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
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a travel survey synthesis model, not LLM benchmarking.
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 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.
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.
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.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian MCMC sampling acceleration rather than small language models.
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.
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.
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.
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.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on probability concentration bounds rather than efficient model serving.
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.
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.
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.
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.
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.
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	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.
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.
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	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	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	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	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	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	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.
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.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on chemical potential modeling rather than efficient model inference.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Develops interatomic potentials for chemistry, not LLM evaluation or benchmarks.
MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching This paper presents a discrete flow-matching generator that evaluates molecular identity probabilities on demand to accelerate high-fidelity molecular generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a molecular flow-matching generative model, not LLM benchmarks.
MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching This paper presents a discrete flow-matching generator that evaluates molecular identity probabilities on demand to accelerate high-fidelity molecular generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with molecular generation rather than safety or reliability guardrails.
MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching This paper presents a discrete flow-matching generator that evaluates molecular identity probabilities on demand to accelerate high-fidelity molecular generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on chemistry-informed molecular generation speed, not efficient language models.
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators This paper presents a distillation method using frozen language models to guide molecular flow generators toward natural language discovery goals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies distillation to scientific discovery rather than deployment safety or guardrails.
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators This paper presents a distillation method using frozen language models to guide molecular flow generators toward natural language discovery goals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a distillation method for molecular generators, not evaluation benchmarks.
Value-Preserving Action Quantization The authors propose an action quantization method that preserves value functions by minimizing reachability error for search-based robot policy learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces robot action quantization for MCTS, not LLM benchmarks.
Value-Preserving Action Quantization The authors propose an action quantization method that preserves value functions by minimizing reachability error for search-based robot policy learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Deals with reinforcement learning action quantization for robotics, not industrial computer vision.
Value-Preserving Action Quantization The authors propose an action quantization method that preserves value functions by minimizing reachability error for search-based robot policy learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on robot trajectory planning rather than deployment safety guardrails.
Value-Preserving Action Quantization The authors propose an action quantization method that preserves value functions by minimizing reachability error for search-based robot policy learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on continuous action space discretization in RL, not model parameter quantization.
Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations The authors compare federated learning algorithms for hospital mortality prediction and report that evaluation protocol choices outweigh aggregation algorithm differences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical federated learning protocols rather than LLM safety or reliability.
Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations The authors compare federated learning algorithms for hospital mortality prediction and report that evaluation protocol choices outweigh aggregation algorithm differences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies federated learning aggregation protocols, not efficient model compression or serving.
Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations The authors compare federated learning algorithms for hospital mortality prediction and report that evaluation protocol choices outweigh aggregation algorithm differences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates tabular federated learning protocols for hospital mortality, not LLMs.
Colored Graph Signal Diffusion Models The authors propose a score-based diffusion framework that controls generation speed across different graph frequency bands via colored noise priors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Presents graph diffusion dynamics, not deployment safety or reliability.
Colored Graph Signal Diffusion Models The authors propose a score-based diffusion framework that controls generation speed across different graph frequency bands via colored noise priors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies graph signal diffusion, not small or efficient language models.
Colored Graph Signal Diffusion Models The authors propose a score-based diffusion framework that controls generation speed across different graph frequency bands via colored noise priors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes graph diffusion processes, not LLM benchmarks or evaluation.
The Surprising Expressivity of a Single Neuron with Autoregressive Chain-of-Thought The authors demonstrate that a single autoregressive neuron using chain-of-thought generation can simulate any feedforward circuit of the same activation type. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of single-neuron expressivity, not LLM evaluation or benchmarks.
The Surprising Expressivity of a Single Neuron with Autoregressive Chain-of-Thought The authors demonstrate that a single autoregressive neuron using chain-of-thought generation can simulate any feedforward circuit of the same activation type. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on circuit simulation theory rather than deployment safety or robustness.
The Surprising Expressivity of a Single Neuron with Autoregressive Chain-of-Thought The authors demonstrate that a single autoregressive neuron using chain-of-thought generation can simulate any feedforward circuit of the same activation type. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical expressivity bounds for single neurons, not practical model efficiency.

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