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
GIST: Generator-state-Informed Supervision for Tabular Foundation Models Proposes averaging query losses over generator states to improve pretraining sample efficiency in tabular foundation models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a training method for tabular models rather than evaluating or benchmarking LLMs.
GIST: Generator-state-Informed Supervision for Tabular Foundation Models Proposes averaging query losses over generator states to improve pretraining sample efficiency in tabular foundation models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on tabular foundation model pretraining loss, not small/efficient model deployment.
GIST: Generator-state-Informed Supervision for Tabular Foundation Models Proposes averaging query losses over generator states to improve pretraining sample efficiency in tabular foundation models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Not focused on AI deployment safety, guardrails, or robustness risks.
Controlled-Minimax: Stabilizing Adversarial Optimization via Control Theory Applies continuous-time control theory to stabilize adversarial minimax optimization against rotational divergence. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses GAN optimization stability rather than deployed AI safety, guardrails, or reliability.
Controlled-Minimax: Stabilizing Adversarial Optimization via Control Theory Applies continuous-time control theory to stabilize adversarial minimax optimization against rotational divergence. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on GAN minimax optimization dynamics, not LLM evaluation methods.
Controlled-Minimax: Stabilizing Adversarial Optimization via Control Theory Applies continuous-time control theory to stabilize adversarial minimax optimization against rotational divergence. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Studies continuous-time GAN training stability, not small models or inference efficiency.
Beyond Second-Order Information: Paid Logistic Regression with Noisy Features Analyzes online binary logistic regression where features are observed noisily subject to variable acquisition costs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Covers feature acquisition costs in online learning, not efficient or small model inference.
Beyond Second-Order Information: Paid Logistic Regression with Noisy Features Analyzes online binary logistic regression where features are observed noisily subject to variable acquisition costs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical online logistic regression paper, unrelated to LLM benchmarking or evaluation.
Beyond Second-Order Information: Paid Logistic Regression with Noisy Features Analyzes online binary logistic regression where features are observed noisily subject to variable acquisition costs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on noisy bandit-style online regression, not AI deployment safety or robustness.
Sparse Dictionary Learning for Measure-Valued Data via Linearized Optimal Transport: Efficient Algorithms and Sample Complexity Studies dictionary learning on probability measures via linearized optimal transport embeddings and sparse autoencoders. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Not related to AI safety, red teaming, jailbreaks, or deployment reliability.
Sparse Dictionary Learning for Measure-Valued Data via Linearized Optimal Transport: Efficient Algorithms and Sample Complexity Studies dictionary learning on probability measures via linearized optimal transport embeddings and sparse autoencoders. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on mathematical measure embeddings rather than small or efficient language model serving.
Sparse Dictionary Learning for Measure-Valued Data via Linearized Optimal Transport: Efficient Algorithms and Sample Complexity Studies dictionary learning on probability measures via linearized optimal transport embeddings and sparse autoencoders. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents theoretical dictionary learning via optimal transport, not LLM benchmark evaluation.
From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance Proposes an imbalance-aware deep nonparametric two-sample test that leverages majority observations through repeated testing evidence. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on theoretical statistical two-sample testing, not LLM evaluation or benchmarks.
From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance Proposes an imbalance-aware deep nonparametric two-sample test that leverages majority observations through repeated testing evidence. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not study model compression, small models, or efficient LLM inference.
From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance Proposes an imbalance-aware deep nonparametric two-sample test that leverages majority observations through repeated testing evidence. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses general distribution testing rather than deployment safety, guardrails, or hallucination.
DataCupid: Stable Matching Repair for Training Data Acquisition with Delayed Feedback Develops a matching repair framework with theoretical performance guarantees for training data acquisition under delayed feedback. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical study on stable matching repair for data acquisition, not LLM benchmarks.
DataCupid: Stable Matching Repair for Training Data Acquisition with Delayed Feedback Develops a matching repair framework with theoretical performance guarantees for training data acquisition under delayed feedback. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Algorithmic matching repair under delayed feedback, not model compression or efficiency.
DataCupid: Stable Matching Repair for Training Data Acquisition with Delayed Feedback Develops a matching repair framework with theoretical performance guarantees for training data acquisition under delayed feedback. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on data acquisition matching repair, not AI safety or deployment guardrails.
Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices Presents a distributed system architecture aggregating idle Apple Silicon devices into a virtual GPU cloud for inference. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses distributed cluster resource management, not AI safety or reliability.
Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices Presents a distributed system architecture aggregating idle Apple Silicon devices into a virtual GPU cloud for inference. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant System paper on distributed GPU inference orchestration, not LLM evaluation.
Diffusion Precipitation Generation with Medium-range Weather Forecasting Employs a conditional diffusion model to generate observation-aligned precipitation forecasts from frozen weather foundation models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Precipitation forecasting generation method, unrelated to AI safety or guardrails.
Diffusion Precipitation Generation with Medium-range Weather Forecasting Employs a conditional diffusion model to generate observation-aligned precipitation forecasts from frozen weather foundation models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a diffusion model for precipitation forecasting, not LLM evaluation.
Diffusion Precipitation Generation with Medium-range Weather Forecasting Employs a conditional diffusion model to generate observation-aligned precipitation forecasts from frozen weather foundation models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on weather diffusion models, not small language models or efficient serving.
NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo Introduces a nuclear-aware determinant ansatz for multi-geometry neural-network variational Monte Carlo calculations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on variational Monte Carlo in quantum chemistry, not AI safety.
NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo Introduces a nuclear-aware determinant ansatz for multi-geometry neural-network variational Monte Carlo calculations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Specialized architecture for molecular electronic structure, not general model compression.
NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo Introduces a nuclear-aware determinant ansatz for multi-geometry neural-network variational Monte Carlo calculations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents a quantum chemistry neural wavefunction ansatz, not LLM benchmarking.
Scan-Parallel Variational Inference for event-driven Latent Jump Models Proposes scan-parallel variational inference for continuous-time latent jump models with state-dependent event intensities. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces variational inference for latent jump models, not LLM evaluation.
Scan-Parallel Variational Inference for event-driven Latent Jump Models Proposes scan-parallel variational inference for continuous-time latent jump models with state-dependent event intensities. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Deals with continuous-time state-space models and inference, not language model efficiency.
Scan-Parallel Variational Inference for event-driven Latent Jump Models Proposes scan-parallel variational inference for continuous-time latent jump models with state-dependent event intensities. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on latent jump models for time series, unrelated to AI safety.
Gaussian-Width Bounds for Offline-to-Online Linear Bandits Derives tighter regret bounds and algorithms for offline-to-online linear bandits using Gaussian concentration tools. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical regret bounds for stochastic linear bandits, not LLM benchmarking.
Gaussian-Width Bounds for Offline-to-Online Linear Bandits Derives tighter regret bounds and algorithms for offline-to-online linear bandits using Gaussian concentration tools. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of linear bandits, unrelated to small language models or efficient serving.
Gaussian-Width Bounds for Offline-to-Online Linear Bandits Derives tighter regret bounds and algorithms for offline-to-online linear bandits using Gaussian concentration tools. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on bandit theoretical regret bounds, not AI safety or system reliability.
Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognition Introduces an evidential transformer using Dempster-Shafer fusion to quantify and handle epistemic and aleatoric multimodal emotion uncertainty. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with multimodal emotion classification under sensor conflict, not LLM deployment safety.
Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognition Introduces an evidential transformer using Dempster-Shafer fusion to quantify and handle epistemic and aleatoric multimodal emotion uncertainty. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on evidential transformers for emotion recognition, not model compression.
Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognition Introduces an evidential transformer using Dempster-Shafer fusion to quantify and handle epistemic and aleatoric multimodal emotion uncertainty. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents an emotion recognition model with uncertainty quantification, not LLM benchmarks.
Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL Presents a policy optimization method that corrects state-distribution shift during long-horizon reinforcement learning with language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes an RL training algorithm for LLM reasoning, not an evaluation framework.
Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL Presents a policy optimization method that corrects state-distribution shift during long-horizon reinforcement learning with language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on RL policy optimization training dynamics, not model compression or serving.
Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL Presents a policy optimization method that corrects state-distribution shift during long-horizon reinforcement learning with language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses RL policy optimization for reasoning tasks, not safety guardrails or red-teaming.
Low-Rank Kolmogorov-Arnold Generator Flows for Interpretable Operator Learning Introduces Kolmogorov-Arnold flow to learn physical operator generators using low-rank univariate function symbols. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies physical operator learning, not AI safety or deployment reliability.
Low-Rank Kolmogorov-Arnold Generator Flows for Interpretable Operator Learning Introduces Kolmogorov-Arnold flow to learn physical operator generators using low-rank univariate function symbols. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on physics operator learning using Kolmogorov-Arnold flows, not small language models.
Low-Rank Kolmogorov-Arnold Generator Flows for Interpretable Operator Learning Introduces Kolmogorov-Arnold flow to learn physical operator generators using low-rank univariate function symbols. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Develops neural operators for physical laws, not LLM benchmarking or evaluation.
Target-Constrained Pooling for Trajectory Extrapolation Proposes target-constrained pooling to improve trajectory extrapolation by combining complementary endpoint estimates from related source signals. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Numerical trajectory extrapolation method, not AI safety, alignment, or guardrails.
Target-Constrained Pooling for Trajectory Extrapolation Proposes target-constrained pooling to improve trajectory extrapolation by combining complementary endpoint estimates from related source signals. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a trajectory extrapolation algorithm for numerical forecasting, not LLM evaluation.
Target-Constrained Pooling for Trajectory Extrapolation Proposes target-constrained pooling to improve trajectory extrapolation by combining complementary endpoint estimates from related source signals. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Proposes target-constrained pooling for time-series trajectories, not model compression.
Towards Easier-to-Tune Training Algorithms: New Results from AlgoPerf Evaluates deep learning optimizers under a self-tuning fixed-hardware benchmark across nine distinct workloads. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Analyzes optimizer performance and tuning, unrelated to AI safety or robustness.
Towards Easier-to-Tune Training Algorithms: New Results from AlgoPerf Evaluates deep learning optimizers under a self-tuning fixed-hardware benchmark across nine distinct workloads. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Benchmark report on optimizer training efficiency, not small language models or efficient inference.
Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring Presents a lightweight vibration sensing framework transforming time-series into time-frequency representations for edge deep learning monitoring. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Deploys tiny vision models for consumer vibration sensing, not language model efficiency.
Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring Presents a lightweight vibration sensing framework transforming time-series into time-frequency representations for edge deep learning monitoring. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on appliance vibration sensing, not LLM benchmarking or evaluation frameworks.
Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring Presents a lightweight vibration sensing framework transforming time-series into time-frequency representations for edge deep learning monitoring. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Condition monitoring for consumer rotating devices, not AI safety or LLM deployment risks.
CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits Proposes a framework combining evolutionary candidate search and contextual bandits to optimize and select LLM agent workflows adaptively. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses agent workflow selection and API cost efficiency, not safety or guardrails.
CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits Proposes a framework combining evolutionary candidate search and contextual bandits to optimize and select LLM agent workflows adaptively. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Optimizes agentic workflows via bandits, not small language models, quantization, or serving.
CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits Proposes a framework combining evolutionary candidate search and contextual bandits to optimize and select LLM agent workflows adaptively. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes contextual bandit workflow optimization for LLM agents, not evaluation methodology.
Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory Introduces Double-Take to mitigate capacity and cross-position binding failures in long-context test-time training architectures. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on memory capacity in long-context processing, not AI safety or deployment risks.
Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory Introduces Double-Take to mitigate capacity and cross-position binding failures in long-context test-time training architectures. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes test-time training memory architecture for long contexts, not LLM evaluation or benchmarks.
Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison Compares human and Llama 3.1 8B facilitation turns across design meeting transcripts using multiple computational behavioral metrics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Analyzes behavioral differences between human and LLM meeting facilitation, not model efficiency.
Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison Compares human and Llama 3.1 8B facilitation turns across design meeting transcripts using multiple computational behavioral metrics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on linguistic and behavioral comparison of meeting facilitation, not AI safety.
Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels Proposes a spectral representation framework to attribute agent failures to specific decisive steps using only trajectory-level outcome labels. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on trajectory failure attribution for agents, not model efficiency or distillation.
Scaling RNA Foundation Models through Iterative Refinement Presents a compact RNA foundation model utilizing learned iterative refinement to allow variable inference depth at fixed parameter scale. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Domain-specific biological foundation model for RNA, unrelated to AI safety or reliability.
Scaling RNA Foundation Models through Iterative Refinement Presents a compact RNA foundation model utilizing learned iterative refinement to allow variable inference depth at fixed parameter scale. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes an RNA biology foundation model, not general LLM benchmarks or evaluation methods.
Scaling RNA Foundation Models through Iterative Refinement Presents a compact RNA foundation model utilizing learned iterative refinement to allow variable inference depth at fixed parameter scale. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on biological RNA modeling with iterative refinement, not general efficient LLM deployment.

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