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
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.
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.
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.
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	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	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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not study deployment safety, jailbreaks, hallucinations, or monitoring.
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.
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.
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 agents via arxiv · 15 Oct, 06:17	Not relevant Does not study multi-step planning, tool use, or agent workflows.
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	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	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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on collaborative reasoning generally, not software engineering or code generation.
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	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	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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on language models rather than multimodal cross-domain models.
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.
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.
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	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 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	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.
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.
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.
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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation The paper introduces a framework combining 3D Gaussian Splatting and physical simulation to evaluate robot policies using handheld fisheye cameras. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic policy simulation evaluation, not language model benchmarks.
YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation The paper introduces a framework combining 3D Gaussian Splatting and physical simulation to evaluate robot policies using handheld fisheye cameras. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses simulation fidelity for robot policies rather than AI safety guardrails.
YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation The paper introduces a framework combining 3D Gaussian Splatting and physical simulation to evaluate robot policies using handheld fisheye cameras. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on real-to-sim 3DGS scene reconstruction for robotic evaluation, not LLM agents.
YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation The paper introduces a framework combining 3D Gaussian Splatting and physical simulation to evaluate robot policies using handheld fisheye cameras. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates robotic manipulation policies in simulation, not industrial inspection.
Enhanced Detective: A Dual-Stage Proactive Defense Against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning The paper introduces a dual-stage robust aggregation defense against data and model poisoning attacks in federated financial fraud detection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies poisoning defenses in FL, not LLM evaluation benchmarks.
Enhanced Detective: A Dual-Stage Proactive Defense Against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning The paper introduces a dual-stage robust aggregation defense against data and model poisoning attacks in federated financial fraud detection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on federated learning fraud models, not LLM agents.
Box-WAM: Box Trajectories as Compact State Representations for World Action Models The paper proposes predicting 3D bounding box trajectories instead of dense video frames as compact representations for world action models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic state representation efficiency, not safety guardrails.
Box-WAM: Box Trajectories as Compact State Representations for World Action Models The paper proposes predicting 3D bounding box trajectories instead of dense video frames as compact representations for world action models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a policy modeling representation, not an LLM benchmark.
Box-WAM: Box Trajectories as Compact State Representations for World Action Models The paper proposes predicting 3D bounding box trajectories instead of dense video frames as compact representations for world action models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic manipulation policies, not industrial defect detection.
Towards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference The paper evaluates the energy efficiency, carbon intensity, and accuracy of ten post-training quantization methods across various language models and GPUs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates quantization energy efficiency, not LLM autonomous agents.
Towards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference The paper evaluates the energy efficiency, carbon intensity, and accuracy of ten post-training quantization methods across various language models and GPUs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies energy and carbon metrics rather than safety, hallucinations, or robustness.
Towards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference The paper evaluates the energy efficiency, carbon intensity, and accuracy of ten post-training quantization methods across various language models and GPUs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on hardware energy profiling rather than LLM benchmark design.
TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency The paper proposes a vision-language-action model where action execution avoids inference latency by decoupling from explicit reasoning output at runtime. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on reducing reasoning latency in robotics, not safety guardrails.
TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency The paper proposes a vision-language-action model where action execution avoids inference latency by decoupling from explicit reasoning output at runtime. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates a specific model architecture, not benchmarking LLMs.
TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency The paper proposes a vision-language-action model where action execution avoids inference latency by decoupling from explicit reasoning output at runtime. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates robotic manipulation and autonomous driving, not industrial visual inspection.
Convex-Analytic View of Query Selection for Safety Verification of Markov Decision Processes The paper presents a convex-analytic framework to select query directions for safety verification in Markov decision processes. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies formal verification of MDPs, not LLM autonomous agents.
Convex-Analytic View of Query Selection for Safety Verification of Markov Decision Processes The paper presents a convex-analytic framework to select query directions for safety verification in Markov decision processes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on mathematical verification algorithms, not LLM evaluation benchmarks.
Convex-Analytic View of Query Selection for Safety Verification of Markov Decision Processes The paper presents a convex-analytic framework to select query directions for safety verification in Markov decision processes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on formal MDP verification rather than LLM safety or guardrails.
Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents The paper analyzes the failure modes of agent memory reuse and evaluates certified memory update strategies when facts change. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents theoretical and empirical bounds on memory safety, not an LLM benchmark.
Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents The paper analyzes the failure modes of agent memory reuse and evaluates certified memory update strategies when facts change. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent reasoning contraction and skill caches rather than RAG.

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