilities Across Heterogeneous Foundation Models The paper introduces SuperAI, a framework to merge complementary capabilities across heterogeneous foundation models using low-dimensional subspace alignment. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on model weight/representation merging, not safety or alignment.
SuperAI: Merging Capabilities Across Heterogeneous Foundation Models The paper introduces SuperAI, a framework to merge complementary capabilities across heterogeneous foundation models using low-dimensional subspace alignment. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on representation subspace alignment across models, not multi-step agents.
Anchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax The paper introduces a rule-based curriculum training system that improves reinforcement learning performance on Craftax without language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies RL game performance, not safety or guardrails.
Anchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax The paper introduces a rule-based curriculum training system that improves reinforcement learning performance on Craftax without language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses non-LLM reinforcement learning curricula for a 2D game, not LLM agents.
Anchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax The paper introduces a rule-based curriculum training system that improves reinforcement learning performance on Craftax without language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies RL game curricula, not model quantization or serving efficiency.
Anchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax The paper introduces a rule-based curriculum training system that improves reinforcement learning performance on Craftax without language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a curriculum training method for RL rather than LLM benchmarking.
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes The paper introduces a zeroth-order influence function framework to enable memory-efficient training data selection using only forward passes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies zeroth-order data attribution rather than model compression or inference acceleration.
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes The paper introduces a zeroth-order influence function framework to enable memory-efficient training data selection using only forward passes. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on influence functions for training data selection, not agents.
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes The paper introduces a zeroth-order influence function framework to enable memory-efficient training data selection using only forward passes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training data attribution technique, not an evaluation benchmark.
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes The paper introduces a zeroth-order influence function framework to enable memory-efficient training data selection using only forward passes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on efficient data selection math rather than deployment safety or jailbreaks.
MultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching The paper introduces MultiFlow, a generative framework that models distributions of fMRI neural responses using stimulus-informed flow matching. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers biological fMRI prediction, not AI safety or deployment guardrails.
MultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching The paper introduces MultiFlow, a generative framework that models distributions of fMRI neural responses using stimulus-informed flow matching. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with neural fMRI encoding, not LLM agents.
MultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching The paper introduces MultiFlow, a generative framework that models distributions of fMRI neural responses using stimulus-informed flow matching. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses computational neuroscience encoding benchmarks rather than LLM evaluation.
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching The paper presents StimFlow, using conditional flow matching to synthesize fMRI brain activity directly in voxel space from visual stimuli. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Aligns visual features to fMRI brain scans rather than general multimodal vision-language tasks.
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching The paper presents StimFlow, using conditional flow matching to synthesize fMRI brain activity directly in voxel space from visual stimuli. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Neuroscience fMRI synthesis, completely unrelated to industrial defect detection.
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching The paper presents StimFlow, using conditional flow matching to synthesize fMRI brain activity directly in voxel space from visual stimuli. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with brain signal generation, not AI deployment safety.

12,301–12,360 of 17,313← Previous Next →