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
A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not address external knowledge retrieval or RAG pipelines.
A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Investigates continuous text generation, not code synthesis.
A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on diffusion for text generation without multimodal inputs.
A Step in the Right Place: Understanding and Optimizing Timestep Placement in Continuous Diffusion Language Models The paper introduces an algorithm to optimize denoising timestep placement in continuous diffusion language models to improve generation quality. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on sampling algorithms rather than evaluation benchmarks.
Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant A general multimodal foundation model, not specifically for code.
Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates a new model release rather than benchmarking methodology.
Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Presents an open multimodal model rather than deployment safety.
Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not focus on retrieval-augmented generation architectures.
Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Describes a base foundation model release, not an agent.
Apertus 1.5: From Open Text Models to Open Multimodal Reasoners The paper presents open-source multimodal models extended to process text, images, and audio with long-context reasoning capabilities. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 8B/70B pretraining rather than efficient model compression.
An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Analyzes screenplay saliency using NLP, not autonomous agent systems.
An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Explores narrative linguistic features rather than AI safety or reliability.
An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, pruning, or efficient inference.
An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses purely on text screenplay analysis without multimodal processing.
An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not explore document retrieval or RAG architectures.
An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies movie screenplays rather than software programming code.
An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays The paper evaluates whether uniform information density and specific linguistic features predict salient scenes in movie screenplays. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies linguistic salience rather than LLM benchmarking methodologies.
The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on general LLM compression rather than code generation.
The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses standard text LLM compression without multimodal data.
The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Investigates structural pruning dynamics, not autonomous agentic workflows.
The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not investigate retrieval systems or knowledge grounding.
The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies model compression dynamics rather than deployment safety guardrails.
The Fragility and Dynamics of LLM Pruning-Recovery Steps The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates pruning recipes rather than creating evaluation benchmarks.
Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Evaluates language models without multimodal text-image-audio inputs.
Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates compression calibration rather than safety or reliability guardrails.
Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on expert pruning, not LLM agent systems.
Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not study document retrieval or augmented generation.
Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies pruning calibration data rather than evaluation benchmark design.
Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning The paper studies how calibration dataset size, language, and data distribution affect post-training expert pruning in mixture-of-experts models. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on general MoE compression, not software engineering.
The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes training distillation gradients rather than evaluation benchmarks.
The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not address external document retrieval or knowledge grounding.
The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on distillation training loss rather than deployment safety.
The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies language model token vocabulary distillation without multimodality.
The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Deals with general vocabulary distillation, not code-specific models.
The Gradient That Top-k Distillation Never Computes The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Investigates theoretical distillation loss gradients, not agentic planning systems.
Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not explore quantization, pruning, or efficient model serving.
Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not evaluate external document retrieval or grounding methods.
Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies iterative machine translation refinement, not autonomous multi-step agents.
Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses entirely on text translation without multimodal inputs.
Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Deals with natural language translation, not code or programming.
Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement The paper investigates how varying the detail and format of feedback affects machine translation refinement in large language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines translation accuracy refinement rather than deployment safety hazards.
A Persian Vision-Language-Action Dataset and Benchmark for Ambiguity- and Infeasibility-Aware Instruction-to-Plan Grounding The paper introduces a Persian vision-language-action dataset and benchmark to evaluate robotic instruction grounding under deliberate ambiguity and infeasibility. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on benchmark creation rather than deployment safety guardrails.
A Persian Vision-Language-Action Dataset and Benchmark for Ambiguity- and Infeasibility-Aware Instruction-to-Plan Grounding The paper introduces a Persian vision-language-action dataset and benchmark to evaluate robotic instruction grounding under deliberate ambiguity and infeasibility. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Household robot vision grounding, not industrial quality control inspection.
A Persian Vision-Language-Action Dataset and Benchmark for Ambiguity- and Infeasibility-Aware Instruction-to-Plan Grounding The paper introduces a Persian vision-language-action dataset and benchmark to evaluate robotic instruction grounding under deliberate ambiguity and infeasibility. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Uses RAG as an ablation baseline, not core focus.
A Persian Vision-Language-Action Dataset and Benchmark for Ambiguity- and Infeasibility-Aware Instruction-to-Plan Grounding The paper introduces a Persian vision-language-action dataset and benchmark to evaluate robotic instruction grounding under deliberate ambiguity and infeasibility. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on embodied action plans, not software code generation.
A Persian Vision-Language-Action Dataset and Benchmark for Ambiguity- and Infeasibility-Aware Instruction-to-Plan Grounding The paper introduces a Persian vision-language-action dataset and benchmark to evaluate robotic instruction grounding under deliberate ambiguity and infeasibility. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, distillation, or efficient inference.
Rate-Resolved Text Evaluation with Masked Diffusion Language Models The paper introduces rate-resolved text evaluation to turn masking rates in diffusion language models into diagnostic scores for text assessment. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression or efficient serving.
Rate-Resolved Text Evaluation with Masked Diffusion Language Models The paper introduces rate-resolved text evaluation to turn masking rates in diffusion language models into diagnostic scores for text assessment. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Text evaluation method, not an autonomous agent system.
Rate-Resolved Text Evaluation with Masked Diffusion Language Models The paper introduces rate-resolved text evaluation to turn masking rates in diffusion language models into diagnostic scores for text assessment. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Not focused on retrieval-augmented generation architectures.
Rate-Resolved Text Evaluation with Masked Diffusion Language Models The paper introduces rate-resolved text evaluation to turn masking rates in diffusion language models into diagnostic scores for text assessment. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Not focused on software code or programming tasks.
Rate-Resolved Text Evaluation with Masked Diffusion Language Models The paper introduces rate-resolved text evaluation to turn masking rates in diffusion language models into diagnostic scores for text assessment. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Evaluates text models, without multimodal capabilities.
Rate-Resolved Text Evaluation with Masked Diffusion Language Models The paper introduces rate-resolved text evaluation to turn masking rates in diffusion language models into diagnostic scores for text assessment. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on evaluation metrics, not safety guardrails.
The Mathematical Collective Unconscious: Jungian Archetypes within the Latent Space The paper examines whether large language model embedding spaces naturally form semantic clusters resembling Jungian archetypes using synthetic and human-authored texts. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Examines literary archetypes, not software code.
The Mathematical Collective Unconscious: Jungian Archetypes within the Latent Space The paper examines whether large language model embedding spaces naturally form semantic clusters resembling Jungian archetypes using synthetic and human-authored texts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Not focused on AI safety, hallucination, or guardrails.
The Mathematical Collective Unconscious: Jungian Archetypes within the Latent Space The paper examines whether large language model embedding spaces naturally form semantic clusters resembling Jungian archetypes using synthetic and human-authored texts. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Embedding geometry study, not about AI agents.
The Mathematical Collective Unconscious: Jungian Archetypes within the Latent Space The paper examines whether large language model embedding spaces naturally form semantic clusters resembling Jungian archetypes using synthetic and human-authored texts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Latent space exploratory study, not a benchmark.
The Mathematical Collective Unconscious: Jungian Archetypes within the Latent Space The paper examines whether large language model embedding spaces naturally form semantic clusters resembling Jungian archetypes using synthetic and human-authored texts. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text embeddings, not multimodal systems.
The Mathematical Collective Unconscious: Jungian Archetypes within the Latent Space The paper examines whether large language model embedding spaces naturally form semantic clusters resembling Jungian archetypes using synthetic and human-authored texts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Does not explore retrieval-augmented generation architectures.
The Mathematical Collective Unconscious: Jungian Archetypes within the Latent Space The paper examines whether large language model embedding spaces naturally form semantic clusters resembling Jungian archetypes using synthetic and human-authored texts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Not focused on efficient models or compression.
Towards Autopilot Labeling for Short Video Content Governance The paper presents a multi-stage automated labeling framework using multimodal models and agents to annotate short videos for content moderation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression or efficient serving.

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