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
FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding This paper introduces FOLD, a training-free speculative decoding approach treating distant tokens as position-free far memory for long-context generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on inference acceleration rather than safety risks, hallucination, or guardrails.
The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation This paper introduces COUNT, scaling language model memory without training parameters by storing precomputed n-gram counts directly in host memory. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with pretraining memory efficiency rather than AI safety or reliability.
The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation This paper introduces COUNT, scaling language model memory without training parameters by storing precomputed n-gram counts directly in host memory. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses purely on text pretraining and n-grams, not multimodal data.
The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation This paper introduces COUNT, scaling language model memory without training parameters by storing precomputed n-gram counts directly in host memory. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses general language model pretraining efficiency, not software or coding tasks.
The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation This paper introduces COUNT, scaling language model memory without training parameters by storing precomputed n-gram counts directly in host memory. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Optimizes language model pretraining via host-stored n-grams, not document RAG.
The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation This paper introduces COUNT, scaling language model memory without training parameters by storing precomputed n-gram counts directly in host memory. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a pretraining method evaluated on loss, not an evaluation benchmark.
The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation This paper introduces COUNT, scaling language model memory without training parameters by storing precomputed n-gram counts directly in host memory. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes n-gram embeddings on host memory, not autonomous agent workflows.
Same Claim, Different Interpretations: How LLM Agents Resolve Ambiguity in Scientific Claims This paper proposes Claim Cards to make agent interpretations explicit when verifying ambiguous natural-language scientific claims. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies interpretation ambiguity in agents rather than retrieval or indexing pipelines.
Same Claim, Different Interpretations: How LLM Agents Resolve Ambiguity in Scientific Claims This paper proposes Claim Cards to make agent interpretations explicit when verifying ambiguous natural-language scientific claims. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes scientific claim verification agents, not programming or code-generation systems.
Same Claim, Different Interpretations: How LLM Agents Resolve Ambiguity in Scientific Claims This paper proposes Claim Cards to make agent interpretations explicit when verifying ambiguous natural-language scientific claims. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates interpretation variance in LLM agents, not model compression or efficiency.
Same Claim, Different Interpretations: How LLM Agents Resolve Ambiguity in Scientific Claims This paper proposes Claim Cards to make agent interpretations explicit when verifying ambiguous natural-language scientific claims. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates textual scientific claims and evidence, without visual or multimodal components.
MolLean: A Process-Verifiable Language for Chemical Reasoning This paper introduces MolLean, a verifiable language and tool interface that grounds chemical reasoning in formal state transitions and explicit checks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates chemical reasoning against direct prompting rather than creating a general LLM benchmark.
MolLean: A Process-Verifiable Language for Chemical Reasoning This paper introduces MolLean, a verifiable language and tool interface that grounds chemical reasoning in formal state transitions and explicit checks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Develops a formal language for chemistry, not general software engineering or code LLMs.
MolLean: A Process-Verifiable Language for Chemical Reasoning This paper introduces MolLean, a verifiable language and tool interface that grounds chemical reasoning in formal state transitions and explicit checks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on chemical domain-specific languages and text reasoning, not multimodal models.
MolLean: A Process-Verifiable Language for Chemical Reasoning This paper introduces MolLean, a verifiable language and tool interface that grounds chemical reasoning in formal state transitions and explicit checks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on symbolic state verification and tool feedback, not external document retrieval.
MolLean: A Process-Verifiable Language for Chemical Reasoning This paper introduces MolLean, a verifiable language and tool interface that grounds chemical reasoning in formal state transitions and explicit checks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on formal chemical reasoning frameworks rather than model quantization or distillation.
ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models This paper introduces ChronoCulture to evaluate how well language models capture temporal changes in cultural attitudes across 72 countries. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates cultural attitudes in general language models, not code generation models.
ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models This paper introduces ChronoCulture to evaluate how well language models capture temporal changes in cultural attitudes across 72 countries. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents an evaluation benchmark rather than small models or inference optimization.
ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models This paper introduces ChronoCulture to evaluate how well language models capture temporal changes in cultural attitudes across 72 countries. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies historical cultural representation rather than safety guardrails or deployment risks.
ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models This paper introduces ChronoCulture to evaluate how well language models capture temporal changes in cultural attitudes across 72 countries. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Introduces a temporal cultural benchmark, not autonomous planning or tool agents.
ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models This paper introduces ChronoCulture to evaluate how well language models capture temporal changes in cultural attitudes across 72 countries. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Evaluates internal model cultural knowledge over time, not external retrieval systems.
ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models This paper introduces ChronoCulture to evaluate how well language models capture temporal changes in cultural attitudes across 72 countries. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Benchmarking focuses on textual survey questions, containing no multimodal modalities.
AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on targeted sub-step policy fine-tuning rather than model compression or inference efficiency.
AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a self-improvement framework rather than a general LLM evaluation benchmark.
AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Uses a coding agent internally, but main contribution is embodied robotic policy improvement.
AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on embodied robotic execution and self-training, not document retrieval.
AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on task success improvement in robotics rather than safety or alignment guardrails.
AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Targets household/embodied robotics (RoboCasa365), not industrial defect detection or manufacturing inspection.
Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models This paper evaluates multilingual bias audits across ten language models, showing that lower stereotype scores often reflect translation artifacts rather than fairer behavior. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Investigates text-only multilingual translation bias without multimodal data.
Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models This paper evaluates multilingual bias audits across ten language models, showing that lower stereotype scores often reflect translation artifacts rather than fairer behavior. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies linguistic bias in multilingual models, not code LLMs or software tasks.
Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models This paper evaluates multilingual bias audits across ten language models, showing that lower stereotype scores often reflect translation artifacts rather than fairer behavior. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates multilingual bias metrics, not model compression or efficient serving.
Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models This paper evaluates multilingual bias audits across ten language models, showing that lower stereotype scores often reflect translation artifacts rather than fairer behavior. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on stereotype evaluation in multilingual models, not retrieval-augmented generation.
Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models This paper evaluates multilingual bias audits across ten language models, showing that lower stereotype scores often reflect translation artifacts rather than fairer behavior. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies bias auditing across multilingual translations, not autonomous tool-using agents.
ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models This paper presents ReaDy, a speculative decoding method using hybrid linear attention recurrent states to provide position-specific context during drafting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an efficient decoding method rather than a general LLM evaluation framework.
ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models This paper presents ReaDy, a speculative decoding method using hybrid linear attention recurrent states to provide position-specific context during drafting. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Deals with internal recurrent state reading for speculative decoding, not external document retrieval.
ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models This paper presents ReaDy, a speculative decoding method using hybrid linear attention recurrent states to provide position-specific context during drafting. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies to autoregressive language model decoding without multimodal inputs or outputs.
ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models This paper presents ReaDy, a speculative decoding method using hybrid linear attention recurrent states to provide position-specific context during drafting. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on decoding acceleration rather than code generation or software maintenance.
ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models This paper presents ReaDy, a speculative decoding method using hybrid linear attention recurrent states to provide position-specific context during drafting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on decoding speedups, not AI safety, guardrails, or alignment.
ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models This paper presents ReaDy, a speculative decoding method using hybrid linear attention recurrent states to provide position-specific context during drafting. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes speculative decoding for hybrid linear models, not autonomous planning agents.
Manacá-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge Documents safety alignment methods, negative preference tuning outcomes, and automated judge limitations for a Portuguese language model. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Aligns a general-purpose language model, not a coding-specific LLM.
Manacá-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge Documents safety alignment methods, negative preference tuning outcomes, and automated judge limitations for a Portuguese language model. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses strictly on Portuguese text alignment, with no multimodal components.
Manacá-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge Documents safety alignment methods, negative preference tuning outcomes, and automated judge limitations for a Portuguese language model. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies post-training safety alignment rather than retrieval-augmented generation architectures.
Manacá-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge Documents safety alignment methods, negative preference tuning outcomes, and automated judge limitations for a Portuguese language model. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on safety alignment of a Portuguese language model, not agents.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Presents an inference efficiency technique, not an AI safety or robustness method.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on long-context KV cache compaction rather than external document retrieval systems.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes KV-cache compression via test-time training, not autonomous agents.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text long-context KV cache compression, not multimodal models.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses long-context attention efficiency rather than code generation or software engineering.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a compression technique evaluated on existing benchmarks, not an evaluation benchmark.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a medical imaging technique rather than a language model benchmark.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical radiotherapy image registration, not industrial or manufacturing defect inspection.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Deals with medical deformable image registration, not autonomous LLM agents.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical radiotherapy registration rather than AI deployment safety or jailbreaks.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on orchestrating coding agents rather than document retrieval or RAG architectures.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with automated scientific research orchestration rather than AI safety or guardrails.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent orchestration rather than efficient model architectures or compression.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Describes an agentic scientific system rather than an LLM evaluation benchmark.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on coding and scientific orchestration, not multimodal vision-language models.
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench Introduces a multi-agent framework where simulated scientists formulate, test, and revise scientific conjectures alongside mathematical equations. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific discovery frameworks rather than model compression or efficiency.
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench Introduces a multi-agent framework where simulated scientists formulate, test, and revise scientific conjectures alongside mathematical equations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific discovery and equation benchmarking rather than AI safety.

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