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
SkillHorizon: Constructing Long-Horizon Agent Tasks through Skill Composition This paper introduces an evaluation benchmark that generates complex, long-horizon agent workflows by composing verifiable business skills. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on task horizon benchmarking rather than deployment safety or guardrails.
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization This paper proposes a steering framework that dynamically personalizes language model responses using low-overhead activation vectors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses personalization adaptation rather than deployment safety, jailbreaks, or hallucinations.
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization This paper proposes a steering framework that dynamically personalizes language model responses using low-overhead activation vectors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an inference steering method rather than an evaluation methodology or benchmark.
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization This paper proposes a steering framework that dynamically personalizes language model responses using low-overhead activation vectors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on activation steering for user personalization, not agent planning or tool use.
Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad This paper introduces a two-layer masked language model approach to generate constrained Thai poetry in a single decoding pass. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on Thai poetry generation rather than standard LLM evaluation frameworks.
Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad This paper introduces a two-layer masked language model approach to generate constrained Thai poetry in a single decoding pass. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with poetic form enforcement rather than safety, red-teaming, or guardrails.
Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad This paper introduces a two-layer masked language model approach to generate constrained Thai poetry in a single decoding pass. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a constrained masked language model for poetry, not an autonomous agent.
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on graph neural network generalization rather than LLM safety or deployment guardrails.
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies algorithmic reasoner generalization, not model compression or efficient serving.
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies recurrent graph reasoners, not LLM agent planning or tool use.
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes failure modes in graph neural networks, not LLM evaluation benchmarks.
Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions This paper studies experimentally how the timing of generative AI access impacts team ideation and creative selection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates human organizational creativity rather than LLM benchmark design or evaluation.
Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions This paper studies experimentally how the timing of generative AI access impacts team ideation and creative selection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on team decision timing rather than technical AI safety or hallucination.
Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions This paper studies experimentally how the timing of generative AI access impacts team ideation and creative selection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies human team workflows with generative tools, not autonomous LLM agents.
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses abstract RL convergence rather than real-world AI deployment safety.
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with reinforcement learning optimization rates, not model efficiency or distillation.
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of Q-learning, not language model agent systems.
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Provides convergence theory for reinforcement learning rather than LLM evaluation.
Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations This paper proposes an adaptive selection method that identifies the top candidates under uncertainty using minimal scenario evaluations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses scenario evaluation algorithms, not LLM testing benchmarks or LLM-as-a-judge.
Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations This paper proposes an adaptive selection method that identifies the top candidates under uncertainty using minimal scenario evaluations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical decision-making algorithm for candidate selection, not LLM agents.
Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations This paper proposes an adaptive selection method that identifies the top candidates under uncertainty using minimal scenario evaluations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on statistical selection guarantees rather than LLM safety or guardrails.
Recurrent In-Context Learning for Tabular Foundation Models This paper introduces parameter-tied recurrent blocks to reduce model size in tabular foundation models without sacrificing performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on parameter sharing efficiency rather than safety or reliability.
Recurrent In-Context Learning for Tabular Foundation Models This paper introduces parameter-tied recurrent blocks to reduce model size in tabular foundation models without sacrificing performance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies tabular foundation model architectures, not autonomous agents.
Recurrent In-Context Learning for Tabular Foundation Models This paper introduces parameter-tied recurrent blocks to reduce model size in tabular foundation models without sacrificing performance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a model architecture rather than an evaluation framework or benchmark.
Can You Trust Your Preference Models? Stackelberg Games for Alignment This paper models preference model alignment as a Stackelberg game to identify comparisons where evaluators are prone to error. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on preference model alignment games, not LLM tool use or multi-agent planning.
On the Complexity Gap Between POMDPs and Multi-Environment POMDPs This paper maps the computational complexity landscape of robust decision-making in multi-environment partially observable environments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies computational complexity theory, not language model benchmarks.
On the Complexity Gap Between POMDPs and Multi-Environment POMDPs This paper maps the computational complexity landscape of robust decision-making in multi-environment partially observable environments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical complexity analysis of POMDPs, not LLM agent workflows.
On the Complexity Gap Between POMDPs and Multi-Environment POMDPs This paper maps the computational complexity landscape of robust decision-making in multi-environment partially observable environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Pure theoretical analysis of robust reward maximization without deployment safety focus.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with general language encoder pre-training, not software engineering models.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates pre-training recipes rather than benchmarking frameworks or evaluation methodology.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses strictly on text-based bidirectional encoders, not multimodal models.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses pre-training optimizations rather than deployment safety, robustness, or jailbreaks.
UltraBERT: Beyond the Limit of BERT Pre-Training This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on pre-training BERT encoders, not LLM multi-step agents.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a classification model, not an agent planning or tool use system.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses clinical text classification, not code generation or software engineering.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates explainability for a classifier rather than deployment safety guardrails or hallucinations.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Processes text-only clinical symptoms rather than multimodal data.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on classification without retrieval-augmented generation mechanisms.
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on an existing benchmark rather than proposing new LLM evaluation methods.
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Benchmark is specifically for visual anomaly detection, not language model evaluation.
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address language model deployment safety, jailbreaks, or guardrails.
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses continual visual anomaly detection, not LLM agent systems.
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on continual learning benchmarks rather than model compression or efficient serving.
Belief Embedding Tree Search This paper proposes a tree search method using belief embeddings for planning in partially observable environments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Deals with reinforcement learning search algorithms rather than LLM benchmarking.
Belief Embedding Tree Search This paper proposes a tree search method using belief embeddings for planning in partially observable environments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on classical POMDP tree search rather than language model agents.
Belief Embedding Tree Search This paper proposes a tree search method using belief embeddings for planning in partially observable environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address deployment safety, guardrails, or hallucination mitigation.
Belief Embedding Tree Search This paper proposes a tree search method using belief embeddings for planning in partially observable environments. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on POMDP belief representation, not LLM compression or inference efficiency.
Trace Evolution Bench: Measuring Whether Agents Improve from Their Own Environment Traces This paper introduces a benchmark to measure whether autonomous coding agents truly improve their policies from interaction traces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates self-evolution measurement protocols rather than deployment guardrails or safety attacks.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Covers speech-to-text transcript correction, not software coding or program repair.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Presents text-based ASR transcript correction, not multimodal vision-language models.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a fine-tuning strategy rather than LLM benchmarking or evaluation tools.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on ASR post-editing supervision, not multi-step autonomous agents.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies data supervision strategies rather than model pruning, distillation, or quantization.
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses speech recognition transcript rewriting, not document retrieval-augmented generation.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical dental imaging rather than manufacturing or industrial inspection.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines dental image segmentation, not deployment safety or alignment risks.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies traditional CNN vision segmentation without multimodal language reasoning.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Applies standard CNN segmenters to dental images, not LLM evaluation benchmarks.
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++ This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers medical radiograph segmentation, not autonomous LLM agents.
Exposing AI Inference as a Telco Network API over the 5G User Plane This paper demonstrates an API and network framework to support split AI inference over 5G mobile networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Covers telecommunications networking protocols, not LLM benchmark evaluation.

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