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 |
|---|---|---|---|
| HexaBee-4B: Six-Language Translation with Multilingual Answering and Bidirectional Supervision This paper introduces HexaBee-4B, a translation model trained across six languages using joint multilingual answering and bidirectional supervision. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Fine-tunes text translation models without using multimodal image or video capabilities. | |
| HexaBee-4B: Six-Language Translation with Multilingual Answering and Bidirectional Supervision This paper introduces HexaBee-4B, a translation model trained across six languages using joint multilingual answering and bidirectional supervision. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Develops a machine translation model rather than retrieval-augmented generation. | |
| HexaBee-4B: Six-Language Translation with Multilingual Answering and Bidirectional Supervision This paper introduces HexaBee-4B, a translation model trained across six languages using joint multilingual answering and bidirectional supervision. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on translation accuracy rather than AI safety, jailbreaks, or guardrails. | |
| Rule vs.Consequence: Dissociable Internal Representations of Moral Reasoning in LLMs This paper uses activation steering and probing to identify distinct internal representations for rule-based versus consequence-based moral reasoning in language models. Peer-reviewed | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Analyzes text-based moral reasoning without multimodal image or audio processing. | |
| Rule vs.Consequence: Dissociable Internal Representations of Moral Reasoning in LLMs This paper uses activation steering and probing to identify distinct internal representations for rule-based versus consequence-based moral reasoning in language models. Peer-reviewed | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Investigates internal model activations rather than retrieval-augmented generation. | |
| Rule vs.Consequence: Dissociable Internal Representations of Moral Reasoning in LLMs This paper uses activation steering and probing to identify distinct internal representations for rule-based versus consequence-based moral reasoning in language models. Peer-reviewed | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Studies internal representations of moral reasoning styles, not autonomous agents. | |
| Rule vs.Consequence: Dissociable Internal Representations of Moral Reasoning in LLMs This paper uses activation steering and probing to identify distinct internal representations for rule-based versus consequence-based moral reasoning in language models. Peer-reviewed | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Examines moral reasoning representations, not code generation or software engineering. | |
| Rule vs.Consequence: Dissociable Internal Representations of Moral Reasoning in LLMs This paper uses activation steering and probing to identify distinct internal representations for rule-based versus consequence-based moral reasoning in language models. Peer-reviewed | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Mechanistic interpretability study, not model compression or efficient serving research. | |
| PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization This paper presents PersonaFactors, a framework that distills reusable preference patterns across tasks for dynamic prompt personalization in language models. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents a dynamic personalization framework rather than an evaluation benchmark. | |
| PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization This paper presents PersonaFactors, a framework that distills reusable preference patterns across tasks for dynamic prompt personalization in language models. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on user personalization performance rather than AI safety or reliability risks. | |
| PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization This paper presents PersonaFactors, a framework that distills reusable preference patterns across tasks for dynamic prompt personalization in language models. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not address model compression, quantization, distillation, or efficient serving. | |
| PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization This paper presents PersonaFactors, a framework that distills reusable preference patterns across tasks for dynamic prompt personalization in language models. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on user preference personalization in LLMs, not multi-step agent actions. | |
| PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization This paper presents PersonaFactors, a framework that distills reusable preference patterns across tasks for dynamic prompt personalization in language models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Addresses user personalization factor modeling, not retrieval-augmented generation systems. | |
| PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization This paper presents PersonaFactors, a framework that distills reusable preference patterns across tasks for dynamic prompt personalization in language models. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Evaluates text-based personalization benchmarks without multimodal model capabilities. | |
| PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization This paper presents PersonaFactors, a framework that distills reusable preference patterns across tasks for dynamic prompt personalization in language models. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies personalized text generation, not code synthesis or software engineering. | |
| CultureBot: A Framework for Retrieval-Augmented Exploration of Cultural Heritage Data This paper introduces an interactive retrieval-augmented framework that combines knowledge graphs and text retrieval to explore digital cultural heritage archives. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Presents a conversational retrieval system rather than autonomous multi-step planning agents. | |
| CultureBot: A Framework for Retrieval-Augmented Exploration of Cultural Heritage Data This paper introduces an interactive retrieval-augmented framework that combines knowledge graphs and text retrieval to explore digital cultural heritage archives. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Develops a cultural heritage search assistant rather than deployment safety guardrails. | |
| CultureBot: A Framework for Retrieval-Augmented Exploration of Cultural Heritage Data This paper introduces an interactive retrieval-augmented framework that combines knowledge graphs and text retrieval to explore digital cultural heritage archives. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Develops a domain retrieval application without focusing on model compression techniques. | |
| CultureBot: A Framework for Retrieval-Augmented Exploration of Cultural Heritage Data This paper introduces an interactive retrieval-augmented framework that combines knowledge graphs and text retrieval to explore digital cultural heritage archives. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on text and knowledge graph retrieval without multimodal vision-language models. | |
| CultureBot: A Framework for Retrieval-Augmented Exploration of Cultural Heritage Data This paper introduces an interactive retrieval-augmented framework that combines knowledge graphs and text retrieval to explore digital cultural heritage archives. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Describes an application framework rather than general LLM evaluation benchmarks. | |
| CultureBot: A Framework for Retrieval-Augmented Exploration of Cultural Heritage Data This paper introduces an interactive retrieval-augmented framework that combines knowledge graphs and text retrieval to explore digital cultural heritage archives. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Addresses cultural heritage catalog search, not programming assistants or code generation. | |
| CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds This paper introduces a multi-agent sandbox benchmark to evaluate language models on state inference, forecasting, and intervention prediction. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on sandbox world models and forecasting rather than retrieval-augmented generation. | |
| CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds This paper introduces a multi-agent sandbox benchmark to evaluate language models on state inference, forecasting, and intervention prediction. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Assesses world-model forecasting capabilities, not software code generation or repair. | |
| CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds This paper introduces a multi-agent sandbox benchmark to evaluate language models on state inference, forecasting, and intervention prediction. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on predictive world modeling rather than safety guardrails or red-teaming. | |
| CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds This paper introduces a multi-agent sandbox benchmark to evaluate language models on state inference, forecasting, and intervention prediction. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Assesses language models on text state representations without visual multimodal processing. | |
| CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds This paper introduces a multi-agent sandbox benchmark to evaluate language models on state inference, forecasting, and intervention prediction. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Presents a forecasting benchmark rather than model compression or efficient serving. | |
| Differential Privacy Protected Preference Alignment on Survey Derived Data This paper presents a label differential privacy framework to protect survey-derived ordinal preference data during language model alignment. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Studies privacy guarantees during training, not inference speedup or model compression. | |
| Differential Privacy Protected Preference Alignment on Survey Derived Data This paper presents a label differential privacy framework to protect survey-derived ordinal preference data during language model alignment. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on text-based survey preference alignment without multimodal capabilities. | |
| Differential Privacy Protected Preference Alignment on Survey Derived Data This paper presents a label differential privacy framework to protect survey-derived ordinal preference data during language model alignment. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Addresses privacy-preserving preference alignment, not document retrieval systems. | |
| Differential Privacy Protected Preference Alignment on Survey Derived Data This paper presents a label differential privacy framework to protect survey-derived ordinal preference data during language model alignment. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies preference alignment on survey data, not code generation or repair. | |
| Differential Privacy Protected Preference Alignment on Survey Derived Data This paper presents a label differential privacy framework to protect survey-derived ordinal preference data during language model alignment. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents privacy-preserving alignment methods rather than LLM evaluation benchmarks. | |
| Differential Privacy Protected Preference Alignment on Survey Derived Data This paper presents a label differential privacy framework to protect survey-derived ordinal preference data during language model alignment. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on differential privacy during preference alignment, not agent execution. | |
| The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Case study of human-LLM co-creation, not autonomous multi-step agent systems. | |
| The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents a qualitative design study rather than standard benchmarks or evaluation methods. | |
| The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Autoethnographic study of building a translation tool, not retrieval-augmented generation. | |
| The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on interpreting workflows and software development without vision-language modeling. | |
| The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on user tool co-creation rather than AI safety or jailbreak defenses. | |
| The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Describes software application development rather than model compression or inference efficiency. | |
| Ontology-Based Post-Coordinated Expression Assembly: from Clinical Narrative to Queryable Knowledge Graphs This paper introduces UNIGRAPHE, a deterministic method to assemble clinical text into queryable knowledge graphs conforming to SNOMED CT grammar. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents an information extraction methodology rather than an LLM evaluation benchmark. | |
| Ontology-Based Post-Coordinated Expression Assembly: from Clinical Narrative to Queryable Knowledge Graphs This paper introduces UNIGRAPHE, a deterministic method to assemble clinical text into queryable knowledge graphs conforming to SNOMED CT grammar. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on clinical oncology narrative extraction, not programming or software engineering. | |
| Ontology-Based Post-Coordinated Expression Assembly: from Clinical Narrative to Queryable Knowledge Graphs This paper introduces UNIGRAPHE, a deterministic method to assemble clinical text into queryable knowledge graphs conforming to SNOMED CT grammar. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Processes clinical text records without multimodal image or audio components. | |
| Ontology-Based Post-Coordinated Expression Assembly: from Clinical Narrative to Queryable Knowledge Graphs This paper introduces UNIGRAPHE, a deterministic method to assemble clinical text into queryable knowledge graphs conforming to SNOMED CT grammar. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on ontology-based information extraction rather than AI safety or reliability. | |
| Ontology-Based Post-Coordinated Expression Assembly: from Clinical Narrative to Queryable Knowledge Graphs This paper introduces UNIGRAPHE, a deterministic method to assemble clinical text into queryable knowledge graphs conforming to SNOMED CT grammar. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not address model efficiency, quantization, distillation, or serving acceleration. | |
| Ontology-Based Post-Coordinated Expression Assembly: from Clinical Narrative to Queryable Knowledge Graphs This paper introduces UNIGRAPHE, a deterministic method to assemble clinical text into queryable knowledge graphs conforming to SNOMED CT grammar. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Constructs knowledge graphs from clinical text, not autonomous agents taking actions. | |
| Communication, Vocalisation, and Language This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Theoretical commentary on LLM language use, not autonomous agentic workflows. | |
| Communication, Vocalisation, and Language This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Conceptual perspective piece rather than an empirical benchmark or evaluation methodology. | |
| Communication, Vocalisation, and Language This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Theoretical commentary focused solely on natural language processing without multimodal models. | |
| Communication, Vocalisation, and Language This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Conceptual commentary on language rather than deployment safety, robustness, or guardrails. | |
| Communication, Vocalisation, and Language This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Does not address model compression, quantization, distillation, or efficient inference. | |
| Communication, Vocalisation, and Language This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Philosophical essay on language meaning rather than retrieval-augmented generation. | |
| Communication, Vocalisation, and Language This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Discusses cultural intelligence and language practices, not programming or code models. | |
| Intent-Triggered Anchoring for Multi-Token Format Units in Parallel Decoding of Diffusion Language Models This paper introduces a training-free decoding rule that anchors multi-token format units to ensure parseable parallel generation in diffusion language models. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Focuses on decoding algorithms rather than benchmarking or evaluating language models. | |
| Intent-Triggered Anchoring for Multi-Token Format Units in Parallel Decoding of Diffusion Language Models This paper introduces a training-free decoding rule that anchors multi-token format units to ensure parseable parallel generation in diffusion language models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on parallel decoding for diffusion models rather than external retrieval. | |
| Intent-Triggered Anchoring for Multi-Token Format Units in Parallel Decoding of Diffusion Language Models This paper introduces a training-free decoding rule that anchors multi-token format units to ensure parseable parallel generation in diffusion language models. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Evaluates medical report formatting and math markers, not software engineering. | |
| Intent-Triggered Anchoring for Multi-Token Format Units in Parallel Decoding of Diffusion Language Models This paper introduces a training-free decoding rule that anchors multi-token format units to ensure parseable parallel generation in diffusion language models. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Applies decoding rules to text diffusion generators, not multimodal vision-language models. | |
| Intent-Triggered Anchoring for Multi-Token Format Units in Parallel Decoding of Diffusion Language Models This paper introduces a training-free decoding rule that anchors multi-token format units to ensure parseable parallel generation in diffusion language models. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Proposes a decoding technique for diffusion language models, not agent planning. | |
| Support or Magnitude? Characterizing Neuron Relevance for Instruction Data Selection This paper examines neuron relevance characterization methods based on magnitude and distribution support for selecting instruction fine-tuning data. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Investigates instruction data selection criteria, not LLM agent workflows. | |
| Support or Magnitude? Characterizing Neuron Relevance for Instruction Data Selection This paper examines neuron relevance characterization methods based on magnitude and distribution support for selecting instruction fine-tuning data. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies general instruction data selection, not code-specific models or benchmarks. | |
| Support or Magnitude? Characterizing Neuron Relevance for Instruction Data Selection This paper examines neuron relevance characterization methods based on magnitude and distribution support for selecting instruction fine-tuning data. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on selecting training instruction data rather than retrieval-augmented generation. | |
| Support or Magnitude? Characterizing Neuron Relevance for Instruction Data Selection This paper examines neuron relevance characterization methods based on magnitude and distribution support for selecting instruction fine-tuning data. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Examines text instruction fine-tuning without any multimodal components. |
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