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
Reinforcement Learning with Hawkes-Structured Networks for Market Making The paper proposes a neural architecture and reinforcement learning method to optimize market making under non-Markovian Hawkes order-flow dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Algorithmic market making, not AI safety and guardrails.
Reinforcement Learning with Hawkes-Structured Networks for Market Making The paper proposes a neural architecture and reinforcement learning method to optimize market making under non-Markovian Hawkes order-flow dynamics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant RL architecture for order-flow dynamics, not model compression.
Reinforcement Learning with Hawkes-Structured Networks for Market Making The paper proposes a neural architecture and reinforcement learning method to optimize market making under non-Markovian Hawkes order-flow dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial market making RL, not LLM benchmarks.
Learning, Compression, and Meta-Complexity The paper surveys theoretical relationships linking machine learning algorithms, data compression methods, and computational meta-complexity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Surveys theoretical computational complexity, not LLM benchmark evaluation.
Learning, Compression, and Meta-Complexity The paper surveys theoretical relationships linking machine learning algorithms, data compression methods, and computational meta-complexity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical data compression and complexity theory, not efficient LLM deployment.
Learning, Compression, and Meta-Complexity The paper surveys theoretical relationships linking machine learning algorithms, data compression methods, and computational meta-complexity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical complexity survey, not AI safety and reliability.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theory of graded sparse mechanisms, not model quantization/distillation.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Generalization bounds in learning theory, not deployment AI safety.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Theoretical representation theorem on multisets, not planning or tool-using agents.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Statistical learning theory, not LLM evaluation or benchmarks.
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation The paper uses a self-supervised convolutional neural network as a preconditioner to accelerate iterative solvers for parametric Helmholtz equations. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Numerical PDE solving via neural preconditioning, not LLM evaluation.
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation The paper uses a self-supervised convolutional neural network as a preconditioner to accelerate iterative solvers for parametric Helmholtz equations. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Preconditioning Helmholtz equations, not small language models.
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation The paper uses a self-supervised convolutional neural network as a preconditioner to accelerate iterative solvers for parametric Helmholtz equations. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Numerical mathematics for PDEs, not AI deployment safety.
DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields The paper establishes two provably convergent differentiable algorithms for pairwise conditional random field regularization in image and signal classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Probabilistic graphical models convergence, not efficient LLM serving.
DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields The paper establishes two provably convergent differentiable algorithms for pairwise conditional random field regularization in image and signal classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with conditional random fields, not LLM evaluation.
DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields The paper establishes two provably convergent differentiable algorithms for pairwise conditional random field regularization in image and signal classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Convergence of random fields, not AI deployment safety.
Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements The paper introduces a computationally efficient method combining prediction error methods and moving horizon estimation to identify nonlinear dynamic models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on dynamical systems identification, not LLM evaluation.
Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements The paper introduces a computationally efficient method combining prediction error methods and moving horizon estimation to identify nonlinear dynamic models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses system identification in dynamics, not deployment safety guardrails.
Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements The paper introduces a computationally efficient method combining prediction error methods and moving horizon estimation to identify nonlinear dynamic models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on dynamical system identification, not model compression.
Optimal Transport PAC-Bayes Bounds The paper derives PAC-Bayesian generalization bounds that substitute standard relative entropy with general optimal transport metrics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical PAC-Bayes theory, not applied AI deployment safety.
Optimal Transport PAC-Bayes Bounds The paper derives PAC-Bayesian generalization bounds that substitute standard relative entropy with general optimal transport metrics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical learning bounds, not LLM evaluation benchmarks.
Optimal Transport PAC-Bayes Bounds The paper derives PAC-Bayesian generalization bounds that substitute standard relative entropy with general optimal transport metrics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical learning theory bounds, not small or efficient models.
The Language of Looking: Motion Tokens Reveal Domain-Dependent Local Structure in Gaze Sequences The paper analyzes human gaze sequences across datasets using motion tokens to demonstrate domain-dependent local transition patterns. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies gaze sequences, not language model evaluation or benchmarks.
The Language of Looking: Motion Tokens Reveal Domain-Dependent Local Structure in Gaze Sequences The paper analyzes human gaze sequences across datasets using motion tokens to demonstrate domain-dependent local transition patterns. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Gaze behavior analysis, unrelated to AI safety or guardrails.
The Language of Looking: Motion Tokens Reveal Domain-Dependent Local Structure in Gaze Sequences The paper analyzes human gaze sequences across datasets using motion tokens to demonstrate domain-dependent local transition patterns. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies human gaze sequence motion tokens, not model compression.
$\texttt{NFDiag}$: High-dimensional MCMC Convergence Diagnosis with Normalizing Flows The paper proposes using normalizing flows to evaluate high-dimensional Markov Chain Monte Carlo convergence from multivariate joint posteriors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Normalizing flows for MCMC, not small or efficient models.
$\texttt{NFDiag}$: High-dimensional MCMC Convergence Diagnosis with Normalizing Flows The paper proposes using normalizing flows to evaluate high-dimensional Markov Chain Monte Carlo convergence from multivariate joint posteriors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical Bayesian inference diagnostics, not deployment safety or robustness.
$\texttt{NFDiag}$: High-dimensional MCMC Convergence Diagnosis with Normalizing Flows The paper proposes using normalizing flows to evaluate high-dimensional Markov Chain Monte Carlo convergence from multivariate joint posteriors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on MCMC convergence diagnostics, not LLM benchmarking.
LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR The paper presents a language-aware framework using language models and mixture-of-experts adaptors to improve speech recognition during code-switching. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses code-switching ASR biasing, not small or efficient models.
LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR The paper presents a language-aware framework using language models and mixture-of-experts adaptors to improve speech recognition during code-switching. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with speech recognition accuracy rather than deployment safety or guardrails.
LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR The paper presents a language-aware framework using language models and mixture-of-experts adaptors to improve speech recognition during code-switching. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on speech recognition biasing rather than LLM evaluation benchmarks.
Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models The paper introduces a data-anchored penalty to prevent distributional bias and leakage in physics-informed diffusion models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses residual regularized diffusion models, not model efficiency or compression.
Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models The paper introduces a data-anchored penalty to prevent distributional bias and leakage in physics-informed diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies physics-informed generative models, not LLM benchmarks or evaluation.
Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models The paper introduces a data-anchored penalty to prevent distributional bias and leakage in physics-informed diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics constraints in generative models rather than deployment safety.
OctMole: Multiscale 3D Molecular Generation with Hierarchical Spatial Flows The paper introduces a multiscale octree flow framework for generating three-dimensional molecular structures and atom placements. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D molecular generation rather than evaluating LLMs.
OctMole: Multiscale 3D Molecular Generation with Hierarchical Spatial Flows The paper introduces a multiscale octree flow framework for generating three-dimensional molecular structures and atom placements. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Not about AI safety, hallucination, guardrails, or model alignment.
OctMole: Multiscale 3D Molecular Generation with Hierarchical Spatial Flows The paper introduces a multiscale octree flow framework for generating three-dimensional molecular structures and atom placements. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a 3D molecular flow model, not model compression or SLMs.
HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning The paper proposes a hyperbolic aggregation framework for self-supervised brain wave representation learning across heterogeneous electrode montages. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on EEG representation learning, not LLM evaluation benchmarks.
HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning The paper proposes a hyperbolic aggregation framework for self-supervised brain wave representation learning across heterogeneous electrode montages. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with brain-region EEG models, not efficient model serving.
HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning The paper proposes a hyperbolic aggregation framework for self-supervised brain wave representation learning across heterogeneous electrode montages. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant EEG representation learning method, not AI safety or guardrails.
Prorsa: Unleashing the Potential of Prefill-Decode Disaggregation in Agentic Reinforcement Learning The paper explores prefill-decode disaggregation architectures to speed up rollout generation during agentic reinforcement learning training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on serving infrastructure for RL rollout, not LLM evaluation.
Prorsa: Unleashing the Potential of Prefill-Decode Disaggregation in Agentic Reinforcement Learning The paper explores prefill-decode disaggregation architectures to speed up rollout generation during agentic reinforcement learning training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant System infrastructure for RL rollouts, not deployment safety or robustness.
DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning The paper proposes an implicit integration approach for flow-based reinforcement learning policies to enable stable off-policy optimization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Implicit flow policy optimization in RL, not model compression.
DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning The paper proposes an implicit integration approach for flow-based reinforcement learning policies to enable stable off-policy optimization. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Covers reinforcement learning continuous control, not industrial vision inspection.
DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning The paper proposes an implicit integration approach for flow-based reinforcement learning policies to enable stable off-policy optimization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning algorithms, not LLM evaluation.
DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning The paper proposes an implicit integration approach for flow-based reinforcement learning policies to enable stable off-policy optimization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Robotics policy optimization technique, not AI deployment safety.
DR-APO: Decoupled-Representation Advantage-Guided Policy Optimization from Continuous Control to Embodied Navigation The paper introduces an advantage-guided policy optimization framework that decouples policy representation from reinforcement learning policy updates. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Reinforcement learning optimization method, not model compression or serving.
DR-APO: Decoupled-Representation Advantage-Guided Policy Optimization from Continuous Control to Embodied Navigation The paper introduces an advantage-guided policy optimization framework that decouples policy representation from reinforcement learning policy updates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on generic policy optimization, not AI safety or guardrails.
DR-APO: Decoupled-Representation Advantage-Guided Policy Optimization from Continuous Control to Embodied Navigation The paper introduces an advantage-guided policy optimization framework that decouples policy representation from reinforcement learning policy updates. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Deals with continuous control reinforcement learning, not industrial computer vision.
DR-APO: Decoupled-Representation Advantage-Guided Policy Optimization from Continuous Control to Embodied Navigation The paper introduces an advantage-guided policy optimization framework that decouples policy representation from reinforcement learning policy updates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL policy optimization, not LLM evaluation benchmarks.
ACED: Reference-Anchored Aggregation of Natural-Language and Executable Evidence for Mathematical Reasoning The paper combines natural language reasoning with executable program evidence using reference anchoring to improve mathematical problem solving. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies aggregation for math reasoning, not model compression or efficiency.
ACED: Reference-Anchored Aggregation of Natural-Language and Executable Evidence for Mathematical Reasoning The paper combines natural language reasoning with executable program evidence using reference anchoring to improve mathematical problem solving. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Ensemble aggregation heuristic for math problems, not deployment safety guardrails.
ACED: Reference-Anchored Aggregation of Natural-Language and Executable Evidence for Mathematical Reasoning The paper combines natural language reasoning with executable program evidence using reference anchoring to improve mathematical problem solving. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on test-time aggregation of mathematical reasoning, not LLM evaluation.
Learning Deployable Causal Action Geometry under Temporal Non-Stationarity The paper presents a causal action geometry framework to estimate stable decision-response shapes from non-stationary time series. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Causal time-series estimation method, not AI safety or red-teaming.
Learning Deployable Causal Action Geometry under Temporal Non-Stationarity The paper presents a causal action geometry framework to estimate stable decision-response shapes from non-stationary time series. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on causal decision-making under drift, not LLM evaluation.
Learning Deployable Causal Action Geometry under Temporal Non-Stationarity The paper presents a causal action geometry framework to estimate stable decision-response shapes from non-stationary time series. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Causal inference under non-stationarity, not efficient model architectures.
IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models The paper introduces an industrial time-series forecasting benchmark to evaluate foundation models on multi-rate and multivariate data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies time series benchmarking, not AI safety or guardrails.
IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models The paper introduces an industrial time-series forecasting benchmark to evaluate foundation models on multi-rate and multivariate data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series benchmarking and simulation, not efficient model serving.
Counting with Attention and LayerNorm: Norm Scaling and Representation Geometry The paper studies sequence-length parameter bounds and representation geometry required for attention layers to perform counting tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of layer norm scaling, not deployment safety.
Counting with Attention and LayerNorm: Norm Scaling and Representation Geometry The paper studies sequence-length parameter bounds and representation geometry required for attention layers to perform counting tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of counting representations, not LLM evaluation benchmarks.

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