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
Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates This paper develops a deferred-decision trajectory optimization controller with conformal prediction guarantees for multi-agent target interception. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers multi-agent target pursuit, not model compression.
Mixture of GFlowNets This paper introduces an asynchronous population of generative flow networks coupled through soft clustering to improve mode coverage. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Explores generative flow policy exploration, not AI deployment safety.
Mixture of GFlowNets This paper introduces an asynchronous population of generative flow networks coupled through soft clustering to improve mode coverage. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates GFlowNet mode coverage, not efficient LLM serving.
Mixture of GFlowNets This paper introduces an asynchronous population of generative flow networks coupled through soft clustering to improve mode coverage. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on GFlowNet sampling algorithms, not LLM evaluation.
LLM Evaluation is a Forecasting Problem This paper frames language model evaluation as a forecasting problem, using time-series techniques on historical data to predict post-deployment performance. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines performance forecasting over time, not model compression.
Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer This paper uses neural network variational Monte Carlo simulations to demonstrate the formation of honeycomb electron-hole crystals in bilayers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Condensed matter physics research, not AI safety.
Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer This paper uses neural network variational Monte Carlo simulations to demonstrate the formation of honeycomb electron-hole crystals in bilayers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Condensed matter physics study, not related to LLM evaluation.
Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer This paper uses neural network variational Monte Carlo simulations to demonstrate the formation of honeycomb electron-hole crystals in bilayers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Physics domain application, not efficient or small models.
Measuring Variable Importance Using Proportional Inclusive Marginal Effects This paper introduces a model-free variable importance measure that uses active variable selection to prevent nonactive variables from receiving spurious credit. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a statistical sensitivity metric, not model compression.
Measuring Variable Importance Using Proportional Inclusive Marginal Effects This paper introduces a model-free variable importance measure that uses active variable selection to prevent nonactive variables from receiving spurious credit. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical variable importance, not LLM evaluation.
Measuring Variable Importance Using Proportional Inclusive Marginal Effects This paper introduces a model-free variable importance measure that uses active variable selection to prevent nonactive variables from receiving spurious credit. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses general feature importance metrics, not AI deployment safety.
In-context classification: Calibration and noise in linear attention with softmax readout This paper analyzes in-context classification in linear attention models, establishing theoretical asymptotic bounds separating calibration from noise effects. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical analysis of attention, not LLM benchmark evaluation.
In-context classification: Calibration and noise in linear attention with softmax readout This paper analyzes in-context classification in linear attention models, establishing theoretical asymptotic bounds separating calibration from noise effects. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies linear attention theory, not model efficiency or distillation.
In-context classification: Calibration and noise in linear attention with softmax readout This paper analyzes in-context classification in linear attention models, establishing theoretical asymptotic bounds separating calibration from noise effects. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes synthetic linear attention models, not deployment safety or guardrails.
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings This paper introduces an interface for extracting context-rich language model embeddings to improve table foundation models on mixed tabular data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates a tabular method, not LLM benchmarking methodology.
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings This paper introduces an interface for extracting context-rich language model embeddings to improve table foundation models on mixed tabular data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on tabular learning accuracy, not AI deployment safety.
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings This paper introduces an interface for extracting context-rich language model embeddings to improve table foundation models on mixed tabular data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Develops tabular embedding representations, not model compression or efficiency.
Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts This paper proposes a nearest-neighbor functional curve completion method for intraday probabilistic revisions of renewable energy forecasts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on renewable energy forecasting, not LLM benchmarking.
Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts This paper proposes a nearest-neighbor functional curve completion method for intraday probabilistic revisions of renewable energy forecasts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies statistical curve completion to power grids, not AI safety.
Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts This paper proposes a nearest-neighbor functional curve completion method for intraday probabilistic revisions of renewable energy forecasts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies power curve completion, not model efficiency techniques.
LocalTrain: Integer-Only Local Learning for Energy-Efficient CNN Training on Edge FPGAs This paper presents an integer-only local learning system on edge FPGAs for energy-efficient convolutional neural network training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents FPGA hardware training, not LLM evaluation methods.
LocalTrain: Integer-Only Local Learning for Energy-Efficient CNN Training on Edge FPGAs This paper presents an integer-only local learning system on edge FPGAs for energy-efficient convolutional neural network training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on edge hardware efficiency rather than deployment safety guardrails.
Masked Diffusion for Travel Diaries Synthesis This paper presents a masked diffusion model that synthesizes complete household travel survey diaries from tokenized sequences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on urban travel diary generation, not AI safety.
Masked Diffusion for Travel Diaries Synthesis This paper presents a masked diffusion model that synthesizes complete household travel survey diaries from tokenized sequences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses domain data synthesis rather than small or efficient models.
Masked Diffusion for Travel Diaries Synthesis This paper presents a masked diffusion model that synthesizes complete household travel survey diaries from tokenized sequences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a travel survey synthesis model, not LLM benchmarking.
Masked Attention Pooling Is Invariant to Padding and Permutation: A Formal Proof in Lean This paper provides a formal Lean proof establishing that guarded masked attention pooling is invariant to padding and row permutation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on formal proof in Lean, not LLM evaluation.
Masked Attention Pooling Is Invariant to Padding and Permutation: A Formal Proof in Lean This paper provides a formal Lean proof establishing that guarded masked attention pooling is invariant to padding and row permutation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical proof of attention invariance, not deployment safety or robustness.
Masked Attention Pooling Is Invariant to Padding and Permutation: A Formal Proof in Lean This paper provides a formal Lean proof establishing that guarded masked attention pooling is invariant to padding and row permutation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with formal verification of pooling, not model compression.
OpenDGO: Benchmarking Multi-Agent Organizations Across Diverse Application Domains This paper introduces a benchmark evaluating multi-agent organizational structures across diverse domains and collaborative interaction patterns. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-agent collaboration architectures rather than model compression or serving efficiency.
OpenDGO: Benchmarking Multi-Agent Organizations Across Diverse Application Domains This paper introduces a benchmark evaluating multi-agent organizational structures across diverse domains and collaborative interaction patterns. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes organizational collaboration benchmarks rather than deployment safety or guardrails.
SCOPE Before You Sample: Conformal Scores Guide Posterior Exploration for Fast and Calibrated Bayesian Predictive Inference This paper proposes a method that uses conformal score gradients to guide Markov chain Monte Carlo exploration for fast, calibrated Bayesian inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Develops conformal Bayesian inference sampling, not LLM evaluation benchmarks.
SCOPE Before You Sample: Conformal Scores Guide Posterior Exploration for Fast and Calibrated Bayesian Predictive Inference This paper proposes a method that uses conformal score gradients to guide Markov chain Monte Carlo exploration for fast, calibrated Bayesian inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses Bayesian coverage calibration rather than AI deployment safety risks.
SCOPE Before You Sample: Conformal Scores Guide Posterior Exploration for Fast and Calibrated Bayesian Predictive Inference This paper proposes a method that uses conformal score gradients to guide Markov chain Monte Carlo exploration for fast, calibrated Bayesian inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian MCMC sampling acceleration rather than small language models.
Implicit and explicit reparameterizations of the Folded Normal and Rice distributions This paper develops and evaluates implicit and explicit reparameterized gradient estimators for Folded Normal and Rice distributions in variational inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Derives statistical gradient estimators for crystallography, not LLM evaluation.
Implicit and explicit reparameterizations of the Folded Normal and Rice distributions This paper develops and evaluates implicit and explicit reparameterized gradient estimators for Folded Normal and Rice distributions in variational inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on variational inference estimators rather than AI safety guardrails.
Implicit and explicit reparameterizations of the Folded Normal and Rice distributions This paper develops and evaluates implicit and explicit reparameterized gradient estimators for Folded Normal and Rice distributions in variational inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical reparameterization tricks rather than small or efficient models.
Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells This paper presents an end-to-end spiking neural network that decodes and reconstructs dynamic visual stimuli directly from biological retinal spike trains. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines biological spiking networks for prostheses, not small language models or serving.
Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells This paper presents an end-to-end spiking neural network that decodes and reconstructs dynamic visual stimuli directly from biological retinal spike trains. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with retinal spike decoding rather than AI safety or reliability.
Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells This paper presents an end-to-end spiking neural network that decodes and reconstructs dynamic visual stimuli directly from biological retinal spike trains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies neuromorphic vision decoding from biological retinas, not LLM benchmarks.
A Generalized McDiarmid Inequality: Sample Reuse and Non-Uniform Unbounded Differences This paper develops a generalized McDiarmid concentration inequality capturing sample reuse and unbounded differences for stochastic optimization and generative modeling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents statistical concentration inequalities, not LLM benchmarks or evaluation.
A Generalized McDiarmid Inequality: Sample Reuse and Non-Uniform Unbounded Differences This paper develops a generalized McDiarmid concentration inequality capturing sample reuse and unbounded differences for stochastic optimization and generative modeling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical concentration inequality derivation, not AI safety or reliability in deployment.
A Generalized McDiarmid Inequality: Sample Reuse and Non-Uniform Unbounded Differences This paper develops a generalized McDiarmid concentration inequality capturing sample reuse and unbounded differences for stochastic optimization and generative modeling. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on probability concentration bounds rather than efficient model serving.
Comprehensive Workflow for Uncertainty-Aware Ionic Liquid Solvent Discovery This paper introduces an uncertainty-aware property prediction workflow to guide out-of-distribution candidate selection for ionic liquid solvent discovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses chemical screening uncertainty rather than deployment safety risks.
Comprehensive Workflow for Uncertainty-Aware Ionic Liquid Solvent Discovery This paper introduces an uncertainty-aware property prediction workflow to guide out-of-distribution candidate selection for ionic liquid solvent discovery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a materials discovery workflow for solvents, not LLM benchmarks.
Comprehensive Workflow for Uncertainty-Aware Ionic Liquid Solvent Discovery This paper introduces an uncertainty-aware property prediction workflow to guide out-of-distribution candidate selection for ionic liquid solvent discovery. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on chemical property discovery rather than model compression or efficiency.
FunDM: Cheap & Label-Free Decision Models from Frozen Language Models This paper presents a label-free decision head for frozen language models that preserves multiple-choice accuracy with reduced layers and memory. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on inference speed and accuracy rather than safety guardrails or hallucination mitigation.
FunDM: Cheap & Label-Free Decision Models from Frozen Language Models This paper presents a label-free decision head for frozen language models that preserves multiple-choice accuracy with reduced layers and memory. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a decision head architecture rather than new benchmarks or evaluation methodologies.
ModRes: A modification of the ResPlan dataset ready for floor plan analysis and graph machine learning This paper presents a cleaned and enriched residential floor plan dataset with graph, geometry, and image representations for machine learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a floor plan dataset for graph machine learning, not LLM evaluation.
ModRes: A modification of the ResPlan dataset ready for floor plan analysis and graph machine learning This paper presents a cleaned and enriched residential floor plan dataset with graph, geometry, and image representations for machine learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Provides an architectural floor plan dataset, not small or efficient models.
ModRes: A modification of the ResPlan dataset ready for floor plan analysis and graph machine learning This paper presents a cleaned and enriched residential floor plan dataset with graph, geometry, and image representations for machine learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses architectural floor plan cleaning rather than AI safety or reliability.
CartoVec: General-Purpose Location Embeddings from Open Map Data This paper introduces a coordinate encoder trained on open map data to produce multi-scale location embeddings without requiring imagery. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on geospatial coordinate encoders rather than efficient model architectures.
CartoVec: General-Purpose Location Embeddings from Open Map Data This paper introduces a coordinate encoder trained on open map data to produce multi-scale location embeddings without requiring imagery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses geographic representation learning rather than deployment safety or reliability.
CartoVec: General-Purpose Location Embeddings from Open Map Data This paper introduces a coordinate encoder trained on open map data to produce multi-scale location embeddings without requiring imagery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents geographic coordinate embeddings, not LLM benchmarks or evaluation methods.
Density-Matrix Reinforcement Learning This paper proposes density-matrix reinforcement learning, representing policies as density matrices to capture relationships and coherences between actions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical RL formulation without explicit focus on safety guardrails or robustness.
Density-Matrix Reinforcement Learning This paper proposes density-matrix reinforcement learning, representing policies as density matrices to capture relationships and coherences between actions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning policy structures rather than model compression or serving.
Density-Matrix Reinforcement Learning This paper proposes density-matrix reinforcement learning, representing policies as density matrices to capture relationships and coherences between actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes theoretical reinforcement learning matrix representations, not LLM evaluation benchmarks.
CodeLift: Your Test Suite Is Secretly a Dense SWE Curriculum This paper introduces a reinforcement learning method that calibrates test pass-rate rewards to create adaptive training curricula for software engineering agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on training coding agents rather than deployment safety or guardrails.
CodeLift: Your Test Suite Is Secretly a Dense SWE Curriculum This paper introduces a reinforcement learning method that calibrates test pass-rate rewards to create adaptive training curricula for software engineering agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent training curricula rather than designing general LLM evaluation benchmarks.
CodeLift: Your Test Suite Is Secretly a Dense SWE Curriculum This paper introduces a reinforcement learning method that calibrates test pass-rate rewards to create adaptive training curricula for software engineering agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL training curricula rather than model compression or inference efficiency.
Solvating machine-learning interatomic potentials for solution-phase chemistry This paper introduces an implicit solvation potential providing solvent-dependent corrections to gas-phase machine learning interatomic potentials without retraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses computational chemistry simulations rather than deployment safety risks.

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