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
NBCLT: Parameter-Agnostic Norm-Ball Clipped Local Training The paper introduces a unified local training framework using norm-ball clipping to improve communication efficiency in distributed and federated learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies federated learning optimization algorithms, not LLM evaluation.
NBCLT: Parameter-Agnostic Norm-Ball Clipped Local Training The paper introduces a unified local training framework using norm-ball clipping to improve communication efficiency in distributed and federated learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical federated learning convergence, not safety guardrails or deployment risks.
Stein Variational Distributional Reinforcement Learning: Score-Guided Particles for Multivariate Return Distributions The paper introduces a score-guided particle framework using Stein variational gradient descent to model multivariate return distributions in reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers distributional reinforcement learning algorithms, not model compression or efficiency.
Stein Variational Distributional Reinforcement Learning: Score-Guided Particles for Multivariate Return Distributions The paper introduces a score-guided particle framework using Stein variational gradient descent to model multivariate return distributions in reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning algorithms, not LLM evaluation benchmarks.
Stein Variational Distributional Reinforcement Learning: Score-Guided Particles for Multivariate Return Distributions The paper introduces a score-guided particle framework using Stein variational gradient descent to model multivariate return distributions in reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses theoretical RL particle representation, not deployment safety or alignment.
Learning Distributions from Samples and Qualities This paper establishes accelerated parameter estimation rates when continuous data samples are accompanied by binary quality labels or pairwise preferences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical estimation paper, not LLM evaluation.
Learning Distributions from Samples and Qualities This paper establishes accelerated parameter estimation rates when continuous data samples are accompanied by binary quality labels or pairwise preferences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with theoretical sample distribution estimation, not AI safety or reliability.
Learning Distributions from Samples and Qualities This paper establishes accelerated parameter estimation rates when continuous data samples are accompanied by binary quality labels or pairwise preferences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers continuous parameter estimation theory, not efficient AI deployment.
CALM-5G: Event-Level Conformal Risk Control for Predictive Service-Assurance Alarms This paper proposes a conformal risk control layer that certifies miss rates and reduces alarm duty cycles in predictive cellular network operations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies conformal prediction to telecommunications alarms, not model efficiency.
CALM-5G: Event-Level Conformal Risk Control for Predictive Service-Assurance Alarms This paper proposes a conformal risk control layer that certifies miss rates and reduces alarm duty cycles in predictive cellular network operations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on 5G network telemetry forecasting, not language model evaluation.
Neural Dimensional Analysis: Foundations of Dimensionless and Scale-Consistent Computation This paper introduces a dimensional analysis framework and diagnostic metrics to maintain scale consistency across Transformer components. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on training stability via dimensional analysis, not deployment safety or reliability.
Neural Dimensional Analysis: Foundations of Dimensionless and Scale-Consistent Computation This paper introduces a dimensional analysis framework and diagnostic metrics to maintain scale consistency across Transformer components. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with training stability theory across scales, not small models or inference compression.
Neural Dimensional Analysis: Foundations of Dimensionless and Scale-Consistent Computation This paper introduces a dimensional analysis framework and diagnostic metrics to maintain scale consistency across Transformer components. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses model architectural scaling principles rather than evaluation benchmarks.
AlignPred: Error-Controlled KV Cache Compression via Alignment and Predictive Coding This paper presents an error-controlled compression codec for key-value caches using coordinate alignment and predictive coding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses serving efficiency and memory compression, not AI safety or guardrails.
AlignPred: Error-Controlled KV Cache Compression via Alignment and Predictive Coding This paper presents an error-controlled compression codec for key-value caches using coordinate alignment and predictive coding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on KV cache compression techniques rather than general LLM evaluation.
Rarity Determines Minimax Rates of Semantic Entropy From Weak Pairwise Judgments With a Known Channel This paper establishes minimax rates for estimating language model semantic entropy using verdicts from weak pairwise equivalence judges. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical information-theoretic analysis of entropy estimation, not model efficiency.
All-electron machine-learned orbital-free density functional for periodic CHNO systems This paper presents an equivariant neural network functional to perform orbital-free density functional theory for periodic CHNO systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Concerns computational chemistry and density functional theory, not LLMs.
All-electron machine-learned orbital-free density functional for periodic CHNO systems This paper presents an equivariant neural network functional to perform orbital-free density functional theory for periodic CHNO systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses molecular orbital modeling, not small language models or efficient inference.
All-electron machine-learned orbital-free density functional for periodic CHNO systems This paper presents an equivariant neural network functional to perform orbital-free density functional theory for periodic CHNO systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantum chemistry calculations, not AI deployment safety.
Exact power indices for plurality-voting ensembles This paper introduces an exact method to compute Banzhaf and Shapley power indices for base classifiers in plurality-voting ensembles. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on game-theoretic power indices, not efficient model architectures.
Exact power indices for plurality-voting ensembles This paper introduces an exact method to compute Banzhaf and Shapley power indices for base classifiers in plurality-voting ensembles. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies voting ensemble attribution, not AI safety or deployment robustness.
Exact power indices for plurality-voting ensembles This paper introduces an exact method to compute Banzhaf and Shapley power indices for base classifiers in plurality-voting ensembles. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical attribution work on ensemble voting, not LLM evaluation.
Counterfactual Certification for Sequential Unlearning with Long-Run Performance Guarantees This paper proposes a counterfactual certification framework to bound performance loss during sequential machine unlearning requests. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical unlearning guarantees, not model compression or efficiency.
Counterfactual Certification for Sequential Unlearning with Long-Run Performance Guarantees This paper proposes a counterfactual certification framework to bound performance loss during sequential machine unlearning requests. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with certified machine unlearning, not LLM evaluation.
Improving Neural Symbolic Regression with a Diverse Prior and Post-training Alignment This paper improves neural symbolic regression by using a tabular prior and reinforcement learning alignment to fit noisy data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on symbolic regression algorithms rather than LLM evaluation or benchmarks.
Improving Neural Symbolic Regression with a Diverse Prior and Post-training Alignment This paper improves neural symbolic regression by using a tabular prior and reinforcement learning alignment to fit noisy data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on symbolic mathematical regression, not deployment safety or jailbreak prevention.
Improving Neural Symbolic Regression with a Diverse Prior and Post-training Alignment This paper improves neural symbolic regression by using a tabular prior and reinforcement learning alignment to fit noisy data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses neural symbolic regression training recipes, not model compression or inference efficiency.
FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation This paper presents an agentic framework and an evaluation rubric for generating explanations of risk labels in graph fraud detection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Presents an agentic tool for fraud explanation rather than safety or alignment guardrails.
FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation This paper presents an agentic framework and an evaluation rubric for generating explanations of risk labels in graph fraud detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph context navigation for fraud explanations, not model compression.
Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees This paper theoretically and empirically compares weighting-based rebalancing strategies to unweighted methods in imbalanced tabular classification tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes class weighting in CART trees, not small or efficient models.
Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees This paper theoretically and empirically compares weighting-based rebalancing strategies to unweighted methods in imbalanced tabular classification tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies class-imbalance weighting in decision trees, not AI safety or deployment guardrails.
Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees This paper theoretically and empirically compares weighting-based rebalancing strategies to unweighted methods in imbalanced tabular classification tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical tabular classification analysis, not language model evaluation.
LADDER: A Long-Horizon Robot Manipulation Benchmark This paper introduces a robot manipulation benchmark to isolate failures across scaling subgoal horizons and orchestration methods. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a robotic manipulation benchmark rather than an LLM evaluation benchmark.
LADDER: A Long-Horizon Robot Manipulation Benchmark This paper introduces a robot manipulation benchmark to isolate failures across scaling subgoal horizons and orchestration methods. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Evaluates robotics manipulation policies, not AI deployment safety or jailbreak risks.
LADDER: A Long-Horizon Robot Manipulation Benchmark This paper introduces a robot manipulation benchmark to isolate failures across scaling subgoal horizons and orchestration methods. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies long-horizon robot policies, not model compression or efficient serving.
LADDER: A Long-Horizon Robot Manipulation Benchmark This paper introduces a robot manipulation benchmark to isolate failures across scaling subgoal horizons and orchestration methods. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Covers general robot manipulation, not industrial inspection or defect detection.
Reliable Scale-Free Bandits via Empirical Likelihood This paper develops an empirical-likelihood bandit policy that provides asymptotic regret guarantees without knowledge of reward scales. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies stochastic bandit regret bounds, not model efficiency or serving.
Reliable Scale-Free Bandits via Empirical Likelihood This paper develops an empirical-likelihood bandit policy that provides asymptotic regret guarantees without knowledge of reward scales. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical study of multi-armed bandits, not LLM benchmarking.
Reliable Scale-Free Bandits via Empirical Likelihood This paper develops an empirical-likelihood bandit policy that provides asymptotic regret guarantees without knowledge of reward scales. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical bandit regret bounds rather than AI safety or guardrails.
Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach This paper proposes a score-matching approach for causal discovery in additive noise models under hard selection bias and truncation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical causal discovery rather than AI deployment safety or robustness.
Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach This paper proposes a score-matching approach for causal discovery in additive noise models under hard selection bias and truncation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical causal discovery work, not language model evaluation.
Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach This paper proposes a score-matching approach for causal discovery in additive noise models under hard selection bias and truncation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses causal discovery under selection bias, not model efficiency or compression.
Failure Recovery via Belief Updates and World Modeling for Long-Horizon Agents This paper introduces a framework combining persistent belief states with implicit world modeling to help long-horizon language agents recover from failures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent planning recovery rather than safety, guardrails, or alignment risks.
Failure Recovery via Belief Updates and World Modeling for Long-Horizon Agents This paper introduces a framework combining persistent belief states with implicit world modeling to help long-horizon language agents recover from failures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an agent framework and distillation method rather than a general evaluation benchmark.
Same Function, Different Trajectory: No Standard Optimizer Is Equivariant to Transformer Reparameterization This paper demonstrates that standard optimizers are sensitive to parameter scaling in Transformers and proposes a symmetry-aware gradient adjustment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimizer reparameterization dynamics rather than LLM evaluation or benchmarks.
Same Function, Different Trajectory: No Standard Optimizer Is Equivariant to Transformer Reparameterization This paper demonstrates that standard optimizers are sensitive to parameter scaling in Transformers and proposes a symmetry-aware gradient adjustment. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns training stability and optimization theory, not deployment safety or reliability.
Same Function, Different Trajectory: No Standard Optimizer Is Equivariant to Transformer Reparameterization This paper demonstrates that standard optimizers are sensitive to parameter scaling in Transformers and proposes a symmetry-aware gradient adjustment. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses training optimization geometry, not model compression or efficient inference methods.
From Spectral Saliency to Finite Deletion: Evaluating Selective Language Model Edits This paper analyzes prediction errors in spectral saliency when deleting singular components to selectively edit language model capabilities. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies targeted capability deletion rather than model compression or inference efficiency.
From Spectral Saliency to Finite Deletion: Evaluating Selective Language Model Edits This paper analyzes prediction errors in spectral saliency when deleting singular components to selectively edit language model capabilities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates selective weight editing methods rather than assessing general LLM capabilities.
The Effect of Layer Ordering in Hybrid Architectures This paper analyzes how layer ordering and selection affect performance across tasks in hybrid attention model architectures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes architecture design theory, not benchmark creation or evaluation.
The Effect of Layer Ordering in Hybrid Architectures This paper analyzes how layer ordering and selection affect performance across tasks in hybrid attention model architectures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on hybrid model architecture optimization, not safety or guardrails.
Distance Prediction for Quantum Codes This paper introduces an attention-based neural network to predict code distances for bivariate bicycle quantum low-density parity-check codes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Quantum code distance calculation, not AI deployment safety.
Distance Prediction for Quantum Codes This paper introduces an attention-based neural network to predict code distances for bivariate bicycle quantum low-density parity-check codes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Application to quantum error correction codes, not efficient AI.
Distance Prediction for Quantum Codes This paper introduces an attention-based neural network to predict code distances for bivariate bicycle quantum low-density parity-check codes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Predicts quantum code distances, not language model evaluation.
Revisiting Error Feedback for Model Quantization This paper formulates a trace-minimization framework to design causal error-feedback coefficients for neural network weight quantization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Compression error feedback, not deployment safety, guardrails, or alignment.
Revisiting Error Feedback for Model Quantization This paper formulates a trace-minimization framework to design causal error-feedback coefficients for neural network weight quantization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on model quantization techniques, not LLM evaluation benchmarks.
Learning a Group Element from Its Action: Galois Correspondences, Optimal ERM, and an Abel-Ruffini Threshold for the Natarajan Dimension This paper analyzes the sample and mistake complexity of learning group elements from actions using Galois correspondences and multiclass dimensions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Abstract learning theory and group theory, not LLM evaluation.
Learning a Group Element from Its Action: Galois Correspondences, Optimal ERM, and an Abel-Ruffini Threshold for the Natarajan Dimension This paper analyzes the sample and mistake complexity of learning group elements from actions using Galois correspondences and multiclass dimensions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical sample complexity analysis, not efficient model deployment.
Learning a Group Element from Its Action: Galois Correspondences, Optimal ERM, and an Abel-Ruffini Threshold for the Natarajan Dimension This paper analyzes the sample and mistake complexity of learning group elements from actions using Galois correspondences and multiclass dimensions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Pure statistical learning theory, not deployed AI safety.
Random Probing for Structural Self-Interactions in Graph Neural Networks This paper proposes a message-passing layer using randomized probing of operator diagonals to capture node self-interactions in graph neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with graph neural network expressivity, not LLM evaluation.

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