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
SEEDS: Preview-Guided Evidence Admission for Search Agents Introduces a preview-guided admission framework allowing search agents to selectively incorporate retrieved candidates into the ongoing reasoning context. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on search reasoning efficiency and quality rather than safety risks.
SEEDS: Preview-Guided Evidence Admission for Search Agents Introduces a preview-guided admission framework allowing search agents to selectively incorporate retrieved candidates into the ongoing reasoning context. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with textual search evidence without multimodal models.
SEEDS: Preview-Guided Evidence Admission for Search Agents Introduces a preview-guided admission framework allowing search agents to selectively incorporate retrieved candidates into the ongoing reasoning context. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses context management in search, not model compression or quantization.
SEEDS: Preview-Guided Evidence Admission for Search Agents Introduces a preview-guided admission framework allowing search agents to selectively incorporate retrieved candidates into the ongoing reasoning context. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on general search agents rather than code generation or program repair.
SEEDS: Preview-Guided Evidence Admission for Search Agents Introduces a preview-guided admission framework allowing search agents to selectively incorporate retrieved candidates into the ongoing reasoning context. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a framework for selective search ingestion, not an evaluation benchmark.
MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph Introduces a benchmark evaluating vision-language models on graph-theoretic reasoning where evidence is split across multiple visual graph views. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on visual graph reasoning rather than retrieval-augmented generation.
MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph Introduces a benchmark evaluating vision-language models on graph-theoretic reasoning where evidence is split across multiple visual graph views. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates abstract visual graph tasks, not industrial manufacturing or inspection.
MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph Introduces a benchmark evaluating vision-language models on graph-theoretic reasoning where evidence is split across multiple visual graph views. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on multi-view reasoning capability rather than safety or reliability.
MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph Introduces a benchmark evaluating vision-language models on graph-theoretic reasoning where evidence is split across multiple visual graph views. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on visual graph theory reasoning, not software engineering or code.
MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph Introduces a benchmark evaluating vision-language models on graph-theoretic reasoning where evidence is split across multiple visual graph views. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression or serving optimization.
MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph Introduces a benchmark evaluating vision-language models on graph-theoretic reasoning where evidence is split across multiple visual graph views. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a benchmark for visual graph reasoning, not multi-step autonomous tool-use agents.
Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction Derives a formula to forecast disagreement between masked text reconstruction models fitted on independent samples from the same population. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies statistical sampling variance in language modeling, not agent frameworks.
Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction Derives a formula to forecast disagreement between masked text reconstruction models fitted on independent samples from the same population. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not focus on retrieval-augmented generation.
Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction Derives a formula to forecast disagreement between masked text reconstruction models fitted on independent samples from the same population. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Deals with theoretical text reconstruction statistics, not code LLMs.
Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction Derives a formula to forecast disagreement between masked text reconstruction models fitted on independent samples from the same population. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not involve model pruning, quantization, or inference serving.
Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction Derives a formula to forecast disagreement between masked text reconstruction models fitted on independent samples from the same population. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Concerned with text masked reconstruction rather than multimodal tasks.
Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction Derives a formula to forecast disagreement between masked text reconstruction models fitted on independent samples from the same population. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on statistical variance in text prediction, not AI safety or jailbreaks.
Language Models Have Interpretable Narrative Mechanisms Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on narrative text mechanisms, not software code.
Language Models Have Interpretable Narrative Mechanisms Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Examines text-only LLMs without multimodal modalities.
Language Models Have Interpretable Narrative Mechanisms Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address document retrieval or grounding.
Language Models Have Interpretable Narrative Mechanisms Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate model quantization or serving efficiency.
Language Models Have Interpretable Narrative Mechanisms Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates mechanistic interpretability rather than providing a benchmark or evaluation method.
Language Models Have Interpretable Narrative Mechanisms Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates internal model interpretability for narrativity, not agent systems.
Language Models Have Interpretable Narrative Mechanisms Identifies internal attention heads, neurons, and sparse autoencoder features that causally influence narrative representation in language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on narrative representations rather than deployment guardrails or safety risks.
Evaluating LLM Creativity through Long-Tail Performance Formalizes language model creativity evaluation as long-tail input-output distribution performance and tests it across multiple benchmarks and models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on general LLM creativity metrics, not code generation.
Evaluating LLM Creativity through Long-Tail Performance Formalizes language model creativity evaluation as long-tail input-output distribution performance and tests it across multiple benchmarks and models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Examines benchmark validity for creativity rather than system safety or red-teaming.
Evaluating LLM Creativity through Long-Tail Performance Formalizes language model creativity evaluation as long-tail input-output distribution performance and tests it across multiple benchmarks and models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Analyzes LLM benchmark distributions, not agent architectures or planning.
Evaluating LLM Creativity through Long-Tail Performance Formalizes language model creativity evaluation as long-tail input-output distribution performance and tests it across multiple benchmarks and models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text language model creativity without multimodal data.
Evaluating LLM Creativity through Long-Tail Performance Formalizes language model creativity evaluation as long-tail input-output distribution performance and tests it across multiple benchmarks and models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval mechanisms or external document grounding.
Evaluating LLM Creativity through Long-Tail Performance Formalizes language model creativity evaluation as long-tail input-output distribution performance and tests it across multiple benchmarks and models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression or inference speed.
Evo$^2$: Evolving the Evolver through Weight-Harness Co-Evolution Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with text agent harnesses without multimodal integration.
Evo$^2$: Evolving the Evolver through Weight-Harness Co-Evolution Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Deals with agent harness co-evolution, not model quantization or serving efficiency.
Evo$^2$: Evolving the Evolver through Weight-Harness Co-Evolution Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on autonomous performance evolution rather than safety guardrails or jailbreaks.
Evo$^2$: Evolving the Evolver through Weight-Harness Co-Evolution Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses general agent harness evolution rather than code generation specific tasks.
Evo$^2$: Evolving the Evolver through Weight-Harness Co-Evolution Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent self-evolution mechanisms rather than document retrieval systems.
Evo$^2$: Evolving the Evolver through Weight-Harness Co-Evolution Proposes a co-evolution framework that coordinates agent harness prompt modifications with model weight updates using self-generated experience. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an agent training framework, not an evaluation benchmark.
RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models Analyzes quantization error propagation in recurrent-depth language models and proposes adaptive mixed-precision schedules for faster prefill inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve external document retrieval or grounding.
RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models Analyzes quantization error propagation in recurrent-depth language models and proposes adaptive mixed-precision schedules for faster prefill inference. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses numerical inference efficiency, not deployment safety or alignment.
RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models Analyzes quantization error propagation in recurrent-depth language models and proposes adaptive mixed-precision schedules for faster prefill inference. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on numerical precision for deep models, not software engineering.
RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models Analyzes quantization error propagation in recurrent-depth language models and proposes adaptive mixed-precision schedules for faster prefill inference. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Deals with recurrent-depth model quantization, not agent orchestration.
RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models Analyzes quantization error propagation in recurrent-depth language models and proposes adaptive mixed-precision schedules for faster prefill inference. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies solely to language model prefill compute, not multimodal inputs.
RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models Analyzes quantization error propagation in recurrent-depth language models and proposes adaptive mixed-precision schedules for faster prefill inference. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an inference speedup technique, not a benchmark.
CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation Presents a two-stage visual-textual alignment and retrieval-augmented framework for generating structured radiology reports from medical scans. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a domain-specific model framework rather than an evaluation benchmark.
CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation Presents a two-stage visual-textual alignment and retrieval-augmented framework for generating structured radiology reports from medical scans. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on medical radiology reporting, not code development.
CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation Presents a two-stage visual-textual alignment and retrieval-augmented framework for generating structured radiology reports from medical scans. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a report generation pipeline rather than multi-step autonomous planning agents.
CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation Presents a two-stage visual-textual alignment and retrieval-augmented framework for generating structured radiology reports from medical scans. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Targets medical report generation quality rather than AI safety or guardrails.
CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation Presents a two-stage visual-textual alignment and retrieval-augmented framework for generating structured radiology reports from medical scans. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression or inference efficiency.
KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse Develops an open-source testbed to evaluate segment-wise key-value cache reuse beyond exact prompt prefixes during language model serving. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses latency and caching efficiency, not AI safety or alignment.
KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse Develops an open-source testbed to evaluate segment-wise key-value cache reuse beyond exact prompt prefixes during language model serving. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a serving system testbed for cache hits, not a standard LLM evaluation benchmark.
KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse Develops an open-source testbed to evaluate segment-wise key-value cache reuse beyond exact prompt prefixes during language model serving. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text LLM serving infrastructure without multimodal data.
KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse Develops an open-source testbed to evaluate segment-wise key-value cache reuse beyond exact prompt prefixes during language model serving. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on serving-level KV-cache memory management rather than multi-step agent planning.
KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse Develops an open-source testbed to evaluate segment-wise key-value cache reuse beyond exact prompt prefixes during language model serving. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Targets serving systems, not code generation or program repair.
From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not investigate retrieval-augmented generation.
From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with speech SSL data quality, not deployment safety or jailbreaks.
From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on Finnish speech self-supervised learning, not autonomous agents.
From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Concerned with speech audio representation, not code generation.
From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an audio pretraining filtering method, not an LLM benchmark.
From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies speech SSL data filtering and architecture comparisons, not model compression.
From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale Evaluates speech self-supervised learning models on Finnish broadcast audio using ASR confidence filtering and controlled architectural comparisons. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses exclusively on audio/speech SSL rather than joint multimodal vision-language models.
BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION Presents a multi-dimensional annotated corpus of Bengali political YouTube discussions to support research on contextual cyberbullying detection. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore model compression, distillation, or efficient inference.

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