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
Joint Embedding with Dynamic Integration network (JEDI-net) The paper proposes clustering observations by feature importance profiles to incorporate latent factor representations into deep learning models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on feature importance clustering, not model efficiency.
Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior The paper develops an approach to map AI semantic blind spots and evaluates the quality of distinct human ideas. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve external document retrieval or grounding methods.
Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior The paper develops an approach to map AI semantic blind spots and evaluates the quality of distinct human ideas. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or serving.
Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior The paper develops an approach to map AI semantic blind spots and evaluates the quality of distinct human ideas. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Analyzes essays and open-ended text, not software code generation.
Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior The paper develops an approach to map AI semantic blind spots and evaluates the quality of distinct human ideas. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Covers text generation tasks, not vision-language or multimodal models.
Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior The paper develops an approach to map AI semantic blind spots and evaluates the quality of distinct human ideas. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies text generation diversity rather than multi-step autonomous planning.
Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior The paper develops an approach to map AI semantic blind spots and evaluates the quality of distinct human ideas. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies idea diversity rather than model hallucination, jailbreaks, or safety.
Do Theorem Provers Need Theorem-Proving Agents? mini-Lean-agent: A Coding-Agent Baseline for Lean The paper presents a minimal coding agent baseline for Lean theorem proving using standard bash tooling instead of specialized harnesses. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates agent tool complexity rather than AI safety guardrails.
Do Theorem Provers Need Theorem-Proving Agents? mini-Lean-agent: A Coding-Agent Baseline for Lean The paper presents a minimal coding agent baseline for Lean theorem proving using standard bash tooling instead of specialized harnesses. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates existing benchmarks rather than proposing a new LLM test.
Budgeted KV-Cache Repair for Evolving Contexts The paper proposes a budgeted KV-cache repair technique to restore model performance efficiently after deleting obsolete context tokens. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an inference acceleration method, not an evaluation benchmark.
Budgeted KV-Cache Repair for Evolving Contexts The paper proposes a budgeted KV-cache repair technique to restore model performance efficiently after deleting obsolete context tokens. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on latency and context cache management, not safety.
On the Statistical Mechanics of Attention The paper presents a statistical mechanics framework using modern Hopfield models to analyze inference reliability and phase transitions in Transformer attention. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical physics of attention rather than deployment safety.
On the Statistical Mechanics of Attention The paper presents a statistical mechanics framework using modern Hopfield models to analyze inference reliability and phase transitions in Transformer attention. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents theoretical physics analysis rather than empirical evaluation benchmarks.
On the Statistical Mechanics of Attention The paper presents a statistical mechanics framework using modern Hopfield models to analyze inference reliability and phase transitions in Transformer attention. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies attention mechanisms via statistical physics, not autonomous agents.
On the Statistical Mechanics of Attention The paper presents a statistical mechanics framework using modern Hopfield models to analyze inference reliability and phase transitions in Transformer attention. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of attention phases rather than model efficiency methods.
MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation The paper introduces a distribution-level evaluation metric for medical image segmentation to assess prediction sets without requiring paired ground-truth masks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates medical imaging rather than industrial manufacturing inspection.
MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation The paper introduces a distribution-level evaluation metric for medical image segmentation to assess prediction sets without requiring paired ground-truth masks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers segmentation metric design rather than deployed LLM safety.
MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation The paper introduces a distribution-level evaluation metric for medical image segmentation to assess prediction sets without requiring paired ground-truth masks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical segmentation masks rather than vision-language models.
MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation The paper introduces a distribution-level evaluation metric for medical image segmentation to assess prediction sets without requiring paired ground-truth masks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses medical image segmentation metrics rather than LLM benchmarks.
MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation The paper introduces a distribution-level evaluation metric for medical image segmentation to assess prediction sets without requiring paired ground-truth masks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with medical segmentation metrics, not AI 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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, pruning, or quantization.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on recommendation loss supervision rather than model compression.
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	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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates generative recommendation IDs, not retrieval-augmented generation.
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.
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.
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	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	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	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	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	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	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	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	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	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 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	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	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.
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.
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 agents via arxiv · 15 Oct, 06:17	Not relevant Addresses geophysics earthquake monitoring, not language model agents.

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