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
ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence The paper introduces a neuro-symbolic benchmark to evaluate whether language model agents actively investigate environments and make causal decisions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on causal experimentation benchmarks rather than safety, red-teaming, or guardrails.
ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI The paper refines a high-level spatial AI orchestration framework into an implementation-focused low-level architecture for governed agent execution. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Presents an agent architecture for geospatial data, not code generation or repair.
ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI The paper refines a high-level spatial AI orchestration framework into an implementation-focused low-level architecture for governed agent execution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a system architecture design rather than an evaluation benchmark.
Deep learning from a control vantage point The paper reviews deep learning architectures and training dynamics through the lens of continuous-time nonlinear control theory. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, quantization, or serving optimizations.
Deep learning from a control vantage point The paper reviews deep learning architectures and training dynamics through the lens of continuous-time nonlinear control theory. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical overview of architectures, not benchmark evaluation or testing methodology.
Deep learning from a control vantage point The paper reviews deep learning architectures and training dynamics through the lens of continuous-time nonlinear control theory. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not study safety, hallucination, prompt injection, or deployment guardrails.
Deep learning from a control vantage point The paper reviews deep learning architectures and training dynamics through the lens of continuous-time nonlinear control theory. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Surveys control theory connections to deep learning architectures, not agent workflows.
Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware The paper measures inference energy efficiency across 86 open-weight language models, showing large efficiency gaps among models of similar capability. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on hardware energy consumption rather than safety, alignment, or jailbreaks.
Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware The paper measures inference energy efficiency across 86 open-weight language models, showing large efficiency gaps among models of similar capability. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies energy efficiency of LLMs, not autonomous agent architectures.
RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work The paper presents a simulated benchmark across eleven unconstrained environments to evaluate vision-language models on documenting real-world human physical work. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation or external document grounding.
RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work The paper presents a simulated benchmark across eleven unconstrained environments to evaluate vision-language models on documenting real-world human physical work. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate model compression, quantization, or inference acceleration.
RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work The paper presents a simulated benchmark across eleven unconstrained environments to evaluate vision-language models on documenting real-world human physical work. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates vision-language model understanding, not multi-step agent planning or tool use.
RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work The paper presents a simulated benchmark across eleven unconstrained environments to evaluate vision-language models on documenting real-world human physical work. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not involve software code generation or developer assistants.
RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work The paper presents a simulated benchmark across eleven unconstrained environments to evaluate vision-language models on documenting real-world human physical work. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on physical work documentation accuracy rather than deployment safety guardrails.
SAT-RL: Training Language Models for Logical Reasoning with Symbolic Solvers The paper presents a reinforcement learning framework that trains language models to solve complex logical reasoning tasks using interactive symbolic solvers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on logical reasoning accuracy rather than deployment safety or jailbreaks.
SAT-RL: Training Language Models for Logical Reasoning with Symbolic Solvers The paper presents a reinforcement learning framework that trains language models to solve complex logical reasoning tasks using interactive symbolic solvers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a training method with solvers rather than an evaluation framework.
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies The paper proposes an uncurated multi-stream latent memory framework that augments vision-language-action robotic policies while maintaining low inference latency. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Develops a latent memory framework for robots rather than general model compression.
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies The paper proposes an uncurated multi-stream latent memory framework that augments vision-language-action robotic policies while maintaining low inference latency. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on low-level robotic manipulation control rather than LLM-based agents.
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies The paper proposes an uncurated multi-stream latent memory framework that augments vision-language-action robotic policies while maintaining low inference latency. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not focus on safety guardrails, jailbreaks, or alignment monitoring.
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies The paper proposes an uncurated multi-stream latent memory framework that augments vision-language-action robotic policies while maintaining low inference latency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on a policy architecture rather than general LLM evaluation benchmarks.
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies The paper proposes an uncurated multi-stream latent memory framework that augments vision-language-action robotic policies while maintaining low inference latency. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with robotic manipulation memory, not industrial defect or visual quality inspection.
Coarse-Graining Recursive Expert Computation: Continuation-Relative Interfaces and Failure Modes The paper introduces an evaluation framework to measure the trade-off between task quality and computation cost when replacing iterative neural modules. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not introduce a standard benchmark or LLM evaluation dataset.
Coarse-Graining Recursive Expert Computation: Continuation-Relative Interfaces and Failure Modes The paper introduces an evaluation framework to measure the trade-off between task quality and computation cost when replacing iterative neural modules. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with neural computation reduction rather than autonomous agent workflows.
Coarse-Graining Recursive Expert Computation: Continuation-Relative Interfaces and Failure Modes The paper introduces an evaluation framework to measure the trade-off between task quality and computation cost when replacing iterative neural modules. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on computational efficiency rather than AI safety, red-teaming, or guardrails.
Frustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support The paper presents an enterprise case study showing that portable skills and structured navigation layers simplify retrieval pipelines in customer support. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on enterprise process automation rather than AI safety guardrails.
Frustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support The paper presents an enterprise case study showing that portable skills and structured navigation layers simplify retrieval pipelines in customer support. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on a deployment case study rather than a general evaluation benchmark.
SceneForesee: Learning Scene Evolution for Spatiotemporal Reasoning The paper introduces a benchmark and framework to evaluate how multimodal models track entities and reason about future scene evolution in videos. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with video scene forecasting, not autonomous agent tool use or planning.
SceneForesee: Learning Scene Evolution for Spatiotemporal Reasoning The paper introduces a benchmark and framework to evaluate how multimodal models track entities and reason about future scene evolution in videos. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on general video reasoning, not manufacturing or industrial inspection.
SceneForesee: Learning Scene Evolution for Spatiotemporal Reasoning The paper introduces a benchmark and framework to evaluate how multimodal models track entities and reason about future scene evolution in videos. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address deployment safety, hallucination, or adversarial robustness.
Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling The paper proposes a data-adaptive importance sampling method to stabilize variance and prevent sample size collapse in asynchronous RL post-training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an RL optimization algorithm rather than an evaluation benchmark.
Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling The paper proposes a data-adaptive importance sampling method to stabilize variance and prevent sample size collapse in asynchronous RL post-training. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL optimization theory rather than autonomous agent planning.
Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling The paper proposes a data-adaptive importance sampling method to stabilize variance and prevent sample size collapse in asynchronous RL post-training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses training variance control rather than deployment safety, alignment, or guardrails.
Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling The paper proposes a data-adaptive importance sampling method to stabilize variance and prevent sample size collapse in asynchronous RL post-training. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL algorithm stability rather than model compression or inference efficiency.
Specification-Based Testing of Large Language Models The paper introduces a testing framework that turns documented model safety claims into specifications to find counterexamples via mutated prompts. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Applies software testing principles to evaluate LLMs, not code generation.
Specification-Based Testing of Large Language Models The paper introduces a testing framework that turns documented model safety claims into specifications to find counterexamples via mutated prompts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on testing model compliance, not multi-step agent actions.
KV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization The paper introduces INT4 KV cache quantization with selective INT8 protection to accelerate asynchronous rollouts in reinforcement learning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL training acceleration rather than agent orchestration.
KV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization The paper introduces INT4 KV cache quantization with selective INT8 protection to accelerate asynchronous rollouts in reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on training efficiency rather than safety or alignment robustness.
KV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization The paper introduces INT4 KV cache quantization with selective INT8 protection to accelerate asynchronous rollouts in reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not introduce general LLM evaluation benchmarks or methodologies.
Jevolution: A Framework for Studying Communication and Collective Dynamics in AI Agents The paper presents an instrumented predator-prey simulation framework to evaluate communication, scheduling, and collective dynamics in remote decision-making agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies ecological simulation dynamics, not enterprise AI safety or guardrails.
Jevolution: A Framework for Studying Communication and Collective Dynamics in AI Agents The paper presents an instrumented predator-prey simulation framework to evaluate communication, scheduling, and collective dynamics in remote decision-making agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a simulation framework for agent communication rather than general LLM evaluation.
Jevolution: A Framework for Studying Communication and Collective Dynamics in AI Agents The paper presents an instrumented predator-prey simulation framework to evaluate communication, scheduling, and collective dynamics in remote decision-making agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model quantization, distillation, or efficient inference.
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval The paper presents a framework that jointly learns query rule assignment and rule-constrained evidence retrieval over structured knowledge bases. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Develops a retrieval method rather than a general evaluation benchmark.
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval The paper presents a framework that jointly learns query rule assignment and rule-constrained evidence retrieval over structured knowledge bases. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on retriever routing algorithms rather than general multi-step agents.
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval The paper presents a framework that jointly learns query rule assignment and rule-constrained evidence retrieval over structured knowledge bases. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate safety vulnerabilities, hallucination mitigation, or deployment guardrails.
Diffusion-Based Auctions for Multi-Winner Banner Ads The paper proposes a multi-winner banner ad auction mechanism using diffusion models guided by bids and text descriptions to generate ads. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not benchmark or evaluate language models.
Diffusion-Based Auctions for Multi-Winner Banner Ads The paper proposes a multi-winner banner ad auction mechanism using diffusion models guided by bids and text descriptions to generate ads. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on auction mechanisms, not LLM agent workflows.
Diffusion-Based Auctions for Multi-Winner Banner Ads The paper proposes a multi-winner banner ad auction mechanism using diffusion models guided by bids and text descriptions to generate ads. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with mechanism design for ads, not AI safety or reliability.
Diffusion-Based Auctions for Multi-Winner Banner Ads The paper proposes a multi-winner banner ad auction mechanism using diffusion models guided by bids and text descriptions to generate ads. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, distillation, or lightweight architectures.
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation The paper introduces a two-stage reasoning framework to generate personalized, safe, and explainable medication recommendations using language models. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not target efficient inference, quantization, or small on-device models.
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation The paper introduces a two-stage reasoning framework to generate personalized, safe, and explainable medication recommendations using language models. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Graph retrieval is used only as background context for medication recommendation.
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation The paper introduces a two-stage reasoning framework to generate personalized, safe, and explainable medication recommendations using language models. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on a specialized clinical recommendation framework, not general LLM benchmarking.
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation The paper introduces a two-stage reasoning framework to generate personalized, safe, and explainable medication recommendations using language models. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical recommendation policies rather than autonomous tool-using agents.
Scale, Drivers, and Limits of Representational Alignment Across Large Language Models The paper presents two geometric methods to evaluate where and why representational structures align across different large language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate safety, jailbreaks, prompt injection, or deployment guardrails.
Scale, Drivers, and Limits of Representational Alignment Across Large Language Models The paper presents two geometric methods to evaluate where and why representational structures align across different large language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal representation analysis, not autonomous agent planning or actions.
Scale, Drivers, and Limits of Representational Alignment Across Large Language Models The paper presents two geometric methods to evaluate where and why representational structures align across different large language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study retrieval-augmented generation pipelines or external knowledge grounding.
Scale, Drivers, and Limits of Representational Alignment Across Large Language Models The paper presents two geometric methods to evaluate where and why representational structures align across different large language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation models.
Scale, Drivers, and Limits of Representational Alignment Across Large Language Models The paper presents two geometric methods to evaluate where and why representational structures align across different large language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, distillation, or serving efficiency.
Scale, Drivers, and Limits of Representational Alignment Across Large Language Models The paper presents two geometric methods to evaluate where and why representational structures align across different large language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses strictly on language models rather than multimodal architectures.
What Counts as the Same Task? Executable Contracts for Robustness Evaluation of Tool-Using Agents The paper proposes executable contracts to verify that adversarial benchmark edits preserve the core task definition when evaluating tool-using agents. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses agent task contracts generally rather than software code generation.
AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a specialized audio generator rather than benchmark evaluations.

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