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
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization The paper introduces a standardized 25-task benchmark and evaluation protocol for docking-based molecular optimization in drug design. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmarking molecular drug design, not language model safety risks.
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization The paper introduces a standardized 25-task benchmark and evaluation protocol for docking-based molecular optimization in drug design. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on molecular docking benchmarks, not small efficient models.
Data Density Scaling Laws for Image Self-Distillation The paper proposes a revised scaling law for image self-distillation that incorporates sample repetition alongside unique sample counts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines sample repetition in image pretraining, not AI safety.
Data Density Scaling Laws for Image Self-Distillation The paper proposes a revised scaling law for image self-distillation that incorporates sample repetition alongside unique sample counts. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual self-distillation scaling laws, not efficient model inference.
Data Density Scaling Laws for Image Self-Distillation The paper proposes a revised scaling law for image self-distillation that incorporates sample repetition alongside unique sample counts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies image self-distillation scaling laws, not LLM evaluation.
IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence The paper establishes constant regret and convergence guarantees for simultaneous and alternating regret matching variants using previous-utility prediction in matrix games. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents game-theoretic regret minimization, not efficient model deployment.
IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence The paper establishes constant regret and convergence guarantees for simultaneous and alternating regret matching variants using previous-utility prediction in matrix games. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Deals with zero-sum game theory algorithms, not LLM benchmarks.
IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence The paper establishes constant regret and convergence guarantees for simultaneous and alternating regret matching variants using previous-utility prediction in matrix games. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical game solver algorithm, not deployment AI safety guardrails.
Discrete Diffusion Models are Minimax Optimal Under Sparsity The paper establishes minimax-optimal statistical rates for discrete diffusion models generating text and categorical data under sparsity assumptions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents theoretical rates for diffusion models, not LLM benchmarks.
Discrete Diffusion Models are Minimax Optimal Under Sparsity The paper establishes minimax-optimal statistical rates for discrete diffusion models generating text and categorical data under sparsity assumptions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Covers theoretical minimax bounds, not practical model efficiency.
Discrete Diffusion Models are Minimax Optimal Under Sparsity The paper establishes minimax-optimal statistical rates for discrete diffusion models generating text and categorical data under sparsity assumptions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical rates of diffusion, not deployment safety.
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs The paper proposes a framework that decouples candidate generation from feasibility assessment using structured sampling for chance-constrained stochastic programs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on chance-constrained mathematical optimization, not LLM evaluation.
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs The paper proposes a framework that decouples candidate generation from feasibility assessment using structured sampling for chance-constrained stochastic programs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies numerical operations research problems, not AI deployment safety.
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs The paper proposes a framework that decouples candidate generation from feasibility assessment using structured sampling for chance-constrained stochastic programs. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with stochastic programming, not efficient language models.
How Many Ranks Does a Token Need? A Noise-Floor Squelch for Rank-One LoRA Experts The paper introduces a router-free method that adaptively thresholds rank-one LoRA adapter responses to remove cross-talk noise per token. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a dynamic LoRA pruning method, not an evaluation benchmark.
How Many Ranks Does a Token Need? A Noise-Floor Squelch for Rank-One LoRA Experts The paper introduces a router-free method that adaptively thresholds rank-one LoRA adapter responses to remove cross-talk noise per token. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on parameter-efficient fine-tuning adaptation, not deployment safety.
Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources The paper develops a closed-form index policy for online task assignment with reusable resources, establishing its asymptotic optimality in fluid limits. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with multi-armed bandits, not efficient language models.
Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources The paper develops a closed-form index policy for online task assignment with reusable resources, establishing its asymptotic optimality in fluid limits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on online resource assignment policies, not LLM evaluation.
Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources The paper develops a closed-form index policy for online task assignment with reusable resources, establishing its asymptotic optimality in fluid limits. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies online scheduling algorithms, not AI safety or reliability.
What Information Buys in Active Inference The paper compares active inference to standard exploration in reinforcement learning and proposes a square-root calibration to prevent sub-optimal actions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical cognitive reinforcement learning study, not deployment AI safety.
What Information Buys in Active Inference The paper compares active inference to standard exploration in reinforcement learning and proposes a square-root calibration to prevent sub-optimal actions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Analyzes theoretical exploration bounds in RL, not small models.
What Information Buys in Active Inference The paper compares active inference to standard exploration in reinforcement learning and proposes a square-root calibration to prevent sub-optimal actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on theoretical active inference regret, not LLM benchmarks.
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes The paper studies deep generative simulation-based inference to estimate phylogenetic tree posteriors from sequences under complex evolutionary models. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on computational biology, not deployment safety or robustness.
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes The paper studies deep generative simulation-based inference to estimate phylogenetic tree posteriors from sequences under complex evolutionary models. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with biological tree generative models, not model efficiency.
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes The paper studies deep generative simulation-based inference to estimate phylogenetic tree posteriors from sequences under complex evolutionary models. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on phylogenetic tree inference, not LLM benchmarks.
Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models The paper presents a weak regression approach combined with conformal prediction to estimate parameters and uncertainty in epidemic models without numerical derivatives. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on physics-informed epidemic regression, not model compression.
Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models The paper presents a weak regression approach combined with conformal prediction to estimate parameters and uncertainty in epidemic models without numerical derivatives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers scientific differential equation regression, not AI safety risks.
Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models The paper presents a weak regression approach combined with conformal prediction to estimate parameters and uncertainty in epidemic models without numerical derivatives. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies parameter estimation for epidemic models, not LLM benchmarking.
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology The paper introduces a multilevel particle marginal Metropolis-Hastings method for joint Bayesian inference of neural network parameters and prior hyperparameters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses theoretical uncertainty estimation, not AI deployment safety risks.
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology The paper introduces a multilevel particle marginal Metropolis-Hastings method for joint Bayesian inference of neural network parameters and prior hyperparameters. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents hierarchical Bayesian MCMC theory, not efficient language models.
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology The paper introduces a multilevel particle marginal Metropolis-Hastings method for joint Bayesian inference of neural network parameters and prior hyperparameters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on MCMC methodology, not LLM evaluation benchmarks.
Backpropagating Verifier Rewards Through Continuous Flow Language Models The paper introduces a post-training framework using intermediate token critics to backpropagate verifier rewards through continuous flow language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an RL framework for continuous flow models, not benchmarks.
Backpropagating Verifier Rewards Through Continuous Flow Language Models The paper introduces a post-training framework using intermediate token critics to backpropagate verifier rewards through continuous flow language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with RL training for continuous flow models, not efficiency.
Backpropagating Verifier Rewards Through Continuous Flow Language Models The paper introduces a post-training framework using intermediate token critics to backpropagate verifier rewards through continuous flow language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies model post-training methods, not safety guardrails or reliability.
Contextual Credit Policy Optimization The paper presents a method that weights trajectory peers by context similarity to improve step-level credit assignment for language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies reinforcement learning optimization, not deployment safety or guardrails.
Contextual Credit Policy Optimization The paper presents a method that weights trajectory peers by context similarity to improve step-level credit assignment for language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an RL credit assignment method, not a benchmark.
Contextual Credit Policy Optimization The paper presents a method that weights trajectory peers by context similarity to improve step-level credit assignment for language model agents. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL credit assignment algorithms, not model compression.
Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments The paper introduces a neural Q-learning framework that clusters treatments by response similarity to improve individualized treatment rules across many options. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical treatment clustering, not deployment AI safety.
Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments The paper introduces a neural Q-learning framework that clusters treatments by response similarity to improve individualized treatment rules across many options. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on biostatistical treatment rule estimation, not LLM evaluation.
Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments The paper introduces a neural Q-learning framework that clusters treatments by response similarity to improve individualized treatment rules across many options. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with individualized treatment rules, not efficient language models.
TRACER: Trace-Regularized Training Against Indirect Prompt Injection The paper introduces an adversarial training framework that hardens models against indirect prompt injection across retrieval and tool interaction channels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on prompt injection defense training, not general benchmarking.
TRACER: Trace-Regularized Training Against Indirect Prompt Injection The paper introduces an adversarial training framework that hardens models against indirect prompt injection across retrieval and tool interaction channels. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with adversarial training against attacks, not model efficiency.
Student Copula Markov Networks The paper extends undirected graphical models to non-Gaussian data using copula densities and provides an estimation algorithm. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical copula graphical model paper, not an LLM benchmark.
Student Copula Markov Networks The paper extends undirected graphical models to non-Gaussian data using copula densities and provides an estimation algorithm. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on probabilistic graphical models, not AI safety deployments.
Student Copula Markov Networks The paper extends undirected graphical models to non-Gaussian data using copula densities and provides an estimation algorithm. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with statistical copula networks, not small language models.
Online Tokenization with Optimal Delay in Linear Time The paper introduces an online tokenization algorithm that processes text streams in linear time with minimal algorithmic delay. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on streaming tokenization algorithms, not AI safety risks.
Online Tokenization with Optimal Delay in Linear Time The paper introduces an online tokenization algorithm that processes text streams in linear time with minimal algorithmic delay. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a tokenization algorithm, not an LLM benchmark.
Towards Training and Inference of Graph Neural Networks in Graph Databases The paper shows how graph databases can execute graph neural network sampling, retrieval, and inference directly without separate in-memory pipelines. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with GNN training infrastructure, not AI safety or hallucination.
Towards Training and Inference of Graph Neural Networks in Graph Databases The paper shows how graph databases can execute graph neural network sampling, retrieval, and inference directly without separate in-memory pipelines. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on GNN database systems, not LLM evaluation benchmarks.
Towards Training and Inference of Graph Neural Networks in Graph Databases The paper shows how graph databases can execute graph neural network sampling, retrieval, and inference directly without separate in-memory pipelines. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies GNNs in graph databases, not small language models.
Demystifying On-Policy Distillation: Where Does the Teacher's Advantage Live? The paper examines teacher interventions along student generation paths to identify how teacher guidance drives performance gains in on-policy distillation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on distillation dynamics rather than safety guardrails or reliability.
Demystifying On-Policy Distillation: Where Does the Teacher's Advantage Live? The paper examines teacher interventions along student generation paths to identify how teacher guidance drives performance gains in on-policy distillation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates distillation mechanics rather than LLM benchmarking suites.
Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems The paper analyzes packet routing throughput when selfish queue agents use no-regret learning algorithms to select buffered servers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Queuing bandit problem unrelated to LLM deployment safety.
Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems The paper analyzes packet routing throughput when selfish queue agents use no-regret learning algorithms to select buffered servers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on packet routing queues, not efficient model inference.
Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems The paper analyzes packet routing throughput when selfish queue agents use no-regret learning algorithms to select buffered servers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes multi-agent queuing theory, not LLM benchmarking.
Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent The paper proposes a Bayesian full waveform inversion framework that uses learned diffusion models as regularizers for geological reconstruction. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses geophysical inversion rather than AI safety or reliability.
Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent The paper proposes a Bayesian full waveform inversion framework that uses learned diffusion models as regularizers for geological reconstruction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Pertains to geophysics waveform inversion, not LLM evaluation.
Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent The paper proposes a Bayesian full waveform inversion framework that uses learned diffusion models as regularizers for geological reconstruction. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on geophysical inverse modeling, not small model efficiency.
Adaptive Computation with Conformal Certification The paper develops an adaptive computation framework offering conformal uncertainty guarantees for processing large scientific and medical imagery efficiently. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific imaging compute adaptation, not small language models.
Adaptive Computation with Conformal Certification The paper develops an adaptive computation framework offering conformal uncertainty guarantees for processing large scientific and medical imagery efficiently. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Deals with conformal risk certification for imaging, not LLM benchmarks.

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