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
TopicMerge: A Divide and Conquer Approach to Topic Modeling The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or distillation.
Counterfactual Gate-Support Decomposition: Separating Selection from Redistribution in Training-Data Filtering The paper proposes an experimental design that separates example selection from mass redistribution to analyze the effects of training-data quality filtering. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies data selection effects rather than AI safety or guardrails.
Counterfactual Gate-Support Decomposition: Separating Selection from Redistribution in Training-Data Filtering The paper proposes an experimental design that separates example selection from mass redistribution to analyze the effects of training-data quality filtering. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on training data selection methodology rather than model efficiency.
Counterfactual Gate-Support Decomposition: Separating Selection from Redistribution in Training-Data Filtering The paper proposes an experimental design that separates example selection from mass redistribution to analyze the effects of training-data quality filtering. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates training data filtering decomposition, not LLM agents.
Counterfactual Gate-Support Decomposition: Separating Selection from Redistribution in Training-Data Filtering The paper proposes an experimental design that separates example selection from mass redistribution to analyze the effects of training-data quality filtering. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Analyzes training-data selection, not retrieval-augmented generation architectures.
Counterfactual Gate-Support Decomposition: Separating Selection from Redistribution in Training-Data Filtering The paper proposes an experimental design that separates example selection from mass redistribution to analyze the effects of training-data quality filtering. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not focus on software engineering or code generation.
Counterfactual Gate-Support Decomposition: Separating Selection from Redistribution in Training-Data Filtering The paper proposes an experimental design that separates example selection from mass redistribution to analyze the effects of training-data quality filtering. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Analyzes text retrieval data filtering without multimodal components.
Target-Token Asymmetry Can Masquerade As Depth Specialization The paper shows that differences in predicted target tokens, such as whitespace, can falsely look like layer specialization across different corpora. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies mechanistic interpretability across token types, not code generation.
Target-Token Asymmetry Can Masquerade As Depth Specialization The paper shows that differences in predicted target tokens, such as whitespace, can falsely look like layer specialization across different corpora. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address safety vulnerabilities, hallucinations, or deployment guardrails.
Target-Token Asymmetry Can Masquerade As Depth Specialization The paper shows that differences in predicted target tokens, such as whitespace, can falsely look like layer specialization across different corpora. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on interpretability analysis rather than model compression techniques.
Target-Token Asymmetry Can Masquerade As Depth Specialization The paper shows that differences in predicted target tokens, such as whitespace, can falsely look like layer specialization across different corpora. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates language models on text data without multimodal elements.
Target-Token Asymmetry Can Masquerade As Depth Specialization The paper shows that differences in predicted target tokens, such as whitespace, can falsely look like layer specialization across different corpora. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate retrieval-augmented generation systems.
Target-Token Asymmetry Can Masquerade As Depth Specialization The paper shows that differences in predicted target tokens, such as whitespace, can falsely look like layer specialization across different corpora. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Analyzes layer-ablation dynamics, not agent planning or tool use.
Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents The paper analyzes prefix replay curricula and handoff rules under reinforcement learning to improve long-horizon task completion for language model agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies reinforcement learning curricula, not retrieval-augmented generation.
Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents The paper analyzes prefix replay curricula and handoff rules under reinforcement learning to improve long-horizon task completion for language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on training curricula under GRPO rather than evaluation methods.
Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents The paper analyzes prefix replay curricula and handoff rules under reinforcement learning to improve long-horizon task completion for language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies task completion learning rather than safety or guardrails.
Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents The paper analyzes prefix replay curricula and handoff rules under reinforcement learning to improve long-horizon task completion for language model agents. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on multi-step tool-use benchmarks rather than software engineering.
Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents The paper analyzes prefix replay curricula and handoff rules under reinforcement learning to improve long-horizon task completion for language model agents. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on language-based tool calling and agent benchmarks.
Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents The paper analyzes prefix replay curricula and handoff rules under reinforcement learning to improve long-horizon task completion for language model agents. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient inference.
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not investigate retrieval-augmented text generation methods.
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression or inference acceleration.
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic visual tracking, not programming code.
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic control rather than model safety or alignment.
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Studies embodied robot person-following, not industrial inspection.
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an embodied tracking policy rather than evaluation frameworks.
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback The paper introduces a framework that decomposes user feedback into atomic rules to retain only reusable preferences for personalizing language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates personalized writing tasks rather than software engineering.
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback The paper introduces a framework that decomposes user feedback into atomic rules to retain only reusable preferences for personalizing language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on personalized skill induction rather than RAG systems.
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback The paper introduces a framework that decomposes user feedback into atomic rules to retain only reusable preferences for personalizing language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not investigate pruning, distillation, or lightweight models.
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback The paper introduces a framework that decomposes user feedback into atomic rules to retain only reusable preferences for personalizing language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies preference learning rather than safety, guardrails, or jailbreaks.
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback The paper introduces a framework that decomposes user feedback into atomic rules to retain only reusable preferences for personalizing language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals purely with textual user feedback and personalization.
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback The paper introduces a framework that decomposes user feedback into atomic rules to retain only reusable preferences for personalizing language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on skill acquisition methods rather than benchmark reliability.
The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation The paper proposes a multiple-summary metric and dataset to evaluate graded proposition salience across diverse text genres. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates proposition salience extraction, not multi-step agent planning.
The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation The paper proposes a multiple-summary metric and dataset to evaluate graded proposition salience across diverse text genres. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on safety vulnerabilities or deployment reliability.
The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation The paper proposes a multiple-summary metric and dataset to evaluate graded proposition salience across diverse text genres. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with written and spoken English text, not multimodal.
The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation The paper proposes a multiple-summary metric and dataset to evaluate graded proposition salience across diverse text genres. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies to general text summarization rather than code generation.
The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation The paper proposes a multiple-summary metric and dataset to evaluate graded proposition salience across diverse text genres. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate external document retrieval or grounding architectures.
The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation The paper proposes a multiple-summary metric and dataset to evaluate graded proposition salience across diverse text genres. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression or efficient serving.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve document or knowledge retrieval systems.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies exploration in diffusion language models, not model efficiency.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion language models without multimodal inputs.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion model reasoning exploration, not code synthesis.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an RL algorithm rather than evaluation methodologies.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning optimization, not safety guardrails.
ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies reinforcement learning for diffusion language models, not agent workflows.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines scaling behavior rather than small efficient model architectures.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Examines spatial reasoning and multimodal loss, not software code.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not study safety, jailbreaks, or deployment reliability.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation systems.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies multimodal pre-training and spatial reasoning, not autonomous agents.
More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates pre-training dynamics rather than LLM benchmark design.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, distillation, or quantization.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Does not address industrial inspection or defect detection tasks.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on multimodal in-context learning, not code generation.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on training agentic retrieval, not benchmark evaluation methodology.
ActICL: Post-Training for Agentic Multimodal In-Context Learning The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on safety, guardrails, or alignment risks.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Investigates audio reasoning post-training, not code or programming tasks.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates audio-language model post-training, not multi-step agents.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies post-training evaluation artifacts, not model compression or efficiency.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies audio-language models, not retrieval-augmented generation systems.
AgriJev: Efficient Agricultural Candidate Scoring with Multi-Scale Adapted Models The paper presents adapted small language models designed to directly score agricultural candidate choices in a single forward pass. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Operates on domain text data without image or multimodal modalities.

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