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
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion model decoding rather than multi-step agent systems.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Uses standard benchmarks to evaluate speedup rather than creating evaluation methods.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference acceleration rather than safety or reliability risks.
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not concern retrieval-augmented generation or search.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Tests narrative temporal extraction rather than tool-using agent systems.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation or software engineering.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates temporal relation extraction from text rather than RAG architectures.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies linguistic reasoning benchmarks rather than deployment safety or jailbreaks.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or inference serving efficiency.
Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text narratives, not multimodal inputs.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a pretraining approach rather than benchmark evaluation methods.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not relate to retrieval-augmented generation.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on cross-modal knowledge distillation, not safety or deployment guardrails.
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech model distillation rather than multi-step agent planning.
Scaling Inference-Time Compute with Teams of Coding Agents The paper studies compute allocation across multi-agent coding teams to identify Pareto-optimal trade-offs between cost and performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on cost and performance trade-offs, not safety or guardrails.
Scaling Inference-Time Compute with Teams of Coding Agents The paper studies compute allocation across multi-agent coding teams to identify Pareto-optimal trade-offs between cost and performance. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses multi-agent compute allocation rather than document retrieval or RAG.
Scaling Inference-Time Compute with Teams of Coding Agents The paper studies compute allocation across multi-agent coding teams to identify Pareto-optimal trade-offs between cost and performance. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on code-generating agent teams, not multimodal models.
Scaling Inference-Time Compute with Teams of Coding Agents The paper studies compute allocation across multi-agent coding teams to identify Pareto-optimal trade-offs between cost and performance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies team configurations rather than introducing evaluation benchmarks.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies internal state reporting rather than proposing general LLM benchmarks.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on language model self-inspection, not multimodal data.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to coding assistants or program generation.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies internal state awareness rather than multi-step tool-using agents.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address external information retrieval or RAG architectures.
A Sense of Proprioception in Language Models The paper trains language models to report internal states through supervised fine-tuning and on-policy learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address inference acceleration, pruning, or model compression.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not cover retrieval-augmented systems or knowledge grounding.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates representation theory rather than evaluation frameworks or benchmarks.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with lexical linguistics, not programming or code models.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Exclusively examines word representations in text transformers.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Theoretical representation study, not addressing model compression or efficiency.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on linguistic representations, not safety or deployment reliability.
Everyone Was Right: Lexical Representations in Language Models Are Both Abstract and Holistic The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical study of lexical representations, not agentic planning or tools.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent delegation between SLMs and LLMs, not multimodal architectures.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent delegation strategies, not retrieval-augmented generation.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses general long-horizon agent execution rather than software engineering.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on compute efficiency and orchestration rather than safety guardrails.
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation The paper proposes an edge-cloud collaboration framework that selectively delegates complex agent steps from local models to cloud models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an operational framework rather than an LLM evaluation standard.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with diacritic restoration rather than autonomous agent systems.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment reliability.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on natural language orthography, not code or software engineering.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Strictly text-focused linguistics, not multimodal.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on a niche Tagalog NLP corpus rather than general LLM benchmarking.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Mentions small models in passing, not focused on model efficiency techniques.
TDR: Tagalog Diacritic Restoration Dataset The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Mentions retrieval only as a dataset construction tool, not RAG systems.
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation The paper studies long chain-of-thought distillation and argues improvements stem from structured reasoning patterns rather than factual correctness. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies reasoning distillation mechanics rather than multi-step tool-using agents.
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation The paper studies long chain-of-thought distillation and argues improvements stem from structured reasoning patterns rather than factual correctness. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval or external knowledge grounding.
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation The paper studies long chain-of-thought distillation and argues improvements stem from structured reasoning patterns rather than factual correctness. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text-based reasoning models, not multimodal tasks.
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation The paper studies long chain-of-thought distillation and argues improvements stem from structured reasoning patterns rather than factual correctness. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes chain-of-thought distillation mechanics rather than benchmarking methodology.
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation The paper studies long chain-of-thought distillation and argues improvements stem from structured reasoning patterns rather than factual correctness. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on distillation performance rather than AI safety or reliability.
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation The paper studies long chain-of-thought distillation and argues improvements stem from structured reasoning patterns rather than factual correctness. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Investigates general reasoning distillation rather than code generation or repair.
StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents The paper presents a runtime framework that tracks evolving repository state and dependencies to assist long-horizon coding agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or inference optimization.
StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents The paper presents a runtime framework that tracks evolving repository state and dependencies to assist long-horizon coding agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Aimed at coding agent capability and state tracking, not safety guardrails or alignment.
StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents The paper presents a runtime framework that tracks evolving repository state and dependencies to assist long-horizon coding agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent state tracking during software tasks rather than external document retrieval.
StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents The paper presents a runtime framework that tracks evolving repository state and dependencies to assist long-horizon coding agents. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based code repositories, not multimodal systems.
StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents The paper presents a runtime framework that tracks evolving repository state and dependencies to assist long-horizon coding agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an agent framework rather than a general evaluation benchmark.
When Does an ASR Error Matter? Task-Role Diagnostics for Tool-Using Voice Agents The paper introduces a diagnostic protocol to evaluate how speech recognition errors affect structured tool calling in voice agents. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to code generation or software engineering agents.
When Does an ASR Error Matter? Task-Role Diagnostics for Tool-Using Voice Agents The paper introduces a diagnostic protocol to evaluate how speech recognition errors affect structured tool calling in voice agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Examines tool calling from speech rather than retrieval-augmented generation systems.
When Does an ASR Error Matter? Task-Role Diagnostics for Tool-Using Voice Agents The paper introduces a diagnostic protocol to evaluate how speech recognition errors affect structured tool calling in voice agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient inference.
When Does an ASR Error Matter? Task-Role Diagnostics for Tool-Using Voice Agents The paper introduces a diagnostic protocol to evaluate how speech recognition errors affect structured tool calling in voice agents. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines cascaded speech recognition and text agents rather than end-to-end multimodal models.
DisaggKD: Efficient Knowledge Distillation through Teacher-Student Disaggregation and a Fused Loss The paper proposes disaggregating teacher and student models across separate nodes with a fused loss to accelerate knowledge distillation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on distillation systems rather than multi-step agent tool use or planning.

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