Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,312In brief24More (on-topic)3,333Not relevant13,753Not assessed87Older351Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | LLMs for code via arxiv · 17 Oct, 06:17 | Not relevant Focuses on MoE inference precision, not programming or code 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 | 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 | Multimodal models via arxiv · 17 Oct, 06:17 | Not relevant Deals with MoE language model architectures, not multimodal inputs. | |
| 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 | AI agents via arxiv · 17 Oct, 06:17 | Not relevant Studies Transformer block pruning, not autonomous planning or agent tool use. | |
| 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 | 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 | 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 safety and reliability in deployment via arxiv · 17 Oct, 06:17 | Not relevant Studies structured pruning mechanics rather than deployment safety, guardrails, or jailbreaks. | |
| 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 | 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 | 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 | 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. | |
| 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. | |
| 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. | |
| 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 | 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 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 | 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. | |
| 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 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 | 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 | Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17 | Not relevant Focuses on speculative decoding speedups rather than external knowledge retrieval. | |
| 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 | Multimodal models via arxiv · 17 Oct, 06:17 | Not relevant Focuses on language model speculative decoding, not multimodal cross-modal 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 | LLMs for code via arxiv · 17 Oct, 06:17 | Not relevant Deals with speech recognition evaluation, not programming or code generation. | |
| 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 | AI safety and reliability in deployment via arxiv · 17 Oct, 06:17 | Not relevant Focuses on transcription scoring accuracy rather than deployment safety or jailbreak prevention. | |
| 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. | |
| 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 | 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. | |
| Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint | LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17 | Not relevant Focuses on translation optimization rather than LLM evaluation methodology or benchmark flaws. | |
| Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint | AI agents via arxiv · 17 Oct, 06:17 | Not relevant Focuses on translation circuits, not multi-step agent planning or tools. | |
| Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint | Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17 | Not relevant Studies internal translation circuits rather than external knowledge retrieval. | |
| Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint | LLMs for code via arxiv · 17 Oct, 06:17 | Not relevant Focuses on natural language translation, not code generation or software engineering. | |
| Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint | AI safety and reliability in deployment via arxiv · 17 Oct, 06:17 | Not relevant Investigates mechanistic interpretability for translation without focusing on safety or jailbreaks. | |
| Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation Identifies translation circuits in multilingual models and optimizes fewer than five percent of parameters for low-resource translation. Preprint | Multimodal models via arxiv · 17 Oct, 06:17 | Not relevant Applies to text-based multilingual translation without any multimodal components. | |
| OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions. Preprint | Multimodal models via arxiv · 17 Oct, 06:17 | Not relevant Focuses on text-based code repositories, not multimodal models. | |
| OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions. Preprint | Small and efficient models via arxiv · 17 Oct, 06:17 | Not relevant Uses distillation for agent training, not for model compression or efficient serving. | |
| OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents Uses multi-scope reminders and on-policy distillation to help coding agents maintain constraints over long software engineering interactions. Preprint | LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17 | Not relevant Uses coding benchmarks for evaluation rather than proposing new LLM benchmark methodologies. |
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