Every paper checked — nothing hidden

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Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
G$^2$WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation Uses a learned scene-coordinate localization module to maintain consistent 3D spatial context for image-goal robot navigation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual navigation rather than manufacturing or industrial defect detection.
G$^2$WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation Uses a learned scene-coordinate localization module to maintain consistent 3D spatial context for image-goal robot navigation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on navigation efficiency rather than AI deployment safety or guardrails.
G$^2$WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation Uses a learned scene-coordinate localization module to maintain consistent 3D spatial context for image-goal robot navigation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses embodied visual navigation rather than language model agent systems.
VLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models Introduces a diagnostic evaluation framework to systematically test generalization and perturbation robustness in vision-language-action robotic models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic manipulation models rather than software LLM agent workflows.
VLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models Introduces a diagnostic evaluation framework to systematically test generalization and perturbation robustness in vision-language-action robotic models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates general robotic manipulation rather than industrial visual inspection or defect detection.
Double-Rollout Reinforcement Learning with Sampling-based Reward Estimation Improves reinforcement learning for reasoning models by sampling continuations from truncated traces to provide more accurate intermediate rewards. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training optimization algorithm rather than an evaluation benchmark.
Double-Rollout Reinforcement Learning with Sampling-based Reward Estimation Improves reinforcement learning for reasoning models by sampling continuations from truncated traces to provide more accurate intermediate rewards. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on training reward estimation rather than deployment safety guardrails or alignment.
Double-Rollout Reinforcement Learning with Sampling-based Reward Estimation Improves reinforcement learning for reasoning models by sampling continuations from truncated traces to provide more accurate intermediate rewards. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses RL training for reasoning models rather than multi-step agent tool use.
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals Presents a deep learning framework that integrates heterogeneous surface EEG and intracranial EEG signals for multimodal seizure classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates medical biosignals rather than language models or LLM benchmarks.
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals Presents a deep learning framework that integrates heterogeneous surface EEG and intracranial EEG signals for multimodal seizure classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on seizure detection rather than AI deployment safety or guardrails.
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals Presents a deep learning framework that integrates heterogeneous surface EEG and intracranial EEG signals for multimodal seizure classification. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on EEG signal classification rather than LLM agents.
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals Presents a deep learning framework that integrates heterogeneous surface EEG and intracranial EEG signals for multimodal seizure classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses clinical signal processing rather than small efficient LLM serving.
MixLID: Token-Level Language Identification in Mixed-Language Text Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Applies to multilingual human text rather than software code or coding assistants.
MixLID: Token-Level Language Identification in Mixed-Language Text Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on token-level language identification rather than LLM agents.
MixLID: Token-Level Language Identification in Mixed-Language Text Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses text-only language identification without multimodal inputs.
MixLID: Token-Level Language Identification in Mixed-Language Text Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a language classification model rather than LLM evaluation methodology.
MixLID: Token-Level Language Identification in Mixed-Language Text Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on multilingual classification accuracy rather than model compression or inference efficiency.
MixLID: Token-Level Language Identification in Mixed-Language Text Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Deals with language identification without any retrieval-augmented generation aspects.
MixLID: Token-Level Language Identification in Mixed-Language Text Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers language identification rather than AI safety, red-teaming, or guardrails.
When 1+1 Backfires: Rethinking Relevance-Diversity Balancing for Sample-Aware Visual Token Reduction Dynamically balances relevance and visual diversity per sample to compress visual tokens in vision-language models more effectively. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Targets inference efficiency rather than deployment safety, hallucinations, or guardrails.
When 1+1 Backfires: Rethinking Relevance-Diversity Balancing for Sample-Aware Visual Token Reduction Dynamically balances relevance and visual diversity per sample to compress visual tokens in vision-language models more effectively. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a token reduction algorithm rather than general LLM evaluation frameworks.
When 1+1 Backfires: Rethinking Relevance-Diversity Balancing for Sample-Aware Visual Token Reduction Dynamically balances relevance and visual diversity per sample to compress visual tokens in vision-language models more effectively. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual token compression rather than multi-step agent actions.
Weak Segmentation-Guided GAN for~Realistic Color Edition Proposes a generative adversarial network guided by differentiable segmentation models to edit image colors realistically and with precise control. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a generative image editing model rather than an evaluation benchmark.
Weak Segmentation-Guided GAN for~Realistic Color Edition Proposes a generative adversarial network guided by differentiable segmentation models to edit image colors realistically and with precise control. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on image color editing GANs rather than LLM agents.
Weak Segmentation-Guided GAN for~Realistic Color Edition Proposes a generative adversarial network guided by differentiable segmentation models to edit image colors realistically and with precise control. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with image color modification rather than AI safety or reliability.
Answer It or Not? Towards Prompt-Adapted Adversarial Images on Vision Language Models Generates prompt-adapted adversarial images that block unauthorized visual questions on vision-language models while still answering legitimate prompts correctly. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on adversarial images for VLMs rather than autonomous agent planning.
Answer It or Not? Towards Prompt-Adapted Adversarial Images on Vision Language Models Generates prompt-adapted adversarial images that block unauthorized visual questions on vision-language models while still answering legitimate prompts correctly. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on adversarial image generation rather than LLM benchmarking methods.
Answer It or Not? Towards Prompt-Adapted Adversarial Images on Vision Language Models Generates prompt-adapted adversarial images that block unauthorized visual questions on vision-language models while still answering legitimate prompts correctly. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies adversarial visual privacy rather than industrial defect detection or inspection.
LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation Evaluates different agent control granularities for post-route FPGA timing closure to balance execution reliability and frequency gains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on an EDA optimization harness rather than general LLM evaluation methods.
LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation Evaluates different agent control granularities for post-route FPGA timing closure to balance execution reliability and frequency gains. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies FPGA timing optimization rather than AI safety guardrails or alignment.
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations Incorporates human understandability scores directly into feature selection to make tabular machine learning explanations easier for users to interpret. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on random forest feature selection rather than small efficient language models.
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations Incorporates human understandability scores directly into feature selection to make tabular machine learning explanations easier for users to interpret. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies tabular explainability rather than language model evaluation or benchmarks.
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations Incorporates human understandability scores directly into feature selection to make tabular machine learning explanations easier for users to interpret. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on tabular feature selection rather than LLM agents or multi-step tool use.
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations Incorporates human understandability scores directly into feature selection to make tabular machine learning explanations easier for users to interpret. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies feature understandability in tabular XAI rather than AI deployment safety guardrails.
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version Combines physical models and state space neural networks in a closed loop to forecast long-term chemically reactive air quality. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers physical air quality prediction rather than AI deployment safety or robustness.
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version Combines physical models and state space neural networks in a closed loop to forecast long-term chemically reactive air quality. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on environmental time series modeling rather than small efficient LLMs.
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version Combines physical models and state space neural networks in a closed loop to forecast long-term chemically reactive air quality. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses air quality forecasting rather than language model agent workflows.
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version Combines physical models and state space neural networks in a closed loop to forecast long-term chemically reactive air quality. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a domain forecasting framework rather than LLM evaluation benchmarks.
REEL: Learning to Retrieve, Evaluate, and Evolve Visual Memory for Any Long-Horizon Multi-Shot Video Generation Introduces a general visual memory planner that retrieves, creates, and updates visual evidence across shots for long-horizon video generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on video generation coherence rather than AI safety or reliability guardrails.
REEL: Learning to Retrieve, Evaluate, and Evolve Visual Memory for Any Long-Horizon Multi-Shot Video Generation Introduces a general visual memory planner that retrieves, creates, and updates visual evidence across shots for long-horizon video generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a video generation framework rather than a general LLM evaluation benchmark.
REEL: Learning to Retrieve, Evaluate, and Evolve Visual Memory for Any Long-Horizon Multi-Shot Video Generation Introduces a general visual memory planner that retrieves, creates, and updates visual evidence across shots for long-horizon video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses general creative video generation rather than industrial inspection or defect detection.
REEL: Learning to Retrieve, Evaluate, and Evolve Visual Memory for Any Long-Horizon Multi-Shot Video Generation Introduces a general visual memory planner that retrieves, creates, and updates visual evidence across shots for long-horizon video generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Applies a memory planner to video generation rather than language model agent systems.
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba Improves long-context handling in Mamba state space models without retraining by using surprisal scores to guide state routing. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on state space model context routing rather than agentic planning.
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba Improves long-context handling in Mamba state space models without retraining by using surprisal scores to guide state routing. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates general long-context benchmarks rather than code generation or repair.
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba Improves long-context handling in Mamba state space models without retraining by using surprisal scores to guide state routing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses internal long-context routing in Mamba models without external retrieval.
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba Improves long-context handling in Mamba state space models without retraining by using surprisal scores to guide state routing. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text sequence processing in state space models without multimodal components.
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba Improves long-context handling in Mamba state space models without retraining by using surprisal scores to guide state routing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on long-range context performance rather than safety guardrails or alignment.
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba Improves long-context handling in Mamba state space models without retraining by using surprisal scores to guide state routing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an architectural routing method rather than an evaluation framework.
Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting Reconstructs divergence-free 3D fluid velocity fields from sparse, irregular observations in a single forward pass using a set-to-field transformer. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns fluid dynamics modeling rather than AI safety or reliability.
Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting Reconstructs divergence-free 3D fluid velocity fields from sparse, irregular observations in a single forward pass using a set-to-field transformer. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Applies transformers to 3D fluid dynamics, not LLM agents.
Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting Reconstructs divergence-free 3D fluid velocity fields from sparse, irregular observations in a single forward pass using a set-to-field transformer. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on scientific machine learning rather than small or efficient language models.
Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting Reconstructs divergence-free 3D fluid velocity fields from sparse, irregular observations in a single forward pass using a set-to-field transformer. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses physical velocity field reconstruction rather than LLM evaluation benchmarks.
Does a Hosted Decision Model Respect Irrelevant Alternatives? A Pre-Registered Test of Option-Set Dependence in Jev Tests a hosted decision model to show that adding irrelevant options systematically shifts output probability distributions between top candidates. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies probability output shifts in classification APIs, not LLM agent workflows.
Does a Hosted Decision Model Respect Irrelevant Alternatives? A Pre-Registered Test of Option-Set Dependence in Jev Tests a hosted decision model to show that adding irrelevant options systematically shifts output probability distributions between top candidates. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates classification decision models rather than code generation or repair.
Does a Hosted Decision Model Respect Irrelevant Alternatives? A Pre-Registered Test of Option-Set Dependence in Jev Tests a hosted decision model to show that adding irrelevant options systematically shifts output probability distributions between top candidates. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or external document grounding.
Does a Hosted Decision Model Respect Irrelevant Alternatives? A Pre-Registered Test of Option-Set Dependence in Jev Tests a hosted decision model to show that adding irrelevant options systematically shifts output probability distributions between top candidates. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text classification decision models without multimodal data.
Does a Hosted Decision Model Respect Irrelevant Alternatives? A Pre-Registered Test of Option-Set Dependence in Jev Tests a hosted decision model to show that adding irrelevant options systematically shifts output probability distributions between top candidates. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on decision behavior evaluation rather than model compression or inference efficiency.
Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs Detects adversarial nodes on evolving graphs without fraud labels by measuring the consistency between node features and aggregated neighborhood contexts. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on graph adversarial detection rather than LLM agents or tool use.
Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs Detects adversarial nodes on evolving graphs without fraud labels by measuring the consistency between node features and aggregated neighborhood contexts. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses graph representation learning rather than model compression or efficient serving.
Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs Detects adversarial nodes on evolving graphs without fraud labels by measuring the consistency between node features and aggregated neighborhood contexts. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a graph fraud detection algorithm rather than an LLM evaluation benchmark.

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