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
DeepThink: A Conceptual Model for Knowledge-Addressable Recurrent Language Modeling This paper proposes DeepThink, a conceptual architecture that decouples knowledge capacity, activation width, and recurrent depth in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a conceptual model architecture, not evaluation benchmarks.
DeepThink: A Conceptual Model for Knowledge-Addressable Recurrent Language Modeling This paper proposes DeepThink, a conceptual architecture that decouples knowledge capacity, activation width, and recurrent depth in language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses entirely on language modeling without multimodal inputs.
DeepThink: A Conceptual Model for Knowledge-Addressable Recurrent Language Modeling This paper proposes DeepThink, a conceptual architecture that decouples knowledge capacity, activation width, and recurrent depth in language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Proposes a recurrent model architecture rather than deployment safety mechanisms.
DeepThink: A Conceptual Model for Knowledge-Addressable Recurrent Language Modeling This paper proposes DeepThink, a conceptual architecture that decouples knowledge capacity, activation width, and recurrent depth in language models. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Proposes a recurrent architecture rather than multi-step agent systems.
DeepThink: A Conceptual Model for Knowledge-Addressable Recurrent Language Modeling This paper proposes DeepThink, a conceptual architecture that decouples knowledge capacity, activation width, and recurrent depth in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on internal parameter-space knowledge routing, not RAG.
DeepThink: A Conceptual Model for Knowledge-Addressable Recurrent Language Modeling This paper proposes DeepThink, a conceptual architecture that decouples knowledge capacity, activation width, and recurrent depth in language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Presents a conceptual language model architecture, not code generation.
Lüshi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model This paper introduces Lüshi-1840, a pre-modern Chinese language model trained on historical texts to evaluate knowledge cutoffs and hindsight bias. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on temporal dataset curation rather than model compression.
Lüshi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model This paper introduces Lüshi-1840, a pre-modern Chinese language model trained on historical texts to evaluate knowledge cutoffs and hindsight bias. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on historical language pretraining, not agent workflows.
Lüshi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model This paper introduces Lüshi-1840, a pre-modern Chinese language model trained on historical texts to evaluate knowledge cutoffs and hindsight bias. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Examines pretraining cutoff corpora rather than retrieval-augmented generation.
Lüshi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model This paper introduces Lüshi-1840, a pre-modern Chinese language model trained on historical texts to evaluate knowledge cutoffs and hindsight bias. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Investigates classical Chinese text modeling, not code.
Lüshi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model This paper introduces Lüshi-1840, a pre-modern Chinese language model trained on historical texts to evaluate knowledge cutoffs and hindsight bias. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with historical Chinese text, not multimodal models.
Lüshi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model This paper introduces Lüshi-1840, a pre-modern Chinese language model trained on historical texts to evaluate knowledge cutoffs and hindsight bias. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Explores historical corpus cutoff rather than deployment safety or jailbreaks.
Error Correction in Trained TTT Layers This paper analyzes prediction-derived error updates and test-time gradient objectives in trained test-time-training layers. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on sequence model layers without multimodal integration.
Error Correction in Trained TTT Layers This paper analyzes prediction-derived error updates and test-time gradient objectives in trained test-time-training layers. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Analyzes model architecture updates rather than code generation.
Error Correction in Trained TTT Layers This paper analyzes prediction-derived error updates and test-time gradient objectives in trained test-time-training layers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes architectural inner-loop updates rather than safety or reliability.
Error Correction in Trained TTT Layers This paper analyzes prediction-derived error updates and test-time gradient objectives in trained test-time-training layers. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies test-time training layer mechanics, not agent tool use or planning.
Error Correction in Trained TTT Layers This paper analyzes prediction-derived error updates and test-time gradient objectives in trained test-time-training layers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Examines internal memory mechanisms, not external knowledge retrieval systems.
Error Correction in Trained TTT Layers This paper analyzes prediction-derived error updates and test-time gradient objectives in trained test-time-training layers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies gradient mechanics in TTT layers rather than evaluation benchmarks.
Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory This paper demonstrates that deep speech representations track word timing along low-dimensional circular trajectories across multiple model architectures. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Investigates speech processing rather than software development or coding.
Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory This paper demonstrates that deep speech representations track word timing along low-dimensional circular trajectories across multiple model architectures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies acoustic representational geometry, not safety or guardrails.
Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory This paper demonstrates that deep speech representations track word timing along low-dimensional circular trajectories across multiple model architectures. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies speech model representations, not LLM agents or planning.
Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory This paper demonstrates that deep speech representations track word timing along low-dimensional circular trajectories across multiple model architectures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes representation geometry rather than benchmark evaluation frameworks.
Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory This paper demonstrates that deep speech representations track word timing along low-dimensional circular trajectories across multiple model architectures. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Analyzes speech-only representation geometry rather than multimodal cross-modality reasoning.
Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory This paper demonstrates that deep speech representations track word timing along low-dimensional circular trajectories across multiple model architectures. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies acoustic representation geometry, not model compression or serving.
Deep Speech Representations Track Word Timing along a Low-Dimensional Circular Trajectory This paper demonstrates that deep speech representations track word timing along low-dimensional circular trajectories across multiple model architectures. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Investigates speech representations rather than retrieval-augmented generation.
RelLedger: Relational Information Governance for LLM Agent Organizations This paper introduces RelLedger, an auditing framework to trace how relational histories drive reporting choices and actions in multi-agent systems. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Examines text-based agent workflows, not multimodal models.
RelLedger: Relational Information Governance for LLM Agent Organizations This paper introduces RelLedger, an auditing framework to trace how relational histories drive reporting choices and actions in multi-agent systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Audits agent-to-agent information sharing rather than RAG architectures.
RelLedger: Relational Information Governance for LLM Agent Organizations This paper introduces RelLedger, an auditing framework to trace how relational histories drive reporting choices and actions in multi-agent systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses agent audit mechanisms rather than model compression or inference efficiency.
RelLedger: Relational Information Governance for LLM Agent Organizations This paper introduces RelLedger, an auditing framework to trace how relational histories drive reporting choices and actions in multi-agent systems. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-agent governance and communication, not coding assistants.
ATTCK-R: Knowledge-Aware Long-Horizon Jailbreaking for Protected LLMs This paper presents ATTCK-R, a planning-driven multi-turn red teaming framework to evaluate language models guarded by prompt safety defenses. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an adversarial red-teaming method, not general evaluation benchmarks.
ATTCK-R: Knowledge-Aware Long-Horizon Jailbreaking for Protected LLMs This paper presents ATTCK-R, a planning-driven multi-turn red teaming framework to evaluate language models guarded by prompt safety defenses. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Evaluates text-based LLM safety rather than multimodal vision-language models.
ATTCK-R: Knowledge-Aware Long-Horizon Jailbreaking for Protected LLMs This paper presents ATTCK-R, a planning-driven multi-turn red teaming framework to evaluate language models guarded by prompt safety defenses. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on adversarial safety attacks rather than code generation or repair.
ATTCK-R: Knowledge-Aware Long-Horizon Jailbreaking for Protected LLMs This paper presents ATTCK-R, a planning-driven multi-turn red teaming framework to evaluate language models guarded by prompt safety defenses. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses safety red-teaming rather than model efficiency or compression.
ATTCK-R: Knowledge-Aware Long-Horizon Jailbreaking for Protected LLMs This paper presents ATTCK-R, a planning-driven multi-turn red teaming framework to evaluate language models guarded by prompt safety defenses. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Examines red-teaming attacks, not retrieval-augmented generation systems.
ATTCK-R: Knowledge-Aware Long-Horizon Jailbreaking for Protected LLMs This paper presents ATTCK-R, a planning-driven multi-turn red teaming framework to evaluate language models guarded by prompt safety defenses. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on adversarial jailbreaking rather than deploying autonomous goal-directed agents.
Strong yet Controllable LLM Abstention without Learning to Abstain This paper proposes TAPS, combining standard accuracy training with post-hoc confidence selection to give controllable abstention rates without specialized tuning. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Evaluates factual QA abstention rather than software engineering tasks.
Strong yet Controllable LLM Abstention without Learning to Abstain This paper proposes TAPS, combining standard accuracy training with post-hoc confidence selection to give controllable abstention rates without specialized tuning. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text QA abstention without multimodal components.
Strong yet Controllable LLM Abstention without Learning to Abstain This paper proposes TAPS, combining standard accuracy training with post-hoc confidence selection to give controllable abstention rates without specialized tuning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Investigates model abstention rather than retrieval or document augmentation.
Strong yet Controllable LLM Abstention without Learning to Abstain This paper proposes TAPS, combining standard accuracy training with post-hoc confidence selection to give controllable abstention rates without specialized tuning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates selective answering methods rather than compression or efficient serving.
Strong yet Controllable LLM Abstention without Learning to Abstain This paper proposes TAPS, combining standard accuracy training with post-hoc confidence selection to give controllable abstention rates without specialized tuning. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on question-answering abstention rather than autonomous agent workflows.
RIFT: Token Influence Follows the Path of Least Resistance This paper proposes RIFT, a method using electrical resistance analogies to trace token influence through transformer attention graphs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies mechanistic token influence rather than safety, jailbreaks, or hallucinations.
RIFT: Token Influence Follows the Path of Least Resistance This paper proposes RIFT, a method using electrical resistance analogies to trace token influence through transformer attention graphs. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Presents an interpretability method for language models, not code generation.
RIFT: Token Influence Follows the Path of Least Resistance This paper proposes RIFT, a method using electrical resistance analogies to trace token influence through transformer attention graphs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces an interpretability tool rather than a model evaluation benchmark.
RIFT: Token Influence Follows the Path of Least Resistance This paper proposes RIFT, a method using electrical resistance analogies to trace token influence through transformer attention graphs. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses entirely on text decoder transformers, not multimodal models.
RIFT: Token Influence Follows the Path of Least Resistance This paper proposes RIFT, a method using electrical resistance analogies to trace token influence through transformer attention graphs. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies token interpretability in transformer layers, not agent planning or tool use.
RIFT: Token Influence Follows the Path of Least Resistance This paper proposes RIFT, a method using electrical resistance analogies to trace token influence through transformer attention graphs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Evaluates internal token influence dynamics rather than RAG architecture or retrieval systems.
A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers RL training infrastructure rather than safety, red-teaming, or guardrails.
A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an RL training framework rather than evaluation benchmarks.
A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Presents a general agent RL training framework, not specific to software engineering.
A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning systems, not retrieval or external database augmentation.
A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on language model RL infrastructure, not multimodal capabilities.
Self-Distillation with Verbal Regularization This paper proposes verbal regularization prompts for on-policy self-distillation to prevent teacher models from taking shortcut reasoning paths. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses general language model reasoning rather than software engineering or coding.
Self-Distillation with Verbal Regularization This paper proposes verbal regularization prompts for on-policy self-distillation to prevent teacher models from taking shortcut reasoning paths. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies distillation methods rather than retrieval-augmented generation systems.
Self-Distillation with Verbal Regularization This paper proposes verbal regularization prompts for on-policy self-distillation to prevent teacher models from taking shortcut reasoning paths. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on reasoning accuracy via distillation, not safety, guardrails, or alignment.
Self-Distillation with Verbal Regularization This paper proposes verbal regularization prompts for on-policy self-distillation to prevent teacher models from taking shortcut reasoning paths. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on self-distillation post-training, not multi-step agent planning or tool use.
Self-Distillation with Verbal Regularization This paper proposes verbal regularization prompts for on-policy self-distillation to prevent teacher models from taking shortcut reasoning paths. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a distillation training technique, not evaluation methods or benchmarks.
Self-Distillation with Verbal Regularization This paper proposes verbal regularization prompts for on-policy self-distillation to prevent teacher models from taking shortcut reasoning paths. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Text-only language model training; does not address multimodal models.
HexaBee-4B: Six-Language Translation with Multilingual Answering and Bidirectional Supervision This paper introduces HexaBee-4B, a translation model trained across six languages using joint multilingual answering and bidirectional supervision. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a trained translation model rather than an evaluation framework or benchmark.
HexaBee-4B: Six-Language Translation with Multilingual Answering and Bidirectional Supervision This paper introduces HexaBee-4B, a translation model trained across six languages using joint multilingual answering and bidirectional supervision. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Specializes a model for human language translation, not code generation.
HexaBee-4B: Six-Language Translation with Multilingual Answering and Bidirectional Supervision This paper introduces HexaBee-4B, a translation model trained across six languages using joint multilingual answering and bidirectional supervision. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on multilingual translation training, not autonomous tool-using agents.

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