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
Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on autonomous driving behavior cloning, not LLM benchmarking.
Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies driving policy imitation rather than model compression.
Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses autonomous vehicle behavior cloning, not general AI deployment safety.
Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Autonomous driving simulation is outside the industrial inspection focus.
HGRP: Heterogeneous Graph Neural Network for Routability Prediction The paper proposes a heterogeneous graph neural network that combines circuit topology, physical layout, and routing resources to predict chip routability. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on circuit design automation rather than deployment safety.
HGRP: Heterogeneous Graph Neural Network for Routability Prediction The paper proposes a heterogeneous graph neural network that combines circuit topology, physical layout, and routing resources to predict chip routability. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies chip placement prediction, not efficient model architectures.
HGRP: Heterogeneous Graph Neural Network for Routability Prediction The paper proposes a heterogeneous graph neural network that combines circuit topology, physical layout, and routing resources to predict chip routability. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on electronic circuit routability prediction, not evaluation frameworks.
Mixed Poisson diffusion models and application to posterior sampling The paper introduces diffusion models driven by Poisson counting processes to solve discrete Poisson inverse problems via posterior sampling. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies Poisson generative modeling rather than efficient inference.
Mixed Poisson diffusion models and application to posterior sampling The paper introduces diffusion models driven by Poisson counting processes to solve discrete Poisson inverse problems via posterior sampling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Mathematical diffusion framework, not deployment safety or reliability.
Mixed Poisson diffusion models and application to posterior sampling The paper introduces diffusion models driven by Poisson counting processes to solve discrete Poisson inverse problems via posterior sampling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on mathematical diffusion models for inverse problems, not benchmarking.
The minimal width of neural networks and immersions of real projective space The paper uses differential topology to establish theoretical lower bounds on the minimal width of universal approximator neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical paper on differential topology and neural network width.
The minimal width of neural networks and immersions of real projective space The paper uses differential topology to establish theoretical lower bounds on the minimal width of universal approximator neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents pure approximation theory bounds, not practical model compression.
The minimal width of neural networks and immersions of real projective space The paper uses differential topology to establish theoretical lower bounds on the minimal width of universal approximator neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical mathematical study on function approximation, not deployment safety.
MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning The paper presents a replay-free dynamic mixture-of-experts method to mitigate recency bias in class-incremental graph learning without needing task identifiers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops a graph learning framework, not an evaluation benchmark.
MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning The paper presents a replay-free dynamic mixture-of-experts method to mitigate recency bias in class-incremental graph learning without needing task identifiers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses continual graph learning rather than AI deployment guardrails.
MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning The paper presents a replay-free dynamic mixture-of-experts method to mitigate recency bias in class-incremental graph learning without needing task identifiers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on class-incremental graph learning, not model compression.
Adversarial Reinforcement Learning for Robust Planetary Landing The paper uses adversarial reinforcement learning with physically constrained perturbations to improve the robustness of autonomous planetary landing controllers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on aerospace trajectory control, not language model benchmarks.
Adversarial Reinforcement Learning for Robust Planetary Landing The paper uses adversarial reinforcement learning with physically constrained perturbations to improve the robustness of autonomous planetary landing controllers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies landing guidance control rather than efficient machine learning.
Adversarial Reinforcement Learning for Robust Planetary Landing The paper uses adversarial reinforcement learning with physically constrained perturbations to improve the robustness of autonomous planetary landing controllers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies aerospace flight trajectory control, not production AI deployment safety.
FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation The paper introduces a rectified flow matching framework to separate overlapping radio-frequency signals in a single channel in one step. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses RF communication processing rather than deployment safety.
FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation The paper introduces a rectified flow matching framework to separate overlapping radio-frequency signals in a single channel in one step. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies radio-frequency signal separation, not language model evaluation.
FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation The paper introduces a rectified flow matching framework to separate overlapping radio-frequency signals in a single channel in one step. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Applies flow matching to RF signals, not model compression.
TANGO: RNA Topology and Geometry Co-Design The paper presents a multi-agent reinforcement learning framework for designing RNA sequences by co-optimizing both secondary topology and tertiary geometry. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents molecular biology sequence design, not language model evaluation.
TANGO: RNA Topology and Geometry Co-Design The paper presents a multi-agent reinforcement learning framework for designing RNA sequences by co-optimizing both secondary topology and tertiary geometry. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on multi-agent reinforcement learning for RNA design, not tool-using language agents.
TANGO: RNA Topology and Geometry Co-Design The paper presents a multi-agent reinforcement learning framework for designing RNA sequences by co-optimizing both secondary topology and tertiary geometry. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on RNA design rather than model efficiency or compression.
TANGO: RNA Topology and Geometry Co-Design The paper presents a multi-agent reinforcement learning framework for designing RNA sequences by co-optimizing both secondary topology and tertiary geometry. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses biomolecular structural engineering rather than AI deployment safety.
From Plato to Aristotle: Toward a General Theory of Representation Hypotheses The paper introduces a lattice-based theoretical framework to reconcile differing hypotheses about how internal representations converge across neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents theoretical representation alignment analysis, not evaluation benchmarks.
From Plato to Aristotle: Toward a General Theory of Representation Hypotheses The paper introduces a lattice-based theoretical framework to reconcile differing hypotheses about how internal representations converge across neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses abstract representation geometry rather than deployment safety.
From Plato to Aristotle: Toward a General Theory of Representation Hypotheses The paper introduces a lattice-based theoretical framework to reconcile differing hypotheses about how internal representations converge across neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies representation convergence theory, not model efficiency or compression.
Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects The paper investigates how conditional flow models learn and potentially hallucinate non-additive interactions in scientific prediction tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates generative scientific flow models, not language model benchmark systems.
Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects The paper investigates how conditional flow models learn and potentially hallucinate non-additive interactions in scientific prediction tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific generative modeling, not model compression or efficiency.
SpectrumQuant: Spectrum Alignment for LLM Quantization This paper proposes a closed-form covariance-aligned rotation method to improve post-training low-bit quantization in large language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on post-training quantization techniques rather than safety or reliability.
SpectrumQuant: Spectrum Alignment for LLM Quantization This paper proposes a closed-form covariance-aligned rotation method to improve post-training low-bit quantization in large language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a quantization method, not a general evaluation benchmark.
Extreme-Scale Super-Resolution of Homogeneous Turbulence from a Single Realization This paper explores extreme super-resolution of turbulent flow fields by training a deep neural network on a single flow realization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies turbulence modeling rather than AI deployment safety risks.
Extreme-Scale Super-Resolution of Homogeneous Turbulence from a Single Realization This paper explores extreme super-resolution of turbulent flow fields by training a deep neural network on a single flow realization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses physical fluid simulation rather than efficient language models.
Extreme-Scale Super-Resolution of Homogeneous Turbulence from a Single Realization This paper explores extreme super-resolution of turbulent flow fields by training a deep neural network on a single flow realization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on fluid dynamics super-resolution, not language model evaluation.
Efficient Test-Time Adaptation For Robot Policies This paper presents an entropy-regularized memory-guided policy optimization method for online test-time adaptation in robot locomotion. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robot locomotion policy adaptation, not industrial visual inspection.
Efficient Test-Time Adaptation For Robot Policies This paper presents an entropy-regularized memory-guided policy optimization method for online test-time adaptation in robot locomotion. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning policy adaptation rather than language models.
Efficient Test-Time Adaptation For Robot Policies This paper presents an entropy-regularized memory-guided policy optimization method for online test-time adaptation in robot locomotion. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses robot locomotion test-time adaptation, not language model deployment safety.
Efficient Test-Time Adaptation For Robot Policies This paper presents an entropy-regularized memory-guided policy optimization method for online test-time adaptation in robot locomotion. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses robotics adaptation instead of small, efficient language models.
Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming This paper evaluates classical and deep learning direction-of-arrival estimators across accuracy and latency metrics for real-time integrated sensing and communication. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on wireless signal DoA estimation, not language model evaluation.
Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming This paper evaluates classical and deep learning direction-of-arrival estimators across accuracy and latency metrics for real-time integrated sensing and communication. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on signal processing and beamforming, not deployment safety.
Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming This paper evaluates classical and deep learning direction-of-arrival estimators across accuracy and latency metrics for real-time integrated sensing and communication. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies radar/communication signal processing rather than efficient language models.
Are We Overthinking One-Step Generation? This paper introduces a two-stage priming framework combining reconstruction pretraining with distributional post-training for one-step generative modeling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion generation speed and quality, not AI safety or guardrails.
Are We Overthinking One-Step Generation? This paper introduces a two-stage priming framework combining reconstruction pretraining with distributional post-training for one-step generative modeling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on one-step diffusion generation training, not LLM evaluation benchmarks.
Training Dynamics of Transformers on Unbounded Inputs: Attention Learning Can Lag Output Layer Learning This paper proves that training shallow transformers on unbounded inputs causes attention weight learning to lag behind output layer learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Explores attention learning dynamics on formal languages, not deployment safety.
Training Dynamics of Transformers on Unbounded Inputs: Attention Learning Can Lag Output Layer Learning This paper proves that training shallow transformers on unbounded inputs causes attention weight learning to lag behind output layer learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical study on transformer training dynamics, not LLM benchmarking.
Training Dynamics of Transformers on Unbounded Inputs: Attention Learning Can Lag Output Layer Learning This paper proves that training shallow transformers on unbounded inputs causes attention weight learning to lag behind output layer learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of attention training dynamics, not model compression or efficiency.
When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch This paper presents a task-conditioned imputation framework and evaluates model robustness under train-test missingness mechanism mismatches. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Tabular data missingness analysis, not deployment safety or guardrails.
When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch This paper presents a task-conditioned imputation framework and evaluates model robustness under train-test missingness mechanism mismatches. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Imputation framework for tabular data, not small or efficient models.
When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch This paper presents a task-conditioned imputation framework and evaluates model robustness under train-test missingness mechanism mismatches. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Addresses missing data imputation on tabular data, not LLM evaluation.
NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection This paper proposes a discrete spatiotemporal tokenization framework for asynchronous event detection and boundary estimation in continuous EEG recordings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Pertains to brain-computer interface signal processing, not AI safety.
NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection This paper proposes a discrete spatiotemporal tokenization framework for asynchronous event detection and boundary estimation in continuous EEG recordings. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Spatiotemporal model for brain-computer interfaces, not LLM efficiency.
NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection This paper proposes a discrete spatiotemporal tokenization framework for asynchronous event detection and boundary estimation in continuous EEG recordings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on EEG event detection architectures, not LLM evaluation.
Beyond Expert Rankings: Contrast Calibration for Mixed-Precision MoE Quantization This paper proposes a contrast-calibrated bit allocation method for mixed-precision quantization of mixture-of-experts language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a quantization technique, not an LLM evaluation framework.
Beyond Expert Rankings: Contrast Calibration for Mixed-Precision MoE Quantization This paper proposes a contrast-calibrated bit allocation method for mixed-precision quantization of mixture-of-experts language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses model compression efficiency, not safety, guardrails, or alignment risks.
Fractional is Better: Learnable Derivative Orders in Neural Operator Learning This paper shows that providing learnable fractional derivative features improves theoretical bounds and empirical accuracy in neural operator learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies neural operators for PDEs, not language model benchmarks.
Fractional is Better: Learnable Derivative Orders in Neural Operator Learning This paper shows that providing learnable fractional derivative features improves theoretical bounds and empirical accuracy in neural operator learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant PDE operator learning theory, unrelated to deployment safety or reliability.
Fractional is Better: Learnable Derivative Orders in Neural Operator Learning This paper shows that providing learnable fractional derivative features improves theoretical bounds and empirical accuracy in neural operator learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on neural operator design for PDE surrogates, not model compression.
ReAl: Refine neural operators via aligning dynamical flow This paper introduces a flow-matching plug-in module to recover high-frequency physical structures in pretrained neural partial differential equation solvers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Physics simulation stability method, not AI deployment safety or hallucination prevention.

6,241–6,300 of 17,313← Previous Next →