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
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical multicalibration sample complexity, not deployment safety guardrails.
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.
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.
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.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses neural interpretability and reliability scoring, not model compression.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies reward signals in reinforcement learning, not operational AI safety.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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	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.
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	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.
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.
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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers LoRA weight merging, not LLM benchmarks 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.
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	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning for robotics, not industrial computer vision.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses robot locomotion control, not AI safety or deployment guardrails.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces an RL algorithm, not LLM evaluation or benchmarks.
Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference The paper derives strong Gaussian approximations for constant learning rate stochastic gradient descent under heavy-tailed noise to enable trajectory-level inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical paper on SGD approximations, not LLM evaluation methods.
Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference The paper derives strong Gaussian approximations for constant learning rate stochastic gradient descent under heavy-tailed noise to enable trajectory-level inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical optimization paper unrelated to operational AI safety or reliability.
Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference The paper derives strong Gaussian approximations for constant learning rate stochastic gradient descent under heavy-tailed noise to enable trajectory-level inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical SGD trajectory bounds, not model efficiency techniques.
Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving The paper evaluates carbon-aware scheduling for geographically distributed language model serving under strict latency service-level objectives using an empirical simulator. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies serving latency and carbon tradeoffs, not LLM evaluation benchmarks.
Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving The paper evaluates carbon-aware scheduling for geographically distributed language model serving under strict latency service-level objectives using an empirical simulator. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses carbon-aware inference scheduling, not safety guardrails or robustness.
LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration The paper combines language models with Bayesian optimization to guide architectural design exploration for heterogeneous chiplet systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Optimizes chiplet hardware layout rather than model efficiency or serving.
LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration The paper combines language models with Bayesian optimization to guide architectural design exploration for heterogeneous chiplet systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies LLMs to chiplet design, not LLM evaluation or benchmarking.
LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration The paper combines language models with Bayesian optimization to guide architectural design exploration for heterogeneous chiplet systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers Bayesian optimization for chiplets, not AI safety or reliability.
From Rainfall to Road-Network Functionality: Transferable Graph Forecasting for Flood-Disrupted Transportation Networks The paper proposes a transferable spatiotemporal graph learning framework to forecast road network disruption caused by extreme rainfall across unseen regions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses spatiotemporal graph learning, not efficient model compression or inference.
From Rainfall to Road-Network Functionality: Transferable Graph Forecasting for Flood-Disrupted Transportation Networks The paper proposes a transferable spatiotemporal graph learning framework to forecast road network disruption caused by extreme rainfall across unseen regions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies flood transportation resilience, not AI safety, guardrails, or hallucination.
From Rainfall to Road-Network Functionality: Transferable Graph Forecasting for Flood-Disrupted Transportation Networks The paper proposes a transferable spatiotemporal graph learning framework to forecast road network disruption caused by extreme rainfall across unseen regions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on flood road forecasting, not LLM benchmarks or evaluation.
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery The paper introduces a Bayesian optimization method that combines language model relational priors with structural descriptors for mixed-variable scientific discovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Integrates LLM priors into optimization, not AI safety or alignment.
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery The paper introduces a Bayesian optimization method that combines language model relational priors with structural descriptors for mixed-variable scientific discovery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies LLMs to Bayesian optimization, not LLM benchmarking or testing.
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery The paper introduces a Bayesian optimization method that combines language model relational priors with structural descriptors for mixed-variable scientific discovery. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific search spaces, not efficient serving or quantization.
Ascent Conditions for Length-Normalized Single-Rollout Policy Updates The paper analyzes policy gradient updates that divide trajectory scores by length, identifying mathematical conditions under which expected returns can decrease. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes reinforcement learning theoretical bounds, not LLM benchmark evaluation.
Ascent Conditions for Length-Normalized Single-Rollout Policy Updates The paper analyzes policy gradient updates that divide trajectory scores by length, identifying mathematical conditions under which expected returns can decrease. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers policy update mathematical theory, not model efficiency or compression.
Ascent Conditions for Length-Normalized Single-Rollout Policy Updates The paper analyzes policy gradient updates that divide trajectory scores by length, identifying mathematical conditions under which expected returns can decrease. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL ascent conditions, not deployment safety or guardrails.

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