Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,313In brief24More (on-topic)3,333Not relevant13,754Not assessed87Older352Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection. Preprint | LLMs for code via arxiv · 12 Oct, 16:28 | Not relevant Concerned with political social media text, not coding or software development. | |
| BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Introduces an annotated dataset for cyberbullying detection, not general LLM benchmarking. | |
| BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:28 | Not relevant Presents a detection dataset rather than LLM safety, red-teaming, or guardrails. | |
| BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection. Preprint | Multimodal models via arxiv · 12 Oct, 16:28 | Not relevant Deals with textual commentary from YouTube, not multimodal model architectures. | |
| BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection. Preprint | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Presents a cyberbullying text corpus rather than an autonomous LLM agent architecture. | |
| BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection. Preprint | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Focuses on social media discourse dataset creation, not RAG systems. | |
| Neurograf v1: An End-to-End Romanian-First Language Model Family Introduces an end-to-end open pipeline, pretraining corpus, and family of decoder-only models trained from scratch for Romanian. Preprint | Multimodal models via arxiv · 12 Oct, 16:28 | Not relevant The models are unimodal text-based language models. | |
| Neurograf v1: An End-to-End Romanian-First Language Model Family Introduces an end-to-end open pipeline, pretraining corpus, and family of decoder-only models trained from scratch for Romanian. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:28 | Not relevant Focuses on low-resource language pretraining rather than safety or reliability. | |
| Neurograf v1: An End-to-End Romanian-First Language Model Family Introduces an end-to-end open pipeline, pretraining corpus, and family of decoder-only models trained from scratch for Romanian. Preprint | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Introduces a pretrained Romanian language model family, not agent planning or tool use. | |
| Neurograf v1: An End-to-End Romanian-First Language Model Family Introduces an end-to-end open pipeline, pretraining corpus, and family of decoder-only models trained from scratch for Romanian. Preprint | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Presents a pretraining pipeline rather than retrieval-augmented generation. | |
| Neurograf v1: An End-to-End Romanian-First Language Model Family Introduces an end-to-end open pipeline, pretraining corpus, and family of decoder-only models trained from scratch for Romanian. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Introduces a model family and dataset rather than a novel LLM evaluation methodology. | |
| Neurograf v1: An End-to-End Romanian-First Language Model Family Introduces an end-to-end open pipeline, pretraining corpus, and family of decoder-only models trained from scratch for Romanian. Preprint | LLMs for code via arxiv · 12 Oct, 16:28 | Not relevant Focuses on Romanian general text models, not software engineering or coding. | |
| Les Misérables Dialogue: A film-based network representation of social communication Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Presents a social network dataset from a movie, not an LLM benchmark. | |
| Les Misérables Dialogue: A film-based network representation of social communication Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables. Preprint | Small and efficient models via arxiv · 12 Oct, 16:28 | Not relevant Not related to model efficiency, quantization, or serving. | |
| Les Misérables Dialogue: A film-based network representation of social communication Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables. Preprint | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Deals with dialogue network analysis, not retrieval-augmented generation. | |
| Les Misérables Dialogue: A film-based network representation of social communication Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables. Preprint | LLMs for code via arxiv · 12 Oct, 16:28 | Not relevant Focuses on narrative dialogue networks, not code generation or repair. | |
| Les Misérables Dialogue: A film-based network representation of social communication Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables. Preprint | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Constructs a temporal text network from a movie, not an AI agent system. | |
| Les Misérables Dialogue: A film-based network representation of social communication Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables. Preprint | Multimodal models via arxiv · 12 Oct, 16:28 | Not relevant Focuses on text dialogue networks rather than multimodal foundation models. | |
| Les Misérables Dialogue: A film-based network representation of social communication Constructs a temporal and multilayer social dialogue network dataset based on the film adaptation of Les Misérables. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:28 | Not relevant Addresses social dialogue graphs rather than AI deployment safety or jailbreaks. | |
| SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents Proposes a reinforcement learning framework that tracks external versus internal knowledge sources to reduce search agent fabrication. Preprint | LLMs for code via arxiv · 12 Oct, 16:28 | Not relevant Focuses on general web search and question answering, not programming or code tasks. | |
| SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents Proposes a reinforcement learning framework that tracks external versus internal knowledge sources to reduce search agent fabrication. Preprint | Multimodal models via arxiv · 12 Oct, 16:28 | Not relevant Centers on text-based search reasoning without multimodal inputs. | |
| SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents Proposes a reinforcement learning framework that tracks external versus internal knowledge sources to reduce search agent fabrication. Preprint | Small and efficient models via arxiv · 12 Oct, 16:28 | Not relevant Focuses on credit assignment and grounding, not inference acceleration or quantization. | |
| SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents Proposes a reinforcement learning framework that tracks external versus internal knowledge sources to reduce search agent fabrication. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Proposes an RL training framework rather than an evaluation benchmark. | |
| Scaling Agentic Data for Long-Horizon Terminal Intelligence Introduces a synthetic data generation framework to produce long-horizon task trajectories for terminal agents. Preprint | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Focuses on terminal agent trajectories rather than retrieval-augmented generation. | |
| Scaling Agentic Data for Long-Horizon Terminal Intelligence Introduces a synthetic data generation framework to produce long-horizon task trajectories for terminal agents. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Proposes a data synthesis engine for training, not an evaluation benchmark. | |
| Scaling Agentic Data for Long-Horizon Terminal Intelligence Introduces a synthetic data generation framework to produce long-horizon task trajectories for terminal agents. Preprint | Multimodal models via arxiv · 12 Oct, 16:28 | Not relevant Operates on terminal text commands without vision or multimodal input. | |
| Scaling Agentic Data for Long-Horizon Terminal Intelligence Introduces a synthetic data generation framework to produce long-horizon task trajectories for terminal agents. Preprint | Small and efficient models via arxiv · 12 Oct, 16:28 | Not relevant Focuses on agentic training trajectories rather than model compression or inference efficiency. | |
| Scaling Agentic Data for Long-Horizon Terminal Intelligence Introduces a synthetic data generation framework to produce long-horizon task trajectories for terminal agents. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:28 | Not relevant Focuses on long-horizon agent capabilities rather than safety guardrails or red-teaming. | |
| PreSTO: Predictive Subtree Prefetching for Fast LLM Power Sampling Presents a predictive subtree prefetching method to accelerate power sampling for test-time language model reasoning. Preprint | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Does not involve external document retrieval or grounding. | |
| PreSTO: Predictive Subtree Prefetching for Fast LLM Power Sampling Presents a predictive subtree prefetching method to accelerate power sampling for test-time language model reasoning. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:28 | Not relevant Focuses on sampling runtime acceleration, not safety, guardrails, or robustness. | |
| PreSTO: Predictive Subtree Prefetching for Fast LLM Power Sampling Presents a predictive subtree prefetching method to accelerate power sampling for test-time language model reasoning. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Proposes an accelerated sampling method, not an evaluation benchmark or diagnostic. | |
| PreSTO: Predictive Subtree Prefetching for Fast LLM Power Sampling Presents a predictive subtree prefetching method to accelerate power sampling for test-time language model reasoning. Preprint | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Focuses on test-time sampling efficiency rather than autonomous agent planning or tool use. | |
| PreSTO: Predictive Subtree Prefetching for Fast LLM Power Sampling Presents a predictive subtree prefetching method to accelerate power sampling for test-time language model reasoning. Preprint | LLMs for code via arxiv · 12 Oct, 16:28 | Not relevant Applies to general reasoning sampling, not specifically to software engineering tasks. | |
| PreSTO: Predictive Subtree Prefetching for Fast LLM Power Sampling Presents a predictive subtree prefetching method to accelerate power sampling for test-time language model reasoning. Preprint | Multimodal models via arxiv · 12 Oct, 16:28 | Not relevant Addresses text LLM sampling without multimodal modalities. | |
| S$^3$R: Fast and Faithful RL Rollouts via Self-Sparse Speculative Decoding Introduces speculative decoding using self-sparse attention to speed up rollout generation during reinforcement learning. Preprint | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Focuses on KV-cache optimization during RL training, not agent workflows or multi-step tool use. | |
| S$^3$R: Fast and Faithful RL Rollouts via Self-Sparse Speculative Decoding Introduces speculative decoding using self-sparse attention to speed up rollout generation during reinforcement learning. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:28 | Not relevant Focuses on rollout efficiency, not AI safety, alignment risks, or guardrails. | |
| S$^3$R: Fast and Faithful RL Rollouts via Self-Sparse Speculative Decoding Introduces speculative decoding using self-sparse attention to speed up rollout generation during reinforcement learning. Preprint | Multimodal models via arxiv · 12 Oct, 16:28 | Not relevant Pertains strictly to language model attention decoding without multimodal data. | |
| S$^3$R: Fast and Faithful RL Rollouts via Self-Sparse Speculative Decoding Introduces speculative decoding using self-sparse attention to speed up rollout generation during reinforcement learning. Preprint | LLMs for code via arxiv · 12 Oct, 16:28 | Not relevant Focuses on RL rollout efficiency in general, not code generation or program repair. | |
| S$^3$R: Fast and Faithful RL Rollouts via Self-Sparse Speculative Decoding Introduces speculative decoding using self-sparse attention to speed up rollout generation during reinforcement learning. Preprint | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Deals with RL training optimization, not document retrieval or grounding. | |
| S$^3$R: Fast and Faithful RL Rollouts via Self-Sparse Speculative Decoding Introduces speculative decoding using self-sparse attention to speed up rollout generation during reinforcement learning. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28 | Not relevant Introduces a decoding method for RL, not an evaluation framework or benchmark. | |
| 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 | Small and efficient models via arxiv · 12 Oct, 16:28 | Not relevant Refers to low-data regimes, not small model compression or inference efficiency. | |
| 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 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 | 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. | |
| 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. | |
| 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 | 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 | Small and efficient models via arxiv · 12 Oct, 16:28 | Not relevant Does not address model compression, pruning, or efficient serving. | |
| 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 | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Does not involve 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 | AI agents via arxiv · 12 Oct, 16:28 | Not relevant Studies visual representation learning, not LLM agents. | |
| 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 | 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 | 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 | 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 | 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 | Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28 | Not relevant Does not cover 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 | LLMs for code via arxiv · 12 Oct, 16:28 | Not relevant Does not focus on code generation or software engineering. |
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