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
DPPM: Dual-Path Parametric Memory for Personalized Language Models The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on personalized interaction memory rather than software engineering or code generation.
DPPM: Dual-Path Parametric Memory for Personalized Language Models The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Encodes history into parametric adapters to avoid retrieval contexts at inference time.
DPPM: Dual-Path Parametric Memory for Personalized Language Models The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on personalization and preference tracking rather than safety or reliability risks.
DPPM: Dual-Path Parametric Memory for Personalized Language Models The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses text-based conversation histories without multimodal integration.
From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies medical prediction from video rather than autonomous LLM agents.
From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on clinical depression assessment rather than software or programming tasks.
From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, distillation, or efficient serving.
From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not explore retrieval-augmented generation or corporate knowledge grounding.
From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a domain-specific diagnostic architecture rather than generic LLM benchmarking.
From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on clinical behavioral modeling rather than AI deployment safety, attacks, or guardrails.
Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Analyzes patch layout mechanisms rather than creating an LLM evaluation benchmark.
Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Examines token-free compute efficiency without addressing deployment safety or alignment.
Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies compute allocation in byte models rather than code models or software engineering.
Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Investigates byte-level text models without multimodal or vision-language data.
Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies byte-level compute allocation rather than retrieval-augmented generation mechanisms.
Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on byte-level patching mechanics rather than LLM agent planning or tool use.
Detecting Spin in Clinical Trials with Large Language Models The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an applied classification system rather than a general LLM evaluation benchmark.
Detecting Spin in Clinical Trials with Large Language Models The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports. Peer-reviewed	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Analyzes clinical trial text without multimodal or visual components.
Detecting Spin in Clinical Trials with Large Language Models The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports. Peer-reviewed	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on clinical trial text analysis rather than software engineering or code generation.
Detecting Spin in Clinical Trials with Large Language Models The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Applies prompt engineering and classification rather than autonomous agent systems.
Detecting Spin in Clinical Trials with Large Language Models The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Evaluates text similarity and LLM classification directly without RAG or external retrieval.
Detecting Spin in Clinical Trials with Large Language Models The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on clinical trial outcome discrepancy detection rather than AI deployment safety risks.
Detecting Spin in Clinical Trials with Large Language Models The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports. Peer-reviewed	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, efficient inference, or edge deployment techniques.
Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies classification performance without addressing deployment safety, risks, or guardrails.
Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates stance detection rather than multi-step planning or tool use in agents.
Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate model pruning, quantization, efficient serving, or small models.
Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on natural language stance detection, not software code or coding assistants.
Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Assesses stance classification directly without retrieval-augmented generation systems.
Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text-only datasets without multimodal inputs.
Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines unlearning in language models without addressing multi-step autonomous agents.
Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an unlearning framework rather than a new evaluation benchmark or audit methodology.
Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text-based language model unlearning without multimodal elements.
Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies parametric unlearning rather than retrieval-augmented generation architectures.
Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Deals with machine unlearning rather than model efficiency, quantization, or distillation.
Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not focus on software engineering, coding assistants, or program repair.
Event-Centric Memory with Query-Aware Graph Augmentation for Long-Term Conversational Agents The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a memory architecture rather than a generic LLM benchmark or evaluation study.
Event-Centric Memory with Query-Aware Graph Augmentation for Long-Term Conversational Agents The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on personalized conversational memory without addressing safety, jailbreaks, or attacks.
Event-Centric Memory with Query-Aware Graph Augmentation for Long-Term Conversational Agents The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text dialogue memory without addressing image, audio, or video modalities.
Event-Centric Memory with Query-Aware Graph Augmentation for Long-Term Conversational Agents The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on memory representations rather than model compression, distillation, or quantization.
Event-Centric Memory with Query-Aware Graph Augmentation for Long-Term Conversational Agents The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses dialogue history memory rather than software code or programming tasks.
Project Greenhouse: Progress Toward Fully Open and Sovereign Agentic Search The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a trained reranker family rather than an evaluation framework or benchmark study.
Project Greenhouse: Progress Toward Fully Open and Sovereign Agentic Search The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Builds a reranker model for search rather than multi-step autonomous planning agents.
Project Greenhouse: Progress Toward Fully Open and Sovereign Agentic Search The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on open and sovereign search architectures, not safety, jailbreaks, or guardrails.
Project Greenhouse: Progress Toward Fully Open and Sovereign Agentic Search The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text search and reranking without multimodal capabilities.
Project Greenhouse: Progress Toward Fully Open and Sovereign Agentic Search The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code generation, program repair, or developer tooling.
When History Helps and Hurts: Selective History Use across Multimodal Turns The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational benchmarking rather than safety guardrails, jailbreaks, or alignment.
When History Helps and Hurts: Selective History Use across Multimodal Turns The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Evaluates conversational context management rather than retrieval-augmented generation from external knowledge.
When History Helps and Hurts: Selective History Use across Multimodal Turns The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies conversational multimodal media understanding rather than software engineering or code.
When History Helps and Hurts: Selective History Use across Multimodal Turns The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore model compression, quantization, distillation, or efficient serving.
When History Helps and Hurts: Selective History Use across Multimodal Turns The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Assesses multimodal conversational history retention rather than autonomous agents with tools.
MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses RL reward hacking internally, but primary contribution is scaling omni-modal RL training.
MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a model and RL training methodology rather than a benchmark or evaluation method.
MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on RL training and self-improvement without investigating external retrieval systems.
MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Mentions coding environments among several domains but focuses fundamentally on foundation model RL training.
MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Scales compute up to large context lengths rather than optimizing for small, efficient models.
Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies decision architectures rather than model quantization, pruning, or efficient inference techniques.
Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Benchmarking accuracy and reasoning rather than deployment safety, red-teaming, or guardrails.
Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on multiple-choice decision models without evaluating external document retrieval or RAG mechanisms.
Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text-based multiple-choice benchmarks without visual or multimodal inputs.
Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates a non-agentic decision model across benchmarks rather than multi-step tool-using agents.

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