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
Incremental Retrieval-Augmented Generation for Human-in-the-Loop Decision Support Evaluates an incremental retrieval-augmented generation framework that progressively populates its knowledge base from human decision precedents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an incremental RAG system rather than a new evaluation framework or benchmark.
Recurrent Harness for Agentic Continual Learning Proposes a recurrent harness framework that uses streaming feedback to revise textual memory for continual agent learning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent reflection revision, not standard RAG knowledge base retrieval or grounding.
Recurrent Harness for Agentic Continual Learning Proposes a recurrent harness framework that uses streaming feedback to revise textual memory for continual agent learning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, distillation, or inference acceleration.
Recurrent Harness for Agentic Continual Learning Proposes a recurrent harness framework that uses streaming feedback to revise textual memory for continual agent learning. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not specifically target code generation or software engineering agents.
Recurrent Harness for Agentic Continual Learning Proposes a recurrent harness framework that uses streaming feedback to revise textual memory for continual agent learning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on continual learning performance rather than deployment safety, guardrails, or alignment.
Recurrent Harness for Agentic Continual Learning Proposes a recurrent harness framework that uses streaming feedback to revise textual memory for continual agent learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates on existing benchmarks rather than proposing a new evaluation methodology or benchmark.
Recurrent Harness for Agentic Continual Learning Proposes a recurrent harness framework that uses streaming feedback to revise textual memory for continual agent learning. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not involve multimodal or vision-language models.
GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks Introduces a benchmark evaluating language models on eliciting geospatial data requirements and constraints from natural language requests. Peer-reviewed	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on natural language queries for geospatial data, not vision-language or multimodal models.
GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks Introduces a benchmark evaluating language models on eliciting geospatial data requirements and constraints from natural language requests. Peer-reviewed	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model efficiency, distillation, or quantization.
GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks Introduces a benchmark evaluating language models on eliciting geospatial data requirements and constraints from natural language requests. Peer-reviewed	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not study code generation, code repair, or programming assistants.
GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks Introduces a benchmark evaluating language models on eliciting geospatial data requirements and constraints from natural language requests. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not focus on safety, jailbreaks, prompt injection, or guardrails.
GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks Introduces a benchmark evaluating language models on eliciting geospatial data requirements and constraints from natural language requests. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on identifying geospatial requirement metadata rather than end-to-end RAG architecture or retrieval grounding.
GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks Introduces a benchmark evaluating language models on eliciting geospatial data requirements and constraints from natural language requests. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates dataset requirement elicitation, not autonomous planning, multi-step actions, or tool use.
Unsupervised Multimodal Reward Modeling Trains an unsupervised vision-language reward model using intermediate layer representations and caption perturbations without human preference labels. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general vision-language reward modeling, not industrial inspection or defect detection.
Unsupervised Multimodal Reward Modeling Trains an unsupervised vision-language reward model using intermediate layer representations and caption perturbations without human preference labels. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies vision-language reward modeling, not retrieval-augmented generation.
Unsupervised Multimodal Reward Modeling Trains an unsupervised vision-language reward model using intermediate layer representations and caption perturbations without human preference labels. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on reward modeling for VLMs, not autonomous multi-step agents.
Unsupervised Multimodal Reward Modeling Trains an unsupervised vision-language reward model using intermediate layer representations and caption perturbations without human preference labels. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a training method for reward models rather than an LLM evaluation benchmark.
Unsupervised Multimodal Reward Modeling Trains an unsupervised vision-language reward model using intermediate layer representations and caption perturbations without human preference labels. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies reward modeling to vision-language captions, not code generation.
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.
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	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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes a time-series forecasting architecture, not language model agents or planning.
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	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	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	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.
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.
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.
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 agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on database semantic query optimization and label caching, not LLM 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	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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an unlearning method rather than a general LLM evaluation benchmark.
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	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	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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on unlearning in reasoning models rather than multi-step tool-using agents.
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	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.
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.
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	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	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	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	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 safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses edge speech enhancement efficiency rather than deployment safety 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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Applies neural architecture search to speech enhancement, not LLM agent systems.
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	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	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	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.
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.
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.

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