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
KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting Develops a temporal encoder injecting engineering domain priors to forecast rare, high-cost events in multivariate time series. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on multivariate time-series forecasting models rather than LLM evaluation.
KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting Develops a temporal encoder injecting engineering domain priors to forecast rare, high-cost events in multivariate time series. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses architecture conditioning for rare events, not model compression or efficient inference.
KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting Develops a temporal encoder injecting engineering domain priors to forecast rare, high-cost events in multivariate time series. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Covers industrial time-series forecasting rather than AI safety, guardrails, or alignment.
Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data Constructs a multimodal simulator dataset and trajectory prediction model for autonomous driving in urban roadway work zones. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with vehicle trajectory forecasting rather than AI safety, guardrails, or LLM reliability.
Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data Constructs a multimodal simulator dataset and trajectory prediction model for autonomous driving in urban roadway work zones. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses autonomous driving trajectory prediction, not LLM agents or tool use.
Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data Constructs a multimodal simulator dataset and trajectory prediction model for autonomous driving in urban roadway work zones. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on trajectory prediction accuracy rather than model compression or efficient serving.
Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data Constructs a multimodal simulator dataset and trajectory prediction model for autonomous driving in urban roadway work zones. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a driving trajectory dataset, not an LLM evaluation benchmark.
Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs Introduces an automated pipeline using language models to construct hierarchical knowledge graphs from complex hardware datasheets. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on an automated extraction pipeline for knowledge graphs, not autonomous multi-step agents.
Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs Introduces an automated pipeline using language models to construct hierarchical knowledge graphs from complex hardware datasheets. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a knowledge graph construction pipeline rather than an LLM benchmark.
Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs Introduces an automated pipeline using language models to construct hierarchical knowledge graphs from complex hardware datasheets. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with datasheet information extraction rather than deployment safety or alignment.
Semantic Views: Certified Reuse of LLM Labels Across Semantic Queries Presents a certified semantic filtering technique that reuses prior model labels to reduce language model calls in database queries. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on database semantic query optimization and label caching, not LLM agents.
Semantic Views: Certified Reuse of LLM Labels Across Semantic Queries Presents a certified semantic filtering technique that reuses prior model labels to reduce language model calls in database queries. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Addresses certified query processing in databases rather than LLM benchmarking or evaluation.
Semantic Views: Certified Reuse of LLM Labels Across Semantic Queries Presents a certified semantic filtering technique that reuses prior model labels to reduce language model calls in database queries. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on query optimization and sample bounds rather than AI safety or guardrails.
REDaCT: Collapse-Free Unlearning in Large Reasoning Models Proposes an inference-time unlearning method that detects leaks and injects corrective embeddings into large reasoning model traces. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on selective forgetting during inference, not model compression or efficient serving.
REDaCT: Collapse-Free Unlearning in Large Reasoning Models Proposes an inference-time unlearning method that detects leaks and injects corrective embeddings into large reasoning model traces. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with large reasoning models and chain-of-thought text, not multimodal models.
REDaCT: Collapse-Free Unlearning in Large Reasoning Models Proposes an inference-time unlearning method that detects leaks and injects corrective embeddings into large reasoning model traces. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on unlearning in reasoning models rather than multi-step tool-using agents.
REDaCT: Collapse-Free Unlearning in Large Reasoning Models Proposes an inference-time unlearning method that detects leaks and injects corrective embeddings into large reasoning model traces. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Addresses machine unlearning, not retrieval-augmented generation or document retrieval.
REDaCT: Collapse-Free Unlearning in Large Reasoning Models Proposes an inference-time unlearning method that detects leaks and injects corrective embeddings into large reasoning model traces. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on chain-of-thought unlearning rather than code generation or software engineering.
REDaCT: Collapse-Free Unlearning in Large Reasoning Models Proposes an inference-time unlearning method that detects leaks and injects corrective embeddings into large reasoning model traces. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an unlearning method rather than a general LLM evaluation benchmark.
Representation Alignment from a Few Paired Data via Distilled Optimal Transport Aligns independently pretrained unimodal encoders using a few paired anchors and distilled entropic optimal transport. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on multimodal embedding alignment rather than deployment safety, guardrails, or robustness.
Representation Alignment from a Few Paired Data via Distilled Optimal Transport Aligns independently pretrained unimodal encoders using a few paired anchors and distilled entropic optimal transport. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses cross-modal representation alignment, not LLM agents, planning, or tool use.
Representation Alignment from a Few Paired Data via Distilled Optimal Transport Aligns independently pretrained unimodal encoders using a few paired anchors and distilled entropic optimal transport. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an alignment technique using optimal transport, not an LLM benchmark or evaluation study.
EvoBelief: Falsifiable World Models for Self-Improving LLM Agents Presents a framework allowing language model agents to evolve environment dynamics beliefs through falsifiable hypothesis testing. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops an agent world-modeling framework rather than an LLM evaluation benchmark.
EvoBelief: Falsifiable World Models for Self-Improving LLM Agents Presents a framework allowing language model agents to evolve environment dynamics beliefs through falsifiable hypothesis testing. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on belief updating and task planning rather than safety, guardrails, or hallucination.
Meta-Reflective Evolution Effectively Improves Production AI Systems Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on evolutionary code optimization rather than model compression or efficient inference.
Meta-Reflective Evolution Effectively Improves Production AI Systems Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Optimizes production workflows via evolutionary code mutation, not retrieval-augmented generation.
Meta-Reflective Evolution Effectively Improves Production AI Systems Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an optimization framework evaluated on benchmarks, not a novel benchmark or evaluation method.
Meta-Reflective Evolution Effectively Improves Production AI Systems Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with code and data engineering workflows, not multimodal vision-language architectures.
Meta-Reflective Evolution Effectively Improves Production AI Systems Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on performance optimization of production systems rather than safety, guardrails, or alignment.
Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search Uses reinforcement learning-based neural architecture search to discover compact models for audio-visual speech enhancement. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech enhancement architecture search rather than LLM benchmarking.
Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search Uses reinforcement learning-based neural architecture search to discover compact models for audio-visual speech enhancement. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Applies neural architecture search to speech enhancement, not LLM agent systems.
Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search Uses reinforcement learning-based neural architecture search to discover compact models for audio-visual speech enhancement. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses edge speech enhancement efficiency rather than deployment safety or alignment.
VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails Develops a framework for evolving deterministic formal policies to serve as runtime guardrails for large language model agents. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Develops runtime guardrails over system events rather than models for code generation.
VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails Develops a framework for evolving deterministic formal policies to serve as runtime guardrails for large language model agents. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with symbolic policy guardrails for agent actions, not multimodal reasoning models.
VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails Develops a framework for evolving deterministic formal policies to serve as runtime guardrails for large language model agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent action guardrails, not retrieval-augmented generation or document indexing.
VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails Develops a framework for evolving deterministic formal policies to serve as runtime guardrails for large language model agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses safety guardrails, not model compression, distillation, or efficient serving.
VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails Develops a framework for evolving deterministic formal policies to serve as runtime guardrails for large language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on runtime safety enforcement rather than LLM benchmarking or evaluation frameworks.
LeWAM: End-to-End World and Action Modeling with JEPAs Presents an end-to-end joint-embedding predictive architecture that combines world modeling and action flow matching for robot planning and control. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on robot control dynamics and planning, not industrial visual inspection or defect detection.
LeWAM: End-to-End World and Action Modeling with JEPAs Presents an end-to-end joint-embedding predictive architecture that combines world modeling and action flow matching for robot planning and control. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops a predictive architecture for control rather than a standard LLM evaluation benchmark.
LeWAM: End-to-End World and Action Modeling with JEPAs Presents an end-to-end joint-embedding predictive architecture that combines world modeling and action flow matching for robot planning and control. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses robotic control and planning efficiency, not deployment safety, guardrails, or alignment.
Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems Demonstrates that reward-hacking strategies can propagate across multiple agents through shared memory and code in multi-agent coding systems. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies multi-agent behavioral failure dynamics rather than general LLM benchmark design.
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering Introduces a counterfactual-guided few-shot distillation framework to train smaller question-answering language models using limited training samples. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Targets text-based question answering distillation, not multimodal image/audio models.
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering Introduces a counterfactual-guided few-shot distillation framework to train smaller question-answering language models using limited training samples. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Applies distillation to open-book QA without introducing or evaluating retrieval or grounding mechanisms.
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering Introduces a counterfactual-guided few-shot distillation framework to train smaller question-answering language models using limited training samples. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a distillation training method rather than an evaluation benchmark or testing methodology.
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering Introduces a counterfactual-guided few-shot distillation framework to train smaller question-answering language models using limited training samples. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on knowledge distillation for question answering, not multi-step LLM planning or agent systems.
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering Introduces a counterfactual-guided few-shot distillation framework to train smaller question-answering language models using limited training samples. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses open-book question answering distillation, not code generation or software engineering.
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering Introduces a counterfactual-guided few-shot distillation framework to train smaller question-answering language models using limited training samples. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses distillation efficiency rather than deployment safety, alignment, or guardrails.
Neural networks are more modular than single neurons suggest Proposes a hierarchy of modularity to identify functional modular organization in neural networks lacking single-neuron specialization. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Analyzes internal neural network modularity at the subspace level, not agentic planning or tools.
Neural networks are more modular than single neurons suggest Proposes a hierarchy of modularity to identify functional modular organization in neural networks lacking single-neuron specialization. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies internal representation structures rather than standard LLM evaluation methods or benchmarks.
Neural networks are more modular than single neurons suggest Proposes a hierarchy of modularity to identify functional modular organization in neural networks lacking single-neuron specialization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Investigates interpretability of neural network representations, not deployment safety or guardrails.
Towards Defining, Measuring, and Leveraging Creativity in AI Applications Proposes formal mathematical postulates and definitions to measure and operationalize computational creativity across domains. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Formulates a conceptual metric for creativity in chess and art, not LLM evaluation benchmarks.
Towards Defining, Measuring, and Leveraging Creativity in AI Applications Proposes formal mathematical postulates and definitions to measure and operationalize computational creativity across domains. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes a mathematical definition of computational creativity, not autonomous LLM agents.
Towards Defining, Measuring, and Leveraging Creativity in AI Applications Proposes formal mathematical postulates and definitions to measure and operationalize computational creativity across domains. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with computational creativity theory rather than safety, reliability, or deployment risks.
When Generative Models Act as Simulators Investigates mappings between generative model latent states and simulator states to enable controllable environment simulation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies world representation mechanics rather than benchmarking or evaluating language models.
When Generative Models Act as Simulators Investigates mappings between generative model latent states and simulator states to enable controllable environment simulation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on state mappings and editability, not model compression, quantization, or efficient serving.
When Generative Models Act as Simulators Investigates mappings between generative model latent states and simulator states to enable controllable environment simulation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Explores latent state editability in generative models, not LLM agent workflows or tool use.
When Generative Models Act as Simulators Investigates mappings between generative model latent states and simulator states to enable controllable environment simulation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Investigates simulator mappings rather than deployment safety, guardrails, or hallucination mitigation.
Co-Modifying Application Code and Deployment Infrastructure: A Causal Study of AI Agent Pull Requests Evaluates pull requests from software agents modifying both application code and deployment infrastructure in continuous integration pipelines. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on software engineering PR workflows rather than AI safety, guardrails, or alignment.
Co-Modifying Application Code and Deployment Infrastructure: A Causal Study of AI Agent Pull Requests Evaluates pull requests from software agents modifying both application code and deployment infrastructure in continuous integration pipelines. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates real-world PR outcomes rather than proposing or testing LLM benchmark methodologies.
Mitigating Capability Degradation in LLM Self-Improvement via Representation Geometry Regularization Introduces representation geometry regularization to prevent capability degradation during on-policy self-distillation in language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on internal representation regularization during self-distillation, not multi-step agent architectures.

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