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
Event-Centric Earthquake Monitoring with a Multi-Station Transformer The paper presents a multi-station transformer model that jointly detects phase arrivals, locations, magnitudes, and associations for earthquake monitoring. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies seismology, not AI deployment reliability or guardrails.
Event-Centric Earthquake Monitoring with a Multi-Station Transformer The paper presents a multi-station transformer model that jointly detects phase arrivals, locations, magnitudes, and associations for earthquake monitoring. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on seismic signal processing, not LLM evaluation benchmarks.
Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation The authors introduce a time-frequency diffusion framework that improves the generation of rare extreme events in time-series data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on time-series generation, not AI deployment safety or guardrails.
Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation The authors introduce a time-frequency diffusion framework that improves the generation of rare extreme events in time-series data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with time-series diffusion models, not language model agents.
Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation The authors introduce a time-frequency diffusion framework that improves the generation of rare extreme events in time-series data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate language models or present an LLM benchmark.
Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation The authors introduce a time-frequency diffusion framework that improves the generation of rare extreme events in time-series data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression, quantization, or efficient model serving.
Agency as a New Organizing Abstraction for Computer Science The paper argues that software agency should be treated as a core organizing abstraction in computer science rather than merely an AI application. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Conceptual perspective paper; does not introduce or analyze LLM benchmarks.
Bottlenecked semantic compression by representation reconstruction The authors propose a visual token compression framework that reduces computational costs by retaining only visual tokens with high reconstruction residuals. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual token compression, not multi-step agent planning.
Bottlenecked semantic compression by representation reconstruction The authors propose a visual token compression framework that reduces computational costs by retaining only visual tokens with high reconstruction residuals. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on general vision transformer token pruning, not industrial manufacturing inspection.
Bottlenecked semantic compression by representation reconstruction The authors propose a visual token compression framework that reduces computational costs by retaining only visual tokens with high reconstruction residuals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate language models or present an LLM benchmark.
Bottlenecked semantic compression by representation reconstruction The authors propose a visual token compression framework that reduces computational costs by retaining only visual tokens with high reconstruction residuals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Why Agentic Workflows Fail: A Taxonomy Derived from Literature and Empirical Data The paper presents a taxonomy that locates agent workflow failures across specific workflow stages and agent functional capabilities. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Uses GitHub data to analyze agent workflow errors, not coding assistants or SWE-bench.
Why Agentic Workflows Fail: A Taxonomy Derived from Literature and Empirical Data The paper presents a taxonomy that locates agent workflow failures across specific workflow stages and agent functional capabilities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Provides a failure taxonomy rather than a model evaluation benchmark.
ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics The authors present a flight navigation policy for quadrotors that penalizes movement outside the camera view to avoid collisions in cluttered environments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not benchmark or evaluate language models.
ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics The authors present a flight navigation policy for quadrotors that penalizes movement outside the camera view to avoid collisions in cluttered environments. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotics obstacle avoidance, not industrial defect inspection.
ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics The authors present a flight navigation policy for quadrotors that penalizes movement outside the camera view to avoid collisions in cluttered environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies physical robot collision avoidance, not generative AI deployment safety.
ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics The authors present a flight navigation policy for quadrotors that penalizes movement outside the camera view to avoid collisions in cluttered environments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on drone navigation policies, not language model agent systems.
ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics The authors present a flight navigation policy for quadrotors that penalizes movement outside the camera view to avoid collisions in cluttered environments. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or efficient inference.
On Making $\mathrm{SE}(2)$-Invariant Networks Optimal The paper develops a plug-and-play module for convolutional neural networks that provides optimal rotation and translation invariance based on invariant theory. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on symmetry groups in CNNs, not LLM agents.
On Making $\mathrm{SE}(2)$-Invariant Networks Optimal The paper develops a plug-and-play module for convolutional neural networks that provides optimal rotation and translation invariance based on invariant theory. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Presents architectural group theory, not AI safety or deployment monitoring.
On Making $\mathrm{SE}(2)$-Invariant Networks Optimal The paper develops a plug-and-play module for convolutional neural networks that provides optimal rotation and translation invariance based on invariant theory. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not address language model evaluation or benchmarks.
Reinforcement Learning In Partially Verifiable Environments for Colon Cancer Treatment Planning The authors formulate colon cancer treatment planning as an interactive reinforcement learning task in settings where actions are only partially verifiable. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL decision trajectory optimization, not deployment guardrails or safety.
Reinforcement Learning In Partially Verifiable Environments for Colon Cancer Treatment Planning The authors formulate colon cancer treatment planning as an interactive reinforcement learning task in settings where actions are only partially verifiable. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL formulation for oncology, not LLM benchmark evaluation.
Reinforcement Learning In Partially Verifiable Environments for Colon Cancer Treatment Planning The authors formulate colon cancer treatment planning as an interactive reinforcement learning task in settings where actions are only partially verifiable. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient model serving.
World-T3: Test-Time Training as Persistent Memory for Real-Time Video World Models The paper proposes using test-time training memory to maintain consistent long-horizon visual scenes in real-time camera-controlled video world models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on video world model generation, not agent workflows or tool use.
World-T3: Test-Time Training as Persistent Memory for Real-Time Video World Models The paper proposes using test-time training memory to maintain consistent long-horizon visual scenes in real-time camera-controlled video world models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a video generation model, not general LLM benchmark evaluation.
World-T3: Test-Time Training as Persistent Memory for Real-Time Video World Models The paper proposes using test-time training memory to maintain consistent long-horizon visual scenes in real-time camera-controlled video world models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not focus on deployment safety, jailbreaks, or guardrails.
World-T3: Test-Time Training as Persistent Memory for Real-Time Video World Models The paper proposes using test-time training memory to maintain consistent long-horizon visual scenes in real-time camera-controlled video world models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative video world models, not industrial defect detection.
SchemaWeaver: Accelerating On-Device Mobile Agentic AI via Schema-Guided Parallel LLM Decoding The authors present a parallel decoding method that speeds up on-device tool calling for language model agents without requiring model retraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a decoding optimization rather than an evaluation benchmark.
SchemaWeaver: Accelerating On-Device Mobile Agentic AI via Schema-Guided Parallel LLM Decoding The authors present a parallel decoding method that speeds up on-device tool calling for language model agents without requiring model retraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference latency rather than safety, guardrails, or robustness.
Large Language Models Extend Known Research But Do Not Create New Research Directions The study evaluates scientific hypotheses generated by language models, concluding that they extend existing knowledge rather than starting new research directions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes hypothesis novelty rather than multi-step agent planning or tool use.
Large Language Models Extend Known Research But Do Not Create New Research Directions The study evaluates scientific hypotheses generated by language models, concluding that they extend existing knowledge rather than starting new research directions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not study deployment safety, jailbreaks, hallucinations, or monitoring.
Know-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models The paper introduces a training-free technique and benchmark to help vision-language models correctly process and reject negated concepts in text prompts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not study multi-step planning, tool use, or agent workflows.
Know-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models The paper introduces a training-free technique and benchmark to help vision-language models correctly process and reject negated concepts in text prompts. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Does not address industrial inspection, defect detection, or manufacturing vision.
Know-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models The paper introduces a training-free technique and benchmark to help vision-language models correctly process and reject negated concepts in text prompts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on multimodal visual negation representation rather than deployment safety.
The belief-action gap in language model agents The authors examine why language model agents fail by analyzing discrepancies between internal representations of state values and actual action choices. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal agent mechanics rather than general LLM evaluation benchmarks.
The belief-action gap in language model agents The authors examine why language model agents fail by analyzing discrepancies between internal representations of state values and actual action choices. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates agents on text-rendered grid worlds, not multimodal understanding.
The belief-action gap in language model agents The authors examine why language model agents fail by analyzing discrepancies between internal representations of state values and actual action choices. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study retrieval-augmented generation or grounding with external databases.
The belief-action gap in language model agents The authors examine why language model agents fail by analyzing discrepancies between internal representations of state values and actual action choices. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate models on software engineering, coding, or program repair.
The belief-action gap in language model agents The authors examine why language model agents fail by analyzing discrepancies between internal representations of state values and actual action choices. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, quantization, or efficient inference.
Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning The paper studies how much models contribute to multi-model teams, showing individual test scores do not correlate well with collaborative performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not focus on safety, guardrails, or hallucination mitigation.
Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning The paper studies how much models contribute to multi-model teams, showing individual test scores do not correlate well with collaborative performance. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on language models rather than multimodal cross-domain models.
Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning The paper studies how much models contribute to multi-model teams, showing individual test scores do not correlate well with collaborative performance. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or external document retrieval.
Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning The paper studies how much models contribute to multi-model teams, showing individual test scores do not correlate well with collaborative performance. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or small efficient models.
Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning The paper studies how much models contribute to multi-model teams, showing individual test scores do not correlate well with collaborative performance. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on collaborative reasoning generally, not software engineering or code generation.
An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classification The authors design an FPGA implementation of spiking neural networks that performs both inference and training on-chip without backpropagation through time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents hardware implementation, not an LLM benchmark.
An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classification The authors design an FPGA implementation of spiking neural networks that performs both inference and training on-chip without backpropagation through time. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on edge hardware design rather than AI safety or guardrails.
An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classification The authors design an FPGA implementation of spiking neural networks that performs both inference and training on-chip without backpropagation through time. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents FPGA hardware for SNNs, not LLM agents.
Assessing the Performance of AI Models in Misinformation Detection Using Predefined Social Noise Constructs The paper evaluates seven machine learning and graph models on detecting misinformation within social media noise from conflict-related tweets. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on classification under social noise, not model compression.
Assessing the Performance of AI Models in Misinformation Detection Using Predefined Social Noise Constructs The paper evaluates seven machine learning and graph models on detecting misinformation within social media noise from conflict-related tweets. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on social media misinformation classification rather than LLM deployment safety.
Assessing the Performance of AI Models in Misinformation Detection Using Predefined Social Noise Constructs The paper evaluates seven machine learning and graph models on detecting misinformation within social media noise from conflict-related tweets. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies social media classification models, not multi-step LLM agents.
Assessing the Performance of AI Models in Misinformation Detection Using Predefined Social Noise Constructs The paper evaluates seven machine learning and graph models on detecting misinformation within social media noise from conflict-related tweets. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates misinformation classifiers, not language model evaluation benchmarks.
REA: From Correct LLM Judgments to Safe Decision Delivery The authors introduce a framework to track safety model judgments through output parsing to prevent malformed responses from causing bad decisions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on guardrail parsing and decision delivery, not agent planning.
REA: From Correct LLM Judgments to Safe Decision Delivery The authors introduce a framework to track safety model judgments through output parsing to prevent malformed responses from causing bad decisions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates guardrail execution pipelines, not general LLM benchmarks.
VeriPIE: Checkable Query Plans and Evidence-Guided Selection for Text-to-SQL The paper presents a neurosymbolic framework combining query plan checks and candidate selection to improve text-to-SQL translation for financial governance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-to-SQL query verification rather than autonomous multi-step agents.
VeriPIE: Checkable Query Plans and Evidence-Guided Selection for Text-to-SQL The paper presents a neurosymbolic framework combining query plan checks and candidate selection to improve text-to-SQL translation for financial governance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on text-to-SQL datasets, but primary contribution is a neurosymbolic verification framework.
Foundation Models for Event Prediction Across Heterogeneous Event Logs The paper presents an in-context foundation model architecture for next-event prediction across diverse operational event logs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Foundation Models for Event Prediction Across Heterogeneous Event Logs The paper presents an in-context foundation model architecture for next-event prediction across diverse operational event logs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a model architecture for event prediction, not an LLM benchmark.
Foundation Models for Event Prediction Across Heterogeneous Event Logs The paper presents an in-context foundation model architecture for next-event prediction across diverse operational event logs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on cross-domain event modeling rather than model compression or efficient serving.
Foundation Models for Event Prediction Across Heterogeneous Event Logs The paper presents an in-context foundation model architecture for next-event prediction across diverse operational event logs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Develops a foundation model for event logs, not autonomous LLM agents.

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