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
Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents The paper presents an evidence admission framework that verifies anomalies before updating the persistent principles of scientific language model agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with autonomous scientific agents, not model compression or efficiency.
Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents The paper presents an evidence admission framework that verifies anomalies before updating the persistent principles of scientific language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an evidence admission agent framework, not benchmark evaluation.
One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates The paper proposes patchwise adaptive normalization to represent one-way coupled exogenous fields in transformer surrogates for partial differential equations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies PDE surrogate modeling architectures, not LLM benchmarking methods.
One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates The paper proposes patchwise adaptive normalization to represent one-way coupled exogenous fields in transformer surrogates for partial differential equations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with scientific PDE surrogates, not general language model compression.
One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates The paper proposes patchwise adaptive normalization to represent one-way coupled exogenous fields in transformer surrogates for partial differential equations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses physics boundary compliance, not AI deployment safety or guardrails.
Accelerating Cross-Model LLM Inference via Attention Map Reuse This paper presents a system that speeds up multi-model language model serving by translating and reusing attention maps across models. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on serving latency reduction, not safety guardrails or alignment.
Accelerating Cross-Model LLM Inference via Attention Map Reuse This paper presents a system that speeds up multi-model language model serving by translating and reusing attention maps across models. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an inference serving system, not an LLM benchmark.
Understanding Hidden Spatial Preferences in Bayesian Optimization This paper identifies spatial boundary biases in Bayesian optimization and modifies exploration using parameterized spatial weight fields. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies spatial preferences in Bayesian optimization, not model compression.
Understanding Hidden Spatial Preferences in Bayesian Optimization This paper identifies spatial boundary biases in Bayesian optimization and modifies exploration using parameterized spatial weight fields. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian optimization exploration, not LLM evaluation methods.
Understanding Hidden Spatial Preferences in Bayesian Optimization This paper identifies spatial boundary biases in Bayesian optimization and modifies exploration using parameterized spatial weight fields. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns acquisition function behavior, not deployment safety or red-teaming.
Linear-Time Non-Local Codec Placement for Machine Learning Model Compression This paper introduces a linear-time algorithm to place codec operators in computational graphs to compress operand traffic during on-device inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses computational graph optimization, not AI safety or deployment guardrails.
Linear-Time Non-Local Codec Placement for Machine Learning Model Compression This paper introduces a linear-time algorithm to place codec operators in computational graphs to compress operand traffic during on-device inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on codec operator placement algorithms, not LLM benchmarking.
DeKoR: Decomposed Knowledge Routing for Efficient GNN--LLM Fusion This paper proposes a decomposed routing framework that distills large language model knowledge into lightweight graph neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers graph knowledge fusion, not deployment safety or reliability risks.
DeKoR: Decomposed Knowledge Routing for Efficient GNN--LLM Fusion This paper proposes a decomposed routing framework that distills large language model knowledge into lightweight graph neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on GNN-LLM fusion architectures, not LLM evaluation or benchmarking.
Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange This paper analyzes the complexity and theoretical benefits of meeting-triggered information exchange in multi-token random-walk decentralized learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines optimization complexity bounds, not deployment safety or robustness.
Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange This paper analyzes the complexity and theoretical benefits of meeting-triggered information exchange in multi-token random-walk decentralized learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies distributed training algorithms, not model compression or efficient serving.
Multi-Token Random-Walk Learning via Meeting-Triggered Information Exchange This paper analyzes the complexity and theoretical benefits of meeting-triggered information exchange in multi-token random-walk decentralized learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes decentralized optimization algorithms, not LLM evaluation methodologies.
VitaBench: Memory Lifecycles Beyond Retrieval This paper introduces a benchmark to evaluate agent memory lifecycle decisions, including compression, consolidation, and deletion. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents a memory evaluation benchmark, not model compression or efficient serving.
VitaBench: Memory Lifecycles Beyond Retrieval This paper introduces a benchmark to evaluate agent memory lifecycle decisions, including compression, consolidation, and deletion. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent memory benchmarking, not safety, guardrails, or alignment.
More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training This paper studies why multi-domain graph pre-training fails to generalize and proposes a fast-slow reciprocal learning framework. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses graph foundation pre-training, not deployment safety or monitoring.
More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training This paper studies why multi-domain graph pre-training fails to generalize and proposes a fast-slow reciprocal learning framework. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph pre-training paradigms, not LLM evaluation benchmarks.
More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training This paper studies why multi-domain graph pre-training fails to generalize and proposes a fast-slow reciprocal learning framework. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies multi-domain graph pre-training, not model compression or serving.
Random Fourier Features in Near-Linear Time This paper provides a tighter analysis enabling near-linear time computation of random Fourier feature maps with near-subgaussian concentration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on kernel feature computation, not small models or inference.
Random Fourier Features in Near-Linear Time This paper provides a tighter analysis enabling near-linear time computation of random Fourier feature maps with near-subgaussian concentration. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers kernel concentration bounds, not deployment safety or reliability.
Random Fourier Features in Near-Linear Time This paper provides a tighter analysis enabling near-linear time computation of random Fourier feature maps with near-subgaussian concentration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies kernel approximation theory, not LLM evaluation methods.
Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization This paper evaluates human opinion measurement interfaces and recommends listwise preference collection for human-in-the-loop optimization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies opinion elicitation interfaces, not safety guardrails or system red-teaming.
Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization This paper evaluates human opinion measurement interfaces and recommends listwise preference collection for human-in-the-loop optimization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes human-in-the-loop interfaces, not model compression or efficient serving.
CrossMean: Model Merging by Pairing Merged and Expert Activations This paper presents an activation-pairing method and selection criterion to improve closed-form model merging without retraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on model merging algorithms, not language model benchmarking.
CrossMean: Model Merging by Pairing Merged and Expert Activations This paper presents an activation-pairing method and selection criterion to improve closed-form model merging without retraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies parameter merging algorithms, not deployed AI safety or robustness.
Formalizing Forgetting: Theory of Forgetting for Online Continual Learning This paper provides theoretical upper bounds on catastrophic forgetting during single-pass online continual learning under convex objectives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies theoretical continual learning, not deployment safety or guardrails.
Formalizing Forgetting: Theory of Forgetting for Online Continual Learning This paper provides theoretical upper bounds on catastrophic forgetting during single-pass online continual learning under convex objectives. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on continual learning theory, not LLM benchmark assessment.
Formalizing Forgetting: Theory of Forgetting for Online Continual Learning This paper provides theoretical upper bounds on catastrophic forgetting during single-pass online continual learning under convex objectives. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates catastrophic forgetting bounds, not model compression or efficiency.
Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence This paper uses Bregman clustering to address opposing member effects within linearized group influence estimates for machine learning models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies dataset attribution methods, not model compression or distillation.
Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence This paper uses Bregman clustering to address opposing member effects within linearized group influence estimates for machine learning models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical influence functions, not LLM benchmark evaluation.
Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence This paper uses Bregman clustering to address opposing member effects within linearized group influence estimates for machine learning models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes sample influence estimators, not deployment guardrails or robustness.
Machine Learning-Based Prediction of Dengue Infection Using Clinical and Hematological Data from Bangladesh: A Comparative Analysis of Benchmark and Extended Prediction Models This paper evaluates standard machine learning models to predict dengue infection from clinical and hematological data in Bangladesh. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Examines tabular clinical models, not language model evaluation methodologies.
Machine Learning-Based Prediction of Dengue Infection Using Clinical and Hematological Data from Bangladesh: A Comparative Analysis of Benchmark and Extended Prediction Models This paper evaluates standard machine learning models to predict dengue infection from clinical and hematological data in Bangladesh. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies dengue prediction models, not efficient model serving techniques.
Machine Learning-Based Prediction of Dengue Infection Using Clinical and Hematological Data from Bangladesh: A Comparative Analysis of Benchmark and Extended Prediction Models This paper evaluates standard machine learning models to predict dengue infection from clinical and hematological data in Bangladesh. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Evaluates clinical diagnostic performance, not AI system deployment safeguards.
Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid This paper proposes a neural architecture that predicts a tatum grid to track musical beats and downbeats without dynamic Bayesian networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates audio music tracking, not LLM evaluation frameworks.
Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid This paper proposes a neural architecture that predicts a tatum grid to track musical beats and downbeats without dynamic Bayesian networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns musical signal processing, not AI safety and reliability.
Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid This paper proposes a neural architecture that predicts a tatum grid to track musical beats and downbeats without dynamic Bayesian networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on musical beat tracking, not efficient language modeling.
Sequence-Structured Decision Learning for Branch-and-Bound in Mixed-Integer Linear Programming This paper introduces a sequence-structured framework that models the global search trajectory to learn branching and node selection in branch-and-bound. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on algorithmic search efficiency, not language model compression.
Sequence-Structured Decision Learning for Branch-and-Bound in Mixed-Integer Linear Programming This paper introduces a sequence-structured framework that models the global search trajectory to learn branching and node selection in branch-and-bound. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses combinatorial optimization decisions, not deployed AI safety risks.
Sequence-Structured Decision Learning for Branch-and-Bound in Mixed-Integer Linear Programming This paper introduces a sequence-structured framework that models the global search trajectory to learn branching and node selection in branch-and-bound. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Concerns mixed-integer linear programming solvers, not LLM benchmark evaluation.
Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification This paper develops stochastic physics-informed neural networks on manifolds to solve stochastic boundary-value problems with random operators. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with scientific computing architectures, not model compression or efficiency.
Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification This paper develops stochastic physics-informed neural networks on manifolds to solve stochastic boundary-value problems with random operators. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies stochastic boundary value problems, not deployment safety or guardrails.
Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification This paper develops stochastic physics-informed neural networks on manifolds to solve stochastic boundary-value problems with random operators. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on solving physics differential equations, not LLM benchmarking.
Disentangling Marginal Capacity from Geometry in Optimal-Transport Label Transfer This paper analyzes error patterns from class frequency imbalances in optimal-transport label transfer and proposes matching target composition marginals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies optimal transport label transfer under class imbalance, not LLM evaluation.
Disentangling Marginal Capacity from Geometry in Optimal-Transport Label Transfer This paper analyzes error patterns from class frequency imbalances in optimal-transport label transfer and proposes matching target composition marginals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes label shift in optimal transport, not AI deployment safety or guardrails.
Disentangling Marginal Capacity from Geometry in Optimal-Transport Label Transfer This paper analyzes error patterns from class frequency imbalances in optimal-transport label transfer and proposes matching target composition marginals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on distribution alignment in label transfer, not efficient model architectures or inference.
Terrarium: Growing Personal Assistants via Scalable Synthesis of App Worlds This paper presents a framework to synthesize executable application environments and training tasks from web information for personal language agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on synthesizing environment suites for assistants, not model compression or efficient inference.
Terrarium: Growing Personal Assistants via Scalable Synthesis of App Worlds This paper presents a framework to synthesize executable application environments and training tasks from web information for personal language agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on synthetic training environments for personal assistants, not AI safety or guardrails.
World2TS: Real-World Event Context for Time-Series Forecasting This paper introduces a benchmark and dataset that pairs numerical time-series data with real-world event context for forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a context dataset for time-series forecasting, not LLM evaluation or benchmarks.
World2TS: Real-World Event Context for Time-Series Forecasting This paper introduces a benchmark and dataset that pairs numerical time-series data with real-world event context for forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series forecasting datasets, not small or efficient model architectures.
World2TS: Real-World Event Context for Time-Series Forecasting This paper introduces a benchmark and dataset that pairs numerical time-series data with real-world event context for forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses context alignment in time-series forecasting, not AI deployment safety or guardrails.
Blending experimental electron density information to protein-ligand interaction This paper introduces a pre-trained vision transformer that combines experimental electron-density maps with atomic coordinates to model protein-ligand interactions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies multimodal molecular structural representations, not efficient LLM inference or compression.
Blending experimental electron density information to protein-ligand interaction This paper introduces a pre-trained vision transformer that combines experimental electron-density maps with atomic coordinates to model protein-ligand interactions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on protein-ligand interaction models, not language model evaluation or benchmarks.
Blending experimental electron density information to protein-ligand interaction This paper introduces a pre-trained vision transformer that combines experimental electron-density maps with atomic coordinates to model protein-ligand interactions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses biomolecular binding affinity models, not AI deployment safety or guardrails.
Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores This paper proposes a post-hoc framework based on Linearized Laplace approximation to quantify uncertainty across downstream tasks in contrastive learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents uncertainty quantification for contrastive embeddings, not LLM evaluation benchmarks.
Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores This paper proposes a post-hoc framework based on Linearized Laplace approximation to quantify uncertainty across downstream tasks in contrastive learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with post-hoc Laplace approximations, not model distillation, pruning, or efficiency.

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