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
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies code fix runtime performance without retrieval-augmented generation components.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on artifact reliance in text detectors, not model compression or efficiency.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Examines text detectors and PDF extraction artifacts without multimodal models.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Examines machine-generated text detectors on scientific literature, not code generation.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Investigates machine-text detection artifacts without retrieval-augmented generation.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies AI-generated text detection rather than autonomous multi-step agent systems.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies token representation and vocabulary compression without retrieval-augmented generation.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes token compression methods rather than novel benchmark suites or evaluation protocols.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on token compression layers rather than autonomous tool-using language agents.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses inference efficiency and token compression rather than AI safety or reliability risks.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates token compression algorithms, unrelated to software engineering or code LLMs.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text token compression and RoPE positioning without multimodal components.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on linguistic polarity licensing without any retrieval-augmented generation.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses linguistic grammaticality evaluation rather than safety, red-teaming, or guardrails.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on multilingual linguistic syntax and semantics without multimodal elements.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Evaluates existing multilingual models without focusing on model compression or efficiency.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines linguistic grammaticality of polarity items, not autonomous multi-step agents.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies negative polarity items in linguistics, unrelated to code synthesis or programming.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Investigates user simulation in multi-turn dialogues, not retrieval-augmented generation.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses conversational assistant feedback rather than software engineering or code models.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies simulator feedback for training conversational assistants rather than safety or guardrails.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies conversation feedback simulation without addressing model compression or efficiency.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on training dialogue user simulators rather than autonomous agent systems.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text-based dialogue interactions without incorporating multimodal inputs.
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Tests agent execution of generic scripts, but the primary focus is security and prompt injection.
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent tool interfaces and files rather than multimodal foundation models.
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Assesses frontier model vulnerabilities without focusing on model compression or small models.
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on security attacks against agent tools rather than retrieval-augmented generation systems.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses evaluation sample efficiency rather than model compression or efficient model serving.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on benchmark downsampling for AML evaluation, not autonomous agentic frameworks.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies anti-money laundering transaction graphs, unrelated to code generation or software.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on graph-to-text prompt evaluations without vision, audio, or multimodal data.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Pertains to graph classification evaluation rather than retrieval-augmented generation.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses benchmark sampling techniques rather than AI safety risks, guardrails, or alignment.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an internal attention residual architecture, not multi-step autonomous agent systems.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Implements depth-wise attention over internal network layers, not document retrieval systems.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Presents a general neural architecture improvement rather than code-specific language models.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general neural network architecture rather than industrial computer vision tasks.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a model architecture rather than an evaluation framework or benchmark suite.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Proposes a general backbone layer architecture rather than multimodal reasoning or VLMs.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on network layer architecture rather than deployment safety, guardrails, or alignment.
Retractions Revisited: A Replication, Extension, and Mechanistic Evaluation of Linguistic Obfuscation Effects in Fraudulent Science The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Exclusively analyzes written scientific texts without multimodal elements.
Retractions Revisited: A Replication, Extension, and Mechanistic Evaluation of Linguistic Obfuscation Effects in Fraudulent Science The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Examines psychological linguistics in fraudulent papers rather than LLM evaluation benchmarks.
Retractions Revisited: A Replication, Extension, and Mechanistic Evaluation of Linguistic Obfuscation Effects in Fraudulent Science The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Investigates linguistic features of fraud without addressing model efficiency or compression.
Retractions Revisited: A Replication, Extension, and Mechanistic Evaluation of Linguistic Obfuscation Effects in Fraudulent Science The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Analyzes linguistic features in scientific papers rather than autonomous agent systems.
Retractions Revisited: A Replication, Extension, and Mechanistic Evaluation of Linguistic Obfuscation Effects in Fraudulent Science The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies psychology and linguistic obfuscation in scientific text, unrelated to software code.
Retractions Revisited: A Replication, Extension, and Mechanistic Evaluation of Linguistic Obfuscation Effects in Fraudulent Science The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Analyzes human fraudulent science texts, not AI deployment safety, guardrails, or alignment.
Retractions Revisited: A Replication, Extension, and Mechanistic Evaluation of Linguistic Obfuscation Effects in Fraudulent Science The paper evaluates linguistic obfuscation patterns and reality-monitoring mechanisms across fraudulent and genuine scientific publications. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on scientific fraud detection using text analysis, not retrieval-augmented generation.
Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on retrieval structuring rather than model compression, quantization, or small models.
Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies retrieval methods to German Legal Code rather than software engineering or code.
Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses citation accuracy in legal RAG, not jailbreaks, guardrails, or safety deployment.
Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops a KG-guided RAG approach rather than an LLM evaluation benchmark.
Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Investigates text-based legal retrieval and reasoning without multimodal modalities.
Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on knowledge graph retrieval for legal text rather than autonomous multi-step agents.
Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on domain QA accuracy in legal text rather than safety, guardrails, or alignment.
Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on legal knowledge pre-training rather than autonomous multi-step agent frameworks.
Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses statutory legal reasoning, not software engineering or code generation.
Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents pre-training objectives for legal reasoning rather than an evaluation framework.
Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies domain pre-training techniques without focusing on model compression or inference speed.
Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Embeds structured legal knowledge into weights during pre-training to remove retrieval needs.

12,721–12,780 of 13,754← Previous Next →