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
Does self-distillation improve reasoning? The paper evaluates whether self-distillation using privileged information improves reasoning capabilities or reflects overfitting to training tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on reasoning self-distillation rather than external document retrieval.
Does self-distillation improve reasoning? The paper evaluates whether self-distillation using privileged information improves reasoning capabilities or reflects overfitting to training tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates self-distillation training methods rather than agent architectures or tool use.
Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents The authors develop a reinforcement learning method to calibrate an agent's per-step confidence against the final outcome of an episode. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses confidence calibration rather than model compression or efficient serving.
Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve This paper proposes a system where language model agents write code to create and refine tools directly over raw knowledge-graph files. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Involves text-rich knowledge graphs without vision, audio, or multimodal data.
Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve This paper proposes a system where language model agents write code to create and refine tools directly over raw knowledge-graph files. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent search performance rather than safety guardrails or reliability.
Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve This paper proposes a system where language model agents write code to create and refine tools directly over raw knowledge-graph files. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on tool creation architectures rather than model compression or efficiency.
Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve This paper proposes a system where language model agents write code to create and refine tools directly over raw knowledge-graph files. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates an agent system on benchmarks rather than introducing evaluation frameworks.
Two Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference The authors compare information precision and preference reweighting mechanisms in active-inference agents managing competing survival needs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Active inference neuroscience study on biological agents, not LLM agent systems.
Two Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference The authors compare information precision and preference reweighting mechanisms in active-inference agents managing competing survival needs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies active inference simulations rather than LLM benchmarks or evaluation.
Two Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference The authors compare information precision and preference reweighting mechanisms in active-inference agents managing competing survival needs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates biological active inference models rather than AI safety or reliability.
Falsification-proof non-market allocation mechanisms The paper characterizes optimal falsification-proof non-market allocation mechanisms when individuals can strategically manipulate their observable characteristics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical economic study with no connection to LLM benchmarks or evaluation.
Falsification-proof non-market allocation mechanisms The paper characterizes optimal falsification-proof non-market allocation mechanisms when individuals can strategically manipulate their observable characteristics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with economic resource allocation rather than AI deployment safety.
Falsification-proof non-market allocation mechanisms The paper characterizes optimal falsification-proof non-market allocation mechanisms when individuals can strategically manipulate their observable characteristics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical economics paper on allocation mechanisms, not language model agents.
Geometric Calibration of Supervised Fine-Tuning This paper presents a post-hoc method that refines fine-tuned models by selectively reverting parameter updates along principal directions of base weights. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a fine-tuning method rather than evaluation benchmarks or methodology.
Geometric Calibration of Supervised Fine-Tuning This paper presents a post-hoc method that refines fine-tuned models by selectively reverting parameter updates along principal directions of base weights. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with weight subspace calibration rather than compression or efficient serving.
Geometric Calibration of Supervised Fine-Tuning This paper presents a post-hoc method that refines fine-tuned models by selectively reverting parameter updates along principal directions of base weights. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates fine-tuning weight geometry rather than autonomous agent systems.
DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models The authors present an approach using local language models to automatically generate conceptual architectural models and reports for digital twins. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Uses LLM-as-a-judge for an application rather than studying evaluation methodology.
DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models The authors present an approach using local language models to automatically generate conceptual architectural models and reports for digital twins. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on digital twin documentation without addressing safety or reliability risks.
DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models The authors present an approach using local language models to automatically generate conceptual architectural models and reports for digital twins. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses LLM prompting loops rather than studying multi-step autonomous agent architectures.
DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models The authors present an approach using local language models to automatically generate conceptual architectural models and reports for digital twins. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Generates conceptual architecture models for digital twins rather than software code.
To Align or Not To Align: Check Your COMPASS Before You Train This paper introduces a metric to estimate the compatibility of pretrained models before running costly alignment or checkpoint merging. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates cross-modal representation alignment rather than deployment safety risks.
To Align or Not To Align: Check Your COMPASS Before You Train This paper introduces a metric to estimate the compatibility of pretrained models before running costly alignment or checkpoint merging. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies encoder alignment compatibility rather than agent tool use or planning.
To Align or Not To Align: Check Your COMPASS Before You Train This paper introduces a metric to estimate the compatibility of pretrained models before running costly alignment or checkpoint merging. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Deals with pre-training encoder alignment, not external retrieval or RAG systems.
To Align or Not To Align: Check Your COMPASS Before You Train This paper introduces a metric to estimate the compatibility of pretrained models before running costly alignment or checkpoint merging. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on model alignment metrics rather than code generation or repair.
To Align or Not To Align: Check Your COMPASS Before You Train This paper introduces a metric to estimate the compatibility of pretrained models before running costly alignment or checkpoint merging. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on model compatibility diagnostics rather than distillation or quantization.
ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction The authors reformulate a recurrent video prediction architecture using gated recurrent units to reduce parameters while maintaining spatiotemporal modeling. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates on Moving MNIST, not industrial defect detection or manufacturing inspection.
ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction The authors reformulate a recurrent video prediction architecture using gated recurrent units to reduce parameters while maintaining spatiotemporal modeling. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on spatiotemporal recurrent architectures for video frames without multimodal text integration.
ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction The authors reformulate a recurrent video prediction architecture using gated recurrent units to reduce parameters while maintaining spatiotemporal modeling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies video prediction architectures rather than deployment safety or guardrails.
ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction The authors reformulate a recurrent video prediction architecture using gated recurrent units to reduce parameters while maintaining spatiotemporal modeling. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Develops a video prediction GRU architecture, not an autonomous agent.
ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction The authors reformulate a recurrent video prediction architecture using gated recurrent units to reduce parameters while maintaining spatiotemporal modeling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Applies a model to existing benchmarks rather than building evaluation methods.
Tiny Recursive Message Passing This paper applies recursive training and discrete deduction procedures to message-passing neural networks to solve constraint-reasoning tasks with few parameters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies algorithmic reasoning efficiency rather than safety or alignment guardrails.
Tiny Recursive Message Passing This paper applies recursive training and discrete deduction procedures to message-passing neural networks to solve constraint-reasoning tasks with few parameters. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates message passing graph architectures, not LLM agent systems.
Tiny Recursive Message Passing This paper applies recursive training and discrete deduction procedures to message-passing neural networks to solve constraint-reasoning tasks with few parameters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates an architecture on puzzles rather than proposing evaluation benchmarks.
AMemOps: Evaluating Memory Updates in Long Audio Streams The authors introduce a benchmark to evaluate how audio models update, retain, or reject recurring acoustic sources in long streams. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates memory updating mechanisms, not model compression or efficient serving.
AMemOps: Evaluating Memory Updates in Long Audio Streams The authors introduce a benchmark to evaluate how audio models update, retain, or reject recurring acoustic sources in long streams. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Tests streaming audio memory operations rather than multi-step agent workflows.
AMemOps: Evaluating Memory Updates in Long Audio Streams The authors introduce a benchmark to evaluate how audio models update, retain, or reject recurring acoustic sources in long streams. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on memory update accuracy rather than safety risks or guardrails.
Kwai Summary Attention V2 Technical Report This technical report presents an updated summary-token attention architecture to optimize cache compression and computation across model layers. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on KV cache attention architecture, not multi-step agent planning or tools.
Kwai Summary Attention V2 Technical Report This technical report presents an updated summary-token attention architecture to optimize cache compression and computation across model layers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Proposes attention compression architecture rather than external document retrieval or grounding.
Kwai Summary Attention V2 Technical Report This technical report presents an updated summary-token attention architecture to optimize cache compression and computation across model layers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a model architecture rather than evaluation benchmarks or methodology.
Kwai Summary Attention V2 Technical Report This technical report presents an updated summary-token attention architecture to optimize cache compression and computation across model layers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on attention efficiency without addressing safety, jailbreaks, or reliability.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on audio and virtual characters, not industrial vision inspection.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on synthetic speech and character perception, not software code.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies human perception of cloned voices, not autonomous LLM agents.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on perceptual psychology of voice cloning rather than multimodal foundation models.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Voice cloning study, completely unrelated to retrieval-augmented generation.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with voice cloning psychology, not model efficiency or quantization.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant User study on voice cloning perception, not an LLM benchmark.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses clinical concept representation, not autonomous agent planning or actions.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text embeddings and medical ontologies, not multimodal models.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies medical vocabulary representations, not code generation or repair.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an embedding representation framework, not an evaluation benchmark.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical ontology aggregation, not model compression or efficiency.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Develops ontology embeddings rather than a retrieval-augmented generation framework.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses representation quality for clinical codes, not safety guardrails or jailbreaks.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on interpretability and attention head ablation, not benchmark design.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies attention head mechanisms rather than model compression or efficient inference.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies factual prompt completion in LLMs, not code generation or review.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Mechanistic interpretability study of attention heads, not agent planning or tool use.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Analyzes attention heads during in-context retrieval rather than RAG architectures.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates text-only transformer attention mechanisms, not multimodal models.

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