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
Nufo: A Benchmark for Ewe Automatic Speech Recognition The paper introduces a 174-hour speech benchmark and evaluates baseline models on Ewe automatic speech recognition. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech recognition benchmarking rather than language model evaluation.
Nufo: A Benchmark for Ewe Automatic Speech Recognition The paper introduces a 174-hour speech benchmark and evaluates baseline models on Ewe automatic speech recognition. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Nufo: A Benchmark for Ewe Automatic Speech Recognition The paper introduces a 174-hour speech benchmark and evaluates baseline models on Ewe automatic speech recognition. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient inference techniques.
Nufo: A Benchmark for Ewe Automatic Speech Recognition The paper introduces a 174-hour speech benchmark and evaluates baseline models on Ewe automatic speech recognition. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers low-resource speech recognition, not autonomous agent workflows.
From Sampled Evidence to Radiology Reports: Objectives, Constraints, and Realization The paper evaluates how objective functions and candidate constraints affect decision conversion when generating radiology reports from sampled evidence. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on evidence aggregation and realization, not external document retrieval.
From Sampled Evidence to Radiology Reports: Objectives, Constraints, and Realization The paper evaluates how objective functions and candidate constraints affect decision conversion when generating radiology reports from sampled evidence. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies clinical report generation pipelines rather than tool-using autonomous agents.
From Sampled Evidence to Radiology Reports: Objectives, Constraints, and Realization The paper evaluates how objective functions and candidate constraints affect decision conversion when generating radiology reports from sampled evidence. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering.
From Sampled Evidence to Radiology Reports: Objectives, Constraints, and Realization The paper evaluates how objective functions and candidate constraints affect decision conversion when generating radiology reports from sampled evidence. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate model compression, quantization, or inference serving.
From Sampled Evidence to Radiology Reports: Objectives, Constraints, and Realization The paper evaluates how objective functions and candidate constraints affect decision conversion when generating radiology reports from sampled evidence. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies report realization trade-offs rather than general LLM benchmarking.
The Curse of Knowledge in Role-Playing Language Models The paper tests whether role-playing language models leak private or unobserved character information across simulated interactive scenes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient serving.
The Curse of Knowledge in Role-Playing Language Models The paper tests whether role-playing language models leak private or unobserved character information across simulated interactive scenes. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based role-playing prompts, not multimodal models.
The Curse of Knowledge in Role-Playing Language Models The paper tests whether role-playing language models leak private or unobserved character information across simulated interactive scenes. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
The Curse of Knowledge in Role-Playing Language Models The paper tests whether role-playing language models leak private or unobserved character information across simulated interactive scenes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates information leakage in role-play, not RAG or document retrieval.
UMCR: Universal Medical Concept Recognition through Guideline Integration and Agentic Linking The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or serving efficiency.
UMCR: Universal Medical Concept Recognition through Guideline Integration and Agentic Linking The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Aimed at ontology linking accuracy rather than safety guardrails or jailbreaks.
UMCR: Universal Medical Concept Recognition through Guideline Integration and Agentic Linking The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Processes clinical text and ontologies, not multimodal vision or audio.
UMCR: Universal Medical Concept Recognition through Guideline Integration and Agentic Linking The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Applies existing MedPath benchmark rather than proposing LLM evaluation methods.
UMCR: Universal Medical Concept Recognition through Guideline Integration and Agentic Linking The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical medical concept recognition rather than code.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Uses standard benchmarks to evaluate speedup rather than creating evaluation methods.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion model decoding rather than multi-step agent systems.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference acceleration rather than safety or reliability risks.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates general decoding speedups, not specialized code assistants.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not concern retrieval-augmented generation or search.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Applies to diffusion language models on text, not multimodal models.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates temporal relation extraction from text rather than RAG architectures.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies linguistic reasoning benchmarks rather than deployment safety or jailbreaks.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Tests narrative temporal extraction rather than tool-using agent systems.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text narratives, not multimodal inputs.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or inference serving efficiency.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech model distillation rather than multi-step agent planning.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a pretraining approach rather than benchmark evaluation methods.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not relate to retrieval-augmented generation.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on cross-modal knowledge distillation, not safety or deployment guardrails.
Scaling Inference-Time Compute with Teams of Coding Agents The paper studies compute allocation across multi-agent coding teams to identify Pareto-optimal trade-offs between cost and performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on cost and performance trade-offs, not safety or guardrails.
Scaling Inference-Time Compute with Teams of Coding Agents The paper studies compute allocation across multi-agent coding teams to identify Pareto-optimal trade-offs between cost and performance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies team configurations rather than introducing evaluation benchmarks.
Scaling Inference-Time Compute with Teams of Coding Agents The paper studies compute allocation across multi-agent coding teams to identify Pareto-optimal trade-offs between cost and performance. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses multi-agent compute allocation rather than document retrieval or RAG.
Scaling Inference-Time Compute with Teams of Coding Agents The paper studies compute allocation across multi-agent coding teams to identify Pareto-optimal trade-offs between cost and performance. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on code-generating agent teams, not multimodal models.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on language model self-inspection, not multimodal data.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to coding assistants or program generation.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies internal state reporting rather than proposing general LLM benchmarks.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address external information retrieval or RAG architectures.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies internal state awareness rather than multi-step tool-using agents.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address inference acceleration, pruning, or model compression.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not cover retrieval-augmented systems or knowledge grounding.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Exclusively examines word representations in text transformers.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates representation theory rather than evaluation frameworks or benchmarks.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with lexical linguistics, not programming or code models.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on linguistic representations, not safety or deployment reliability.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical study of lexical representations, not agentic planning or tools.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Theoretical representation study, not addressing model compression or efficiency.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on compute efficiency and orchestration rather than safety guardrails.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an operational framework rather than an LLM evaluation standard.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses general long-horizon agent execution rather than software engineering.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent delegation between SLMs and LLMs, not multimodal architectures.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent delegation strategies, not retrieval-augmented generation.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Mentions small models in passing, not focused on model efficiency techniques.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on a niche Tagalog NLP corpus rather than general LLM benchmarking.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with diacritic restoration rather than autonomous agent systems.

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