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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
On Global Joint Lower Bounds in Zeroth-Order Nonconvex Optimization The paper proves theoretical joint lower bounds on oracle complexity for zeroth-order nonconvex stochastic optimization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Purely theoretical optimization analysis, not model efficiency.
On Global Joint Lower Bounds in Zeroth-Order Nonconvex Optimization The paper proves theoretical joint lower bounds on oracle complexity for zeroth-order nonconvex stochastic optimization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical nonconvex optimization bounds, not LLM benchmarks.
On Global Joint Lower Bounds in Zeroth-Order Nonconvex Optimization The paper proves theoretical joint lower bounds on oracle complexity for zeroth-order nonconvex stochastic optimization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Mathematical optimization complexity, not AI safety or alignment.
Annealed Lévy Diffusion via Gaussian Surrogate Reverse Dynamics The paper proposes a generative diffusion framework that anneals heavy-tailed noise into Gaussian noise to combine exploration with stable refinement. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with continuous diffusion processes, not model compression.
Annealed Lévy Diffusion via Gaussian Surrogate Reverse Dynamics The paper proposes a generative diffusion framework that anneals heavy-tailed noise into Gaussian noise to combine exploration with stable refinement. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers diffusion formulations, not LLM safety or guardrails.
Annealed Lévy Diffusion via Gaussian Surrogate Reverse Dynamics The paper proposes a generative diffusion framework that anneals heavy-tailed noise into Gaussian noise to combine exploration with stable refinement. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies diffusion sampling mathematics, not LLM evaluation benchmarks.
ReGRA: Residual-Gradient Adaptation for Complementary Low-Rank Fine-Tuning The paper introduces a two-stage low-rank fine-tuning method that uses residual gradients to add complementary adaptation directions without expanding the rank. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a fine-tuning method, not a benchmarking suite.
ReGRA: Residual-Gradient Adaptation for Complementary Low-Rank Fine-Tuning The paper introduces a two-stage low-rank fine-tuning method that uses residual gradients to add complementary adaptation directions without expanding the rank. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on parameter-efficient adaptation, not deployment safety or guardrails.
TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models The paper presents an agentic system that iteratively writes and adjusts forecasting pipelines around frozen time-series models to improve predictions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on automated time-series pipelines, not LLM evaluation benchmarks.
TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models The paper presents an agentic system that iteratively writes and adjusts forecasting pipelines around frozen time-series models to improve predictions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers time-series pipeline tuning, not AI safety or alignment.
TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models The paper presents an agentic system that iteratively writes and adjusts forecasting pipelines around frozen time-series models to improve predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with agentic pipeline optimization, not model compression.
CAST: History-Adaptive Spectral Clipping for LLM Pretraining Introduces an adaptive spectral gradient clipping method to stabilize and accelerate large language model pretraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a pretraining gradient clipping optimizer, not evaluation.
CAST: History-Adaptive Spectral Clipping for LLM Pretraining Introduces an adaptive spectral gradient clipping method to stabilize and accelerate large language model pretraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on pretraining optimization stability, not deployment safety.
Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers Investigates why looped transformers fail to retain or reuse earlier correct intermediate states when extended past training depth. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Explores mechanistic recurrent reasoning, not AI safety or guardrails.
Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers Investigates why looped transformers fail to retain or reuse earlier correct intermediate states when extended past training depth. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates recurrent transformer mechanics, not LLM evaluation benchmarks.
When Does the Dependency Graph Help Code-to-Architecture Mapping? Evaluates whether dependency graphs improve automated mapping between source code files and system architectures. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, pruning, or efficient inference.
When Does the Dependency Graph Help Code-to-Architecture Mapping? Evaluates whether dependency graphs improve automated mapping between source code files and system architectures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers code architectural debt, not deployment safety or alignment.
When Does the Dependency Graph Help Code-to-Architecture Mapping? Evaluates whether dependency graphs improve automated mapping between source code files and system architectures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies software dependency mapping rather than LLM evaluation benchmarks.
From Global to Tail Convergence: Dynamics of $α$-Stable Score-Based Generative Models Provides theoretical convergence bounds for score-based generative models driven by heavy-tailed stable processes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical diffusion dynamics, not efficient serving or quantization.
From Global to Tail Convergence: Dynamics of $α$-Stable Score-Based Generative Models Provides theoretical convergence bounds for score-based generative models driven by heavy-tailed stable processes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of score-based models, not LLM benchmarking.
From Global to Tail Convergence: Dynamics of $α$-Stable Score-Based Generative Models Provides theoretical convergence bounds for score-based generative models driven by heavy-tailed stable processes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with theoretical score dynamics, not AI safety guardrails.
SNACK: A Sequential Notation Framework for Probabilistic Graph Generation Proposes a sequential string notation to represent and generate valid graphs using standard sequence models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Introduces string representation for graphs, not model compression.
SNACK: A Sequential Notation Framework for Probabilistic Graph Generation Proposes a sequential string notation to represent and generate valid graphs using standard sequence models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses molecular graph generation, not AI deployment safety.
SNACK: A Sequential Notation Framework for Probabilistic Graph Generation Proposes a sequential string notation to represent and generate valid graphs using standard sequence models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on graph generation notation, not LLM benchmarks.
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification Formulates clinical population health stratification as a multi-treatment causal ranking problem to prioritize interventions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical stratification, not AI safety or alignment.
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification Formulates clinical population health stratification as a multi-treatment causal ranking problem to prioritize interventions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Healthcare causal ranking framework, not LLM benchmark evaluation.
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification Formulates clinical population health stratification as a multi-treatment causal ranking problem to prioritize interventions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with causal patient stratification, not model efficiency.
Reinforcement Learning via Advantage Guidance Uses supervised advantage conditioning to train reinforcement learning policies with improved stability. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies general RL policy extraction, not model compression techniques.
Reinforcement Learning via Advantage Guidance Uses supervised advantage conditioning to train reinforcement learning policies with improved stability. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a reinforcement learning algorithm, not LLM benchmarks.
Reinforcement Learning via Advantage Guidance Uses supervised advantage conditioning to train reinforcement learning policies with improved stability. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant General RL policy extraction, not deployment safety or alignment.
Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning Applies positive-unlabeled learning to establish mathematically exact negative guidance in diffusion generative models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with diffusion sampling guidance, not small efficient models.
Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning Applies positive-unlabeled learning to establish mathematically exact negative guidance in diffusion generative models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Covers negative guidance in diffusion models, not LLM evaluation.
Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning Applies positive-unlabeled learning to establish mathematically exact negative guidance in diffusion generative models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Diffusion negative sampling theory, not deployment safety or guardrails.
Efficient contextual partial monitoring with general function approximation Presents an efficient regression-based algorithm for contextual partial monitoring games with side information. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on theoretical sequential learning, not deployment safety.
Efficient contextual partial monitoring with general function approximation Presents an efficient regression-based algorithm for contextual partial monitoring games with side information. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Covers partial monitoring games, not model compression or efficiency.
Efficient contextual partial monitoring with general function approximation Presents an efficient regression-based algorithm for contextual partial monitoring games with side information. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical bandit and game theory paper, not LLM evaluation.
Calibration and Risk-Budgeted Decisions with System One Models Studies statistical calibration techniques to bound risk when fast language models make automated decisions under uncertainty. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on decision calibration and risk budgeting rather than benchmarks.
Calibration and Risk-Budgeted Decisions with System One Models Studies statistical calibration techniques to bound risk when fast language models make automated decisions under uncertainty. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model pruning, distillation, or inference optimization.
When Does Depth Help? Understanding Layerwise Inference in Tabular Foundation Models Analyzes layerwise inference across tabular foundation models to understand how depth and intermediate representations affect predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies layer dynamics in tabular models without efficient serving methods.
When Does Depth Help? Understanding Layerwise Inference in Tabular Foundation Models Analyzes layerwise inference across tabular foundation models to understand how depth and intermediate representations affect predictions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates tabular representations, not deployment safety or alignment.
When Does Depth Help? Understanding Layerwise Inference in Tabular Foundation Models Analyzes layerwise inference across tabular foundation models to understand how depth and intermediate representations affect predictions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes internal representations of tabular models, not LLM benchmarking.
JointServe: Serving Multi-Model Agents on Memory-Constrained GPUs Presents a serving system that dynamically manages GPU memory and prefetches weights for multi-model agent workflows. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on GPU serving systems, not safety or guardrails.
JointServe: Serving Multi-Model Agents on Memory-Constrained GPUs Presents a serving system that dynamically manages GPU memory and prefetches weights for multi-model agent workflows. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on memory serving architecture rather than LLM evaluation benchmarks.
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting Introduces continuous-time inter-variable attention inside differential equation solvers for long-term multivariate time-series forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on time series ODE architectures, not LLM efficiency.
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting Introduces continuous-time inter-variable attention inside differential equation solvers for long-term multivariate time-series forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies time series forecasting, not LLM safety, guardrails, or alignment.
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting Introduces continuous-time inter-variable attention inside differential equation solvers for long-term multivariate time-series forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Addresses continuous-time time series forecasting, not LLM evaluation frameworks.
Rent-Seeking Language Models: Reward Models Set the Prices, Frequent Audits Curb the Rent Models reward overoptimization in reinforcement learning with human feedback as economic rent-seeking driven by proxy reward signals. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, distillation, or serving efficiency.
Rent-Seeking Language Models: Reward Models Set the Prices, Frequent Audits Curb the Rent Models reward overoptimization in reinforcement learning with human feedback as economic rent-seeking driven by proxy reward signals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on RLHF reward gaming dynamics, not benchmark design or evaluation methods.
Energy Consumption Analysis of Discrete Diffusion and Autoregressive Language Models Benchmarks the inference energy efficiency of diffusion language models compared to standard autoregressive architectures. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on inference hardware energy consumption, not deployment safety or robustness.
Energy Consumption Analysis of Discrete Diffusion and Autoregressive Language Models Benchmarks the inference energy efficiency of diffusion language models compared to standard autoregressive architectures. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes energy consumption metrics rather than evaluating model task capabilities or benchmarks.
Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance Applies large language models to filter anomalies in industrial sensor training data using unstructured operational logs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with industrial sensor data cleaning, not AI deployment safety.
Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance Applies large language models to filter anomalies in industrial sensor training data using unstructured operational logs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Uses LLMs for tabular data filtering, not LLM benchmark evaluation.
Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance Applies large language models to filter anomalies in industrial sensor training data using unstructured operational logs. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on industrial soft-sensor modeling, not efficient language models.
On the Optimisation Landscape of Overparameterised Weighted MMD Analyzes the optimization landscape of overparameterized weighted maximum mean discrepancy to provide finite-particle guarantees. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Purely mathematical analysis of maximum mean discrepancy optimisation landscapes.
On the Optimisation Landscape of Overparameterised Weighted MMD Analyzes the optimization landscape of overparameterized weighted maximum mean discrepancy to provide finite-particle guarantees. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses theoretical kernel optimisation, not small or efficient models.
On the Optimisation Landscape of Overparameterised Weighted MMD Analyzes the optimization landscape of overparameterized weighted maximum mean discrepancy to provide finite-particle guarantees. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Mathematical optimisation theory without focus on deployment safety or reliability.
SPARS: A Stability-Plasticity-Aware Region Selection Framework for Continual Learning Mitigates catastrophic forgetting in continual learning through a hybrid approach combining parameter isolation and local regularization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a continual learning algorithm, not an LLM evaluation framework.
SPARS: A Stability-Plasticity-Aware Region Selection Framework for Continual Learning Mitigates catastrophic forgetting in continual learning through a hybrid approach combining parameter isolation and local regularization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on catastrophic forgetting in continual learning, not deployment safety.
SPARS: A Stability-Plasticity-Aware Region Selection Framework for Continual Learning Mitigates catastrophic forgetting in continual learning through a hybrid approach combining parameter isolation and local regularization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses continual learning forgetting rather than model compression or inference efficiency.
Occupancy Modeling via Drifting: One-Step Generation Enables Direct Policy Extraction Proposes a one-step generative approach to model occupancy for simplified policy extraction in reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with reinforcement learning sampling, not AI safety or guardrails.

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