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
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses RF communication processing rather than deployment safety.
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.
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 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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents molecular biology sequence design, not language model evaluation.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on fluid dynamics super-resolution, not language model evaluation.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies turbulence modeling rather than AI deployment safety risks.
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	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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses robotics adaptation instead of small, efficient 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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning policy adaptation rather than 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	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	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	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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of attention training dynamics, not model compression or efficiency.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Explores attention learning dynamics on formal languages, not deployment safety.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on EEG event detection architectures, 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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Spatiotemporal model for brain-computer interfaces, not LLM efficiency.
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.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies neural operators for PDEs, not language model benchmarks.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Neural operator refinement for PDEs, not LLM evaluation benchmarks.
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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant PDE surrogate modeling technique, not small models or efficient inference.
Hypothesis Selection via Weighted Medians This paper provides a fast hypothesis selection algorithm using weighted medians that improves runtime dependence on failure probability. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical distribution learning algorithms, not model compression or efficient serving.
Hypothesis Selection via Weighted Medians This paper provides a fast hypothesis selection algorithm using weighted medians that improves runtime dependence on failure probability. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Pure statistical hypothesis testing theory, not LLM benchmark evaluation.
Hypothesis Selection via Weighted Medians This paper provides a fast hypothesis selection algorithm using weighted medians that improves runtime dependence on failure probability. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical statistics on sample complexity, not AI safety or deployment reliability.
From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort This paper presents a mathematical review analyzing when human corrections translate into reusable learning versus interactive refinements in image segmentation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on interactive image segmentation learning, not AI safety guardrails.
From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort This paper presents a mathematical review analyzing when human corrections translate into reusable learning versus interactive refinements in image segmentation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Reviews human feedback in image segmentation, not model efficiency or distillation.
From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort This paper presents a mathematical review analyzing when human corrections translate into reusable learning versus interactive refinements in image segmentation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Reviews interactive segmentation feedback dynamics, not LLM benchmarking or evaluation.
HRTF Upsampling and Personalization via Neural Operators This paper develops a neural operator framework to upsample and personalize continuous head-related transfer function fields from sparse spatial measurements. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on binaural audio upsampling, not LLM benchmarking.
HRTF Upsampling and Personalization via Neural Operators This paper develops a neural operator framework to upsample and personalize continuous head-related transfer function fields from sparse spatial measurements. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Domain-specific spatial audio modeling, not AI safety or reliability.
HRTF Upsampling and Personalization via Neural Operators This paper develops a neural operator framework to upsample and personalize continuous head-related transfer function fields from sparse spatial measurements. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Neural operator for head-related transfer functions, not LLM compression or inference efficiency.
Training Language Models to Fail Gracefully This paper introduces a reinforcement learning approach that trains language models to execute safe fallbacks instead of unreliable guesses. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a training method for fallback behavior, not an evaluation benchmark.
Training Language Models to Fail Gracefully This paper introduces a reinforcement learning approach that trains language models to execute safe fallbacks instead of unreliable guesses. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents an RL training method for fallbacks, not model compression or serving.
Posterior Frequencies and the Cost of Not Fitting: A Correction to a Memorization Lower Bound This paper provides counterexamples to an established memorization lower bound and derives exact replacements using full-sample posterior probabilities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on mathematical bounds of sample memorization, not AI safety monitoring.
Posterior Frequencies and the Cost of Not Fitting: A Correction to a Memorization Lower Bound This paper provides counterexamples to an established memorization lower bound and derives exact replacements using full-sample posterior probabilities. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical bound correction for statistical learning, not efficient model architectures.
Posterior Frequencies and the Cost of Not Fitting: A Correction to a Memorization Lower Bound This paper provides counterexamples to an established memorization lower bound and derives exact replacements using full-sample posterior probabilities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Statistical learning theory on memorization bounds, not LLM evaluation benchmarks.
Hyperspherical Contrastive Representations Unlock Planning and Exploration This paper presents a self-supervised reinforcement learning method that uses hyperspherical representations and spherical interpolation to infer planning waypoints. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Proposes contrastive representations for robot planning, not small model optimization.
Hyperspherical Contrastive Representations Unlock Planning and Exploration This paper presents a self-supervised reinforcement learning method that uses hyperspherical representations and spherical interpolation to infer planning waypoints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies robotic exploration planning, not language model safety or guardrails.
Hyperspherical Contrastive Representations Unlock Planning and Exploration This paper presents a self-supervised reinforcement learning method that uses hyperspherical representations and spherical interpolation to infer planning waypoints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic manipulation representations, not LLM benchmarks.

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