Every paper checked — nothing hidden

All papers

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

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Imperfect Is Perfect: Learned Column Prefilling for Branch-Price-and-Cut The paper uses neural networks to predict initial columns and dual centers, speeding up branch-price-and-cut solvers for vehicle routing problems. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies discrete optimization algorithms, not AI safety or reliability.
Imperfect Is Perfect: Learned Column Prefilling for Branch-Price-and-Cut The paper uses neural networks to predict initial columns and dual centers, speeding up branch-price-and-cut solvers for vehicle routing problems. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Addresses operations research optimization, not LLM evaluation methods.
Imperfect Is Perfect: Learned Column Prefilling for Branch-Price-and-Cut The paper uses neural networks to predict initial columns and dual centers, speeding up branch-price-and-cut solvers for vehicle routing problems. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on vehicle routing optimization, not efficient neural models.
CausalEvo: Causal Skill Self-Evolution for Health LLM Agents The paper introduces a causal evaluation method for healthcare language model agents to iteratively select and retain useful task skills. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on agent skill evolution, not benchmark methodology or contamination.
CausalEvo: Causal Skill Self-Evolution for Health LLM Agents The paper introduces a causal evaluation method for healthcare language model agents to iteratively select and retain useful task skills. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on agent skill optimization, not model compression or serving.
Acceleration and its Limits in Gaussian Variational Inference The paper studies query complexity bounds and limits of momentum-based acceleration in Gaussian variational inference for strongly log-concave distributions. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on variational inference theory, not LLM benchmark evaluation.
Acceleration and its Limits in Gaussian Variational Inference The paper studies query complexity bounds and limits of momentum-based acceleration in Gaussian variational inference for strongly log-concave distributions. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on optimization complexity, not AI safety or deployment robustness.
Acceleration and its Limits in Gaussian Variational Inference The paper studies query complexity bounds and limits of momentum-based acceleration in Gaussian variational inference for strongly log-concave distributions. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Addresses optimization theory in statistics, not efficient model architectures.
On the Last-Iterate Convergence of Clipped Gradient Methods The paper provides theoretical last-iterate convergence guarantees for clipped stochastic gradient methods under heavy-tailed noise. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical analysis of optimization bounds, not model efficiency or serving.
On the Last-Iterate Convergence of Clipped Gradient Methods The paper provides theoretical last-iterate convergence guarantees for clipped stochastic gradient methods under heavy-tailed noise. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies gradient descent math, not safety, guardrails, or alignment.
On the Last-Iterate Convergence of Clipped Gradient Methods The paper provides theoretical last-iterate convergence guarantees for clipped stochastic gradient methods under heavy-tailed noise. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on theoretical optimization convergence, not LLM evaluation benchmarks.
SALTO: Solver-Anchored Lifted Transfer Operators for Nonlinear Event Inference The paper introduces an operator-learning framework using linear transfer operators to infer localized finite-amplitude source events in nonlinear dynamical systems. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Nonlinear dynamical system inference paper, not LLM benchmark evaluation.
SALTO: Solver-Anchored Lifted Transfer Operators for Nonlinear Event Inference The paper introduces an operator-learning framework using linear transfer operators to infer localized finite-amplitude source events in nonlinear dynamical systems. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Dynamical transfer operator method, not model quantization, distillation, or efficient serving.
SALTO: Solver-Anchored Lifted Transfer Operators for Nonlinear Event Inference The paper introduces an operator-learning framework using linear transfer operators to infer localized finite-amplitude source events in nonlinear dynamical systems. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Scientific inverse problem modeling, not AI safety, jailbreak defense, or guardrails.
Score Mismatch and Set-Constrained Sampling in Zero-Shot Conditional Discrete Diffusion The paper provides theoretical bounds on the approximation error caused by score mismatch during zero-shot conditional sampling in discrete diffusion models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical discrete diffusion analysis, not model compression, distillation, or serving efficiency.
Score Mismatch and Set-Constrained Sampling in Zero-Shot Conditional Discrete Diffusion The paper provides theoretical bounds on the approximation error caused by score mismatch during zero-shot conditional sampling in discrete diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on discrete diffusion distribution shifts, not deployment safety or guardrails.
Score Mismatch and Set-Constrained Sampling in Zero-Shot Conditional Discrete Diffusion The paper provides theoretical bounds on the approximation error caused by score mismatch during zero-shot conditional sampling in discrete diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical analysis of diffusion sampling bias, not LLM benchmark evaluation.
Adaptive Randomized Neural Networks for PDEs: Structure-Aware Feature Redistribution and Residual-Driven Block Enrichment The paper introduces a two-stage adaptive feature construction method to improve randomized neural networks for solving partial differential equations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant PDE solver method, not language model evaluation or benchmark methodology.
Adaptive Randomized Neural Networks for PDEs: Structure-Aware Feature Redistribution and Residual-Driven Block Enrichment The paper introduces a two-stage adaptive feature construction method to improve randomized neural networks for solving partial differential equations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Numerical PDE solution technique, not deployment safety or robustness in LLMs.
Adaptive Randomized Neural Networks for PDEs: Structure-Aware Feature Redistribution and Residual-Driven Block Enrichment The paper introduces a two-stage adaptive feature construction method to improve randomized neural networks for solving partial differential equations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Randomized neural network for PDEs, not efficient language model serving or compression.
StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction The paper proposes a heavy-tailed score-matching framework for limit order book trend prediction without needing reverse diffusion sampling at inference. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Diffusion modeling for finance, not small language models or efficient inference.
StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction The paper proposes a heavy-tailed score-matching framework for limit order book trend prediction without needing reverse diffusion sampling at inference. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Limit order book forecasting, not AI safety, alignment, or guardrail systems.
StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction The paper proposes a heavy-tailed score-matching framework for limit order book trend prediction without needing reverse diffusion sampling at inference. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Financial time series modeling paper, not LLM evaluation or benchmark design.
Noise-Space Reward Alignment for Few-Step Generative Models The paper proposes Noise Policy Optimization to align few-step generative models with black-box rewards by steering their initial input noise space. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Presents reward alignment for generative models, not benchmark dataset or evaluation analysis.
Noise-Space Reward Alignment for Few-Step Generative Models The paper proposes Noise Policy Optimization to align few-step generative models with black-box rewards by steering their initial input noise space. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on reward alignment in generative models, not deployment safety or jailbreak prevention.
Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble The paper presents an attention-based neural network and logistic regression ensemble to predict live birth outcomes before fresh embryo transfer. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Tabular clinical prediction for IVF, not LLM benchmarking or evaluation tools.
Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble The paper presents an attention-based neural network and logistic regression ensemble to predict live birth outcomes before fresh embryo transfer. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant IVF clinical outcome prediction, not LLM safety, guardrails, or red-teaming.
Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble The paper presents an attention-based neural network and logistic regression ensemble to predict live birth outcomes before fresh embryo transfer. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Clinical tabular ensemble model, not small language models or inference serving.
Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process The paper analyzes theoretical properties of the multiplicative half-Cauchy process and provides a calibration rule linking prior beliefs to its scale parameter. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical statistics paper on stochastic processes, not LLM evaluation.
Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process The paper analyzes theoretical properties of the multiplicative half-Cauchy process and provides a calibration rule linking prior beliefs to its scale parameter. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Mathematical stochastic process analysis, not AI deployment safety or jailbreak defense.
Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process The paper analyzes theoretical properties of the multiplicative half-Cauchy process and provides a calibration rule linking prior beliefs to its scale parameter. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Pure Bayesian statistical theory, not model compression or inference efficiency.
Coordination-Free Workload Regularization for Variable-Cost Distributed Training The paper proposes a rank-local data-loading policy that rebalances variable-cost training batches without cross-node coordination to speed up distributed training. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Hardware workload balancing, not AI deployment safety, alignment, or guardrails.
Coordination-Free Workload Regularization for Variable-Cost Distributed Training The paper proposes a rank-local data-loading policy that rebalances variable-cost training batches without cross-node coordination to speed up distributed training. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on distributed training systems, not LLM evaluation benchmarks or metrics.
Sensor Diagnosis for Fuel and Emissions Monitoring in Heavy-Duty Diesel Vehicles The paper evaluates two machine learning procedures to diagnose faulty sensors and correct fuel and emission estimates in heavy-duty diesel engines. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Deals with diesel sensor diagnostics, not LLM benchmark evaluation or judge testing.
Sensor Diagnosis for Fuel and Emissions Monitoring in Heavy-Duty Diesel Vehicles The paper evaluates two machine learning procedures to diagnose faulty sensors and correct fuel and emission estimates in heavy-duty diesel engines. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Automotive engine sensor monitoring, not LLM safety, red-teaming, or guardrails.
Sensor Diagnosis for Fuel and Emissions Monitoring in Heavy-Duty Diesel Vehicles The paper evaluates two machine learning procedures to diagnose faulty sensors and correct fuel and emission estimates in heavy-duty diesel engines. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Automotive sensor fault diagnosis, not small language models or efficient inference.
Resisting Harmful Instructions through Training-Free Weight Editing The paper introduces a training-free weight-editing method to reduce large language model compliance with harmful instructions such as lying and sexism. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Presents a model editing technique rather than a benchmark or evaluation methodology.
Resisting Harmful Instructions through Training-Free Weight Editing The paper introduces a training-free weight-editing method to reduce large language model compliance with harmful instructions such as lying and sexism. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on alignment editing, not quantization, pruning, or efficient inference serving.
Poisoning the corpora of protein design systems during pre-training and retrieval The paper investigates data poisoning attacks targeting pre-training and retrieval corpora of protein language models and generative design systems. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on protein model data poisoning rather than LLM benchmarking or evaluation.
Poisoning the corpora of protein design systems during pre-training and retrieval The paper investigates data poisoning attacks targeting pre-training and retrieval corpora of protein language models and generative design systems. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Deals with protein generator security, not small model compression or inference efficiency.
Hard Way, The Only Way: Provable Merits of Training on Hardest Examples The paper provides a high-dimensional theoretical analysis of data pruning strategies when training models on the hardest examples. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Mathematical sample pruning analysis, not AI safety guardrails or deployment risks.
Hard Way, The Only Way: Provable Merits of Training on Hardest Examples The paper provides a high-dimensional theoretical analysis of data pruning strategies when training models on the hardest examples. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical analysis of data pruning rates, not LLM benchmark evaluation.
Hard Way, The Only Way: Provable Merits of Training on Hardest Examples The paper provides a high-dimensional theoretical analysis of data pruning strategies when training models on the hardest examples. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies sample pruning theory in high dimensions, not model compression or serving.
Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs The authors introduce a graph neural ordinary differential equation framework to forecast multiscale spatiotemporal epidemic spreading dynamics. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Epidemiological neural ODE architecture, not small or efficient language model serving.
Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs The authors introduce a graph neural ordinary differential equation framework to forecast multiscale spatiotemporal epidemic spreading dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Addresses epidemic dynamic forecasting models, not LLM benchmarks or evaluation methods.
Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs The authors introduce a graph neural ordinary differential equation framework to forecast multiscale spatiotemporal epidemic spreading dynamics. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on spatio-temporal epidemic modeling, not LLM deployment safety or guardrails.
Not All Change Is Damage: Update-Matched Unlearning for Large Language Models The paper introduces an update-matched evaluation reference to balance unlearning effectiveness with downstream learning ability in language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on machine unlearning methods rather than LLM benchmarking or judge reliability.
Not All Change Is Damage: Update-Matched Unlearning for Large Language Models The paper introduces an update-matched evaluation reference to balance unlearning effectiveness with downstream learning ability in language models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates knowledge unlearning rather than model quantization, distillation, or serving speed.
Compatibility Matters: A Framework for Token-Level Teacher Selection in On-Policy Distillation The authors propose a theoretical and empirical framework to route token-level teacher selection during on-policy student distillation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Centers on distillation policy routing, not deployment safety or alignment robustness.
Compatibility Matters: A Framework for Token-Level Teacher Selection in On-Policy Distillation The authors propose a theoretical and empirical framework to route token-level teacher selection during on-policy student distillation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Introduces a distillation framework rather than benchmark datasets or evaluation methodologies.
Rethinking Learning from Label Proportions via Moment Matching The paper presents a moment-matching framework for weakly supervised learning when labels are grouped into bags non-randomly. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Weakly supervised classification theory, not small model distillation or efficient inference.
Rethinking Learning from Label Proportions via Moment Matching The paper presents a moment-matching framework for weakly supervised learning when labels are grouped into bags non-randomly. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Addresses learning from label proportions, not LLM benchmark reliability or evaluation.
Rethinking Learning from Label Proportions via Moment Matching The paper presents a moment-matching framework for weakly supervised learning when labels are grouped into bags non-randomly. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on label-proportion statistical learning, not deployment safety guardrails or robustness.
Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means The authors establish regret lower bounds for online quantum state learning under logarithmic loss with applications to tomography. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on theoretical quantum state learning regret bounds, not LLM benchmarks.
Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means The authors establish regret lower bounds for online quantum state learning under logarithmic loss with applications to tomography. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Mathematical quantum learning paper, not model compression or efficient LLM inference.
Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means The authors establish regret lower bounds for online quantum state learning under logarithmic loss with applications to tomography. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with quantum tomography regret bounds, not AI deployment safety or jailbreaks.
Can Capability Enhancement be Localized? The study investigates whether removing small subsets of attention heads can improve language model performance on specific tasks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on capability improvement via head removal, not safety guardrails or robustness.
Can Capability Enhancement be Localized? The study investigates whether removing small subsets of attention heads can improve language model performance on specific tasks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Examines head pruning mechanics rather than benchmarking or evaluation methodology.
Personalized Projected Ascent for Federated Stackelberg Optimization The paper analyzes personalized projected ascent algorithms for federated minimax problems with client-specific maximization variables. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical optimization paper, not LLM safety, guardrails, or deployment reliability.
Personalized Projected Ascent for Federated Stackelberg Optimization The paper analyzes personalized projected ascent algorithms for federated minimax problems with client-specific maximization variables. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Analyzes federated minimax optimization rates, not LLM benchmark or evaluation methodology.

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