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
Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms This paper presents an autonomous system combining LiDAR SLAM, visual imagery, and thermal sensing to inspect under-panel components of solar farms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers hardware defect inspection rather than AI model safety and guardrails.
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address document retrieval or knowledge grounding.
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Operates on text-based reasoning models without multimodal inputs.
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an optimization recipe rather than an evaluation benchmark or judge audit.
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Optimizes reasoning tokens generally rather than focusing on software engineering or code generation.
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on token-level policy optimization for reasoning generation rather than multi-step agents.
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on compute efficiency and adaptive thinking rather than AI safety or alignment.
Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis This paper introduces a framework combining language model agents with deterministic software controls for reliable sensor-based industrial fault diagnosis. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Pertains to industrial sensor diagnosis, not LLM guardrails, hallucination, or safety.
Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis This paper introduces a framework combining language model agents with deterministic software controls for reliable sensor-based industrial fault diagnosis. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent-driven fault diagnosis rather than model distillation or efficient inference.
Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis This paper introduces a framework combining language model agents with deterministic software controls for reliable sensor-based industrial fault diagnosis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a diagnostic framework rather than a general LLM benchmark or evaluation methodology.
Posterior-Averaged Bellman Flow Matching for Distributional Critics This paper proposes a flow-matching method that averages Bellman target directions under a posterior distribution to improve reinforcement learning critics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an algorithmic RL training method rather than an LLM evaluation benchmark.
Posterior-Averaged Bellman Flow Matching for Distributional Critics This paper proposes a flow-matching method that averages Bellman target directions under a posterior distribution to improve reinforcement learning critics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses temporal-difference flow matching rather than model compression or efficient serving.
Posterior-Averaged Bellman Flow Matching for Distributional Critics This paper proposes a flow-matching method that averages Bellman target directions under a posterior distribution to improve reinforcement learning critics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Pure RL theory without a focus on LLM safety, guardrails, or alignment.
Posterior-Averaged Bellman Flow Matching for Distributional Critics This paper proposes a flow-matching method that averages Bellman target directions under a posterior distribution to improve reinforcement learning critics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical reinforcement learning distributional critics, not LLM agents.
GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation This paper introduces a benchmark to evaluate whether forecasting models can incorporate forward-looking contextual information into accurate time-series predictions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on numerical time series forecasting accuracy rather than AI safety or guardrails.
GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation This paper introduces a benchmark to evaluate whether forecasting models can incorporate forward-looking contextual information into accurate time-series predictions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Forecasting benchmark paper; agentic pipelines are used merely to build annotations.
GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation This paper introduces a benchmark to evaluate whether forecasting models can incorporate forward-looking contextual information into accurate time-series predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates time series forecasting rather than efficient model architectures or inference.
Learning from fracture: Teaching language models to design through physical experience This paper introduces a framework that enables language models to optimize physical structural designs through simulated fracture experience and hypothesis revision. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on structural fracture optimization, not AI safety or deployment reliability.
Learning from fracture: Teaching language models to design through physical experience This paper introduces a framework that enables language models to optimize physical structural designs through simulated fracture experience and hypothesis revision. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient inference techniques.
Learning from fracture: Teaching language models to design through physical experience This paper introduces a framework that enables language models to optimize physical structural designs through simulated fracture experience and hypothesis revision. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a scientific design framework rather than a general LLM evaluation benchmark.
Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution This paper presents a benchmark to evaluate performance, resource usage, and privacy trade-offs when language model agents collaborate across edge and cloud environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines privacy exposure alongside resource trade-offs, but its main focus is agent benchmarking and orchestration.
Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers This paper conducts an adversarial evaluation comparing a probability-based safety judge against large language model judges and safety classifiers across multiple attack types. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates safety classification tools rather than multi-step agent planning or tool use.
Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR This survey analyzes how verifier noise affects reinforcement learning with verifiable rewards across 58 studies and summarizes practical mitigation techniques. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on reasoning verifiers in LLMs, not multimodal architectures.
Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR This survey analyzes how verifier noise affects reinforcement learning with verifiable rewards across 58 studies and summarizes practical mitigation techniques. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Considers unit tests only as an example of reward checkers, not LLMs for code.
Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR This survey analyzes how verifier noise affects reinforcement learning with verifiable rewards across 58 studies and summarizes practical mitigation techniques. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Surveys verifier noise in RLVR rather than building or deploying LLM agents.
Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR This survey analyzes how verifier noise affects reinforcement learning with verifiable rewards across 58 studies and summarizes practical mitigation techniques. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses RL reward verifier errors rather than model compression, quantization, or serving efficiency.
Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR This survey analyzes how verifier noise affects reinforcement learning with verifiable rewards across 58 studies and summarizes practical mitigation techniques. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on RLVR reward verification, not retrieval-augmented generation.
QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing Introduces QK-Edit, a training-free query-key attention manipulation method for text-guided image and video editing in multimodal diffusion transformers. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses text-guided media editing rather than industrial inspection or manufacturing defect detection.
QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing Introduces QK-Edit, a training-free query-key attention manipulation method for text-guided image and video editing in multimodal diffusion transformers. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion-based image and video editing, not language model agents.
QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing Introduces QK-Edit, a training-free query-key attention manipulation method for text-guided image and video editing in multimodal diffusion transformers. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an image/video editing method rather than an evaluation benchmark or methodology.
QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing Introduces QK-Edit, a training-free query-key attention manipulation method for text-guided image and video editing in multimodal diffusion transformers. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing Proposes Diff Diffusion, a diffusion-based model that predicts localized code edit sequences rather than regenerating entire source files. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant The framework operates strictly on source code text, not multimodal inputs.
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing Proposes Diff Diffusion, a diffusion-based model that predicts localized code edit sequences rather than regenerating entire source files. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates code editing performance but does not contribute novel general LLM benchmarks or evaluation methods.
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing Proposes Diff Diffusion, a diffusion-based model that predicts localized code edit sequences rather than regenerating entire source files. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on latency and code editing accuracy, not safety, guardrails, or alignment.
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing Proposes Diff Diffusion, a diffusion-based model that predicts localized code edit sequences rather than regenerating entire source files. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion models for code editing rather than multi-step agent planning or tool use.
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing Proposes Diff Diffusion, a diffusion-based model that predicts localized code edit sequences rather than regenerating entire source files. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address external document retrieval or grounding architectures.
Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design Introduces Agentic-MatriBO, a modular Bayesian optimization framework using LLM agents and deterministic compilation for material and chemical experiment design. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on Bayesian optimization for chemistry, not model compression or serving.
Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design Introduces Agentic-MatriBO, a modular Bayesian optimization framework using LLM agents and deterministic compilation for material and chemical experiment design. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an optimization framework, not a general evaluation benchmark.
Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design Introduces Agentic-MatriBO, a modular Bayesian optimization framework using LLM agents and deterministic compilation for material and chemical experiment design. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with materials design optimization, not deployment safety or jailbreaks.
A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading Provides a tutorial analyzing theoretical expressiveness gains and parameter tradeoffs when re-uploading data in quantum amplitude embedding architectures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers quantum circuit encoding, not AI safety or reliability.
A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading Provides a tutorial analyzing theoretical expressiveness gains and parameter tradeoffs when re-uploading data in quantum amplitude embedding architectures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Provides a theoretical tutorial on quantum embeddings, not LLM benchmarking.
A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading Provides a tutorial analyzing theoretical expressiveness gains and parameter tradeoffs when re-uploading data in quantum amplitude embedding architectures. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Discusses quantum machine learning encoding, not classical small or efficient LLMs.
A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading Provides a tutorial analyzing theoretical expressiveness gains and parameter tradeoffs when re-uploading data in quantum amplitude embedding architectures. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a tutorial on quantum amplitude embeddings, unrelated to AI agents.
Learning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models Presents ADMA, an adaptive deliberation controller that dynamically adjusts multi-agent critique and coordination to improve reasoning accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on an adaptive multi-agent coordination system rather than benchmark design.
FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on catalog retrieval evaluation without multimodal integration as main focus.
FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Uses vector quantization for clustering, not efficient model deployment or inference.
FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers fashion search retrieval, not autonomous planning agents.
FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses e-commerce catalog search, not industrial manufacturing defect detection.
FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with product search evaluation, not AI safety or alignment.
MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision Releases MMTA, a multimodal benchmark testing the ability of language models to jointly analyze time series, text, and visual data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on benchmarking multimodal perception, not safety or guardrails.
MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision Releases MMTA, a multimodal benchmark testing the ability of language models to jointly analyze time series, text, and visual data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents an evaluation benchmark rather than efficient model compression.
MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision Releases MMTA, a multimodal benchmark testing the ability of language models to jointly analyze time series, text, and visual data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates multimodal model perception on time series, not tool-using agents.
Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills Introduces Socratic Skill Learning, interleaving expert guidance with student practice to produce reusable and transferable agent skills across tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent skill acquisition, not safety guardrails or red-teaming.
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses standard time series forecasting, not AI safety or robustness.
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on temporal modeling architectures, not model compression or serving.
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a forecasting model architecture, not an evaluation benchmark.
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with time-series forecasting, not LLM agents or planning.
RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions Presents RT-J, a relational transformer pretrained on diverse databases to achieve strong in-context table predictions with few sample examples. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a tabular foundation model, not an autonomous agent system.
RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions Presents RT-J, a relational transformer pretrained on diverse databases to achieve strong in-context table predictions with few sample examples. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on relational data prediction, not safety or alignment.
RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions Presents RT-J, a relational transformer pretrained on diverse databases to achieve strong in-context table predictions with few sample examples. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a model architecture and pretraining method, not an evaluation standard.

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