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
Diagnosing Generalization Boundaries in AI-Based Vulnerability Detection via Calibrated Embedding Distances The paper evaluates embedding distance metrics to detect generalization boundaries and distribution shifts in AI-based code vulnerability models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on model representation geometry and degradation, not model compression or efficiency.
Learning to See through Action: Predictive Curiosity for Robots The paper proposes an active robot visual learning framework driven by self-supervised curiosity and world model prediction errors. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant General robotic visual exploration, not manufacturing quality control or defect detection.
Learning to See through Action: Predictive Curiosity for Robots The paper proposes an active robot visual learning framework driven by self-supervised curiosity and world model prediction errors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses curiosity-driven robotic learning, not efficient model inference or compression.
Learning to See through Action: Predictive Curiosity for Robots The paper proposes an active robot visual learning framework driven by self-supervised curiosity and world model prediction errors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies active robotic exploration policies, not safety guardrails or hallucination mitigation.
Learning to See through Action: Predictive Curiosity for Robots The paper proposes an active robot visual learning framework driven by self-supervised curiosity and world model prediction errors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic curiosity frameworks, not LLM benchmarks or evaluations.
Decentralized Bandit Learning of Markov Perfect Equilibria The paper proves convergence guarantees for decentralized bandit learning in infinite-horizon, general-sum Markov games. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses continuous Markov game theory, not model efficiency or distillation.
Decentralized Bandit Learning of Markov Perfect Equilibria The paper proves convergence guarantees for decentralized bandit learning in infinite-horizon, general-sum Markov games. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical multi-agent game theory study, not LLM evaluation.
Decentralized Bandit Learning of Markov Perfect Equilibria The paper proves convergence guarantees for decentralized bandit learning in infinite-horizon, general-sum Markov games. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies bandit equilibria convergence, not AI safety guardrails or reliability.
STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization The paper presents a trust-region Bayesian optimization framework designed for noisy, high-dimensional blackbox problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Optimization framework paper, not an LLM evaluation benchmark.
STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization The paper presents a trust-region Bayesian optimization framework designed for noisy, high-dimensional blackbox problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian optimization algorithms, not efficient model serving or compression.
STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization The paper presents a trust-region Bayesian optimization framework designed for noisy, high-dimensional blackbox problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies noisy Bayesian optimization, not deployment safety or hallucination prevention.
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs The paper presents a continuous-time neural ODE solver for simulating cloth dynamics at arbitrary timestamps. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on neural ODE simulation, not LLM benchmarks or evaluation.
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs The paper presents a continuous-time neural ODE solver for simulating cloth dynamics at arbitrary timestamps. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Physics simulation of cloth, not industrial defect detection or visual inspection.
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs The paper presents a continuous-time neural ODE solver for simulating cloth dynamics at arbitrary timestamps. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Concerns physical cloth simulation solvers, not small language models or model compression.
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs The paper presents a continuous-time neural ODE solver for simulating cloth dynamics at arbitrary timestamps. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses deformable cloth modeling, not AI safety or deployment robustness.
On the Depth Transfer of Neural Optimizers via L-Smoothness The paper analyzes depth transfer of hyperparameters in neural networks using an L-smoothness theoretical framework. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimization dynamics rather than AI safety or reliability monitoring.
On the Depth Transfer of Neural Optimizers via L-Smoothness The paper analyzes depth transfer of hyperparameters in neural networks using an L-smoothness theoretical framework. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes hyperparameter depth transfer dynamics, not LLM evaluation benchmarks.
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes The paper presents a conformal prediction method to reliably infer Signal Temporal Logic formulas for multiclass time series classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, quantization, or efficient inference.
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes The paper presents a conformal prediction method to reliably infer Signal Temporal Logic formulas for multiclass time series classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers conformal prediction for time-series logic, not LLM evaluation benchmarks.
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes The paper presents a conformal prediction method to reliably infer Signal Temporal Logic formulas for multiclass time series classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with signal temporal logic inference, not AI deployment safety or jailbreaks.
The Sample Complexity of Multicalibration with an ERM Oracle The paper bounds the sample and query complexity of multicalibration using an empirical risk minimization oracle. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical multicalibration sample complexity, not deployment safety guardrails.
The Sample Complexity of Multicalibration with an ERM Oracle The paper bounds the sample and query complexity of multicalibration using an empirical risk minimization oracle. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses learning theory bounds, not small or efficient model compression.
The Sample Complexity of Multicalibration with an ERM Oracle The paper bounds the sample and query complexity of multicalibration using an empirical risk minimization oracle. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical sample complexity study, not about LLM benchmark design or evaluation.
ConformalGuard: False-Positive-Controlled Action Gating for LM Classifiers The paper proposes a conformal calibration layer to provide finite-sample control over false positives in language model classification workflows. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a conformal calibration layer, not model compression or serving speedup.
ConformalGuard: False-Positive-Controlled Action Gating for LM Classifiers The paper proposes a conformal calibration layer to provide finite-sample control over false positives in language model classification workflows. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on deployment threshold calibration, not benchmarking or evaluation datasets.
History, Adaptation, and Hidden Gain: Two Prospective Tests of Learned Predictive Control in a Synthetic Neural Population The paper tests predictive neural control against classical controllers in simulated spiking neural populations under varying sensitivity conditions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on biological neural control systems, not enterprise AI safety.
History, Adaptation, and Hidden Gain: Two Prospective Tests of Learned Predictive Control in a Synthetic Neural Population The paper tests predictive neural control against classical controllers in simulated spiking neural populations under varying sensitivity conditions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates neural control systems, not LLM evaluation benchmarks.
History, Adaptation, and Hidden Gain: Two Prospective Tests of Learned Predictive Control in a Synthetic Neural Population The paper tests predictive neural control against classical controllers in simulated spiking neural populations under varying sensitivity conditions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on synthetic neural population control, not small efficient language models.
When More Memory Fails: Hidden Feasibility Holes in Dynamic Tensor Rematerialization The paper investigates dynamic tensor rematerialization, identifying cases where increasing memory budgets causes training to unexpectedly run out of memory. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on training memory management bugs, not operational AI safety.
When More Memory Fails: Hidden Feasibility Holes in Dynamic Tensor Rematerialization The paper investigates dynamic tensor rematerialization, identifying cases where increasing memory budgets causes training to unexpectedly run out of memory. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates tensor rematerialization behavior, not LLM evaluation benchmarks.
DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting The paper presents an adaptation loss incorporating return direction and price trajectory calibration for test-time financial time series forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses test-time adaptation loss, not model distillation or serving efficiency.
DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting The paper presents an adaptation loss incorporating return direction and price trajectory calibration for test-time financial time series forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial forecasting error metrics, not AI safety or guardrails.
DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting The paper presents an adaptation loss incorporating return direction and price trajectory calibration for test-time financial time series forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial time-series loss functions, not LLM evaluation.
Decomposing A Neural Learner The paper proposes a probabilistic decomposition of neural models that separates predictable features from an input-dependent reliability score. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a general probabilistic framework, not LLM evaluation benchmarks.
Decomposing A Neural Learner The paper proposes a probabilistic decomposition of neural models that separates predictable features from an input-dependent reliability score. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses neural interpretability and reliability scoring, not model compression.
Clinical value claims in multimodal AI outpace supporting evidence The paper audits multimodal clinical machine learning studies, finding that incomplete unimodal baselines frequently inflate claimed gains. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Critiques clinical validation literature, not efficient model compression or serving.
Sparse-TD-JEPA: Improving Zero-Shot Reinforcement Learning With Sparse Latent Task Representations The paper applies a sparsity penalty to task representations to sharpen reward signals in zero-shot reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses reinforcement learning task representation, not LLM benchmarking.
Sparse-TD-JEPA: Improving Zero-Shot Reinforcement Learning With Sparse Latent Task Representations The paper applies a sparsity penalty to task representations to sharpen reward signals in zero-shot reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates zero-shot RL latent representations, not efficient model compression.
Sparse-TD-JEPA: Improving Zero-Shot Reinforcement Learning With Sparse Latent Task Representations The paper applies a sparsity penalty to task representations to sharpen reward signals in zero-shot reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies reward signals in reinforcement learning, not operational AI safety.
PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation The paper introduces a backpropagation-free training method for generative relaxation models suitable for analog neuromorphic hardware execution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on neuromorphic hardware training, not AI safety or deployment robustness.
PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation The paper introduces a backpropagation-free training method for generative relaxation models suitable for analog neuromorphic hardware execution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes alternative training for vision generation, not LLM evaluation.
From Transfer Probes to Expert Grouping: Evidence from LoRA Post-Training The paper examines using transfer probes to group business tasks when sharing Low-Rank Adaptation experts under fixed parameter budgets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies expert routing heuristics, not LLM evaluation frameworks.
From Transfer Probes to Expert Grouping: Evidence from LoRA Post-Training The paper examines using transfer probes to group business tasks when sharing Low-Rank Adaptation experts under fixed parameter budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines multi-tenant LoRA routing efficiency, not AI safety or jailbreaks.
Learning to Extrapolate in Depth with Looped Transformers The paper studies depth extrapolation in looped transformers for graph connectivity, identifying finite iterations beyond which test performance degrades. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates theoretical transformer depth extrapolation, not LLM benchmarks.
Learning to Extrapolate in Depth with Looped Transformers The paper studies depth extrapolation in looped transformers for graph connectivity, identifying finite iterations beyond which test performance degrades. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of transformer iteration limits, not AI safety or deployment risks.
A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes The paper presents a mass-conserving neural network architecture designed for interpretable rainfall, evapotranspiration, and runoff simulation across diverse river catchments. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on hydrological modeling, not small efficient language models.
A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes The paper presents a mass-conserving neural network architecture designed for interpretable rainfall, evapotranspiration, and runoff simulation across diverse river catchments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies physical hydrology simulations, not AI deployment safety or jailbreaks.
A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes The paper presents a mass-conserving neural network architecture designed for interpretable rainfall, evapotranspiration, and runoff simulation across diverse river catchments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on catchment hydrology modeling, not LLM evaluation.
DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models The paper introduces an adapter method that disentangles subject identity from task content during downstream fine-tuning of brainwave foundation models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses cross-subject EEG transfer, not AI safety guardrails or reliability.
DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models The paper introduces an adapter method that disentangles subject identity from task content during downstream fine-tuning of brainwave foundation models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on EEG representation disentanglement, not small or efficient models.
DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models The paper introduces an adapter method that disentangles subject identity from task content during downstream fine-tuning of brainwave foundation models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates EEG models, not LLM evaluation benchmarks or methodologies.
Identication and Estimation of Nonsymmetric Determinantal Point Processes The paper develops identification theory and asymptotic estimation methods for nonsymmetric determinantal point processes capable of modeling positive dependence. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistics on point processes, not LLM evaluation.
Identication and Estimation of Nonsymmetric Determinantal Point Processes The paper develops identification theory and asymptotic estimation methods for nonsymmetric determinantal point processes capable of modeling positive dependence. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on nonsymmetric point process estimation, not model efficiency.
Identication and Estimation of Nonsymmetric Determinantal Point Processes The paper develops identification theory and asymptotic estimation methods for nonsymmetric determinantal point processes capable of modeling positive dependence. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies statistical point processes to breach data, not LLM safety or deployment guardrails.
Learn the Dynamics, Enforce the Physics: Generative Latent Operators for High-Dimensional Beam Evolution The paper presents a physics-constrained generative latent operator framework to simulate multiscale particle accelerator beam dynamics efficiently. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Surrogate model for particle physics, not small language models or efficient LLM inference.
Learn the Dynamics, Enforce the Physics: Generative Latent Operators for High-Dimensional Beam Evolution The paper presents a physics-constrained generative latent operator framework to simulate multiscale particle accelerator beam dynamics efficiently. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with accelerator physics simulation, not AI deployment safety.
Learn the Dynamics, Enforce the Physics: Generative Latent Operators for High-Dimensional Beam Evolution The paper presents a physics-constrained generative latent operator framework to simulate multiscale particle accelerator beam dynamics efficiently. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on particle beam simulation, not LLM benchmarking or evaluation.
PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging The paper proposes a method to align and merge multiple Low-Rank Adaptations using a shared basis guided by pretrained model weights. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on parameter merging techniques, not AI safety or robustness.
PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging The paper proposes a method to align and merge multiple Low-Rank Adaptations using a shared basis guided by pretrained model weights. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers LoRA weight merging, not LLM benchmarks or evaluation.
Chunk-wise Model-based Policy Optimization The paper introduces a chunk-wise model-based reinforcement learning approach that uses single-pass rollouts to optimize policies without recurrent backpropagation through time. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL rollout efficiency rather than small model compression.

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