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
ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization This paper introduces ProTok, a unified discrete tokenizer for protein sequence, structure, and functional annotations using modular multi-codebook quantization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Biological tokenization, not AI safety or deployment robustness.
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements This paper presents Lifting, a framework combining repetition encoding and neural decoding for mid-circuit quantum state discrimination on superconducting hardware. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Hardware-level quantum state discrimination, not LLM evaluation.
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements This paper presents Lifting, a framework combining repetition encoding and neural decoding for mid-circuit quantum state discrimination on superconducting hardware. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Superconducting quantum hardware reliability, not AI deployment safety.
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements This paper presents Lifting, a framework combining repetition encoding and neural decoding for mid-circuit quantum state discrimination on superconducting hardware. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Quantum measurement framework, not efficient model architectures.
SoK: Rethinking Membership Inference in Tabular Machine Learning This paper provides a systematization of knowledge on membership inference attacks in tabular machine learning across small-to-medium datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Privacy evaluation of tabular models, not model efficiency.
SoK: Rethinking Membership Inference in Tabular Machine Learning This paper provides a systematization of knowledge on membership inference attacks in tabular machine learning across small-to-medium datasets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates membership inference attacks, not LLM capabilities or benchmarks.
Recalibrating Many Questions with Few Labels: Minimax Rates and Identification Limits This paper provides minimax rates and limits for recalibrating predictions across many binary questions when each question has few ground-truth labels. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical calibration analysis, not efficient serving or compression.
Recalibrating Many Questions with Few Labels: Minimax Rates and Identification Limits This paper provides minimax rates and limits for recalibrating predictions across many binary questions when each question has few ground-truth labels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical recalibration rates, not AI safety or jailbreak defense.
Recalibrating Many Questions with Few Labels: Minimax Rates and Identification Limits This paper provides minimax rates and limits for recalibrating predictions across many binary questions when each question has few ground-truth labels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical probability recalibration bounds, not LLM benchmark testing.
Steering the Fidelity in State Preparation on Noisy Bosonic Systems with Risk-Seeking Optimization This paper introduces a risk-seeking optimization method to prepare target quantum states with high fidelity on noisy bosonic quantum hardware. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with quantum state optimization, not LLM evaluation.
Steering the Fidelity in State Preparation on Noisy Bosonic Systems with Risk-Seeking Optimization This paper introduces a risk-seeking optimization method to prepare target quantum states with high fidelity on noisy bosonic quantum hardware. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantum computing states, not model efficiency.
Steering the Fidelity in State Preparation on Noisy Bosonic Systems with Risk-Seeking Optimization This paper introduces a risk-seeking optimization method to prepare target quantum states with high fidelity on noisy bosonic quantum hardware. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Quantum physics state preparation, unrelated to AI deployment safety.
How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO) This paper proposes LlMBO, applying Bayesian optimization to ultra-low-dimensional fine-tuning as an alternative to gradient-based policy optimization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on parameter-efficient tuning, not safety or guardrails.
How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO) This paper proposes LlMBO, applying Bayesian optimization to ultra-low-dimensional fine-tuning as an alternative to gradient-based policy optimization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a fine-tuning optimization method, not benchmark design.
Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits This paper analyzes heterogeneous asynchronous multi-player bandits, proves the centralized allocation problem is NP-hard, and studies greedy approximation policies. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Multi-player bandit policies, not small or efficient LLMs.
Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits This paper analyzes heterogeneous asynchronous multi-player bandits, proves the centralized allocation problem is NP-hard, and studies greedy approximation policies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Multi-armed bandit optimization theory, not LLM evaluation.
Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits This paper analyzes heterogeneous asynchronous multi-player bandits, proves the centralized allocation problem is NP-hard, and studies greedy approximation policies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical bandit resource allocation, not AI safety guardrails.
Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too This paper introduces CACTUS, an ADMM-based optimizer that speeds up curvature-aware pruning and preserves influence functions in compressed language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on model compression speed and perplexity, not safety.
Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too This paper introduces CACTUS, an ADMM-based optimizer that speeds up curvature-aware pruning and preserves influence functions in compressed language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on pruning techniques, not benchmark evaluation methodology.
Euclidean Distance-Based Flow Matching This paper replaces squared Euclidean distance with Euclidean distance in Flow Matching to improve distributional robustness against data contamination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Flow matching theoretical objective, not LLM benchmark evaluation.
Euclidean Distance-Based Flow Matching This paper replaces squared Euclidean distance with Euclidean distance in Flow Matching to improve distributional robustness against data contamination. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Generative flow matching method, not model efficiency techniques.
Euclidean Distance-Based Flow Matching This paper replaces squared Euclidean distance with Euclidean distance in Flow Matching to improve distributional robustness against data contamination. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical robustness against distribution contamination, not AI deployment safety.
Understanding Circulant Permutation to Extend the CMinHash Estimator The paper derives explicit variance approximations to extend circulant permutation techniques to different MinHash estimators. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on MinHash variance properties, not AI safety.
Understanding Circulant Permutation to Extend the CMinHash Estimator The paper derives explicit variance approximations to extend circulant permutation techniques to different MinHash estimators. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Algorithmic hashing theory, not small or efficient LLMs.
Understanding Circulant Permutation to Extend the CMinHash Estimator The paper derives explicit variance approximations to extend circulant permutation techniques to different MinHash estimators. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Variance analysis for MinHash, not LLM evaluation.
Last-Iterate Convergence of Clipped SGD with Linear Decay The paper provides last-iterate convergence bounds for clipped stochastic gradient descent under heavy-tailed noise and causal adversarial perturbations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Convergence bounds for optimization, not model compression or efficiency.
Last-Iterate Convergence of Clipped SGD with Linear Decay The paper provides last-iterate convergence bounds for clipped stochastic gradient descent under heavy-tailed noise and causal adversarial perturbations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Optimization theory for SGD, not LLM benchmark evaluation.
Last-Iterate Convergence of Clipped SGD with Linear Decay The paper provides last-iterate convergence bounds for clipped stochastic gradient descent under heavy-tailed noise and causal adversarial perturbations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical SGD convergence under noise, not safety.
Linear Last-Iterate Convergence Rates for Optimistic Learning in Harmonic Games The paper establishes linear last-iterate convergence rates for optimistic learning algorithms in multiplayer harmonic games. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with game-theoretic equilibria, not AI deployment safety.
Linear Last-Iterate Convergence Rates for Optimistic Learning in Harmonic Games The paper establishes linear last-iterate convergence rates for optimistic learning algorithms in multiplayer harmonic games. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical game theory analysis, not efficient model deployment.
Linear Last-Iterate Convergence Rates for Optimistic Learning in Harmonic Games The paper establishes linear last-iterate convergence rates for optimistic learning algorithms in multiplayer harmonic games. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Game-theoretic convergence proof, unrelated to LLM evaluation.
Fairness under Prediction Error in Predict-then-Optimize Pipelines The paper analyzes how prediction errors affect demographic parity fairness violations in budget-constrained allocation problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses algorithmic fairness, not model compression or efficiency.
Fairness under Prediction Error in Predict-then-Optimize Pipelines The paper analyzes how prediction errors affect demographic parity fairness violations in budget-constrained allocation problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies optimization fairness pipelines, not language model evaluation.
Deterministic Prediction as Global Velocity: Flow Matching for Probabilistic Time-Series Forecast The paper proposes a flow-matching model that integrates deterministic forecasting as a velocity component for stable probabilistic time-series predictions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series generative modeling, not LLM evaluation.
Deterministic Prediction as Global Velocity: Flow Matching for Probabilistic Time-Series Forecast The paper proposes a flow-matching model that integrates deterministic forecasting as a velocity component for stable probabilistic time-series predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses time-series forecasting architectures, not small or efficient LLMs.
Deterministic Prediction as Global Velocity: Flow Matching for Probabilistic Time-Series Forecast The paper proposes a flow-matching model that integrates deterministic forecasting as a velocity component for stable probabilistic time-series predictions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers forecasting uncertainty quantification, not deployment safety guardrails.
Minimax rates for pointwise CATE estimation: the role of design smoothness The paper derives minimax risk bounds showing how covariate design smoothness affects pointwise conditional average treatment effect estimation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with theoretical statistics, not model efficiency or compression.
Minimax rates for pointwise CATE estimation: the role of design smoothness The paper derives minimax risk bounds showing how covariate design smoothness affects pointwise conditional average treatment effect estimation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical causal estimation theory, not LLM benchmarks.
Minimax rates for pointwise CATE estimation: the role of design smoothness The paper derives minimax risk bounds showing how covariate design smoothness affects pointwise conditional average treatment effect estimation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical causal bounds, not deployment safety or robustness.
MultiAlloc: Budget-Aware Precision Allocation for KV Cache Quantization The paper presents a profiling-guided framework to allocate precision budgets adaptively across layers and tokens during KV cache quantization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on memory compression and quantization, not deployment safety or guardrails.
MultiAlloc: Budget-Aware Precision Allocation for KV Cache Quantization The paper presents a profiling-guided framework to allocate precision budgets adaptively across layers and tokens during KV cache quantization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a KV cache quantization method, not an evaluation benchmark.
BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling The paper introduces a training-free conditional sampling method for pretrained Schrödinger bridge generative models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on stochastic transport sampling, not AI deployment safety or guardrails.
BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling The paper introduces a training-free conditional sampling method for pretrained Schrödinger bridge generative models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on conditional sampling algorithms, not LLM evaluation methods.
BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling The paper introduces a training-free conditional sampling method for pretrained Schrödinger bridge generative models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses continuous transport sampling, not small or efficient language models.
A Refresh Interval Does Not Fully Specify a Refresh Schedule: Learning-Rate-Dependent Placement in Kronecker-Preconditioned Language Model Training The paper demonstrates that the placement timing of curvature updates in Kronecker-preconditioned optimizers significantly affects language model training loss. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies optimizer refresh schedules, not LLM evaluation or benchmarking frameworks.
A Refresh Interval Does Not Fully Specify a Refresh Schedule: Learning-Rate-Dependent Placement in Kronecker-Preconditioned Language Model Training The paper demonstrates that the placement timing of curvature updates in Kronecker-preconditioned optimizers significantly affects language model training loss. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimizer scheduling during pretraining, not model compression or inference efficiency.
A Refresh Interval Does Not Fully Specify a Refresh Schedule: Learning-Rate-Dependent Placement in Kronecker-Preconditioned Language Model Training The paper demonstrates that the placement timing of curvature updates in Kronecker-preconditioned optimizers significantly affects language model training loss. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on pretraining optimizer schedules, not deployment safety or robustness.
On Collusion-Free Teaching in Planar Graphs The paper proves theoretical teaching complexity bounds for closed neighborhoods in planar graphs without collusion. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical teaching complexity, not model compression or efficiency.
On Collusion-Free Teaching in Planar Graphs The paper proves theoretical teaching complexity bounds for closed neighborhoods in planar graphs without collusion. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical machine learning paper on graph teaching complexity, not LLM benchmarks.
On Collusion-Free Teaching in Planar Graphs The paper proves theoretical teaching complexity bounds for closed neighborhoods in planar graphs without collusion. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical learning theory paper, not AI safety or deployment robustness.
Learning Source Structure: Bayesian Prediction with Transformers The paper compares transformer sequence predictions against an optimal Bayesian predictor across structured Markov sources. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical study of synthetic sequence priors, not deployment safety or guardrails.
Learning Source Structure: Bayesian Prediction with Transformers The paper compares transformer sequence predictions against an optimal Bayesian predictor across structured Markov sources. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies theoretical sequence prediction, not model compression or efficient serving.
Learning Source Structure: Bayesian Prediction with Transformers The paper compares transformer sequence predictions against an optimal Bayesian predictor across structured Markov sources. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical Bayesian sequence prediction, not LLM evaluation benchmarks.
Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains The paper proposes a reinforcement learning method to selectively transfer data for carbon-footprint modeling in new manufacturing settings. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on tabular carbon footprint prediction, not AI safety or LLM guardrails.
Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains The paper proposes a reinforcement learning method to selectively transfer data for carbon-footprint modeling in new manufacturing settings. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies transfer learning for manufacturing metrics, not LLM evaluation.
Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains The paper proposes a reinforcement learning method to selectively transfer data for carbon-footprint modeling in new manufacturing settings. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with domain transfer policies, not small or efficient LLM architectures.
SP$^2$ec: Adaptive Self-Speculative Decoding for Vision-Language Models The paper presents an adaptive, training-free speculative decoding method to speed up inference for vision-language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on inference acceleration techniques rather than model evaluation.
SP$^2$ec: Adaptive Self-Speculative Decoding for Vision-Language Models The paper presents an adaptive, training-free speculative decoding method to speed up inference for vision-language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on decoding acceleration, not safety, jailbreaks, or robustness.
Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization The paper develops an off-policy reinforcement learning method with token-wise importance sampling and proximal KL regularization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning optimization algorithms rather than deployment safety or guardrails.
Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization The paper develops an off-policy reinforcement learning method with token-wise importance sampling and proximal KL regularization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL optimization algorithms rather than model compression or efficient serving.

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