Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,313In brief24More (on-topic)3,333Not relevant13,754Not assessed87Older352Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| 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. | |
| 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 | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Studies human-in-the-loop DSL design, not autonomous multi-step agents. | |
| 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 | 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 safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Addresses spatial layout synthesis, not safety guardrails or reliability risks. | |
| 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 | 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 | 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 | 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 | 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 | 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 | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on sensor activity recognition workflows rather than multi-step tool agents. | |
| 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. | |
| 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 | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Introduces an abstention mechanism for neural classifiers, not an LLM benchmark. | |
| 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. | |
| 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 | 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 | 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 | 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 | 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 | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on representation subspace alignment across models, not multi-step agents. | |
| 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. | |
| 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. | |
| 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 | 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 | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Studies RL game performance, not safety or guardrails. | |
| 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 | 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 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. | |
| 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. | |
| 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 | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Addresses computational neuroscience encoding benchmarks rather than LLM evaluation. | |
| 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. | |
| 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 | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Evaluates neural response synthesis, not general LLM benchmarking. | |
| 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 agents via arxiv · 15 Oct, 06:17 | Not relevant Synthesizes fMRI signals from images, not LLM agent systems. | |
| 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. | |
| 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. | |
| Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Studies physical visual adversarial attacks, not LLM agents. | |
| Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Presents an adversarial attack method rather than standard evaluation methodologies. | |
| Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Concerns video/world models rather than industrial inspection or manufacturing defect detection. | |
| CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph The paper presents CodeWalk, a method to create challenging coding benchmarks at low cost by programmatically composing verified atomic problems. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Discusses benchmark composition rather than model quantization or inference efficiency. | |
| CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph The paper presents CodeWalk, a method to create challenging coding benchmarks at low cost by programmatically composing verified atomic problems. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Generates synthetic programming problems rather than planning agent systems. | |
| CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph The paper presents CodeWalk, a method to create challenging coding benchmarks at low cost by programmatically composing verified atomic problems. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on programming task generation rather than safety guardrails or robustness. | |
| Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations The paper releases a studio-quality audio dataset of Korean traditional singing with dense annotations for vocal expressions and melodic contour. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Concerns music transcription, not AI safety or deployment risks. | |
| Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations The paper releases a studio-quality audio dataset of Korean traditional singing with dense annotations for vocal expressions and melodic contour. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on traditional vocal audio datasets, not AI agents. | |
| Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations The paper releases a studio-quality audio dataset of Korean traditional singing with dense annotations for vocal expressions and melodic contour. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Introduces a traditional Korean music audio dataset, not LLM benchmarks. | |
| Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Applies neural networks to medical diagnostics, not LLM agents. | |
| Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Covers biomedical classification rather than AI system safety and reliability. | |
| Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Evaluates a clinical model, not LLM evaluation frameworks. | |
| Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals The paper introduces an explainable Siamese deep learning framework combining synchronized EEG and eye-tracking signals for early ALS detection. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not address model compression, quantization, or efficient LLM serving. | |
| Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Presents theoretical robustness bounds without focusing on compression or serving efficiency. | |
| Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Studies vision classifier certification, not agent planning or tool use. | |
| Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification The paper proposes certified adversarial detection using Lipschitz-margin analysis to detect perturbations rather than forcing invariant predictions. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on certified adversarial detection methods, not general evaluation benchmarks. | |
| TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation The paper introduces TRACE to monitor harmful response risks during generation, edit token activations selectively, and maintain auditable decision traces. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Presents a safety steering framework rather than a general evaluation benchmark. | |
| TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation The paper introduces TRACE to monitor harmful response risks during generation, edit token activations selectively, and maintain auditable decision traces. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Addresses text-generation guardrails, not autonomous multi-step agents. |
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