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
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.
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	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	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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents a forecasting benchmark rather than model compression or efficient serving.
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.
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	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	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	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	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	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	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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Describes software application development rather than model compression or inference efficiency.
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	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	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.
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	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 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	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	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	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	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Discusses cultural intelligence and language practices, not programming or code 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	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	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	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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Philosophical essay on language meaning rather than retrieval-augmented generation.
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	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	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	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	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	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	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Examines text instruction fine-tuning without any multimodal components.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes data curation metrics rather than benchmarks or evaluation frameworks.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies training data selection rather than safety guardrails, hallucinations, or attacks.
Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards This paper investigates gradient spectral properties during reinforcement learning with verifiable rewards to design an adaptive-rank optimizer for reasoning models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Optimizes training dynamics for reasoning rather than assessing safety or reliability vulnerabilities.
Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards This paper investigates gradient spectral properties during reinforcement learning with verifiable rewards to design an adaptive-rank optimizer for reasoning models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Presents a general optimization algorithm for LLM reasoning, not software engineering.
Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards This paper investigates gradient spectral properties during reinforcement learning with verifiable rewards to design an adaptive-rank optimizer for reasoning models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a training optimizer for reinforcement learning, not agent tool execution.
Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards This paper investigates gradient spectral properties during reinforcement learning with verifiable rewards to design an adaptive-rank optimizer for reasoning models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on optimization methods rather than benchmarking or evaluation protocols.
Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards This paper investigates gradient spectral properties during reinforcement learning with verifiable rewards to design an adaptive-rank optimizer for reasoning models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on language model training optimization without multimodal data.
Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards This paper investigates gradient spectral properties during reinforcement learning with verifiable rewards to design an adaptive-rank optimizer for reasoning models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies optimizer geometry in RLVR rather than retrieval or document grounding.
Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Works strictly on text sentence representations without any multimodal features.
Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on compositional sentence embeddings, not agent planning or tool execution.
Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses representation learning without evaluating deployment safety, alignment, or guardrails.
Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Proposes a recursive hypernetwork architecture without addressing compression or serving efficiency.
Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies natural language sentence syntax and semantics, not code generation.
Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Proposes sentence representation learning rather than retrieval-augmented generation systems.
Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates representations on probing tasks rather than providing an LLM evaluation benchmark.
SIREN: Situation-aware Index for Response and Emergency ActioNability in Crisis Translation This paper introduces an evaluation index to measure whether machine translation of emergency communication preserves critical safety actionability. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on crisis translation metrics, not autonomous planning or tool-using agents.
SIREN: Situation-aware Index for Response and Emergency ActioNability in Crisis Translation This paper introduces an evaluation index to measure whether machine translation of emergency communication preserves critical safety actionability. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text translation in emergencies, with no multimodal modalities evaluated.
SIREN: Situation-aware Index for Response and Emergency ActioNability in Crisis Translation This paper introduces an evaluation index to measure whether machine translation of emergency communication preserves critical safety actionability. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates natural language emergency translation, not software engineering or coding.
SIREN: Situation-aware Index for Response and Emergency ActioNability in Crisis Translation This paper introduces an evaluation index to measure whether machine translation of emergency communication preserves critical safety actionability. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses machine translation evaluation rather than retrieval-augmented generation systems.
SIREN: Situation-aware Index for Response and Emergency ActioNability in Crisis Translation This paper introduces an evaluation index to measure whether machine translation of emergency communication preserves critical safety actionability. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Assesses translation quality and impact rather than model compression or inference efficiency.

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