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
Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on satirical news explanation, unrelated to code synthesis, repair, or software engineering.
Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Only uses open-weights models as a baseline; does not propose compression, quantization, or serving efficiencies.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses unimodal sequence modeling without multimodal data.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a sequence modeling method rather than an evaluation benchmark.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an alternative sequence modeling architecture, not an autonomous agent system.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with architecture design rather than deployment safety or guardrails.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with sequence modeling architectures, not retrieval-augmented generation.
Time Delay System for Sequence Modeling Proposes a sequence modeling framework using dynamic nonlinear time delays based on semantic similarity between current and past inputs. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on general sequence modeling, not software engineering or code.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational recall rather than safety or jailbreaks.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Pertains to text dialogue memory without multimodal input.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses multi-party conversation memory, not code generation.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on memory representations rather than model compression or inference efficiency.
Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory Presents a temporal memory architecture using hierarchical tree structures to trace evolving multi-party conversational histories for agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a memory framework rather than an evaluation benchmark.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies diffusion to discrete text generation, not multimodal data.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a new generation model architecture, not an evaluation benchmark.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on generative decoding speed and modeling, not AI safety or red-teaming.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on generic text generation, not software code synthesis.
Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation Proposes separating continuous diffusion language generation into high-level flow-generated predictive states and block-level autoregressive token decoding. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Introduces an architecture for parallel generation, not agent workflows.
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.
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	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	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	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.
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	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	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	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.
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.
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	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	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	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	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	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	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.
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	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	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Examines text-only LLMs without multimodal modalities.
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.
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	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	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval mechanisms or external document grounding.
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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an agent training framework, not an evaluation benchmark.
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	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	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with text agent harnesses without multimodal integration.
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	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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an inference speedup technique, not a 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.

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