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
FragFlex: Fragment-Based Flexible Molecular Generation for Exploring Vast Chemical Space This paper presents a discrete diffusion model that operates at the fragment level to generate variable-sized molecular graphs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Molecular generation diffusion paper, not LLM evaluation.
FENAS: A Few-Shot Meta-Ensembling Framework for Efficient Neural Architecture Search with Synergized Zero-Cost Proxies This paper proposes a few-shot meta-ensembling framework that combines zero-cost proxies to rank neural architectures efficiently. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on neural architecture search, not LLM benchmarking.
FENAS: A Few-Shot Meta-Ensembling Framework for Efficient Neural Architecture Search with Synergized Zero-Cost Proxies This paper proposes a few-shot meta-ensembling framework that combines zero-cost proxies to rank neural architectures efficiently. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant About architecture search proxies, not AI safety or guardrails.
Deep Neural Mixed Models for Nonlinear Longitudinal Prediction with Subject-Specific Heterogeneity This paper presents a deep mixed model combining neural networks with trainable subject-specific effects for longitudinal data prediction. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Longitudinal health statistics, not AI deployment safety.
Deep Neural Mixed Models for Nonlinear Longitudinal Prediction with Subject-Specific Heterogeneity This paper presents a deep mixed model combining neural networks with trainable subject-specific effects for longitudinal data prediction. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Biostatistical neural net, not small efficient model research.
Deep Neural Mixed Models for Nonlinear Longitudinal Prediction with Subject-Specific Heterogeneity This paper presents a deep mixed model combining neural networks with trainable subject-specific effects for longitudinal data prediction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Statistical longitudinal regression method, not LLM evaluation.
Full Waveform Inversion with a Learned Preconditioner This paper uses a neural operator trained on reverse-time-migration images as a preconditioner to accelerate full-waveform inversion. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Geophysical inversion study, not an LLM benchmark.
Full Waveform Inversion with a Learned Preconditioner This paper uses a neural operator trained on reverse-time-migration images as a preconditioner to accelerate full-waveform inversion. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Neural preconditioner for seismic inversion, not model compression.
Full Waveform Inversion with a Learned Preconditioner This paper uses a neural operator trained on reverse-time-migration images as a preconditioner to accelerate full-waveform inversion. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Geophysics paper, not related to AI deployment safety.
CoLA: Multi-VLA Coordination via Learned Adapters This paper introduces learned communication adapters and action heads to coordinate multiple vision-language-action robotic policies during collaborative tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-agent robotic coordination, not model compression.
CoLA: Multi-VLA Coordination via Learned Adapters This paper introduces learned communication adapters and action heads to coordinate multiple vision-language-action robotic policies during collaborative tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents multi-robot coordination architecture, not general LLM benchmarks.
CoLA: Multi-VLA Coordination via Learned Adapters This paper introduces learned communication adapters and action heads to coordinate multiple vision-language-action robotic policies during collaborative tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Robotic manipulation research, not manufacturing defect or quality control inspection.
CoLA: Multi-VLA Coordination via Learned Adapters This paper introduces learned communication adapters and action heads to coordinate multiple vision-language-action robotic policies during collaborative tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Explores agent communication, not deployment safety or guardrails.
JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings This paper introduces an annotated audio dataset of jazz recordings for beat tracking, chord estimation, and music structure analysis. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Audio-only music dataset, not multimodal vision-language model research.
JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings This paper introduces an annotated audio dataset of jazz recordings for beat tracking, chord estimation, and music structure analysis. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Audio dataset paper, not related to small efficient models.
JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings This paper introduces an annotated audio dataset of jazz recordings for beat tracking, chord estimation, and music structure analysis. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Does not address AI safety, jailbreaks, or reliability.
JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings This paper introduces an annotated audio dataset of jazz recordings for beat tracking, chord estimation, and music structure analysis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Music retrieval dataset, not an LLM evaluation benchmark.
ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs This paper introduces a reinforcement learning framework that trains coding agents to anticipate execution outcomes before taking tool actions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on agentic policy optimization, not model compression or efficiency.
ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs This paper introduces a reinforcement learning framework that trains coding agents to anticipate execution outcomes before taking tool actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Develops an agent training framework, not an evaluation benchmark.
ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs This paper introduces a reinforcement learning framework that trains coding agents to anticipate execution outcomes before taking tool actions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Centers on coding agent training rather than deployment safety.
LiveAnon: Improving Privacy and Utility in Streaming Speaker Anonymization with Codec Language Models This paper proposes a voice-conversion system using codec language models to preserve speech emotion while anonymizing speaker identity in real-time streams. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Speech anonymization paper, not an LLM evaluation benchmark.
LiveAnon: Improving Privacy and Utility in Streaming Speaker Anonymization with Codec Language Models This paper proposes a voice-conversion system using codec language models to preserve speech emotion while anonymizing speaker identity in real-time streams. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers speech privacy rather than LLM safety and reliability.
LiveAnon: Improving Privacy and Utility in Streaming Speaker Anonymization with Codec Language Models This paper proposes a voice-conversion system using codec language models to preserve speech emotion while anonymizing speaker identity in real-time streams. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on audio anonymization rather than small efficient LLMs.
Approximation in Contrastive Representation Learning This paper analyzes contrastive representation learning theoretically by framing inner-product scores as spectral approximations of pointwise mutual information. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of contrastive learning, not an LLM benchmark.
Approximation in Contrastive Representation Learning This paper analyzes contrastive representation learning theoretically by framing inner-product scores as spectral approximations of pointwise mutual information. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical representation learning study, not about efficient model inference.
Approximation in Contrastive Representation Learning This paper analyzes contrastive representation learning theoretically by framing inner-product scores as spectral approximations of pointwise mutual information. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical paper without connection to deployment safety or reliability.
DC-Flow: Target-Conditioned Cyclic Peptide Design via Delayed-Coupling Flow Matching This paper introduces a flow matching framework that decouples backbone generation from sequence and side-chain design for target-conditioned cyclic peptide synthesis. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant About peptide generative modeling, not efficient language model serving.
DC-Flow: Target-Conditioned Cyclic Peptide Design via Delayed-Coupling Flow Matching This paper introduces a flow matching framework that decouples backbone generation from sequence and side-chain design for target-conditioned cyclic peptide synthesis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on molecular peptide generation, not LLM evaluation.
DC-Flow: Target-Conditioned Cyclic Peptide Design via Delayed-Coupling Flow Matching This paper introduces a flow matching framework that decouples backbone generation from sequence and side-chain design for target-conditioned cyclic peptide synthesis. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Not about AI deployment safety, hallucination, or system guardrails.
WUSPruner: Second-Moment-Optimal Transforms for Structured Pruning This paper presents a transform-based framework for structured pruning in language models that optimizes basis selection using activation and weight statistics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on model pruning rather than safety, alignment, or guardrails.
WUSPruner: Second-Moment-Optimal Transforms for Structured Pruning This paper presents a transform-based framework for structured pruning in language models that optimizes basis selection using activation and weight statistics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with pruning algorithms, not LLM evaluation benchmarks.
GA-SVD: Generation-aware Singular Value Decomposition for LLM Compression The paper proposes a generation-aware singular value decomposition method to compress large language models while preserving generative output quality. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Does not address safety, hallucination, guardrails, or deployment reliability.
GA-SVD: Generation-aware Singular Value Decomposition for LLM Compression The paper proposes a generation-aware singular value decomposition method to compress large language models while preserving generative output quality. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on compression technique, not benchmarking or evaluation methodology.
Harnessing VLM in Embodied RL: From Reward Regressor to Success Detector via Exploration-Augmented Key-Observation Gating The paper proposes using vision-language models as success detectors rather than reward regressors for embodied reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on embodied RL reward formulation rather than AI safety or reliability.
Harnessing VLM in Embodied RL: From Reward Regressor to Success Detector via Exploration-Augmented Key-Observation Gating The paper proposes using vision-language models as success detectors rather than reward regressors for embodied reinforcement learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning reward design, not industrial visual inspection.
Harnessing VLM in Embodied RL: From Reward Regressor to Success Detector via Exploration-Augmented Key-Observation Gating The paper proposes using vision-language models as success detectors rather than reward regressors for embodied reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on policy reward formulation, not language model evaluation benchmarks.
Harnessing VLM in Embodied RL: From Reward Regressor to Success Detector via Exploration-Augmented Key-Observation Gating The paper proposes using vision-language models as success detectors rather than reward regressors for embodied reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses reinforcement learning reward gating rather than small model efficiency.
Riemannian Graph Tokenizer for Structural Knowledge Transfer The paper proposes a Riemannian graph tokenizer that quantizes structural patterns to support cross-domain graph knowledge transfer. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph tokenization rather than AI safety or reliability.
Riemannian Graph Tokenizer for Structural Knowledge Transfer The paper proposes a Riemannian graph tokenizer that quantizes structural patterns to support cross-domain graph knowledge transfer. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on geometric graph representation rather than small language model efficiency.
Riemannian Graph Tokenizer for Structural Knowledge Transfer The paper proposes a Riemannian graph tokenizer that quantizes structural patterns to support cross-domain graph knowledge transfer. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph tokenization techniques, not language model evaluation.
Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes The paper develops a neural network constrained by a personalized four-parameter physiological model to forecast blood glucose levels in diabetes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on glucose prediction networks rather than small language model efficiency.
Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes The paper develops a neural network constrained by a personalized four-parameter physiological model to forecast blood glucose levels in diabetes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Pertains to clinical glucose forecasting, not language model evaluation.
Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes The paper develops a neural network constrained by a personalized four-parameter physiological model to forecast blood glucose levels in diabetes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers physiological glucose forecasting rather than AI safety or reliability.
Split Conformal Interval Length under Huber Contamination The paper establishes theoretical bounds on interval lengths for split conformal prediction under Huber data contamination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies statistical conformal prediction interval bounds, not language model benchmarks.
Split Conformal Interval Length under Huber Contamination The paper establishes theoretical bounds on interval lengths for split conformal prediction under Huber data contamination. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical interval analysis rather than small model efficiency.
RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study The paper demonstrates that convolutional neural networks can classify transmitter digital signal processing implementations from over-the-air radio signals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on RF signal fingerprinting, not language model evaluation.
RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study The paper demonstrates that convolutional neural networks can classify transmitter digital signal processing implementations from over-the-air radio signals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with radio transmitter identification rather than AI safety risks.
RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study The paper demonstrates that convolutional neural networks can classify transmitter digital signal processing implementations from over-the-air radio signals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on radio transmitter identification, not small language model efficiency.
Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nyström Approximation for Scalable Adaptive Learning The paper presents a continuous joint optimization framework for support vector machines that updates multipliers and kernel hyperparameters together. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses kernel machine optimization rather than AI safety or reliability.
Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nyström Approximation for Scalable Adaptive Learning The paper presents a continuous joint optimization framework for support vector machines that updates multipliers and kernel hyperparameters together. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses classical SVM optimization rather than small language model efficiency.
Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nyström Approximation for Scalable Adaptive Learning The paper presents a continuous joint optimization framework for support vector machines that updates multipliers and kernel hyperparameters together. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on Support Vector Machine optimization, not language model evaluation.
Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models The paper proposes sensitivity fidelity as an evaluation criterion to verify physical consistency in deep-learning Earth-system forecasting models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on Earth-system model physics rather than AI safety or reliability.
Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models The paper proposes sensitivity fidelity as an evaluation criterion to verify physical consistency in deep-learning Earth-system forecasting models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses Earth-system physical models rather than language model evaluation benchmarks.
Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models The paper proposes sensitivity fidelity as an evaluation criterion to verify physical consistency in deep-learning Earth-system forecasting models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on physical consistency in climate models, not efficient language models.
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning The paper presents an accelerated federated learning method that assigns client-specific memory windows based on individual noise and drift rates. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated data drift optimization rather than small model efficiency.
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning The paper presents an accelerated federated learning method that assigns client-specific memory windows based on individual noise and drift rates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with federated learning convergence bounds rather than deployment safety.
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning The paper presents an accelerated federated learning method that assigns client-specific memory windows based on individual noise and drift rates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated optimization algorithms, not language model evaluation.
Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors The paper proposes a robot control framework using predictive architectures and flow matching to generate action chunks without autoregressive decoding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on inference latency and control rather than safety or reliability guardrails.
Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors The paper proposes a robot control framework using predictive architectures and flow matching to generate action chunks without autoregressive decoding. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general robot manipulation policies, not industrial defect inspection.
Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors The paper proposes a robot control framework using predictive architectures and flow matching to generate action chunks without autoregressive decoding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an embodied AI architecture, not an evaluation framework for language models.

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