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
AI Models Can Provably Hide Arbitrary Capabilities The paper shows that backdoor mechanisms can cryptographically hide arbitrary neural network capabilities until activated by secret triggers. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on hiding capabilities cryptographically rather than model efficiency or compression.
Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates existing sentiment benchmarks rather than contributing a new benchmark or evaluation method.
Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents sequence labeling for sentiment analysis rather than LLM agent systems.
Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Studies text sentiment analysis without visual or multimodal modalities.
Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Uses simpler sequence labeling architectures, but does not study model quantization, distillation, or serving.
Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies dependency graph parsing to sentiment analysis, not software engineering or code.
Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation or external knowledge retrieval.
Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses sentiment parsing without examining deployment safety risks or guardrails.
4-Tensor Attention Model for Semantic Physical Reality The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address programming, software engineering, or code generation.
4-Tensor Attention Model for Semantic Physical Reality The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an architectural model rather than an LLM evaluation benchmark.
4-Tensor Attention Model for Semantic Physical Reality The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on scene state transitions rather than retrieval-augmented generation systems.
4-Tensor Attention Model for Semantic Physical Reality The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on attention efficiency and state modeling, not safety or guardrails.
4-Tensor Attention Model for Semantic Physical Reality The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Tests semantic states on text stories rather than multimodal vision-language models.
4-Tensor Attention Model for Semantic Physical Reality The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an attention mechanism for state prediction without multi-step LLM agent systems.
Thinking Inertia: LLMs Keep Thinking When Told Not To The paper defines evaluation metrics to show that language models continue generating reasoning traces even when instructed not to think. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Analyzes text-only reasoning traces without multimodal capabilities.
Thinking Inertia: LLMs Keep Thinking When Told Not To The paper defines evaluation metrics to show that language models continue generating reasoning traces even when instructed not to think. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not investigate code generation, software development, or program repair.
Thinking Inertia: LLMs Keep Thinking When Told Not To The paper defines evaluation metrics to show that language models continue generating reasoning traces even when instructed not to think. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies reasoning mode suppression in LLMs rather than multi-step autonomous tool use.
Thinking Inertia: LLMs Keep Thinking When Told Not To The paper defines evaluation metrics to show that language models continue generating reasoning traces even when instructed not to think. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval mechanisms, search, or external grounding.
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.
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	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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a memory mechanism rather than a new general LLM evaluation methodology.
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.
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	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	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	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	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.
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.
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.
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	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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Analyzes patch layout mechanisms rather than creating an 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	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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, efficient inference, or edge deployment techniques.
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	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	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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Applies prompt engineering and classification rather than autonomous agent 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	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	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	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	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.
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.
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	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.
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	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.
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	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	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	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses dialogue history memory rather than software code or programming tasks.
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.
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.

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