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
Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate code generation, software engineering assistants, or program repair.
Natural Language to First-Order Logic LLM-based Autoformalization The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with natural language and formal logic representations without multimodal data.
Natural Language to First-Order Logic LLM-based Autoformalization The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Surveys logic formalization methods rather than AI safety, jailbreaks, or guardrails.
Natural Language to First-Order Logic LLM-based Autoformalization The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Surveys translation of text to first-order logic rather than multi-step autonomous agents.
Natural Language to First-Order Logic LLM-based Autoformalization The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Surveys logic autoformalization rather than model compression or efficient serving.
Natural Language to First-Order Logic LLM-based Autoformalization The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on formal logic translation rather than retrieval-augmented generation.
ILM: An AI-Powered Storytelling Educational Tool The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies NLP to educational storytelling rather than code synthesis or software engineering.
ILM: An AI-Powered Storytelling Educational Tool The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Uses LLM-as-a-judge as a minor component rather than studying LLM evaluation methodology.
ILM: An AI-Powered Storytelling Educational Tool The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text narratives and knowledge graphs without multimodal model components.
ILM: An AI-Powered Storytelling Educational Tool The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Presents an educational platform rather than model compression or efficient serving.
ILM: An AI-Powered Storytelling Educational Tool The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents an educational application rather than an autonomous planning agent.
ILM: An AI-Powered Storytelling Educational Tool The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Presents an educational tool rather than investigating safety, jailbreaks, or robustness.
A persistent accuracy ceiling in automated verbal deception detection The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code generation, program repair, or software engineering.
A persistent accuracy ceiling in automated verbal deception detection The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Meta-analyzes verbal deception detection models rather than agent planning or tool use.
A persistent accuracy ceiling in automated verbal deception detection The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Surveys deception classification models rather than model compression or inference efficiency.
A persistent accuracy ceiling in automated verbal deception detection The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Analyzes verbal deception text models without multimodal integration.
A persistent accuracy ceiling in automated verbal deception detection The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on deception detection rather than retrieval-augmented generation systems.
Rehearse Everything, Remember Nothing: Attic-KV Rehearses What Will Be Read The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on long-context KV caching rather than code generation or software tasks.
Rehearse Everything, Remember Nothing: Attic-KV Rehearses What Will Be Read The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses KV cache compression rather than multi-step agent planning or tool use.
Rehearse Everything, Remember Nothing: Attic-KV Rehearses What Will Be Read The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a KV cache compression algorithm rather than a general evaluation benchmark.
Rehearse Everything, Remember Nothing: Attic-KV Rehearses What Will Be Read The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses cache memory efficiency rather than AI safety or guardrails.
Rehearse Everything, Remember Nothing: Attic-KV Rehearses What Will Be Read The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text LLM KV cache management without multimodal inputs.
Language-Specific Effects of Tokenizer Choice in Multilingual Language Models The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies multilingual natural language tokenization rather than programming code.
Language-Specific Effects of Tokenizer Choice in Multilingual Language Models The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies multilingual tokenization effects rather than autonomous agent systems.
Language-Specific Effects of Tokenizer Choice in Multilingual Language Models The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Analyzes text tokenizers across languages without multimodal data.
Language-Specific Effects of Tokenizer Choice in Multilingual Language Models The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on multilingual representation quality rather than model compression or efficient serving.
Language-Specific Effects of Tokenizer Choice in Multilingual Language Models The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Evaluates language modeling performance rather than AI safety or reliability.
Language-Specific Effects of Tokenizer Choice in Multilingual Language Models The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Analyzes subword tokenizers rather than retrieval-augmented generation systems.
When KL Regularization Misfires in Group Policy Optimization Analyzes interaction failures between KL regularization and rewards in group policy optimization and proposes a calibrated surrogate objective. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates mathematical reasoning rather than code generation or program repair.
When KL Regularization Misfires in Group Policy Optimization Analyzes interaction failures between KL regularization and rewards in group policy optimization and proposes a calibrated surrogate objective. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on reinforcement learning policy optimization rather than multi-step agent architectures.
When KL Regularization Misfires in Group Policy Optimization Analyzes interaction failures between KL regularization and rewards in group policy optimization and proposes a calibrated surrogate objective. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Addresses RL policy optimization rather than retrieval-augmented generation.
When KL Regularization Misfires in Group Policy Optimization Analyzes interaction failures between KL regularization and rewards in group policy optimization and proposes a calibrated surrogate objective. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an optimization algorithm rather than an evaluation benchmark or dataset.
When KL Regularization Misfires in Group Policy Optimization Analyzes interaction failures between KL regularization and rewards in group policy optimization and proposes a calibrated surrogate objective. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on RL loss formulations rather than model compression or inference efficiency.
When KL Regularization Misfires in Group Policy Optimization Analyzes interaction failures between KL regularization and rewards in group policy optimization and proposes a calibrated surrogate objective. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with language model policy optimization without multimodal inputs.
SteerablePlex: Can We Steer Full-Duplex Models? Introduces an instruction-following benchmark and training recipe to improve goal adherence in full-duplex conversational models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Addresses conversational steerability rather than retrieval-augmented generation.
SteerablePlex: Can We Steer Full-Duplex Models? Introduces an instruction-following benchmark and training recipe to improve goal adherence in full-duplex conversational models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Deals with speech dialog control rather than code generation or software engineering.
SteerablePlex: Can We Steer Full-Duplex Models? Introduces an instruction-following benchmark and training recipe to improve goal adherence in full-duplex conversational models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on steerability and RL training rather than model compression or efficient serving.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies NLP to scientific papers and patents rather than code or software engineering.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on chronological pretraining rather than model compression or inference efficiency.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents pretrained transformer encoders rather than multi-step agent systems.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with scientific and patent text without multimodal data.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on avoiding chronological lookahead bias rather than deployment guardrails or safety.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops a domain-specific encoder family rather than a general LLM evaluation benchmark.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Introduces scientific encoder models rather than retrieval-augmented generation pipelines.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on acoustic speech generation inference rather than multi-step agent planning.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with spoken dialog inference latency rather than retrieval-augmented generation.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an inference acceleration framework rather than an evaluation benchmark.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on spoken dialog diffusion models rather than code generation.
DiffuPlex: Accelerating Full-Duplex Spoken Dialog Models via Rolling Masked Diffusion Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Targets inference speedup rather than deployment safety, jailbreaks, or guardrails.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on research workflow outcome prediction rather than software engineering.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies predictive models for experiment selection rather than model compression techniques.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on predicting training experiment results without multimodal integration.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies research optimization rather than deployment safety, jailbreaks, or robustness.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies experiment outcome modeling rather than document retrieval-augmented generation.
Language Models as AI Research World Models Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on world models for experiment prediction rather than LLM benchmarking methods.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on LLM serving infrastructure without multimodal components.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Deals with LLM inference systems rather than code synthesis or program repair.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on systems-level token routing across LLMs rather than agentic planning.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an inference system architecture rather than a benchmarking framework.
TokenRouter: Efficient Serving System for Token-Level LLM Routing Designs an inference serving system optimized for dynamic, token-level routing across multiple language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with serving infrastructure rather than retrieval-augmented generation.

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