Every paper checked — nothing hidden

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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
RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent trajectory control rather than RAG architectures.
RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Does not introduce a new benchmark or evaluation method.
RIVO: Lightweight Adaptive Rerouting for Deep Research Agents RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Text-only agent framework, not involving multimodal models.
More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Applies to clinical text records, not multimodal data.
More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Replaces agent reasoning with deterministic rule execution for clinical decisions.
More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on clinical guideline execution rather than software engineering.
More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a hybrid decision framework rather than evaluation benchmarks.
More Reasoning Is Not Always Better: Executing Clinical Guidelines with LLMs and Rules MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Combines rule engines with LLMs, not retrieval-augmented generation.
Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on educational reading curation, not AI safety.
Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Processes text abstracts and objectives, not multimodal models.
Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Applies NLP pipelines to curriculum curation, not autonomous agents.
Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Uses sentence embeddings for reading alignment, not RAG systems.
Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies standard BERT models rather than model compression.
Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Applies NLP to dental education reading lists, not code.
Natural Language Processing for Aligning Readings with Learning Objectives and Assessing Topic Balance in Dental Education The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a domain-specific curriculum workflow, not an LLM benchmark.
Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Investigates pretraining dynamics, not retrieval-augmented generation.
Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies model pretraining dynamics, not LLM agents or planning.
Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies foundational pretraining rather than software engineering.
Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines pretraining efficiency rather than deployment safety.
Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on language model pretraining without multimodal components.
Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates pretraining optimization rather than benchmark development.
Mixed Feelings, Single Polarity The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies NLP emotion classification, not LLM agents or planning.
Mixed Feelings, Single Polarity The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies emotion classification datasets, not deployment safety.
Mixed Feelings, Single Polarity The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes emotion label mappings rather than model efficiency.
Mixed Feelings, Single Polarity The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies text sentiment analysis, not multimodal systems.
Mixed Feelings, Single Polarity The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on emotion polarity labeling, not retrieval systems.
Mixed Feelings, Single Polarity The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Examines classical sentiment dataset annotations rather than LLM benchmarks.
Mixed Feelings, Single Polarity The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Investigates sentiment and emotion datasets, not code generation.
PAIR: Memory-Efficient and Fast Inference with Reading--Reasoning Fusion PAIR splits context processing and answer reasoning between two different-sized models, fusing predictions at the logit level for efficient inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on inference latency and memory rather than safety.
PAIR: Memory-Efficient and Fast Inference with Reading--Reasoning Fusion PAIR splits context processing and answer reasoning between two different-sized models, fusing predictions at the logit level for efficient inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an inference architecture rather than evaluation benchmarks.
PAIR: Memory-Efficient and Fast Inference with Reading--Reasoning Fusion PAIR splits context processing and answer reasoning between two different-sized models, fusing predictions at the logit level for efficient inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies context inference fusion rather than external retrieval systems.
PAIR: Memory-Efficient and Fast Inference with Reading--Reasoning Fusion PAIR splits context processing and answer reasoning between two different-sized models, fusing predictions at the logit level for efficient inference. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Applies to context-based QA rather than software engineering.
PAIR: Memory-Efficient and Fast Inference with Reading--Reasoning Fusion PAIR splits context processing and answer reasoning between two different-sized models, fusing predictions at the logit level for efficient inference. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on language model context processing without multimodal inputs.
PAIR: Memory-Efficient and Fast Inference with Reading--Reasoning Fusion PAIR splits context processing and answer reasoning between two different-sized models, fusing predictions at the logit level for efficient inference. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on split-model inference rather than multi-step agent planning.
Improving the Evaluation of Biomedical Question Answering with Few Human Preference Signals This paper uses limited human preference signals to calibrate automated evaluation metrics for biomedical retrieval-augmented question answering. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Evaluates biomedical QA, not software engineering.
Improving the Evaluation of Biomedical Question Answering with Few Human Preference Signals This paper uses limited human preference signals to calibrate automated evaluation metrics for biomedical retrieval-augmented question answering. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses evaluation metric alignment rather than AI safety guardrails.
Improving the Evaluation of Biomedical Question Answering with Few Human Preference Signals This paper uses limited human preference signals to calibrate automated evaluation metrics for biomedical retrieval-augmented question answering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on evaluation methodology, not model compression or efficiency.
Improving the Evaluation of Biomedical Question Answering with Few Human Preference Signals This paper uses limited human preference signals to calibrate automated evaluation metrics for biomedical retrieval-augmented question answering. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text biomedical QA, not multimodal data.
Improving the Evaluation of Biomedical Question Answering with Few Human Preference Signals This paper uses limited human preference signals to calibrate automated evaluation metrics for biomedical retrieval-augmented question answering. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on evaluation metrics for QA rather than agent planning.
Beyond the greenwash: do LLMs' ecologically sound decisions hold up under pressure? This paper measures whether ecological alignment in large language models survives under pressure using a calibrated pretraining ladder. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies pretraining alignment and evaluation rather than model compression.
Beyond the greenwash: do LLMs' ecologically sound decisions hold up under pressure? This paper measures whether ecological alignment in large language models survives under pressure using a calibrated pretraining ladder. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on behavioral alignment testing rather than agent workflows.
Beyond the greenwash: do LLMs' ecologically sound decisions hold up under pressure? This paper measures whether ecological alignment in large language models survives under pressure using a calibrated pretraining ladder. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Evaluates language models without multimodal components.
Beyond the greenwash: do LLMs' ecologically sound decisions hold up under pressure? This paper measures whether ecological alignment in large language models survives under pressure using a calibrated pretraining ladder. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies ecological bias and decision-making, not software engineering.
Beyond the greenwash: do LLMs' ecologically sound decisions hold up under pressure? This paper measures whether ecological alignment in large language models survives under pressure using a calibrated pretraining ladder. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Investigates model disposition under pressure, not retrieval systems.
Scaling Multi-Agent Intelligence with Substrate-Centric Coordination ScaleMAS coordinates multi-agent systems using an asynchronous shared datastore with limited search to improve scaling and fault tolerance. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on agent coordination mechanisms rather than model compression.
Scaling Multi-Agent Intelligence with Substrate-Centric Coordination ScaleMAS coordinates multi-agent systems using an asynchronous shared datastore with limited search to improve scaling and fault tolerance. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with multi-agent coordination without multimodal data.
Scaling Multi-Agent Intelligence with Substrate-Centric Coordination ScaleMAS coordinates multi-agent systems using an asynchronous shared datastore with limited search to improve scaling and fault tolerance. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses general multi-agent coordination rather than software engineering.
Scaling Multi-Agent Intelligence with Substrate-Centric Coordination ScaleMAS coordinates multi-agent systems using an asynchronous shared datastore with limited search to improve scaling and fault tolerance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a multi-agent framework rather than evaluation benchmarks.
Scaling Multi-Agent Intelligence with Substrate-Centric Coordination ScaleMAS coordinates multi-agent systems using an asynchronous shared datastore with limited search to improve scaling and fault tolerance. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-agent messaging substrates rather than external knowledge RAG.
Hashrope: A Persistent Data Structure for Mechanisms of LLM Context Management This paper presents Hashrope, a unified persistent data structure supporting incremental edits, branching, and cache reuse for language model serving. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on inference serving mechanics rather than deployment safety.
Hashrope: A Persistent Data Structure for Mechanisms of LLM Context Management This paper presents Hashrope, a unified persistent data structure supporting incremental edits, branching, and cache reuse for language model serving. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses text context serving engines, not multimodal systems.
Hashrope: A Persistent Data Structure for Mechanisms of LLM Context Management This paper presents Hashrope, a unified persistent data structure supporting incremental edits, branching, and cache reuse for language model serving. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on serving data structures rather than code assistants.
Hashrope: A Persistent Data Structure for Mechanisms of LLM Context Management This paper presents Hashrope, a unified persistent data structure supporting incremental edits, branching, and cache reuse for language model serving. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a context management data structure, not an evaluation benchmark.
Hashrope: A Persistent Data Structure for Mechanisms of LLM Context Management This paper presents Hashrope, a unified persistent data structure supporting incremental edits, branching, and cache reuse for language model serving. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Covers data structures for serving stacks, not agent planning.
Hashrope: A Persistent Data Structure for Mechanisms of LLM Context Management This paper presents Hashrope, a unified persistent data structure supporting incremental edits, branching, and cache reuse for language model serving. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on KV cache and context management, not RAG retrieval.
Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models This paper presents CacheFuse, a training-free technique enabling heterogeneous language models to communicate directly by sharing key-value cache states. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on inter-model communication speed, not deployment safety.
Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models This paper presents CacheFuse, a training-free technique enabling heterogeneous language models to communicate directly by sharing key-value cache states. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies inter-model KV-cache sharing rather than document retrieval or RAG.
Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models This paper presents CacheFuse, a training-free technique enabling heterogeneous language models to communicate directly by sharing key-value cache states. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on language model latent communication, not multimodal models.
Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models This paper presents CacheFuse, a training-free technique enabling heterogeneous language models to communicate directly by sharing key-value cache states. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on general inter-model communication rather than software engineering.
Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models This paper presents CacheFuse, a training-free technique enabling heterogeneous language models to communicate directly by sharing key-value cache states. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a collaboration method rather than benchmark evaluation methodologies.

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