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
AI-Assisted Tumor-Growth Law Discovery from Sparse Data: Refitting and LLM-Based Ranking The paper evaluates symbolic regression, refitting techniques, and language model rankings for inferring tumor-growth laws from sparse observation data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on tumor growth discovery rather than model compression.
AI-Assisted Tumor-Growth Law Discovery from Sparse Data: Refitting and LLM-Based Ranking The paper evaluates symbolic regression, refitting techniques, and language model rankings for inferring tumor-growth laws from sparse observation data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies symbolic regression fitting rather than AI safety guardrails.
ML Engineering Agents - A Data Management Perspective The tutorial examines machine learning engineering agents from a data management view, highlighting challenges in pipeline execution, data discovery, and debugging. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers ML pipeline agents rather than LLM benchmark design.
ML Engineering Agents - A Data Management Perspective The tutorial examines machine learning engineering agents from a data management view, highlighting challenges in pipeline execution, data discovery, and debugging. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Discusses pipeline automation agents, not model compression or serving.
ML Engineering Agents - A Data Management Perspective The tutorial examines machine learning engineering agents from a data management view, highlighting challenges in pipeline execution, data discovery, and debugging. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers data engineering for ML agents, not core AI safety.
Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning The paper presents a temporal reinforcement learning framework that selects physiologically aligned time windows for motor imagery brain-computer interfaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses BCI classification rather than AI safety or reliability.
Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning The paper presents a temporal reinforcement learning framework that selects physiologically aligned time windows for motor imagery brain-computer interfaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on EEG signal processing, not LLM benchmark evaluation.
Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning The paper presents a temporal reinforcement learning framework that selects physiologically aligned time windows for motor imagery brain-computer interfaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on brain-computer interfaces, not model compression or efficiency.
Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization The paper provides theoretical convergence and tracking error bounds for Matrix Shampoo optimization in nonstationary stochastic settings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Mathematical optimization theory, not small or efficient models.
Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization The paper provides theoretical convergence and tracking error bounds for Matrix Shampoo optimization in nonstationary stochastic settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical optimizer bounds, not AI safety and reliability.
Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization The paper provides theoretical convergence and tracking error bounds for Matrix Shampoo optimization in nonstationary stochastic settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical convergence of an optimization algorithm, not LLM evaluation.
Making Tensor Decoupling More Robust and Less Parametric The paper provides two modifications to make tensor decoupling methods more robust to magnitude mismatches and eliminate manual spline tuning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical function approximation, not deployment safety.
Making Tensor Decoupling More Robust and Less Parametric The paper provides two modifications to make tensor decoupling methods more robust to magnitude mismatches and eliminate manual spline tuning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses spline-based tensor decoupling, not model compression.
Making Tensor Decoupling More Robust and Less Parametric The paper provides two modifications to make tensor decoupling methods more robust to magnitude mismatches and eliminate manual spline tuning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers tensor decoupling algorithms, not LLM evaluation.
Learning to Design Convex Relaxations for Nonconvex Optimization The paper presents AutoConvexRelax, which learns a reinforcement learning policy to automatically construct valid convex relaxations for nonconvex optimization problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical optimization surrogates, not small models.
Learning to Design Convex Relaxations for Nonconvex Optimization The paper presents AutoConvexRelax, which learns a reinforcement learning policy to automatically construct valid convex relaxations for nonconvex optimization problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with convex relaxations in optimization, not LLM evaluation.
Learning to Design Convex Relaxations for Nonconvex Optimization The paper presents AutoConvexRelax, which learns a reinforcement learning policy to automatically construct valid convex relaxations for nonconvex optimization problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies mathematical relaxation proofs, not AI deployment safety.
DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers The paper proposes a multi-agent diffusion transformer framework to generate realistic air traffic scenes and trajectory patterns from recorded data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Generates traffic scenes, not LLM benchmarking or evaluation.
DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers The paper proposes a multi-agent diffusion transformer framework to generate realistic air traffic scenes and trajectory patterns from recorded data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Diffusion transformer for trajectory data, not model efficiency.
DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers The paper proposes a multi-agent diffusion transformer framework to generate realistic air traffic scenes and trajectory patterns from recorded data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Simulates airspace scenarios, not AI deployment safety or guardrails.
DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers The paper proposes a multi-agent diffusion transformer framework to generate realistic air traffic scenes and trajectory patterns from recorded data. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on trajectory generation models, not multi-step LLM agents.
DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers The paper proposes a multi-agent diffusion transformer framework to generate realistic air traffic scenes and trajectory patterns from recorded data. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies air traffic trajectories, not industrial computer vision.
Learning to Design SR Solvers: Harness Optimization for Symbolic Regression The paper introduces HarnessSR to jointly optimize agent workflows and prompts for discovering symbolic regression equations from data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies prompt optimization for regression, not deployment safety.
Learning to Design SR Solvers: Harness Optimization for Symbolic Regression The paper introduces HarnessSR to jointly optimize agent workflows and prompts for discovering symbolic regression equations from data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses symbolic regression workflows, not efficient or small models.
Learning to Design SR Solvers: Harness Optimization for Symbolic Regression The paper introduces HarnessSR to jointly optimize agent workflows and prompts for discovering symbolic regression equations from data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimizing symbolic regression agents, not LLM benchmarks.
Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation The paper presents GenTuRBO, using pretrained generative models as adaptive proposal trust regions for discrete Bayesian optimization tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on discrete Bayesian optimization, not deployment safety.
Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation The paper presents GenTuRBO, using pretrained generative models as adaptive proposal trust regions for discrete Bayesian optimization tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Optimization framework for discrete designs, not model efficiency.
Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation The paper presents GenTuRBO, using pretrained generative models as adaptive proposal trust regions for discrete Bayesian optimization tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies discrete Bayesian optimisation, not LLM benchmark evaluation.
Constant-Depth Rational-Weight GNNs and their Connection to Schanuel's Conjecture The paper proves constant-depth rational-weight graph neural networks match the 1-WL expressiveness bound under Schanuel's conjecture from number theory. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical graph expressiveness proof, not deployment safety.
Constant-Depth Rational-Weight GNNs and their Connection to Schanuel's Conjecture The paper proves constant-depth rational-weight graph neural networks match the 1-WL expressiveness bound under Schanuel's conjecture from number theory. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical GNN expressiveness, not model compression.
Constant-Depth Rational-Weight GNNs and their Connection to Schanuel's Conjecture The paper proves constant-depth rational-weight graph neural networks match the 1-WL expressiveness bound under Schanuel's conjecture from number theory. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical study of GNN expressiveness, not LLM evaluation.
Intrinsic Geometry Matching: learning observation-equivariant process geometry The paper proposes a method to learn the intrinsic geometry and diffusion operators of underlying processes from distorted, indirect observations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on geometric representation learning, not AI safety.
Intrinsic Geometry Matching: learning observation-equivariant process geometry The paper proposes a method to learn the intrinsic geometry and diffusion operators of underlying processes from distorted, indirect observations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical manifold learning method, not model efficiency.
Intrinsic Geometry Matching: learning observation-equivariant process geometry The paper proposes a method to learn the intrinsic geometry and diffusion operators of underlying processes from distorted, indirect observations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents differential geometry learning, not LLM evaluation.
A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration The paper introduces a physics-coupled graph neural operator to predict mid-plane stress tensor fields in concrete during projectile penetration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Neural operator for materials physics, not efficient models.
A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration The paper introduces a physics-coupled graph neural operator to predict mid-plane stress tensor fields in concrete during projectile penetration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Predicts concrete stress fields, not LLM benchmark evaluation.
A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration The paper introduces a physics-coupled graph neural operator to predict mid-plane stress tensor fields in concrete during projectile penetration. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Structural engineering physics surrogate, not AI deployment safety.
Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates This paper develops a deferred-decision trajectory optimization controller with conformal prediction guarantees for multi-agent target interception. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers multi-agent target pursuit, not model compression.
Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates This paper develops a deferred-decision trajectory optimization controller with conformal prediction guarantees for multi-agent target interception. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies target interception control, not AI safety and reliability.
Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates This paper develops a deferred-decision trajectory optimization controller with conformal prediction guarantees for multi-agent target interception. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses robotics trajectory optimization, not LLM evaluation.
Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates This paper develops a deferred-decision trajectory optimization controller with conformal prediction guarantees for multi-agent target interception. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on trajectory interception planning, not industrial visual inspection.
Mixture of GFlowNets This paper introduces an asynchronous population of generative flow networks coupled through soft clustering to improve mode coverage. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Explores generative flow policy exploration, not AI deployment safety.
Mixture of GFlowNets This paper introduces an asynchronous population of generative flow networks coupled through soft clustering to improve mode coverage. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on GFlowNet sampling algorithms, not LLM evaluation.
Mixture of GFlowNets This paper introduces an asynchronous population of generative flow networks coupled through soft clustering to improve mode coverage. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates GFlowNet mode coverage, not efficient LLM serving.
LLM Evaluation is a Forecasting Problem This paper frames language model evaluation as a forecasting problem, using time-series techniques on historical data to predict post-deployment performance. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines performance forecasting over time, not model compression.
Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer This paper uses neural network variational Monte Carlo simulations to demonstrate the formation of honeycomb electron-hole crystals in bilayers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Physics domain application, not efficient or small models.
Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer This paper uses neural network variational Monte Carlo simulations to demonstrate the formation of honeycomb electron-hole crystals in bilayers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Condensed matter physics research, not AI safety.
Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer This paper uses neural network variational Monte Carlo simulations to demonstrate the formation of honeycomb electron-hole crystals in bilayers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Condensed matter physics study, not related to LLM evaluation.
Measuring Variable Importance Using Proportional Inclusive Marginal Effects This paper introduces a model-free variable importance measure that uses active variable selection to prevent nonactive variables from receiving spurious credit. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses general feature importance metrics, not AI deployment safety.
Measuring Variable Importance Using Proportional Inclusive Marginal Effects This paper introduces a model-free variable importance measure that uses active variable selection to prevent nonactive variables from receiving spurious credit. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical variable importance, not LLM evaluation.
Measuring Variable Importance Using Proportional Inclusive Marginal Effects This paper introduces a model-free variable importance measure that uses active variable selection to prevent nonactive variables from receiving spurious credit. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a statistical sensitivity metric, not model compression.
In-context classification: Calibration and noise in linear attention with softmax readout This paper analyzes in-context classification in linear attention models, establishing theoretical asymptotic bounds separating calibration from noise effects. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies linear attention theory, not model efficiency or distillation.
In-context classification: Calibration and noise in linear attention with softmax readout This paper analyzes in-context classification in linear attention models, establishing theoretical asymptotic bounds separating calibration from noise effects. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical analysis of attention, not LLM benchmark evaluation.
In-context classification: Calibration and noise in linear attention with softmax readout This paper analyzes in-context classification in linear attention models, establishing theoretical asymptotic bounds separating calibration from noise effects. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes synthetic linear attention models, not deployment safety or guardrails.
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings This paper introduces an interface for extracting context-rich language model embeddings to improve table foundation models on mixed tabular data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates a tabular method, not LLM benchmarking methodology.
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings This paper introduces an interface for extracting context-rich language model embeddings to improve table foundation models on mixed tabular data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on tabular learning accuracy, not AI deployment safety.
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings This paper introduces an interface for extracting context-rich language model embeddings to improve table foundation models on mixed tabular data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Develops tabular embedding representations, not model compression or efficiency.
Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts This paper proposes a nearest-neighbor functional curve completion method for intraday probabilistic revisions of renewable energy forecasts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies power curve completion, not model efficiency techniques.
Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts This paper proposes a nearest-neighbor functional curve completion method for intraday probabilistic revisions of renewable energy forecasts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies statistical curve completion to power grids, not AI safety.
Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts This paper proposes a nearest-neighbor functional curve completion method for intraday probabilistic revisions of renewable energy forecasts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on renewable energy forecasting, not LLM benchmarking.

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