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
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Analyzes representation loss rather than model compression or inference speed.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on VLM internal representations, not retrieval-augmented generation.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies internal representations of VLMs, not agent planning or tool execution.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on recursive self-improvement and world models, not retrieval-augmented generation.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on policy improvement loops, not model compression or inference efficiency.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a self-improvement method rather than an evaluation framework.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies world models across domains rather than focusing on multimodal model capabilities.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on performance improvement rather than deployment safety or guardrails.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Code repair is only one evaluation domain among many for a general self-improvement method.
Epistemic Disturbance in the Graph Model for Conflict Resolution: State-Preserving Actions, Four-Valued Assessments, and the Distinction between Capability and Intention The paper extends the Graph Model for Conflict Resolution by incorporating epistemic states and state-preserving actions into its decision-making framework. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Addresses formal conflict graph modeling, not LLM evaluation benchmarks.
Epistemic Disturbance in the Graph Model for Conflict Resolution: State-Preserving Actions, Four-Valued Assessments, and the Distinction between Capability and Intention The paper extends the Graph Model for Conflict Resolution by incorporating epistemic states and state-preserving actions into its decision-making framework. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on game-theoretic conflict models rather than operational AI deployment safety.
Epistemic Disturbance in the Graph Model for Conflict Resolution: State-Preserving Actions, Four-Valued Assessments, and the Distinction between Capability and Intention The paper extends the Graph Model for Conflict Resolution by incorporating epistemic states and state-preserving actions into its decision-making framework. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Formal game-theoretic conflict resolution paper, not LLM agents or tool use.
Can Jev be Your Q or Policy in Reinforcement Learning? Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Explores decision foundation models in RL pipelines, not multi-step language agents.
Can Jev be Your Q or Policy in Reinforcement Learning? Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Investigates RL sample efficiency rather than safety or guardrails.
Can Jev be Your Q or Policy in Reinforcement Learning? Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Explores using a foundation model in RL rather than model compression or efficient serving.
Can Jev be Your Q or Policy in Reinforcement Learning? Studies how the non-generative decision model Jev can serve as a prior or policy component within reinforcement learning frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an RL algorithmic integration rather than an evaluation benchmark.
Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model Introduces a unified benchmark and pretrained encoder-decoder model for evaluating enzymatic reaction prediction and enzyme commission classifications. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with computational biology rather than AI safety or reliability.
Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model Introduces a unified benchmark and pretrained encoder-decoder model for evaluating enzymatic reaction prediction and enzyme commission classifications. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on enzymatic reaction prediction models, not autonomous agents.
Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model Introduces a unified benchmark and pretrained encoder-decoder model for evaluating enzymatic reaction prediction and enzyme commission classifications. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a biochemical reaction benchmark rather than an LLM evaluation framework.
A 3D Characterization Framework for Intelligent Sequential Decision Making Proposes a three-dimensional framework evaluating decision-making artificial intelligence methods across sequential formalisms, autonomy levels, and computational cost. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on puzzle solving and decision formalisms, not safety or guardrails.
Intervention anchors and scientific verification in synthetic vascular predictive representations Presents a mathematical audit on how intervention anchors and coordinate transport affect stability in synthetic vascular predictive representations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Pertains to synthetic vascular representation audits, not AI agents.
Intervention anchors and scientific verification in synthetic vascular predictive representations Presents a mathematical audit on how intervention anchors and coordinate transport affect stability in synthetic vascular predictive representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on synthetic biomedical representation math rather than LLM benchmarks.
Intervention anchors and scientific verification in synthetic vascular predictive representations Presents a mathematical audit on how intervention anchors and coordinate transport affect stability in synthetic vascular predictive representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Covers biomedical coordinate verification rather than AI system deployment safety.
DeltaReplay: Task-Relative Memory Reuse for Mobile GUI Agents Introduces a step-level memory reuse framework using transition graphs to adapt relevant prior mobile graphical interface execution trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an agent framework rather than an LLM evaluation benchmark.
DeltaReplay: Task-Relative Memory Reuse for Mobile GUI Agents Introduces a step-level memory reuse framework using transition graphs to adapt relevant prior mobile graphical interface execution trajectories. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on GUI task completion efficiency rather than safety or reliability.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on context parameterization, not software engineering or code generation.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with text documents and language models, not multimodal models.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Proposes an architectural efficiency technique, not safety or guardrails.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a parameter mapping technique rather than an evaluation benchmark.
Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models Presents a hypernetwork that maps document contexts directly into low-rank adapter weights for large language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes a hypernetwork for model adapters, not autonomous agents.
MultiWorldBench: Do Independently Controlled Views Describe One Shared World? Introduces a Minecraft diagnostic benchmark testing whether multiplayer world models maintain consistent and persistent representations across independently controlled views. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines multi-view world models rather than planning agents.
MultiWorldBench: Do Independently Controlled Views Describe One Shared World? Introduces a Minecraft diagnostic benchmark testing whether multiplayer world models maintain consistent and persistent representations across independently controlled views. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on synthetic Minecraft world simulation, not industrial computer vision.
MultiWorldBench: Do Independently Controlled Views Describe One Shared World? Introduces a Minecraft diagnostic benchmark testing whether multiplayer world models maintain consistent and persistent representations across independently controlled views. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Evaluates simulated world models rather than deployment safety or jailbreak risks.
MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning Presents a multi-dimensional data pruning framework that filters code repositories by syntax, diversity, and representation for efficient retriever fine-tuning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a data pruning method rather than an evaluation benchmark.
MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning Presents a multi-dimensional data pruning framework that filters code repositories by syntax, diversity, and representation for efficient retriever fine-tuning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses data pruning efficiency, not AI safety or red-teaming.
MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning Presents a multi-dimensional data pruning framework that filters code repositories by syntax, diversity, and representation for efficient retriever fine-tuning. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on code retrieval training data pruning, not multi-step agents.
MemTrial: Learning When to Trust Memory in LLM Portfolio Agents Presents a portfolio management agent that uses fractional factorial counterfactual evaluation and Banzhaf values to isolate memory utility from market movements. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Designs an agent decision mechanism rather than an LLM evaluation benchmark.
MemTrial: Learning When to Trust Memory in LLM Portfolio Agents Presents a portfolio management agent that uses fractional factorial counterfactual evaluation and Banzhaf values to isolate memory utility from market movements. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on financial decision policy optimization rather than AI safety guardrails.
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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a time series foundation model, not an agent system.
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.
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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on pretraining time-series architectures, not model compression or efficiency.
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	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Pertains to time-series forecasting accuracy, not AI safety or alignment.
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.
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	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.
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 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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on Bayesian inference formulation, not model compression or efficient serving.
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.
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	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.
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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents hardware acceleration mechanisms rather than model evaluation methods.
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.
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.
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	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies safety refusal evaluation rather than retrieval-augmented generation architectures.

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