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 |
|---|---|---|---|
| FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Not related to programming, software maintenance, or code. | |
| FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Introduces a conversational framework, not general benchmark evaluation. | |
| FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint | Small and efficient models via arxiv · 15 Oct, 15:06 | Not relevant Does not cover model pruning, quantization, or efficient serving. | |
| FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Focuses on conversation support alignment, not multi-step tool-use agents. | |
| FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Focuses on conversational empathy, not guardrails or safety attacks. | |
| FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not involve external retrieval or knowledge base grounding. | |
| What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not explore retrieval augmentation or external knowledge grounding. | |
| What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Examines ambiguity estimation, not multi-step autonomous planning agents. | |
| What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint | Small and efficient models via arxiv · 15 Oct, 15:06 | Not relevant Does not address model compression, quantization, or efficient serving. | |
| What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Uses Text-to-SQL as an evaluation setting, not code generation. | |
| What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Focuses on text QA and SQL ambiguities, not multimodality. | |
| Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Focuses on text pre-training, not multimodal or vision tasks. | |
| Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Not related to code generation or software engineering tasks. | |
| Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Addresses multilingual data ordering, not safety or guardrails. | |
| Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Focuses on pre-training regimens, not benchmark evaluation methodology. | |
| Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Examines multilingual pre-training data schedules, not autonomous agents. | |
| Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not address external database retrieval or grounding. | |
| Why Generate a Click? Plug-in Decision Heads for Web Agents This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Focuses on web agent execution efficiency, not safety guardrails. | |
| Why Generate a Click? Plug-in Decision Heads for Web Agents This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Evaluates web agent performance, but focus is on architecture. | |
| Why Generate a Click? Plug-in Decision Heads for Web Agents This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Focuses on GUI web navigation, not software code writing. | |
| Why Generate a Click? Plug-in Decision Heads for Web Agents This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not focus on document retrieval or external knowledge bases. | |
| Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Surveys mechanistic interpretability methods, not autonomous tool-using agents. | |
| Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Surveys feature interpretability, not standard LLM evaluation benchmarks. | |
| Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not focus on retrieval-augmented generation systems. | |
| Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint | Small and efficient models via arxiv · 15 Oct, 15:06 | Not relevant Covers interpretability analysis rather than serving or inference efficiency. | |
| Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Focuses on language model feature representations, not multimodal models. | |
| Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Does not target software engineering or code LLMs. | |
| Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Focuses on response diversity, not deployment safety or jailbreaks. | |
| Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not address external document retrieval or grounding. | |
| Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Not related to coding assistants or software repair. | |
| Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Focuses on embedding search for diverse generation, not agents. | |
| Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Deals with text embedding spaces and language generation. | |
| Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint | Small and efficient models via arxiv · 15 Oct, 15:06 | Not relevant Does not cover model compression, pruning, or quantization. | |
| Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Focuses on generation diversity rather than benchmark evaluation. | |
| RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Develops generative reward models, not general evaluation benchmarks. | |
| RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Not focused on code synthesis, inspection, or program repair. | |
| RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Focuses on reward model distillation, not autonomous planning agents. | |
| RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Targets text preference reward models, not multimodal tasks. | |
| RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not address document retrieval or knowledge augmentation. | |
| TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Focuses on inference acceleration rather than evaluation metrics. | |
| TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Accelerates token generation without addressing AI safety or guardrails. | |
| TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not involve external retrieval or knowledge bases. | |
| TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Focuses on text LLM speculative decoding, not multimodal models. | |
| TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Addresses general speculative decoding, not software engineering models. | |
| TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Focuses on speculative decoding speed, not autonomous agentic systems. | |
| When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint | Small and efficient models via arxiv · 15 Oct, 15:06 | Not relevant Does not address model efficiency, quantization, or distillation. | |
| When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Analyzes career path prediction models, not planning agents. | |
| When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Focuses on career trajectory tasks rather than LLM benchmarks. | |
| When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Not focused on safety, prompt injection, or hallucination. | |
| When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Uses textual job descriptions, without vision or speech modalities. | |
| When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not examine retrieval-augmented generation architectures. | |
| When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Not related to code generation or software development. | |
| Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Focuses on multilingual text scaling, not cross-modal processing. | |
| Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Focuses on scaling laws, not safety, guardrails, or hallucination. | |
| Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not address external retrieval or grounding techniques. | |
| Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Investigates scaling laws rather than benchmark evaluation reliability. | |
| Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Focuses on multilingual mixture-of-experts architecture, not agents. | |
| Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Not focused on software engineering or code modeling. | |
| Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint | Small and efficient models via arxiv · 15 Oct, 15:06 | Not relevant Does not study model compression, pruning, or quantization. | |
| Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Addresses cross-modal knowledge updates, not AI safety or jailbreaks. |
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