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
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates linguistic dynamics across platforms rather than proposing an LLM benchmark.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on linguistic diffusion and community dynamics, not model compression.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Studies forum text and lexical persistence, with no multimodal modalities.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Examines multi-agent language drift, not programming tools or code models.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Analyzes linguistic diffusion in agent communities, not retrieval-augmented generation.
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual document retrieval architectures, not autonomous decision agents.
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual document search, not coding assistants or software engineering.
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Optimizes multimodal retrieval efficiency rather than safety, guardrails, or alignment.
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a retrieval framework evaluated on existing benchmarks, not a benchmark itself.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Analyzes text-based social media hate speech, with no multimodal input.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmarking hate speech detection, not model compression or inference efficiency.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Introduces a classification/reasoning benchmark, not retrieval-augmented generation systems.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates hate speech detection tasks, not autonomous agentic systems.
From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech This paper introduces a multi-hop reasoning benchmark to evaluate fine-grained, explainable hate speech analysis in Persian. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on natural language hate speech analysis, not coding assistants.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on draft-model attention mechanisms, not document retrieval or knowledge grounding.
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	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Deals with inference acceleration techniques rather than code synthesis or repair.
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 agents via arxiv · 15 Oct, 15:06	Not relevant Presents a speculative decoding strategy, not autonomous multi-step planning agents.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an inference speedup technique rather than an evaluation benchmark.
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.
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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates long-context text decoding, not multimodal image/audio systems.
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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates textual scientific claims and evidence, without visual or multimodal components.
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.
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.
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	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	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	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.
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	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	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	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	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	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	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.
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	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.
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.
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	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	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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates multilingual bias metrics, not model compression or efficient serving.
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	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	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 agents via arxiv · 15 Oct, 15:06	Not relevant Proposes speculative decoding for hybrid linear models, not autonomous planning 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.
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.
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.

7,141–7,200 of 17,313← Previous Next →