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
SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection Proposes a prompt-guided text augmentation framework to assist fake news detection in low-resource settings. Peer-reviewed	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not involve multimodal models.
SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection Proposes a prompt-guided text augmentation framework to assist fake news detection in low-resource settings. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on prompt-guided data augmentation for classification, not agent workflows.
SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection Proposes a prompt-guided text augmentation framework to assist fake news detection in low-resource settings. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation pipelines.
SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection Proposes a prompt-guided text augmentation framework to assist fake news detection in low-resource settings. Peer-reviewed	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not involve software engineering or code LLMs.
SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection Proposes a prompt-guided text augmentation framework to assist fake news detection in low-resource settings. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a data augmentation method rather than a novel evaluation methodology.
SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection Proposes a prompt-guided text augmentation framework to assist fake news detection in low-resource settings. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Targets fake news classification, not LLM safety, red-teaming, or guardrails.
What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings Designs diagnostic probes to determine whether audio-language evaluation models judge music based on audio or prompt content. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval-augmented generation.
What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings Designs diagnostic probes to determine whether audio-language evaluation models judge music based on audio or prompt content. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on evaluation bias rather than safety risks, jailbreaks, or guardrails.
What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings Designs diagnostic probes to determine whether audio-language evaluation models judge music based on audio or prompt content. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code LLMs or software development.
What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings Designs diagnostic probes to determine whether audio-language evaluation models judge music based on audio or prompt content. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates audio-language evaluators rather than LLM agents.
What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings Designs diagnostic probes to determine whether audio-language evaluation models judge music based on audio or prompt content. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, pruning, or efficient serving.
Vision Transformers Learn Word Meaning from Images Alone Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies representational semantics rather than LLM benchmarking methodology.
Vision Transformers Learn Word Meaning from Images Alone Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Investigates self-supervised semantic emergence, not industrial visual inspection.
Vision Transformers Learn Word Meaning from Images Alone Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study deployment safety, jailbreaks, or guardrails.
Vision Transformers Learn Word Meaning from Images Alone Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not cover retrieval-augmented generation.
Vision Transformers Learn Word Meaning from Images Alone Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code models or software engineering.
Vision Transformers Learn Word Meaning from Images Alone Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression or inference efficiency.
Vision Transformers Learn Word Meaning from Images Alone Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies visual representation learning, not LLM agents.
CAST: Credit Alignment for Cross-Tokenizer On-Policy Distillation Proposes a chunk-level credit assignment method for on-policy distillation across language models with different tokenizers. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes a policy distillation algorithm, not autonomous agent planning.
CAST: Credit Alignment for Cross-Tokenizer On-Policy Distillation Proposes a chunk-level credit assignment method for on-policy distillation across language models with different tokenizers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not relate to retrieval-augmented generation.
CAST: Credit Alignment for Cross-Tokenizer On-Policy Distillation Proposes a chunk-level credit assignment method for on-policy distillation across language models with different tokenizers. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a distillation method rather than an evaluation benchmark.
CAST: Credit Alignment for Cross-Tokenizer On-Policy Distillation Proposes a chunk-level credit assignment method for on-policy distillation across language models with different tokenizers. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study deployment safety, jailbreaks, or guardrails.
CAST: Credit Alignment for Cross-Tokenizer On-Policy Distillation Proposes a chunk-level credit assignment method for on-policy distillation across language models with different tokenizers. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on multimodal architectures.
CAST: Credit Alignment for Cross-Tokenizer On-Policy Distillation Proposes a chunk-level credit assignment method for on-policy distillation across language models with different tokenizers. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not focus on code generation or software engineering.
Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence Introduces a retrieval method combining token-level matching and document-level embeddings for memory-augmented language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not investigate safety risks, prompt injections, or red-teaming.
Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence Introduces a retrieval method combining token-level matching and document-level embeddings for memory-augmented language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a retrieval method rather than a novel LLM evaluation benchmark.
Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence Introduces a retrieval method combining token-level matching and document-level embeddings for memory-augmented language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not involve multimodal models or vision-language tasks.
Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence Introduces a retrieval method combining token-level matching and document-level embeddings for memory-augmented language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code generation or software development.
Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence Introduces a retrieval method combining token-level matching and document-level embeddings for memory-augmented language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on retriever ranking performance rather than model compression.
Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence Introduces a retrieval method combining token-level matching and document-level embeddings for memory-augmented language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on retrieval mechanisms rather than multi-step agent systems.
Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study Evaluates whether automated prompt optimizers inadvertently cause regressions in unscored prompt constraints and requirements. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, distillation, or serving.
Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study Evaluates whether automated prompt optimizers inadvertently cause regressions in unscored prompt constraints and requirements. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on multimodal models.
Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study Evaluates whether automated prompt optimizers inadvertently cause regressions in unscored prompt constraints and requirements. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not focus on retrieval-augmented generation.
Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study Evaluates whether automated prompt optimizers inadvertently cause regressions in unscored prompt constraints and requirements. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate code-specific models or software engineering tasks.
Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study Evaluates whether automated prompt optimizers inadvertently cause regressions in unscored prompt constraints and requirements. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Analyzes automated prompt optimizers rather than multi-step agent architectures.
Host-Aware Web Crawling for LLM Pretraining Data Studies host-level budget allocation and crawling policies to collect pretraining web data for language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not involve code generation or software engineering.
Host-Aware Web Crawling for LLM Pretraining Data Studies host-level budget allocation and crawling policies to collect pretraining web data for language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on web crawling for pretraining, not autonomous agents.
Host-Aware Web Crawling for LLM Pretraining Data Studies host-level budget allocation and crawling policies to collect pretraining web data for language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Covers pretraining data collection rather than inference-time retrieval systems.
Host-Aware Web Crawling for LLM Pretraining Data Studies host-level budget allocation and crawling policies to collect pretraining web data for language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not examine multimodal architectures or datasets.
Host-Aware Web Crawling for LLM Pretraining Data Studies host-level budget allocation and crawling policies to collect pretraining web data for language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate model safety, jailbreaks, or deployment guardrails.
Host-Aware Web Crawling for LLM Pretraining Data Studies host-level budget allocation and crawling policies to collect pretraining web data for language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies data collection strategies rather than LLM benchmarking or evaluation.
Host-Aware Web Crawling for LLM Pretraining Data Studies host-level budget allocation and crawling policies to collect pretraining web data for language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression, quantization, or efficient inference.
Taking Tokenization off the Host for Agentic LLM Serving Develops a GPU-accelerated tokenizer using CUDA graphs to reduce time-to-first-token in agentic language model serving. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on GPU tokenization rather than RAG or retrieval.
Taking Tokenization off the Host for Agentic LLM Serving Develops a GPU-accelerated tokenizer using CUDA graphs to reduce time-to-first-token in agentic language model serving. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code generation or software engineering LLMs.
Taking Tokenization off the Host for Agentic LLM Serving Develops a GPU-accelerated tokenizer using CUDA graphs to reduce time-to-first-token in agentic language model serving. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses tokenization serving infrastructure, not agent decision-making or workflows.
Taking Tokenization off the Host for Agentic LLM Serving Develops a GPU-accelerated tokenizer using CUDA graphs to reduce time-to-first-token in agentic language model serving. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not propose evaluation frameworks or benchmarks.
Taking Tokenization off the Host for Agentic LLM Serving Develops a GPU-accelerated tokenizer using CUDA graphs to reduce time-to-first-token in agentic language model serving. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with text tokenization systems rather than multimodal architectures.
Taking Tokenization off the Host for Agentic LLM Serving Develops a GPU-accelerated tokenizer using CUDA graphs to reduce time-to-first-token in agentic language model serving. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on serving throughput, not safety or guardrails.
Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on attention dynamics in generative models rather than compression or serving.
Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates attention mechanisms rather than LLM benchmarking techniques.
Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval-augmented generation.
Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Studies foundational attention mechanics in generative models, not industrial vision tasks.
Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code generation or software tooling.
Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies attention mechanisms in text-to-image models, not agent planning.
Exploring Attention Sinks in Text-to-Image Transformers from Diffusion and Autoregressive Models Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address deployment safety, guardrails, or jailbreaks.
Test-Time Memory Construction Presents a framework that defers memory construction to test time to answer queries across long interaction histories. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses memory retrieval, not deployment safety or adversarial robustness.
Test-Time Memory Construction Presents a framework that defers memory construction to test time to answer queries across long interaction histories. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not involve multimodal representations or models.
Test-Time Memory Construction Presents a framework that defers memory construction to test time to answer queries across long interaction histories. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on memory management rather than small models, quantization, or pruning.
Test-Time Memory Construction Presents a framework that defers memory construction to test time to answer queries across long interaction histories. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not focus on code synthesis or software engineering.
Test-Time Memory Construction Presents a framework that defers memory construction to test time to answer queries across long interaction histories. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a memory retrieval architecture rather than LLM benchmarking methods.

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