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
AgriJev: Efficient Agricultural Candidate Scoring with Multi-Scale Adapted Models The paper presents adapted small language models designed to directly score agricultural candidate choices in a single forward pass. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on single-step candidate scoring, not multi-step agentic workflows.
AgriJev: Efficient Agricultural Candidate Scoring with Multi-Scale Adapted Models The paper presents adapted small language models designed to directly score agricultural candidate choices in a single forward pass. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a domain-adapted scoring model rather than an evaluation benchmark.
AgriJev: Efficient Agricultural Candidate Scoring with Multi-Scale Adapted Models The paper presents adapted small language models designed to directly score agricultural candidate choices in a single forward pass. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on domain candidate scoring efficiency, not safety or guardrails.
AgriJev: Efficient Agricultural Candidate Scoring with Multi-Scale Adapted Models The paper presents adapted small language models designed to directly score agricultural candidate choices in a single forward pass. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Specialized for agricultural domain decision tasks, not software code.
AgriJev: Efficient Agricultural Candidate Scoring with Multi-Scale Adapted Models The paper presents adapted small language models designed to directly score agricultural candidate choices in a single forward pass. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Operates on domain text data without image or multimodal modalities.
Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Covers speech-to-text post-processing, not document retrieval systems.
Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an end-to-end normalization model, not an LLM benchmark.
Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text normalization post-ASR rather than multimodal foundation models.
Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses text normalization for speech recognition, not software code.
Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy. Peer-reviewed	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on streaming text normalization, not autonomous planning agents.
Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses text formatting accuracy, not deployment safety or jailbreaks.
Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Application-specific speech pipeline optimization, not general model efficiency or compression.
One Block Ahead: Promoting Sampler State in Diffusion Language Models The paper proposes a decoding algorithm that denoises consecutive blocks concurrently by promoting sampler states in diffusion language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on decoding algorithms for diffusion models, not agents.
One Block Ahead: Promoting Sampler State in Diffusion Language Models The paper proposes a decoding algorithm that denoises consecutive blocks concurrently by promoting sampler states in diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a decoding algorithm rather than an evaluation benchmark.
One Block Ahead: Promoting Sampler State in Diffusion Language Models The paper proposes a decoding algorithm that denoises consecutive blocks concurrently by promoting sampler states in diffusion language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant General diffusion language model decoding, not specialized for code.
One Block Ahead: Promoting Sampler State in Diffusion Language Models The paper proposes a decoding algorithm that denoises consecutive blocks concurrently by promoting sampler states in diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on inference throughput acceleration, not AI safety.
One Block Ahead: Promoting Sampler State in Diffusion Language Models The paper proposes a decoding algorithm that denoises consecutive blocks concurrently by promoting sampler states in diffusion language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Pertains to diffusion LM decoding, not retrieval-augmented systems.
One Block Ahead: Promoting Sampler State in Diffusion Language Models The paper proposes a decoding algorithm that denoises consecutive blocks concurrently by promoting sampler states in diffusion language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion language models for text generation.
Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following The paper analyzes reward hacking in verifiable instruction following and introduces a likelihood bonus method to maintain answer quality. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on constraint satisfaction and reward hacking, not retrieval.
Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following The paper analyzes reward hacking in verifiable instruction following and introduces a likelihood bonus method to maintain answer quality. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with text-based instruction following and reward alignment.
Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following The paper analyzes reward hacking in verifiable instruction following and introduces a likelihood bonus method to maintain answer quality. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses reward hacking mitigation, not model compression or efficiency.
Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following The paper analyzes reward hacking in verifiable instruction following and introduces a likelihood bonus method to maintain answer quality. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant General instruction-following alignment, not focused on software code.
Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following The paper analyzes reward hacking in verifiable instruction following and introduces a likelihood bonus method to maintain answer quality. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL alignment and instruction following, not multi-step agents.
A Unified Response Framework for Questions and Tutoring Dialogues The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Analyzes text tutoring dialogues without multimodal inputs.
A Unified Response Framework for Questions and Tutoring Dialogues The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers educational response evaluation, not AI safety or jailbreaks.
A Unified Response Framework for Questions and Tutoring Dialogues The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an educational IRT modeling framework, not an LLM benchmark.
A Unified Response Framework for Questions and Tutoring Dialogues The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes math educational dialogues, not software engineering or code.
A Unified Response Framework for Questions and Tutoring Dialogues The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on psychometric student modeling, not autonomous LLM agents.
A Unified Response Framework for Questions and Tutoring Dialogues The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Deals with student dialogue assessment, not retrieval-augmented generation.
A Unified Response Framework for Questions and Tutoring Dialogues The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents dialogue modeling for education, not efficient model architectures.
GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation The paper introduces a threat modeling framework examining adversarial query demotion and graph edit attacks on graph-based RAG systems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines GraphRAG security vulnerabilities, not model compression or efficiency.
GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation The paper introduces a threat modeling framework examining adversarial query demotion and graph edit attacks on graph-based RAG systems. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text and knowledge-graph retrieval, not multimodal models.
GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation The paper introduces a threat modeling framework examining adversarial query demotion and graph edit attacks on graph-based RAG systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an adversarial threat framework for GraphRAG, not a standard benchmark.
GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation The paper introduces a threat modeling framework examining adversarial query demotion and graph edit attacks on graph-based RAG systems. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies attacks on GraphRAG evidence pipelines, not agent execution.
GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation The paper introduces a threat modeling framework examining adversarial query demotion and graph edit attacks on graph-based RAG systems. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on GraphRAG retrieval security, not code models.
INTACT: Inducing Latent Reasoning on a Frozen Backbone The paper presents a method using an external encoder to induce latent soft-token reasoning on frozen language model backbones. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses internal model reasoning representation, not external document retrieval.
INTACT: Inducing Latent Reasoning on a Frozen Backbone The paper presents a method using an external encoder to induce latent soft-token reasoning on frozen language model backbones. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a latent reasoning method, not an evaluation framework.
INTACT: Inducing Latent Reasoning on a Frozen Backbone The paper presents a method using an external encoder to induce latent soft-token reasoning on frozen language model backbones. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Pertains to text reasoning tokens without multimodal modalities.
INTACT: Inducing Latent Reasoning on a Frozen Backbone The paper presents a method using an external encoder to induce latent soft-token reasoning on frozen language model backbones. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on reasoning speedup and accuracy, not safety or guardrails.
INTACT: Inducing Latent Reasoning on a Frozen Backbone The paper presents a method using an external encoder to induce latent soft-token reasoning on frozen language model backbones. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on internal latent reasoning prefixes, not tool-augmented agents.
INTACT: Inducing Latent Reasoning on a Frozen Backbone The paper presents a method using an external encoder to induce latent soft-token reasoning on frozen language model backbones. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant General mathematical and reasoning acceleration, not code-specific modeling.
Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs The paper introduces a multi-agent framework that reframes harmful requests using Moral Foundations Theory to test model jailbreak vulnerabilities. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Uses multi-agent framing merely as an attack tool rather than studying agent systems.
Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs The paper introduces a multi-agent framework that reframes harmful requests using Moral Foundations Theory to test model jailbreak vulnerabilities. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on safety vulnerabilities, not model compression or efficiency.
Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs The paper introduces a multi-agent framework that reframes harmful requests using Moral Foundations Theory to test model jailbreak vulnerabilities. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses safety jailbreaks, not software engineering or code models.
Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs The paper introduces a multi-agent framework that reframes harmful requests using Moral Foundations Theory to test model jailbreak vulnerabilities. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Studies text prompt reframing attacks, without multimodal data.
Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs The paper introduces a multi-agent framework that reframes harmful requests using Moral Foundations Theory to test model jailbreak vulnerabilities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates jailbreak vulnerabilities rather than proposing evaluation standards.
Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs The paper introduces a multi-agent framework that reframes harmful requests using Moral Foundations Theory to test model jailbreak vulnerabilities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on prompt jailbreaking, not knowledge retrieval or grounding.
PA1-OPD: One-Step Prefix-Augmented On-Policy Distillation for Language Model Pretraining The paper proposes a prefix-augmented distillation method providing one-step student branch feedback during language model pretraining. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Covers pretraining knowledge distillation, not external retrieval or RAG.
PA1-OPD: One-Step Prefix-Augmented On-Policy Distillation for Language Model Pretraining The paper proposes a prefix-augmented distillation method providing one-step student branch feedback during language model pretraining. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents pretraining distillation methods, not multi-step agent systems.
PA1-OPD: One-Step Prefix-Augmented On-Policy Distillation for Language Model Pretraining The paper proposes a prefix-augmented distillation method providing one-step student branch feedback during language model pretraining. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Pretraining distillation technique, not specialized for code LLMs.
PA1-OPD: One-Step Prefix-Augmented On-Policy Distillation for Language Model Pretraining The paper proposes a prefix-augmented distillation method providing one-step student branch feedback during language model pretraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on language model training efficiency, not safety or guardrails.
PA1-OPD: One-Step Prefix-Augmented On-Policy Distillation for Language Model Pretraining The paper proposes a prefix-augmented distillation method providing one-step student branch feedback during language model pretraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a training method rather than an evaluation benchmark.
PA1-OPD: One-Step Prefix-Augmented On-Policy Distillation for Language Model Pretraining The paper proposes a prefix-augmented distillation method providing one-step student branch feedback during language model pretraining. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text language model pretraining distillation.
PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference The paper proposes a pair-norm guided mixed-precision quantization method for key caches during long-output transformer inference. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on KV cache quantization mechanics, not tool-using agent architectures.
PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference The paper proposes a pair-norm guided mixed-precision quantization method for key caches during long-output transformer inference. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies to decoder-only text language models without multimodal data.
PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference The paper proposes a pair-norm guided mixed-precision quantization method for key caches during long-output transformer inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a quantization algorithm, not an evaluation framework or dataset.
PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference The paper proposes a pair-norm guided mixed-precision quantization method for key caches during long-output transformer inference. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant General LLM KV cache quantization method, not specific to software engineering.
PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference The paper proposes a pair-norm guided mixed-precision quantization method for key caches during long-output transformer inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Applies to long-output inference quantization, not document retrieval systems.
PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference The paper proposes a pair-norm guided mixed-precision quantization method for key caches during long-output transformer inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers inference hardware optimization, not safety vulnerabilities or robustness.
Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answers The paper reports a pre-registered test measuring the stability of history-based forecasts for model brand mentions over twenty days. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes general model brand mentions, unrelated to software code tasks.

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