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 |
|---|---|---|---|
| Towards Autopilot Labeling for Short Video Content Governance The paper presents a multi-stage automated labeling framework using multimodal models and agents to annotate short videos for content moderation. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Not focused on retrieval-augmented generation architectures. | |
| Towards Autopilot Labeling for Short Video Content Governance The paper presents a multi-stage automated labeling framework using multimodal models and agents to annotate short videos for content moderation. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents an automated labeling system, not an evaluation benchmark. | |
| Towards Autopilot Labeling for Short Video Content Governance The paper presents a multi-stage automated labeling framework using multimodal models and agents to annotate short videos for content moderation. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on video governance, not software engineering. | |
| Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Reviews dialogue tutoring systems, not multimodal models. | |
| Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Synthesis of educational dialogue systems, not general agents. | |
| Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not address retrieval-augmented generation. | |
| Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on pedagogical efficacy, not deployment safety risks. | |
| Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on tutoring dialogues, not code generation. | |
| Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Not focused on model size or inference efficiency. | |
| Generalizable Model Editing Could Be More Than Direct Generalization The authors analyze limitations in direct model editing benchmarks and compare them against observed generalization in practical chat settings. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not address model compression or inference efficiency. | |
| Generalizable Model Editing Could Be More Than Direct Generalization The authors analyze limitations in direct model editing benchmarks and compare them against observed generalization in practical chat settings. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on weight editing rather than retrieval augmentation. | |
| Generalizable Model Editing Could Be More Than Direct Generalization The authors analyze limitations in direct model editing benchmarks and compare them against observed generalization in practical chat settings. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Studies model editing, not autonomous multi-step agents. | |
| Generalizable Model Editing Could Be More Than Direct Generalization The authors analyze limitations in direct model editing benchmarks and compare them against observed generalization in practical chat settings. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on text LLM editing, not multimodal models. | |
| Generalizable Model Editing Could Be More Than Direct Generalization The authors analyze limitations in direct model editing benchmarks and compare them against observed generalization in practical chat settings. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not focused on code generation or software engineering. | |
| Preserving Updates in Low-Bit Recurrent State Quantization This paper proposes a low-rank bypass to accumulate small updates and reduce compounding errors in quantized recurrent model states. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Architecture-level state quantization, not about AI agents. | |
| Preserving Updates in Low-Bit Recurrent State Quantization This paper proposes a low-rank bypass to accumulate small updates and reduce compounding errors in quantized recurrent model states. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not related to programming assistants or code generation. | |
| Preserving Updates in Low-Bit Recurrent State Quantization This paper proposes a low-rank bypass to accumulate small updates and reduce compounding errors in quantized recurrent model states. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Not focused on external retrieval or grounding. | |
| Preserving Updates in Low-Bit Recurrent State Quantization This paper proposes a low-rank bypass to accumulate small updates and reduce compounding errors in quantized recurrent model states. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Does not propose benchmarks or evaluation methodologies. | |
| Preserving Updates in Low-Bit Recurrent State Quantization This paper proposes a low-rank bypass to accumulate small updates and reduce compounding errors in quantized recurrent model states. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Addresses memory efficiency rather than safety or alignment. | |
| Preserving Updates in Low-Bit Recurrent State Quantization This paper proposes a low-rank bypass to accumulate small updates and reduce compounding errors in quantized recurrent model states. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on recurrent model states, not multimodal tasks. | |
| TL;QR: Querying Temporal Discursive Patterns in Textual Corpora TL;QR pairs language-model passage judgments with a symbolic temporal logic engine to track evolving patterns in textual corpora. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on text querying rather than safety or guardrails. | |
| TL;QR: Querying Temporal Discursive Patterns in Textual Corpora TL;QR pairs language-model passage judgments with a symbolic temporal logic engine to track evolving patterns in textual corpora. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Text-only query system, no multimodal data. | |
| TL;QR: Querying Temporal Discursive Patterns in Textual Corpora TL;QR pairs language-model passage judgments with a symbolic temporal logic engine to track evolving patterns in textual corpora. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Proposes a querying engine, not a model benchmark. | |
| TL;QR: Querying Temporal Discursive Patterns in Textual Corpora TL;QR pairs language-model passage judgments with a symbolic temporal logic engine to track evolving patterns in textual corpora. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Applies to discourse corpora, not code or programming. | |
| TL;QR: Querying Temporal Discursive Patterns in Textual Corpora TL;QR pairs language-model passage judgments with a symbolic temporal logic engine to track evolving patterns in textual corpora. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Declarative query engine, not an autonomous agent framework. | |
| TL;QR: Querying Temporal Discursive Patterns in Textual Corpora TL;QR pairs language-model passage judgments with a symbolic temporal logic engine to track evolving patterns in textual corpora. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Not focused on model quantization, distillation, or pruning. | |
| NepPlural: A Human-Anchored Benchmark for Persona Annotation in Nepali Migration Discourse NepPlural provides an annotated benchmark of Nepali discourse to evaluate how reliably language models capture diverse socio-cultural personas. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not related to coding assistants or software engineering. | |
| NepPlural: A Human-Anchored Benchmark for Persona Annotation in Nepali Migration Discourse NepPlural provides an annotated benchmark of Nepali discourse to evaluate how reliably language models capture diverse socio-cultural personas. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not focus on retrieval-augmented generation. | |
| NepPlural: A Human-Anchored Benchmark for Persona Annotation in Nepali Migration Discourse NepPlural provides an annotated benchmark of Nepali discourse to evaluate how reliably language models capture diverse socio-cultural personas. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not explore model compression or efficiency techniques. | |
| NepPlural: A Human-Anchored Benchmark for Persona Annotation in Nepali Migration Discourse NepPlural provides an annotated benchmark of Nepali discourse to evaluate how reliably language models capture diverse socio-cultural personas. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Benchmark for persona annotation, not agent systems. | |
| NepPlural: A Human-Anchored Benchmark for Persona Annotation in Nepali Migration Discourse NepPlural provides an annotated benchmark of Nepali discourse to evaluate how reliably language models capture diverse socio-cultural personas. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Examines text comments only, without multimodal components. | |
| Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not study compression or efficient model serving. | |
| Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Empirical study on domain transfer, not benchmark design. | |
| Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on natural language sentiment, not code generation. | |
| Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Not related to retrieval-augmented systems. | |
| Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Deals strictly with text datasets. | |
| Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Studies sentiment classification, not agentic planning. | |
| Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on classification accuracy across domains, not safety. | |
| Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on text classification, not AI agents. | |
| Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Applies standard training techniques, no novel evaluation paradigm. | |
| Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Does not evaluate safety, jailbreaks, or deployment risks. | |
| Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Deals with Marathi text sentiment, not code. | |
| Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Standard classification task, not retrieval-augmented generation. | |
| Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Text-only classification study. | |
| Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Not focused on model compression or efficient inference. | |
| RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not focused on code synthesis or software engineering. | |
| RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on policy optimization, not multimodal models. | |
| RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Addresses reinforcement learning policies rather than RAG systems. | |
| RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on task performance rather than safety guardrails. | |
| RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Not focused on model quantization, pruning, or efficiency. | |
| RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Does not introduce a new benchmark or judge method. | |
| Who Sets the Pace of Judge Hacking? Judge Capability versus the Policy-Judge Gap This work analyzes whether policy capability gaps or judge models dictate reward hacking when training models against language-model judges. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Exclusively considers text LLMs and text judges. | |
| Who Sets the Pace of Judge Hacking? Judge Capability versus the Policy-Judge Gap This work analyzes whether policy capability gaps or judge models dictate reward hacking when training models against language-model judges. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on judge capability and RL, not compression. | |
| Who Sets the Pace of Judge Hacking? Judge Capability versus the Policy-Judge Gap This work analyzes whether policy capability gaps or judge models dictate reward hacking when training models against language-model judges. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Studies RL judge exploitation, not autonomous tool agents. | |
| Who Sets the Pace of Judge Hacking? Judge Capability versus the Policy-Judge Gap This work analyzes whether policy capability gaps or judge models dictate reward hacking when training models against language-model judges. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Does not address retrieval or external database augmentation. | |
| Who Sets the Pace of Judge Hacking? Judge Capability versus the Policy-Judge Gap This work analyzes whether policy capability gaps or judge models dictate reward hacking when training models against language-model judges. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not focused on code generation or software engineering. | |
| 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 | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on model compression, not retrieval or grounding. | |
| 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 agents via arxiv · 15 Oct, 06:18 | Not relevant Hardware pruning method, not related to AI agents. | |
| 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 | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Does not introduce LLM benchmarks or evaluation methods. | |
| 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 | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Not focused on code assistants or programming models. |
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