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
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or efficient deployment.
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Introduces a distribution shift benchmark, not agent planning or tool use.
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples Introduces a benchmark to test model generalization under systematic convenience sampling biases in tabular data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not cover retrieval-augmented generation or grounding mechanisms.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text and structured table data, not vision or audio modalities.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on privacy-preserving representation synthesis, not autonomous agents.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not address retrieval-augmented generation architectures or workflows.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not related to software engineering or code synthesis.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a data synthesis method rather than an LLM evaluation benchmark.
SHIP: Structured and Hierarchical Insight with Privacy The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model pruning, distillation, or efficient inference.
Multimodal AI Detection for Math-Heavy Scientific Text The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Not about model compression, quantization, or lightweight architectures.
Multimodal AI Detection for Math-Heavy Scientific Text The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation or enterprise knowledge search.
Multimodal AI Detection for Math-Heavy Scientific Text The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on AI text detection rather than autonomous planning agents.
Multimodal AI Detection for Math-Heavy Scientific Text The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Investigates AI-generated content detectors rather than broad LLM capability benchmarks.
Multimodal AI Detection for Math-Heavy Scientific Text The paper examines AI-generated text detection in scientific papers by combining visual formatting, PDF text, and LaTeX representations. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Studies scientific math text detection rather than code generation.
Compression Does Not End at Selection: Post-Selection Positional Geometry for Sparse KV Caches The paper proposes RoPERelay to reorganize positional geometry and rotary embeddings after sparse KV cache token eviction. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not related to code generation, debugging, or programming assistants.
Compression Does Not End at Selection: Post-Selection Positional Geometry for Sparse KV Caches The paper proposes RoPERelay to reorganize positional geometry and rotary embeddings after sparse KV cache token eviction. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Optimizes internal attention key-value caches rather than external document retrieval.
Compression Does Not End at Selection: Post-Selection Positional Geometry for Sparse KV Caches The paper proposes RoPERelay to reorganize positional geometry and rotary embeddings after sparse KV cache token eviction. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not address AI safety risks, alignment, or guardrail systems.
Compression Does Not End at Selection: Post-Selection Positional Geometry for Sparse KV Caches The paper proposes RoPERelay to reorganize positional geometry and rotary embeddings after sparse KV cache token eviction. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Studies text-based language model attention caches, not multimodal inputs.
Compression Does Not End at Selection: Post-Selection Positional Geometry for Sparse KV Caches The paper proposes RoPERelay to reorganize positional geometry and rotary embeddings after sparse KV cache token eviction. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents an inference optimization technique rather than an evaluation benchmark.
Compression Does Not End at Selection: Post-Selection Positional Geometry for Sparse KV Caches The paper proposes RoPERelay to reorganize positional geometry and rotary embeddings after sparse KV cache token eviction. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Addresses KV cache positional geometry, not autonomous multi-step agents.
From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention The paper introduces a causal intervention framework to distill transferable self-generated in-context learning demonstrations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on prompt distillation methods rather than LLM benchmarking standards.
From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention The paper introduces a causal intervention framework to distill transferable self-generated in-context learning demonstrations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Not about parameter pruning, quantization, or efficient model serving.
From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention The paper introduces a causal intervention framework to distill transferable self-generated in-context learning demonstrations. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Uses code execution as a causal tool, but is not about software engineering.
From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention The paper introduces a causal intervention framework to distill transferable self-generated in-context learning demonstrations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation or external document corpora.
From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention The paper introduces a causal intervention framework to distill transferable self-generated in-context learning demonstrations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on reasoning distillation rather than deployment safety or jailbreaks.
From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention The paper introduces a causal intervention framework to distill transferable self-generated in-context learning demonstrations. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates text reasoning benchmarks without vision or audio data.
From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention The paper introduces a causal intervention framework to distill transferable self-generated in-context learning demonstrations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies in-context demonstration selection, not multi-step agent planning.
Stop Training Against Soft Users: Mentalized Adversarial Self-Play for Proactive Dialogue The paper introduces an adversarial self-play framework modeling user mental states to improve proactive dialogue agent planning. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Concerns textual proactive dialogue without multimodal components.
Stop Training Against Soft Users: Mentalized Adversarial Self-Play for Proactive Dialogue The paper introduces an adversarial self-play framework modeling user mental states to improve proactive dialogue agent planning. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not explore model compression, distillation, or inference optimization.
Stop Training Against Soft Users: Mentalized Adversarial Self-Play for Proactive Dialogue The paper introduces an adversarial self-play framework modeling user mental states to improve proactive dialogue agent planning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not address external document retrieval or grounding mechanisms.
Stop Training Against Soft Users: Mentalized Adversarial Self-Play for Proactive Dialogue The paper introduces an adversarial self-play framework modeling user mental states to improve proactive dialogue agent planning. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on dialogue policy robustness rather than AI safety or guardrails.
Stop Training Against Soft Users: Mentalized Adversarial Self-Play for Proactive Dialogue The paper introduces an adversarial self-play framework modeling user mental states to improve proactive dialogue agent planning. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Studies conversational dialogue rather than code generation or software repair.
Stop Training Against Soft Users: Mentalized Adversarial Self-Play for Proactive Dialogue The paper introduces an adversarial self-play framework modeling user mental states to improve proactive dialogue agent planning. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a self-play training framework rather than an evaluation benchmark.
Automatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System The paper presents an automated speech recognition and sentence completion system to assist dementia patients speaking Bengali. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents assistive speech models, not autonomous planning LLM agents.
Automatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System The paper presents an automated speech recognition and sentence completion system to assist dementia patients speaking Bengali. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Application is for healthcare dialogue, not software engineering or coding.
Automatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System The paper presents an automated speech recognition and sentence completion system to assist dementia patients speaking Bengali. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not focus on general model compression, quantization, or serving.
Automatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System The paper presents an automated speech recognition and sentence completion system to assist dementia patients speaking Bengali. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Not centered on safety alignment, hallucination prevention, or red teaming.
Automatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System The paper presents an automated speech recognition and sentence completion system to assist dementia patients speaking Bengali. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not study retrieval-augmented generation or knowledge grounding.
Automatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System The paper presents an automated speech recognition and sentence completion system to assist dementia patients speaking Bengali. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes an assistive application rather than LLM benchmarking methodologies.
From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models The paper surveys memory-centric compression techniques for managing inference resources and context windows in large language models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Surveys context compression across tasks rather than autonomous agent architectures.
From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models The paper surveys memory-centric compression techniques for managing inference resources and context windows in large language models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not focus on deployment safety, jailbreaks, or guardrails.
From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models The paper surveys memory-centric compression techniques for managing inference resources and context windows in large language models. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text context window compression, not multimodal models.
From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models The paper surveys memory-centric compression techniques for managing inference resources and context windows in large language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant A survey of memory compression techniques rather than an evaluation benchmark.
From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models The paper surveys memory-centric compression techniques for managing inference resources and context windows in large language models. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not specific to software engineering or code generation models.
Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG The paper introduces ExecutionRAG to show how search drift causes apparent retrieval failures in multi-hop question answering. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not about code generation, software development, or program repair.
Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG The paper introduces ExecutionRAG to show how search drift causes apparent retrieval failures in multi-hop question answering. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Examines multi-hop retrieval dynamics rather than general agent execution environments.
Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG The paper introduces ExecutionRAG to show how search drift causes apparent retrieval failures in multi-hop question answering. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not study model compression, parameter reduction, or quantization.
Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG The paper introduces ExecutionRAG to show how search drift causes apparent retrieval failures in multi-hop question answering. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Analyzes retrieval coverage rather than safety, jailbreaks, or malicious behavior.
Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG The paper introduces ExecutionRAG to show how search drift causes apparent retrieval failures in multi-hop question answering. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text retrieval rather than vision-language or multimodal inputs.
KBS: A Korean Benchmark Suite for Foundation Model Evaluation The paper introduces the Korean Benchmark Suite to evaluate foundation models across six natural language capabilities. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant General language capability benchmark, not focused on code generation or repair.
KBS: A Korean Benchmark Suite for Foundation Model Evaluation The paper introduces the Korean Benchmark Suite to evaluate foundation models across six natural language capabilities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Not focused on retrieval-augmented generation or external document grounding.
KBS: A Korean Benchmark Suite for Foundation Model Evaluation The paper introduces the Korean Benchmark Suite to evaluate foundation models across six natural language capabilities. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not study model efficiency, distillation, or quantization techniques.
KBS: A Korean Benchmark Suite for Foundation Model Evaluation The paper introduces the Korean Benchmark Suite to evaluate foundation models across six natural language capabilities. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates text capabilities without image, video, or audio multimodal inputs.
KBS: A Korean Benchmark Suite for Foundation Model Evaluation The paper introduces the Korean Benchmark Suite to evaluate foundation models across six natural language capabilities. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on broad evaluation capabilities rather than safety or reliability guardrails.
KBS: A Korean Benchmark Suite for Foundation Model Evaluation The paper introduces the Korean Benchmark Suite to evaluate foundation models across six natural language capabilities. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents an evaluation suite, not agentic planning or multi-agent workflows.
What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination The paper audits data decontamination methods on math corpora to quantify false removals and benchmark leakage risks. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Evaluates math corpora rather than software engineering or coding tasks.
What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination The paper audits data decontamination methods on math corpora to quantify false removals and benchmark leakage risks. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Audits training datasets rather than multi-step agent planning or tools.
What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination The paper audits data decontamination methods on math corpora to quantify false removals and benchmark leakage risks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or efficient architectures.
What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination The paper audits data decontamination methods on math corpora to quantify false removals and benchmark leakage risks. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Examines text-based math training data rather than multimodal systems.
What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination The paper audits data decontamination methods on math corpora to quantify false removals and benchmark leakage risks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on training data decontamination, not inference-time document retrieval.

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