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
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on parameter-efficient RL fine-tuning, not multi-step agent planning.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses catastrophic forgetting in training rather than safety guardrails or jailbreaks.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Addresses text-based reinforcement learning fine-tuning, not multimodal architectures.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies continual RL training methods rather than LLM benchmark designs.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Investigates attention head fine-tuning rather than retrieval-augmented generation.
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning The paper studies ranking and partitioning attention heads by gradient norm for sparse and continual reinforcement learning fine-tuning. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Tests general fine-tuning across tasks without a primary focus on code assistance.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on question-answering accuracy rather than deployment safety, jailbreaks, or guardrails.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates question answering methods rather than proposing new evaluation benchmarks.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on reinforcement learning credit assignment rather than model compression or inference efficiency.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Applies to text and structured knowledge graphs, not multimodal vision models.
OpCredit: Operation-Aware Policy Learning for Agentic Knowledge Graph Question Answering The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on knowledge graph queries, not code generation or software repair.
Emotion Is Not External Knowledge: Internal Attention Signals for Affective State Reasoning The paper presents an internal attention boosting method to improve how language models infer implicit conversational affective states. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Investigates internal attention head routing, not autonomous agentic systems.
Emotion Is Not External Knowledge: Internal Attention Signals for Affective State Reasoning The paper presents an internal attention boosting method to improve how language models infer implicit conversational affective states. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies emotion classification accuracy rather than AI deployment safety or jailbreak prevention.
Emotion Is Not External Knowledge: Internal Attention Signals for Affective State Reasoning The paper presents an internal attention boosting method to improve how language models infer implicit conversational affective states. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on multi-turn text dialogue, not multimodal cross-modal models.
Emotion Is Not External Knowledge: Internal Attention Signals for Affective State Reasoning The paper presents an internal attention boosting method to improve how language models infer implicit conversational affective states. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Studies implicit conversational states in model weights rather than external retrieval.
Emotion Is Not External Knowledge: Internal Attention Signals for Affective State Reasoning The paper presents an internal attention boosting method to improve how language models infer implicit conversational affective states. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on attention boosting techniques rather than LLM evaluation methodology.
Emotion Is Not External Knowledge: Internal Attention Signals for Affective State Reasoning The paper presents an internal attention boosting method to improve how language models infer implicit conversational affective states. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies attention steering rather than model compression, distillation, or serving efficiency.
Emotion Is Not External Knowledge: Internal Attention Signals for Affective State Reasoning The paper presents an internal attention boosting method to improve how language models infer implicit conversational affective states. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on affective state reasoning, not code generation or program repair.
RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design The paper introduces RenoLayout to generate residential renovation floorplans and furniture layouts under structural and physical constraints. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on structured floorplan geometry generation rather than multimodal vision-language models.
RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design The paper introduces RenoLayout to generate residential renovation floorplans and furniture layouts under structural and physical constraints. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Deals with architectural design synthesis rather than AI safety or guardrails.
RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design The paper introduces RenoLayout to generate residential renovation floorplans and furniture layouts under structural and physical constraints. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model compression, quantization, distillation, or serving efficiency.
RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design The paper introduces RenoLayout to generate residential renovation floorplans and furniture layouts under structural and physical constraints. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Addresses residential design generation, not retrieval-augmented generation from databases.
RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design The paper introduces RenoLayout to generate residential renovation floorplans and furniture layouts under structural and physical constraints. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a floorplan generation framework rather than an LLM evaluation benchmark.
RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design The paper introduces RenoLayout to generate residential renovation floorplans and furniture layouts under structural and physical constraints. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on CAD floorplan generation, not multi-step LLM agent systems.
RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design The paper introduces RenoLayout to generate residential renovation floorplans and furniture layouts under structural and physical constraints. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Generates residential geometry code rather than general software engineering or coding assistants.
DPA-MoE: A Runtime Precision Control Framework for MoE Inference Introduces a dynamic precision control framework that allocates weight chunks to mixture-of-experts models during inference. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates quantization precision rather than general LLM benchmarking methodologies.
DPA-MoE: A Runtime Precision Control Framework for MoE Inference Introduces a dynamic precision control framework that allocates weight chunks to mixture-of-experts models during inference. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Deals with MoE language model architectures, not multimodal inputs.
DPA-MoE: A Runtime Precision Control Framework for MoE Inference Introduces a dynamic precision control framework that allocates weight chunks to mixture-of-experts models during inference. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents a runtime precision framework, not agent planning or tool execution.
DPA-MoE: A Runtime Precision Control Framework for MoE Inference Introduces a dynamic precision control framework that allocates weight chunks to mixture-of-experts models during inference. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on serving throughput and quantization rather than AI safety or guardrails.
DPA-MoE: A Runtime Precision Control Framework for MoE Inference Introduces a dynamic precision control framework that allocates weight chunks to mixture-of-experts models during inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on MoE weight quantization rather than retrieval-augmented generation.
DPA-MoE: A Runtime Precision Control Framework for MoE Inference Introduces a dynamic precision control framework that allocates weight chunks to mixture-of-experts models during inference. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on MoE inference precision, not programming or code generation.
Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models Analyzes how calibration language choices affect block-influence depth pruning rankings in multilingual language models. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Examines multilingual language pruning, not software engineering or coding models.
Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models Analyzes how calibration language choices affect block-influence depth pruning rankings in multilingual language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Analyzes pruning calibration transfer rather than LLM benchmarking or evaluation frameworks.
Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models Analyzes how calibration language choices affect block-influence depth pruning rankings in multilingual language models. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses exclusively on text-based multilingual models, not multimodal systems.
Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models Analyzes how calibration language choices affect block-influence depth pruning rankings in multilingual language models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies structured pruning mechanics rather than deployment safety, guardrails, or jailbreaks.
Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models Analyzes how calibration language choices affect block-influence depth pruning rankings in multilingual language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Investigates depth pruning rather than external knowledge retrieval or RAG.
Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models Analyzes how calibration language choices affect block-influence depth pruning rankings in multilingual language models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies Transformer block pruning, not autonomous planning or agent tool use.
Beyond Invariance: Factor-wise Selective Treatment of Shifts for Multimodal Learning Proposes a factor-wise representation framework to selectively treat different distribution shifts in multimodal machine learning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Examines representation shifts rather than retrieval-augmented generation pipelines.
Beyond Invariance: Factor-wise Selective Treatment of Shifts for Multimodal Learning Proposes a factor-wise representation framework to selectively treat different distribution shifts in multimodal machine learning. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Addresses out-of-distribution multimodal learning rather than model compression or inference efficiency.
Beyond Invariance: Factor-wise Selective Treatment of Shifts for Multimodal Learning Proposes a factor-wise representation framework to selectively treat different distribution shifts in multimodal machine learning. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on multimodal sentiment datasets, not code generation or software tasks.
Beyond Invariance: Factor-wise Selective Treatment of Shifts for Multimodal Learning Proposes a factor-wise representation framework to selectively treat different distribution shifts in multimodal machine learning. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies robust representation learning rather than benchmarking methodologies.
Beyond Invariance: Factor-wise Selective Treatment of Shifts for Multimodal Learning Proposes a factor-wise representation framework to selectively treat different distribution shifts in multimodal machine learning. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on multimodal distribution shifts, not autonomous agent planning.
Beyond Invariance: Factor-wise Selective Treatment of Shifts for Multimodal Learning Proposes a factor-wise representation framework to selectively treat different distribution shifts in multimodal machine learning. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on multimodal classification robustness rather than safety, red-teaming, or guardrails.
Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction Evaluates prompt optimization methods and identifies effective prompting strategies for social media stress and emotion classification. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies prompt tuning for emotion detection rather than general LLM evaluation frameworks.
Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction Evaluates prompt optimization methods and identifies effective prompting strategies for social media stress and emotion classification. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies emotion classification accuracy rather than deployment guardrails or safety risks.
Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction Evaluates prompt optimization methods and identifies effective prompting strategies for social media stress and emotion classification. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Operates strictly on text data from social media, not multimodal data.
Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction Evaluates prompt optimization methods and identifies effective prompting strategies for social media stress and emotion classification. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on prompting strategies for classification, not autonomous agentic workflows.
Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction Evaluates prompt optimization methods and identifies effective prompting strategies for social media stress and emotion classification. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Investigates prompt engineering for classification without external document retrieval.
Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction Evaluates prompt optimization methods and identifies effective prompting strategies for social media stress and emotion classification. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Analyzes emotion prediction prompts rather than software engineering or code models.
Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction Evaluates prompt optimization methods and identifies effective prompting strategies for social media stress and emotion classification. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Analyzes prompting strategies rather than model compression, distillation, or efficient inference.
HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding Introduces a hybrid speculative decoding framework splitting draft tasks between mobile neural processing units and cloud servers. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on language model speculative decoding, not multimodal cross-modal architectures.
HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding Introduces a hybrid speculative decoding framework splitting draft tasks between mobile neural processing units and cloud servers. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Presents an inference acceleration framework, not code synthesis or code repair.
HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding Introduces a hybrid speculative decoding framework splitting draft tasks between mobile neural processing units and cloud servers. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies speculative decoding inference, not autonomous agent planning or tools.
HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding Introduces a hybrid speculative decoding framework splitting draft tasks between mobile neural processing units and cloud servers. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates inference performance on benchmarks rather than benchmarking methodologies.
HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding Introduces a hybrid speculative decoding framework splitting draft tasks between mobile neural processing units and cloud servers. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on mobile inference efficiency rather than safety, alignment, or hallucination prevention.
HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding Introduces a hybrid speculative decoding framework splitting draft tasks between mobile neural processing units and cloud servers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on speculative decoding speedups rather than external knowledge retrieval.
SITAR: A Situational Distortion Metric for Automatic Speech Recognition Evaluation Develops a lightweight situational distortion metric to evaluate speech recognition errors based on practical conversational severity. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on evaluating speech transcription text rather than multimodal vision-language architectures.
SITAR: A Situational Distortion Metric for Automatic Speech Recognition Evaluation Develops a lightweight situational distortion metric to evaluate speech recognition errors based on practical conversational severity. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Builds a lightweight metric regressor rather than studying model compression or efficient serving.
SITAR: A Situational Distortion Metric for Automatic Speech Recognition Evaluation Develops a lightweight situational distortion metric to evaluate speech recognition errors based on practical conversational severity. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Introduces an ASR evaluation metric, not LLM agent systems.
SITAR: A Situational Distortion Metric for Automatic Speech Recognition Evaluation Develops a lightweight situational distortion metric to evaluate speech recognition errors based on practical conversational severity. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on speech transcription metrics rather than external document retrieval systems.

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