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
FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks The paper proposes a directed graph neural network approach to forecast twelve-hour wildfire spread using spatial and environmental attributes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses environmental hazard modeling rather than AI deployment safety.
FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks The paper proposes a directed graph neural network approach to forecast twelve-hour wildfire spread using spatial and environmental attributes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on geospatial graph models rather than model compression.
FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks The paper proposes a directed graph neural network approach to forecast twelve-hour wildfire spread using spatial and environmental attributes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Wildfire spread prediction with GNNs, not LLM evaluation.
Spectral-MoE: Heterogeneous Per-Layer Expert Utilization in Dense-to-MoE Conversion The paper introduces a method to convert dense models into mixture-of-experts by dynamically allocating different numbers of active experts across layers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on inference acceleration and MoE conversion, not safety.
Spectral-MoE: Heterogeneous Per-Layer Expert Utilization in Dense-to-MoE Conversion The paper introduces a method to convert dense models into mixture-of-experts by dynamically allocating different numbers of active experts across layers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on MoE model architecture optimization, not LLM benchmarking.
From Detection To Anticipation: Continuous Onset Forecasting For Stuttered Speech The paper develops a continuous forecasting method using speech representations to anticipate stuttering events shortly before they occur. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on speech onset forecasting rather than LLM efficiency.
From Detection To Anticipation: Continuous Onset Forecasting For Stuttered Speech The paper develops a continuous forecasting method using speech representations to anticipate stuttering events shortly before they occur. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies speech dysfluency forecasting, not LLM benchmark evaluation.
From Detection To Anticipation: Continuous Onset Forecasting For Stuttered Speech The paper develops a continuous forecasting method using speech representations to anticipate stuttering events shortly before they occur. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Applies speech models to stuttering, unrelated to AI deployment safety.
LLMs program using Reusable and Composable Abstract Representations The paper demonstrates that language models use modular abstract representations to generalize code generation across different programming languages and strategies. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies activation geometry in code LLMs, not model compression.
LLMs program using Reusable and Composable Abstract Representations The paper demonstrates that language models use modular abstract representations to generalize code generation across different programming languages and strategies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on mechanistic representation analysis rather than LLM benchmarking methods.
LLMs program using Reusable and Composable Abstract Representations The paper demonstrates that language models use modular abstract representations to generalize code generation across different programming languages and strategies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines internal representations of code, not safety or guardrails.
FinGREAT: Evaluating LLMs on Financial Graph-guided Retrieval And Typing The paper benchmarks language models on extracting and categorizing double-entry accounting relationships from corporate financial disclosure filings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmarking financial extraction rather than safety or guardrails.
FinGREAT: Evaluating LLMs on Financial Graph-guided Retrieval And Typing The paper benchmarks language models on extracting and categorizing double-entry accounting relationships from corporate financial disclosure filings. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Introduces a financial benchmark rather than model compression techniques.
Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation The paper presents a framework using Wasserstein geometry and Shapley values to audit and decompose intersectional fairness disparities across multiple attributes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical fairness metrics rather than LLM evaluation benchmarks.
Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation The paper presents a framework using Wasserstein geometry and Shapley values to audit and decompose intersectional fairness disparities across multiple attributes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical fairness decomposition, unrelated to efficient model inference.
Beyond Time Induction: Structured Conditional Neural Solvers for Parametric Stochastic-Volatility Pricing The paper introduces a neural solver architecture to accurately compute solutions and sensitivities for parametric stochastic volatility partial differential equations. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on neural PDE solvers rather than LLM efficiency.
Beyond Time Induction: Structured Conditional Neural Solvers for Parametric Stochastic-Volatility Pricing The paper introduces a neural solver architecture to accurately compute solutions and sensitivities for parametric stochastic volatility partial differential equations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies neural solvers to stochastic PDEs, not LLM evaluation.
Beyond Time Induction: Structured Conditional Neural Solvers for Parametric Stochastic-Volatility Pricing The paper introduces a neural solver architecture to accurately compute solutions and sensitivities for parametric stochastic volatility partial differential equations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with financial PDE solving rather than AI deployment safety.
Outcome-Routed Distillation and Reinforcement Learning for Diffusion Language Models The paper presents an outcome-routed distillation and reinforcement learning method tailored for training mixture-of-experts diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on training distillation algorithms rather than safety guardrails.
Outcome-Routed Distillation and Reinforcement Learning for Diffusion Language Models The paper presents an outcome-routed distillation and reinforcement learning method tailored for training mixture-of-experts diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops a distillation training pipeline, not an evaluation framework.
Exact Scaling Laws for a Random-Features Bigram Model The paper derives theoretical scaling laws for prediction regret in a capacity-constrained bigram model with power-law token frequencies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical derivation of scaling laws, not empirical benchmark evaluation.
Exact Scaling Laws for a Random-Features Bigram Model The paper derives theoretical scaling laws for prediction regret in a capacity-constrained bigram model with power-law token frequencies. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Analyzes bigram capacity limits rather than small model efficiency techniques.
Exact Scaling Laws for a Random-Features Bigram Model The paper derives theoretical scaling laws for prediction regret in a capacity-constrained bigram model with power-law token frequencies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical scaling work without AI safety or reliability focus.
Verifier-Grounded Agentic RL for Provably Correct Code The paper introduces an agentic reinforcement learning approach that uses formal program verifier diagnostics as intermediate rewards to produce verified code. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, pruning, or efficient inference.
Verifier-Grounded Agentic RL for Provably Correct Code The paper introduces an agentic reinforcement learning approach that uses formal program verifier diagnostics as intermediate rewards to produce verified code. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an RL training environment rather than LLM benchmarking methods.
Historical-Feature Residual Adaptation for Sequential Screening The paper proposes an acquisition-time residual adaptation method that uses historical features to improve hit rates in sequential screening. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Deals with sequential screening policies, not LLM benchmark evaluation.
Historical-Feature Residual Adaptation for Sequential Screening The paper proposes an acquisition-time residual adaptation method that uses historical features to improve hit rates in sequential screening. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on screening policies rather than AI safety or reliability.
Historical-Feature Residual Adaptation for Sequential Screening The paper proposes an acquisition-time residual adaptation method that uses historical features to improve hit rates in sequential screening. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not investigate model compression or efficient model serving.
Why don't Graph Transformers attend to the same token? The paper examines why molecular graph transformers avoid attention sink patterns seen in language models and highlights attention design impacts. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies attention sink mechanisms rather than model compression or efficiency.
Why don't Graph Transformers attend to the same token? The paper examines why molecular graph transformers avoid attention sink patterns seen in language models and highlights attention design impacts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates attention mechanisms, not AI safety or deployment reliability.
Why don't Graph Transformers attend to the same token? The paper examines why molecular graph transformers avoid attention sink patterns seen in language models and highlights attention design impacts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes internal attention mechanisms in graph transformers, not benchmark evaluation.
BFHD: Bidirectional Feature Harmonization Decomposition for Heterogeneous Clinical Assessments The paper introduces a framework to translate and decompose clinical measurement records across heterogeneous assessment systems within predefined error tolerances. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression or efficient inference.
BFHD: Bidirectional Feature Harmonization Decomposition for Heterogeneous Clinical Assessments The paper introduces a framework to translate and decompose clinical measurement records across heterogeneous assessment systems within predefined error tolerances. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical measurement harmonization, not LLM evaluation.
BFHD: Bidirectional Feature Harmonization Decomposition for Heterogeneous Clinical Assessments The paper introduces a framework to translate and decompose clinical measurement records across heterogeneous assessment systems within predefined error tolerances. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses clinical tabular data harmonization, not LLM deployment safety.
Dynamic Graph World Modelling: Learning Future Dynamics from Temporal Context The paper presents a dynamic graph framework to jointly forecast evolving node and relation representations from historical graph contexts. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on graph modeling rather than small efficient architectures.
Dynamic Graph World Modelling: Learning Future Dynamics from Temporal Context The paper presents a dynamic graph framework to jointly forecast evolving node and relation representations from historical graph contexts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Dynamic Graph World Modelling: Learning Future Dynamics from Temporal Context The paper presents a dynamic graph framework to jointly forecast evolving node and relation representations from historical graph contexts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a dynamic graph architecture, not LLM evaluation benchmarks.
One-Step Prediction Fidelity Is a Poor Proxy for Planning Performance in JEPA World Models The paper shows that one-step prediction accuracy in joint-embedding world models is an unreliable indicator of downstream planning performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on safety, guardrails, or deployment reliability.
One-Step Prediction Fidelity Is a Poor Proxy for Planning Performance in JEPA World Models The paper shows that one-step prediction accuracy in joint-embedding world models is an unreliable indicator of downstream planning performance. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies prediction fidelity vs planning metrics, not model efficiency.
RA$^2$: Retain-Anchored Attraction Preserves Forget-Adjacent Utility in LLM Unlearning The paper proposes a latent-space unlearning method that preserves model performance on benign tasks conceptually adjacent to forgotten domains. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model efficiency, compression, or fast inference.
RA$^2$: Retain-Anchored Attraction Preserves Forget-Adjacent Utility in LLM Unlearning The paper proposes a latent-space unlearning method that preserves model performance on benign tasks conceptually adjacent to forgotten domains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on machine unlearning methodology, not LLM evaluation frameworks or benchmarks.
Fully sequential Gaussian process regression for preference learning The paper presents a sequential Gaussian process method and sampling policy for continuous optimization based on pairwise preference comparisons. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Bayesian optimization theory, not model compression or efficient LLM inference.
Fully sequential Gaussian process regression for preference learning The paper presents a sequential Gaussian process method and sampling policy for continuous optimization based on pairwise preference comparisons. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI deployment safety or model robustness.
Fully sequential Gaussian process regression for preference learning The paper presents a sequential Gaussian process method and sampling policy for continuous optimization based on pairwise preference comparisons. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Sequential Gaussian process regression theory, not LLM evaluation.
Selective Next-Location Prediction with MetaModelSelect: A Lightweight Post-Hoc Confidence Metamodel The paper applies a lightweight post-hoc classifier to evaluate confidence and filter out incorrect next-location predictions from recurrent models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Specific cellular location predictor, not small language models or inference acceleration.
Selective Next-Location Prediction with MetaModelSelect: A Lightweight Post-Hoc Confidence Metamodel The paper applies a lightweight post-hoc classifier to evaluate confidence and filter out incorrect next-location predictions from recurrent models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Post-hoc confidence metamodel for cellular next-location prediction, not LLM evaluation.
ActMOF: Mapping Reticular Synthesis through Active Learning The paper introduces an experimental benchmark to evaluate active learning and Bayesian optimization strategies for synthesizing metal-organic frameworks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI deployment safety or language model guardrails.
ActMOF: Mapping Reticular Synthesis through Active Learning The paper introduces an experimental benchmark to evaluate active learning and Bayesian optimization strategies for synthesizing metal-organic frameworks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Active learning for chemistry experiments, not model compression or SLMs.
ActMOF: Mapping Reticular Synthesis through Active Learning The paper introduces an experimental benchmark to evaluate active learning and Bayesian optimization strategies for synthesizing metal-organic frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Benchmark for chemical reticular synthesis active learning, not LLM evaluation.
W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition The paper introduces a self-supervised wavelet predictive architecture to extract multiscale motion features from wearable activity sensors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Wearable sensor activity recognition, not language model evaluation.
W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition The paper introduces a self-supervised wavelet predictive architecture to extract multiscale motion features from wearable activity sensors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI safety, hallucinations, or red-teaming.
W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition The paper introduces a self-supervised wavelet predictive architecture to extract multiscale motion features from wearable activity sensors. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Sensor signal representation learning, not small language models or inference efficiency.
Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving The paper benchmarks four reinforcement learning algorithms on safety, data efficiency, and stability for autonomous vehicle intersection navigation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on AV driving control algorithms rather than deployable AI safety guardrails.
Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving The paper benchmarks four reinforcement learning algorithms on safety, data efficiency, and stability for autonomous vehicle intersection navigation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Autonomous driving control comparison, not small language models or compression.
Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving The paper benchmarks four reinforcement learning algorithms on safety, data efficiency, and stability for autonomous vehicle intersection navigation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Benchmarking autonomous vehicle RL algorithms, not language model evaluation.
Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents The paper introduces a progress-aware distillation method to prevent small language model agents from stalling in repetitive loops during long tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on distillation training methods rather than new LLM evaluation benchmarks.
Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent The paper establishes theoretical last-iterate convergence rates for random-reshuffling stochastic gradient descent using larger stepsizes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI deployment safety or reliability guardrails.
Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent The paper establishes theoretical last-iterate convergence rates for random-reshuffling stochastic gradient descent using larger stepsizes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical optimization theory for SGD, not LLM evaluation.
Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent The paper establishes theoretical last-iterate convergence rates for random-reshuffling stochastic gradient descent using larger stepsizes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant General convex optimization convergence analysis, not model compression or efficient serving.
State-Matched Sequence Conformal Prediction The paper proposes a sequential conformal prediction algorithm that groups calibration scores by state similarity rather than pure temporal recency. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study small language models or inference optimization.

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