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 |
|---|---|---|---|
| Heterogeneous Structured Pruning Under Resource Constraints for Hardware-Efficient Large Language Models Heterogeneous Structured Pruning selects sublayer-specific sparsity patterns guided by latency and memory models for hardware-efficient inference. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on latency and memory efficiency, not safety. | |
| Heterogeneous Structured Pruning Under Resource Constraints for Hardware-Efficient Large Language Models Heterogeneous Structured Pruning selects sublayer-specific sparsity patterns guided by latency and memory models for hardware-efficient inference. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Evaluates text LLMs, not multimodal architectures. | |
| Local Citation Recommendation Without Graph Signals The paper introduces a dataset and citation recommendation system that retrieves relevant scientific papers without relying on citation graphs. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on retrieval accuracy, not AI safety risks. | |
| Local Citation Recommendation Without Graph Signals The paper introduces a dataset and citation recommendation system that retrieves relevant scientific papers without relying on citation graphs. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Processes textual papers only, no multimodal content. | |
| Local Citation Recommendation Without Graph Signals The paper introduces a dataset and citation recommendation system that retrieves relevant scientific papers without relying on citation graphs. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not study model pruning, quantization, or distillation. | |
| Local Citation Recommendation Without Graph Signals The paper introduces a dataset and citation recommendation system that retrieves relevant scientific papers without relying on citation graphs. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Presents citation retrieval, not autonomous planning agents. | |
| Local Citation Recommendation Without Graph Signals The paper introduces a dataset and citation recommendation system that retrieves relevant scientific papers without relying on citation graphs. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on scientific literature, not code. | |
| Latent Taxonomies: How Property Induction Develops in Language Model Pre-training This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Studies pre-training phenomena rather than testing benchmarks. | |
| Latent Taxonomies: How Property Induction Develops in Language Model Pre-training This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Studies pretraining dynamics, not multi-step agent behavior. | |
| Latent Taxonomies: How Property Induction Develops in Language Model Pre-training This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not focused on code generation or software engineering. | |
| Latent Taxonomies: How Property Induction Develops in Language Model Pre-training This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Analyzes scaling effects, not compression or efficient inference. | |
| Latent Taxonomies: How Property Induction Develops in Language Model Pre-training This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on pretraining memory and induction, not RAG. | |
| Latent Taxonomies: How Property Induction Develops in Language Model Pre-training This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Not focused on safety, jailbreaks, or deployment guardrails. | |
| Latent Taxonomies: How Property Induction Develops in Language Model Pre-training This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Investigates textual taxonomies only, not multimodal data. | |
| KhyontekAI at CheckThat! 2026: Multilingual Dense Retrieval and Cross-Encoder Reranking for Scientific Web Claims The authors present a multilingual dense retrieval and reranking framework to link scientific web claims to their source publications. Peer-reviewed | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not address model compression or efficient serving. | |
| KhyontekAI at CheckThat! 2026: Multilingual Dense Retrieval and Cross-Encoder Reranking for Scientific Web Claims The authors present a multilingual dense retrieval and reranking framework to link scientific web claims to their source publications. Peer-reviewed | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Does not evaluate safety, jailbreaks, or guardrails. | |
| KhyontekAI at CheckThat! 2026: Multilingual Dense Retrieval and Cross-Encoder Reranking for Scientific Web Claims The authors present a multilingual dense retrieval and reranking framework to link scientific web claims to their source publications. Peer-reviewed | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Deals exclusively with text and metadata. | |
| KhyontekAI at CheckThat! 2026: Multilingual Dense Retrieval and Cross-Encoder Reranking for Scientific Web Claims The authors present a multilingual dense retrieval and reranking framework to link scientific web claims to their source publications. Peer-reviewed | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents a competition system, not an evaluation framework. | |
| KhyontekAI at CheckThat! 2026: Multilingual Dense Retrieval and Cross-Encoder Reranking for Scientific Web Claims The authors present a multilingual dense retrieval and reranking framework to link scientific web claims to their source publications. Peer-reviewed | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on scientific claims, not software or code. | |
| KhyontekAI at CheckThat! 2026: Multilingual Dense Retrieval and Cross-Encoder Reranking for Scientific Web Claims The authors present a multilingual dense retrieval and reranking framework to link scientific web claims to their source publications. Peer-reviewed | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Document retrieval framework, not an autonomous agent system. | |
| Binding Phoneme Identity to Position in Neural Models and the Human Brain The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Cognitive and representational neuroscience paper, not about agents. | |
| Binding Phoneme Identity to Position in Neural Models and the Human Brain The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Does not introduce benchmarks or LLM evaluation frameworks. | |
| Binding Phoneme Identity to Position in Neural Models and the Human Brain The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Does not address deployment safety, hallucination, or alignment. | |
| Binding Phoneme Identity to Position in Neural Models and the Human Brain The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on text and phonemes, not multimodal models. | |
| Binding Phoneme Identity to Position in Neural Models and the Human Brain The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Investigates phoneme representations, not model efficiency. | |
| Binding Phoneme Identity to Position in Neural Models and the Human Brain The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not focused on programming or code synthesis. | |
| Binding Phoneme Identity to Position in Neural Models and the Human Brain The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Not related to retrieval or document augmentation. | |
| What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions This paper compares observational feature discovery scores in sparse autoencoders against actual causal intervention effects on model outputs. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Mechanistic interpretability study, not about agentic workflows. | |
| What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions This paper compares observational feature discovery scores in sparse autoencoders against actual causal intervention effects on model outputs. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Does not involve multimodal representations or models. | |
| What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions This paper compares observational feature discovery scores in sparse autoencoders against actual causal intervention effects on model outputs. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Studies feature steering and ablation, not model compression. | |
| What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions This paper compares observational feature discovery scores in sparse autoencoders against actual causal intervention effects on model outputs. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Not related to retrieval-augmented generation. | |
| What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions This paper compares observational feature discovery scores in sparse autoencoders against actual causal intervention effects on model outputs. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not about coding assistants or software engineering tasks. | |
| Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh This work evaluates machine-text detectors on Kazakh text using pre-ChatGPT human writing and shows significant false-positive rates. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Not related to model efficiency, quantization, or distillation. | |
| Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh This work evaluates machine-text detectors on Kazakh text using pre-ChatGPT human writing and shows significant false-positive rates. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Text-only dataset and detectors, no multimodal components. | |
| Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh This work evaluates machine-text detectors on Kazakh text using pre-ChatGPT human writing and shows significant false-positive rates. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Evaluates text detectors, not AI agents. | |
| Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh This work evaluates machine-text detectors on Kazakh text using pre-ChatGPT human writing and shows significant false-positive rates. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not address retrieval-augmented generation. | |
| Unchecked or Falsely Accused? Evaluating AI-Generated Text Detectors on Kazakh This work evaluates machine-text detectors on Kazakh text using pre-ChatGPT human writing and shows significant false-positive rates. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on natural language text, not code generation. | |
| RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not study model compression, quantization, or efficient architectures. | |
| RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not focused on code generation or software engineering tasks. | |
| RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on task completion utility rather than safety or reliability. | |
| RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Text-only agent framework, not involving multimodal models. | |
| RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Does not introduce a new benchmark or evaluation method. | |
| RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on agent trajectory control rather than RAG architectures. | |
| More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Combines rule engines with LLMs, not retrieval-augmented generation. | |
| More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Applies to clinical text records, not multimodal data. | |
| More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Proposes a hybrid decision framework rather than evaluation benchmarks. | |
| More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on clinical guideline execution rather than software engineering. | |
| More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Replaces agent reasoning with deterministic rule execution for clinical decisions. | |
| Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Applies standard BERT models rather than model compression. | |
| Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on educational reading curation, not AI safety. | |
| Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Processes text abstracts and objectives, not multimodal models. | |
| Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents a domain-specific curriculum workflow, not an LLM benchmark. | |
| Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Applies NLP pipelines to curriculum curation, not autonomous agents. | |
| Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Applies NLP to dental education reading lists, not code. | |
| Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Uses sentence embeddings for reading alignment, not RAG systems. | |
| Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies foundational pretraining rather than software engineering. | |
| Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on language model pretraining without multimodal components. | |
| Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Examines pretraining efficiency rather than deployment safety. | |
| Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Investigates pretraining optimization rather than benchmark development. | |
| Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Investigates pretraining dynamics, not retrieval-augmented generation. |
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