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
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an RL algorithm rather than evaluation methodologies.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion model reasoning exploration, not code synthesis.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve document or knowledge retrieval systems.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation systems.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates pre-training dynamics rather than LLM benchmark design.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Examines spatial reasoning and multimodal loss, not software code.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies multimodal pre-training and spatial reasoning, not autonomous agents.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not study safety, jailbreaks, or deployment reliability.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines scaling behavior rather than small efficient model architectures.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, distillation, or quantization.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Does not address industrial inspection or defect detection tasks.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on safety, guardrails, or alignment risks.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on training agentic retrieval, not benchmark evaluation methodology.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on multimodal in-context learning, not code generation.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates audio-language model post-training, not multi-step agents.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Investigates audio reasoning post-training, not code or programming tasks.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies audio-language models, not retrieval-augmented generation systems.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies post-training evaluation artifacts, not model compression or efficiency.
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	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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Operates on domain text data without image or multimodal modalities.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Scores candidate options directly without external document retrieval mechanisms.
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.
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	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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Application-specific speech pipeline optimization, not general model efficiency or compression.
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	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	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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text normalization post-ASR rather than multimodal foundation models.
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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion language models for text generation.
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 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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a decoding algorithm rather than an evaluation benchmark.
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	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL alignment and instruction following, not multi-step agents.
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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on constraint satisfaction and reward hacking, not retrieval.
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 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.
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 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.
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	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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an adversarial threat framework for GraphRAG, not a standard benchmark.
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	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant General mathematical and reasoning acceleration, not code-specific modeling.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses internal model reasoning representation, not external document retrieval.

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