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
claimcheck: Measuring and Improving Specification Faithfulness The paper presents a benchmark to evaluate whether formal specifications generated by language models accurately reflect natural-language requirements. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Exclusively covers text and formal code, not multimodal models.
claimcheck: Measuring and Improving Specification Faithfulness The paper presents a benchmark to evaluate whether formal specifications generated by language models accurately reflect natural-language requirements. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines formal specification verification rather than autonomous tool-using agents.
claimcheck: Measuring and Improving Specification Faithfulness The paper presents a benchmark to evaluate whether formal specifications generated by language models accurately reflect natural-language requirements. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on code specifications, not retrieval-augmented generation.
claimcheck: Measuring and Improving Specification Faithfulness The paper presents a benchmark to evaluate whether formal specifications generated by language models accurately reflect natural-language requirements. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, pruning, or quantization.
When Goals Leak Across Relationships: How Cached Goal Representations Shape Cross-Client Decisions The paper examines how shared KV caches cause language model agents to leak goals across competing clients, offering internal intervention techniques. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on representation analysis and intervention, not benchmark development.
HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation The paper proposes a hierarchical and attribute-aware supervision method to improve training efficiency and long-tail performance in generative recommendations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a training method rather than LLM evaluation benchmarks.
HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation The paper proposes a hierarchical and attribute-aware supervision method to improve training efficiency and long-tail performance in generative recommendations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates generative recommendation IDs, not retrieval-augmented generation.
HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation The paper proposes a hierarchical and attribute-aware supervision method to improve training efficiency and long-tail performance in generative recommendations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on recommendation accuracy rather than safety or reliability.
HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation The paper proposes a hierarchical and attribute-aware supervision method to improve training efficiency and long-tail performance in generative recommendations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers recommendation model training objectives rather than autonomous agents.
HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation The paper proposes a hierarchical and attribute-aware supervision method to improve training efficiency and long-tail performance in generative recommendations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on recommendation loss supervision rather than model compression.
What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs The authors analyze compatibility across multi-schema latent spaces in motion models, showing shared representations require explicit paired data to align. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates motion VAEs rather than vision-language or multimodal LLMs.
What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs The authors analyze compatibility across multi-schema latent spaces in motion models, showing shared representations require explicit paired data to align. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies motion VAE latent representations, not multi-step tool-using agents.
What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs The authors analyze compatibility across multi-schema latent spaces in motion models, showing shared representations require explicit paired data to align. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with human motion representation rather than industrial inspection.
What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs The authors analyze compatibility across multi-schema latent spaces in motion models, showing shared representations require explicit paired data to align. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes representation compatibility rather than LLM benchmarks or evaluation.
What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs The authors analyze compatibility across multi-schema latent spaces in motion models, showing shared representations require explicit paired data to align. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses latent space compatibility rather than deployment safety.
Get In, Decide, Prove: A Merchant-Side Readiness Benchmark for Agentic Commerce The paper introduces a benchmark assessing whether merchant websites provide adequate access, terms, and evidence for autonomous shopping agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on e-commerce transaction readiness, not safety guardrails or robustness.
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on small models, quantization, or efficient inference.
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not address vision, audio, or multimodal model capabilities.
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on evolutionary heuristic design for combinatorial optimization, not LLM agents.
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Generates heuristic algorithms for optimization, not software engineering or coding assistants.
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an optimization algorithm rather than an LLM evaluation benchmark.
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or document search.
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design The authors propose an evolutionary framework that optimizes instance coverage and updates language model weights to discover complementary optimization heuristics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not focus on AI safety, jailbreaks, or deployment guardrails.
Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion The paper introduces a test-time wrapper that distorts exposed output probabilities to defend black-box machine learning models from query-based adversarial attacks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on defending classifiers from query attacks, not LLM agents.
Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion The paper introduces a test-time wrapper that distorts exposed output probabilities to defend black-box machine learning models from query-based adversarial attacks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on adversarial defense mechanics rather than LLM benchmark evaluation.
Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion The paper introduces a test-time wrapper that distorts exposed output probabilities to defend black-box machine learning models from query-based adversarial attacks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or inference efficiency.
Cross-Market Meta-Learning for Electricity Price Forecasting The authors introduce a topology-aware transformer meta-trained across multiple electricity markets to forecast day-ahead electricity prices for unseen grids. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses energy time-series forecasting, not LLM evaluation.
Cross-Market Meta-Learning for Electricity Price Forecasting The authors introduce a topology-aware transformer meta-trained across multiple electricity markets to forecast day-ahead electricity prices for unseen grids. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies energy market price forecasting, not deployment safety or guardrails.
Cross-Market Meta-Learning for Electricity Price Forecasting The authors introduce a topology-aware transformer meta-trained across multiple electricity markets to forecast day-ahead electricity prices for unseen grids. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or efficient inference.
Cross-Market Meta-Learning for Electricity Price Forecasting The authors introduce a topology-aware transformer meta-trained across multiple electricity markets to forecast day-ahead electricity prices for unseen grids. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on electricity price forecasting, not language model agents.
Learning to Play: Reinforcement Learning and Games The authors provide a comprehensive textbook covering reinforcement learning methods, game-playing algorithms, and key historical milestones. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning for games rather than LLM agents.
Learning to Play: Reinforcement Learning and Games The authors provide a comprehensive textbook covering reinforcement learning methods, game-playing algorithms, and key historical milestones. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Book on game-playing RL methods, not LLM evaluation or benchmarks.
Learning to Play: Reinforcement Learning and Games The authors provide a comprehensive textbook covering reinforcement learning methods, game-playing algorithms, and key historical milestones. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers reinforcement learning in games, not AI deployment safety.
Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting The paper develops a sequential betting approach to reliably detect data distribution shifts in unstructured text and image streams over time. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address small models, quantization, or inference speedups.
Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting The paper develops a sequential betting approach to reliably detect data distribution shifts in unstructured text and image streams over time. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with distribution shift detection, not language model agents.
Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting The paper develops a sequential betting approach to reliably detect data distribution shifts in unstructured text and image streams over time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on statistical drift testing rather than LLM capability benchmarking.
Generalizable field of view extension for diffusion MRI via conditional flow matching The authors propose a conditional flow-matching generative model to extend the field of view in diffusion brain MRI scans across scanners. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on MRI image reconstruction, not LLM benchmark evaluation.
Generalizable field of view extension for diffusion MRI via conditional flow matching The authors propose a conditional flow-matching generative model to extend the field of view in diffusion brain MRI scans across scanners. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies medical imaging algorithms, not deployed AI safety or guardrails.
Generalizable field of view extension for diffusion MRI via conditional flow matching The authors propose a conditional flow-matching generative model to extend the field of view in diffusion brain MRI scans across scanners. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Applies diffusion models to medical MRI, not language model agents.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses geophysics earthquake monitoring, not language model agents.
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	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.
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.
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	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 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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Provides a failure taxonomy rather than a model evaluation benchmark.
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.
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.
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 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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies physical robot collision avoidance, not generative AI deployment safety.
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.
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.

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