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
Non-Euclidean Gradient Descent Has Many Edges of Stability This paper studies stability thresholds and convergence boundaries of non-Euclidean gradient descent on quadratic objectives. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical optimization paper, not an LLM benchmark.
Non-Euclidean Gradient Descent Has Many Edges of Stability This paper studies stability thresholds and convergence boundaries of non-Euclidean gradient descent on quadratic objectives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of optimization dynamics, not AI safety.
Zeroth-Order Fine-Tuning of LLMs in Learnable Low-Rank Subspaces This paper proposes a zeroth-order fine-tuning method for language models that optimizes within task-adaptive learnable low-rank subspaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Zeroth-order optimization method, not an LLM benchmark.
Zeroth-Order Fine-Tuning of LLMs in Learnable Low-Rank Subspaces This paper proposes a zeroth-order fine-tuning method for language models that optimizes within task-adaptive learnable low-rank subspaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on training optimization, not deployment safety or robustness.
Explicit Bounds on Hoffman Constants for Sparse Polyhedral Regularizers This paper derives theoretical bounds on Hoffman constants to analyze the conditioning and convergence of sparse regularized optimization problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Pure mathematical optimization theory, not an LLM benchmark.
Explicit Bounds on Hoffman Constants for Sparse Polyhedral Regularizers This paper derives theoretical bounds on Hoffman constants to analyze the conditioning and convergence of sparse regularized optimization problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of optimization, not AI deployment safety.
Explicit Bounds on Hoffman Constants for Sparse Polyhedral Regularizers This paper derives theoretical bounds on Hoffman constants to analyze the conditioning and convergence of sparse regularized optimization problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical regularizer bounds, not efficient model deployment.
ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation This paper introduces an observation speculation framework using small world models to accelerate reinforcement learning rollouts for tool-using agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Inference acceleration method, not an LLM benchmark.
ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation This paper introduces an observation speculation framework using small world models to accelerate reinforcement learning rollouts for tool-using agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on training latency reduction, not AI safety.
Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection This paper introduces a dual-latent variational autoencoder that separates temporal dynamics from cross-sensor correlations for time-series anomaly detection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Time series anomaly detection, not LLM evaluation benchmarks.
Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection This paper introduces a dual-latent variational autoencoder that separates temporal dynamics from cross-sensor correlations for time-series anomaly detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Time series reconstruction VAE, not efficient language models.
Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection This paper introduces a dual-latent variational autoencoder that separates temporal dynamics from cross-sensor correlations for time-series anomaly detection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Multivariate sensor anomaly detection, not AI deployment safety.
Imperfect Verifiers, Exact Targets: Inference-Time Scaling for Continuous Generation This paper introduces an inference-time scaling method using paired denoising and renoising to sample from target distributions despite imperfect verifiers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Continuous generative sampling theory, not efficient model compression.
Imperfect Verifiers, Exact Targets: Inference-Time Scaling for Continuous Generation This paper introduces an inference-time scaling method using paired denoising and renoising to sample from target distributions despite imperfect verifiers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Sampling theory for continuous models, not deployment safety.
Imperfect Verifiers, Exact Targets: Inference-Time Scaling for Continuous Generation This paper introduces an inference-time scaling method using paired denoising and renoising to sample from target distributions despite imperfect verifiers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Inference-time sampling algorithm, not an LLM benchmark.
What a Data Release Must Preserve for Off-Policy Evaluation This paper derives theoretical conditions on what decision-time history data releases must retain to ensure valid off-policy evaluation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical release theory, not small efficient language models.
What a Data Release Must Preserve for Off-Policy Evaluation This paper derives theoretical conditions on what decision-time history data releases must retain to ensure valid off-policy evaluation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical off-policy evaluation statistics, not LLM benchmark study.
What a Data Release Must Preserve for Off-Policy Evaluation This paper derives theoretical conditions on what decision-time history data releases must retain to ensure valid off-policy evaluation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical policy theory, not AI safety or deployment guardrails.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Chemistry generative model, not AI deployment safety.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on drug discovery diffusion, not efficient LLM serving.
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	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.
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.
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	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	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	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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Music retrieval dataset, not an LLM evaluation benchmark.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Centers on coding agent training rather than deployment safety.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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	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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph tokenization techniques, not language model evaluation.
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.

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