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
Correlational Training of Morphological Neural Networks Presents a correlation-based multiplicative weight update method for training morphological neural network layers without standard gradient backpropagation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on backpropagation alternatives for morphological layers, not AI deployment safety or guardrails.
Correlational Training of Morphological Neural Networks Presents a correlation-based multiplicative weight update method for training morphological neural network layers without standard gradient backpropagation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates optimization methods for morphological networks, not model compression, distillation, or quantization.
Correlational Training of Morphological Neural Networks Presents a correlation-based multiplicative weight update method for training morphological neural network layers without standard gradient backpropagation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents an alternative update rule for morphological layers, not an evaluation framework or LLM benchmark.
TraceRelay: Attention-Aligned Recurrence over Rolling Traces Introduces an attention-aligned recurrent architecture that maintains persistent state representations across sequences using rolling low-dimensional traces. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a recurrent neural architecture, not an evaluation framework or benchmark for language models.
TraceRelay: Attention-Aligned Recurrence over Rolling Traces Introduces an attention-aligned recurrent architecture that maintains persistent state representations across sequences using rolling low-dimensional traces. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on architecture design and synthetic sequence length generalization, not deployment safety or guardrails.
The Ball and the Box: Two Geometries of Computation in Superposition Derives mathematical dimension thresholds required to compute Boolean gates using neural representations stored in superposition. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Explores theoretical representation capacity in superposition, not practical model compression or efficient serving.
The Ball and the Box: Two Geometries of Computation in Superposition Derives mathematical dimension thresholds required to compute Boolean gates using neural representations stored in superposition. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Mathematical analysis of representation geometry, not practical AI safety, guardrails, or alignment.
The Ball and the Box: Two Geometries of Computation in Superposition Derives mathematical dimension thresholds required to compute Boolean gates using neural representations stored in superposition. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of Boolean computation in superposition, not LLM evaluation benchmarks or evaluation protocols.
SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing Proposes label-free test-time adaptation using spatial redundancy to improve respiration sensing on massive MIMO base stations against interference. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents wireless signal processing for vital sign sensing, not LLM evaluation or benchmarking methods.
SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing Proposes label-free test-time adaptation using spatial redundancy to improve respiration sensing on massive MIMO base stations against interference. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses MIMO radar signal processing adaptation, not efficient model architectures or LLM compression.
SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing Proposes label-free test-time adaptation using spatial redundancy to improve respiration sensing on massive MIMO base stations against interference. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on RF interference mitigation in wireless sensing, not AI model alignment or deployment safety risks.
Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization Reviews literature on adaptive combinatorial optimization combining machine learning policies with quantum computing workflows. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Surveys quantum optimization algorithms, not neural network efficiency, distillation, or small language models.
Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization Reviews literature on adaptive combinatorial optimization combining machine learning policies with quantum computing workflows. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on quantum combinatorial algorithms, not AI safety, red-teaming, hallucination, or operational guardrails.
Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization Reviews literature on adaptive combinatorial optimization combining machine learning policies with quantum computing workflows. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Reviews quantum computing and combinatorial optimization, not LLM evaluation or benchmark methodologies.
RAGenome: Scaling Retrieval-Based Genomic Language Models to Long Contexts Presents a retrieval-augmented genomic language model scaling pretraining context length to capture long-range biological sequence interactions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on scaling genomic models to long sequences, not model compression or small efficient architectures.
RAGenome: Scaling Retrieval-Based Genomic Language Models to Long Contexts Presents a retrieval-augmented genomic language model scaling pretraining context length to capture long-range biological sequence interactions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a retrieval-based genomic model, not an LLM evaluation dataset, benchmark, or evaluation methodology.
RAGenome: Scaling Retrieval-Based Genomic Language Models to Long Contexts Presents a retrieval-augmented genomic language model scaling pretraining context length to capture long-range biological sequence interactions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Applies retrieval to genomic biology, not AI deployment safety, alignment, or system guardrails.
Optimal random quantisers for spherically symmetric distributions Analyzes optimal random quantisers for high-dimensional spherically symmetric distributions to reduce distortion with moderate sample sizes. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on mathematical quantization theory for probability distributions, not LLM benchmarks or evaluation methods.
Optimal random quantisers for spherically symmetric distributions Analyzes optimal random quantisers for high-dimensional spherically symmetric distributions to reduce distortion with moderate sample sizes. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical statistics paper, not related to AI safety, security, guardrails, or alignment.
Optimal random quantisers for spherically symmetric distributions Analyzes optimal random quantisers for high-dimensional spherically symmetric distributions to reduce distortion with moderate sample sizes. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Pure mathematical statistics on vector quantisation of symmetric distributions, not neural network compression or efficient inference.
In-Ride Alcohol-Impairment Detection in E-Scooterists with False-Alarm Control Develops an onboard inertial and throttle sensor detector with false-alarm controls to identify alcohol-impaired e-scooter riders during trips. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents a sensor-based classifier for e-scooters, not a benchmark or framework for LLM evaluation.
In-Ride Alcohol-Impairment Detection in E-Scooterists with False-Alarm Control Develops an onboard inertial and throttle sensor detector with false-alarm controls to identify alcohol-impaired e-scooter riders during trips. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on edge sensor classification for intoxication, not language model compression or efficiency.
In-Ride Alcohol-Impairment Detection in E-Scooterists with False-Alarm Control Develops an onboard inertial and throttle sensor detector with false-alarm controls to identify alcohol-impaired e-scooter riders during trips. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with physical vehicle safety via sensor anomaly detection, not AI model deployment safety or alignment.
Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning Compresses tabular in-context learning key-value caches into query-calibrated prototypes to improve throughput without sacrificing accuracy. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses latency and cache compression efficiency, not system safety, alignment, or hallucination guardrails.
Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning Compresses tabular in-context learning key-value caches into query-calibrated prototypes to improve throughput without sacrificing accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a KV cache compression algorithm for tabular learning, not an LLM evaluation benchmark.
Softmax Attention on Gaussian Mixtures: Linear When It Can, Selective When It Must Analyzes the infinite-prompt limit of softmax attention on Gaussian mixtures to explain when attention behaves linearly or selectively. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Mathematical analysis of attention limits, not operational AI safety, guardrails, or deployment reliability.
Softmax Attention on Gaussian Mixtures: Linear When It Can, Selective When It Must Analyzes the infinite-prompt limit of softmax attention on Gaussian mixtures to explain when attention behaves linearly or selectively. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of attention mechanisms on Gaussian mixtures, not LLM benchmark evaluation or testing.
Softmax Attention on Gaussian Mixtures: Linear When It Can, Selective When It Must Analyzes the infinite-prompt limit of softmax attention on Gaussian mixtures to explain when attention behaves linearly or selectively. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical study of softmax attention properties, not practical small model design, quantization, or distillation.
PRAXIS: Learning Dynamics of Self-Improving Models with Symbolic Archives Models self-improving systems with symbolic archives as dynamical processes, establishing theoretical optimization bounds and empirical stability. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies optimization dynamics in self-improving systems, not general LLM evaluation frameworks or benchmark integrity.
PRAXIS: Learning Dynamics of Self-Improving Models with Symbolic Archives Models self-improving systems with symbolic archives as dynamical processes, establishing theoretical optimization bounds and empirical stability. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on co-evolutionary learning theory and symbolic archives, not model compression or efficient inference.
PRAXIS: Learning Dynamics of Self-Improving Models with Symbolic Archives Models self-improving systems with symbolic archives as dynamical processes, establishing theoretical optimization bounds and empirical stability. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical formulation of nonstationary objective drift, not AI alignment, jailbreaks, hallucination, or deployment safety.
Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy Evaluates a pipeline combining speaker diarization and self-supervised speech representations to detect dysarthria during awake craniotomy procedures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on clinical speech classification representations, not model compression or efficient model architectures.
Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy Evaluates a pipeline combining speaker diarization and self-supervised speech representations to detect dysarthria during awake craniotomy procedures. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Pertains to intra-operative medical diagnostics rather than deployment guardrails, hallucination, or AI safety risks.
Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy Evaluates a pipeline combining speaker diarization and self-supervised speech representations to detect dysarthria during awake craniotomy procedures. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates a specialized acoustic clinical pipeline, not general LLM evaluation benchmarks or evaluation methodologies.
Recovery Guarantees for Posterior Sampling of One-Bit Compressed Sensing Establishes theoretical sample complexity bounds for posterior sampling in noisy one-bit compressed sensing under prior distribution mismatch. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical study on sample complexity in compressed sensing, not LLM evaluation or benchmark design.
Recovery Guarantees for Posterior Sampling of One-Bit Compressed Sensing Establishes theoretical sample complexity bounds for posterior sampling in noisy one-bit compressed sensing under prior distribution mismatch. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on mathematical signal reconstruction guarantees, not AI deployment safety, robustness, or alignment risks.
Recovery Guarantees for Posterior Sampling of One-Bit Compressed Sensing Establishes theoretical sample complexity bounds for posterior sampling in noisy one-bit compressed sensing under prior distribution mismatch. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses signal processing recovery bounds in compressed sensing, not model quantization, distillation, or efficient inference.
Structural and Representational Agency in LLM-Driven Timbre Generation and Interpolation Examines the disparity between reachability and self-reported parameters in an LLM-driven audio interface for timbre generation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers creative audio interface discrepancies rather than AI safety, red-teaming, guardrails, or deployment reliability.
Structural and Representational Agency in LLM-Driven Timbre Generation and Interpolation Examines the disparity between reachability and self-reported parameters in an LLM-driven audio interface for timbre generation. Preprint	Multimodal models via arxiv · 12 Oct, 16:29	Not relevant Examines audio synthesis interface parameters and generative timbre, not multimodal foundation models or vision-language reasoning.
Structural and Representational Agency in LLM-Driven Timbre Generation and Interpolation Examines the disparity between reachability and self-reported parameters in an LLM-driven audio interface for timbre generation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not explore small models, model distillation, quantization, or efficient deployment techniques.
Structural and Representational Agency in LLM-Driven Timbre Generation and Interpolation Examines the disparity between reachability and self-reported parameters in an LLM-driven audio interface for timbre generation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Analyzes user interfaces and synthesizer parameter accuracy in audio generation, not general LLM benchmarking methods.
DADP: Dynamic Activity-Dependent Pruning, A Reverse Hebbian-Inspired Structural Pruning Method Proposes a dynamic structural pruning method using biologically inspired plasticity to reduce neural network parameters during training. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a neural network pruning technique rather than an LLM evaluation benchmark or evaluation framework.
DADP: Dynamic Activity-Dependent Pruning, A Reverse Hebbian-Inspired Structural Pruning Method Proposes a dynamic structural pruning method using biologically inspired plasticity to reduce neural network parameters during training. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on model sparsity and parameter efficiency, not safety, security, jailbreak mitigation, or alignment.
Conditional Kernel Stein Discrepancy Develops a kernel Stein discrepancy framework using operator-valued kernels to assess conditional goodness-of-fit for unnormalized models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents statistical goodness-of-fit testing theory using kernel Stein discrepancies, not LLM benchmarking or evaluation methodology.
Conditional Kernel Stein Discrepancy Develops a kernel Stein discrepancy framework using operator-valued kernels to assess conditional goodness-of-fit for unnormalized models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical statistical testing paper without any connection to model compression, small models, or efficient inference.
Conditional Kernel Stein Discrepancy Develops a kernel Stein discrepancy framework using operator-valued kernels to assess conditional goodness-of-fit for unnormalized models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on statistical hypothesis testing, not AI deployment safety, hallucination mitigation, or robustness guardrails.
Understanding Latent-Dimension Scaling in Dynamical-System Learning through Spectral Reliability Analyzes how latent-dimension scaling impacts dynamical-system learning and rollout error using Koopman operator spectral reliability metrics. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on theoretical dynamical systems and Koopman operator spectral analysis, not LLM evaluation benchmarks.
Understanding Latent-Dimension Scaling in Dynamical-System Learning through Spectral Reliability Analyzes how latent-dimension scaling impacts dynamical-system learning and rollout error using Koopman operator spectral reliability metrics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses spectral reliability in physics/dynamical systems, not deployment safety, guardrails, or hallucination in AI systems.
Understanding Latent-Dimension Scaling in Dynamical-System Learning through Spectral Reliability Analyzes how latent-dimension scaling impacts dynamical-system learning and rollout error using Koopman operator spectral reliability metrics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Deals with dynamical-system state representation scaling, not language model compression, quantization, or efficient serving.
Learning structured linear dynamical systems from missing observations Establishes non-asymptotic statistical error bounds and optimization convergence for learning structured linear dynamical systems from highly subsampled observations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Statistical dynamical system recovery, not deployment safety or alignment.
Learning structured linear dynamical systems from missing observations Establishes non-asymptotic statistical error bounds and optimization convergence for learning structured linear dynamical systems from highly subsampled observations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical recovery of dynamical systems, not small language models.
Learning structured linear dynamical systems from missing observations Establishes non-asymptotic statistical error bounds and optimization convergence for learning structured linear dynamical systems from highly subsampled observations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on linear dynamical systems with missing observations, not LLM evaluation.
Automated Assembly Instruction Generation from CAD Models Using Grounded Large Language Models: A Human-in-the-Loop Framework Builds a human-in-the-loop framework using grounded language models to automatically generate natural-language assembly instructions from CAD models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Applies standard LLMs to CAD models, not model efficiency or distillation.
Automated Assembly Instruction Generation from CAD Models Using Grounded Large Language Models: A Human-in-the-Loop Framework Builds a human-in-the-loop framework using grounded language models to automatically generate natural-language assembly instructions from CAD models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents an instruction generation framework, not an LLM benchmark.
RobustLDS: Learning linear dynamical systems under adversarial corruptions Develops robust estimators and optimization algorithms to learn linear dynamical systems from trajectories corrupted by adversarial outliers. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses identification of dynamical systems, not LLM evaluation.
RobustLDS: Learning linear dynamical systems under adversarial corruptions Develops robust estimators and optimization algorithms to learn linear dynamical systems from trajectories corrupted by adversarial outliers. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical estimation of dynamical systems, not small model efficiency.
Cost-Aware Mixture-of-Experts Coordination for Model Markets Proposes a market-level mixture-of-experts coordination framework using cost-aware gating and revenue allocation across heterogeneous model services. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Economic coordination mechanism, not deployment safety or jailbreak prevention.
Cost-Aware Mixture-of-Experts Coordination for Model Markets Proposes a market-level mixture-of-experts coordination framework using cost-aware gating and revenue allocation across heterogeneous model services. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Market coordination for mixture-of-experts, not LLM benchmarks.
Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations Presents a probabilistic framework to disaggregate coarse socioeconomic observations into fine spatial scales using satellite embeddings. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with geospatial count estimation, not AI deployment safety.
Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations Presents a probabilistic framework to disaggregate coarse socioeconomic observations into fine spatial scales using satellite embeddings. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Socioeconomic spatial modeling, not efficient language models or pruning.

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