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
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Mentions retrieval only as a dataset construction tool, not RAG 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.
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	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	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	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	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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an agent framework rather than a general evaluation benchmark.
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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based code repositories, not multimodal 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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Not centered on evaluation methodology or LLM benchmarks.
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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on programming or software engineering tasks.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation or external document retrieval.
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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text language model distillation, not multimodal architectures.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns training efficiency rather than safety, jailbreaks, or hallucination mitigation.
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.
Decouple to Forget: Semantic Mediation for Sensitive Unlearning in Large Language Models The paper proposes an unlearning method that removes sensitive facts from model responses while preserving surrounding non-sensitive factual context. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals strictly with text unlearning, not multimodal models.
Decouple to Forget: Semantic Mediation for Sensitive Unlearning in Large Language Models The paper proposes an unlearning method that removes sensitive facts from model responses while preserving surrounding non-sensitive factual context. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on LLM unlearning rather than multi-step agent planning or tool use.
Decouple to Forget: Semantic Mediation for Sensitive Unlearning in Large Language Models The paper proposes an unlearning method that removes sensitive facts from model responses while preserving surrounding non-sensitive factual context. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Concerns knowledge erasure rather than model compression, quantization, or serving efficiency.
Decouple to Forget: Semantic Mediation for Sensitive Unlearning in Large Language Models The paper proposes an unlearning method that removes sensitive facts from model responses while preserving surrounding non-sensitive factual context. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on code generation, software engineering, or program repair.
Decouple to Forget: Semantic Mediation for Sensitive Unlearning in Large Language Models The paper proposes an unlearning method that removes sensitive facts from model responses while preserving surrounding non-sensitive factual context. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or external document grounding.
Decouple to Forget: Semantic Mediation for Sensitive Unlearning in Large Language Models The paper proposes an unlearning method that removes sensitive facts from model responses while preserving surrounding non-sensitive factual context. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates unlearning methods rather than proposing new general evaluation frameworks.
Worth Their Weights? Evaluating Generative Models for Discourse Parsing The paper compares discriminative and generative language models for rhetorical structure parsing and elementary discourse unit segmentation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates discourse parsing rather than autonomous multi-step agents.
Worth Their Weights? Evaluating Generative Models for Discourse Parsing The paper compares discriminative and generative language models for rhetorical structure parsing and elementary discourse unit segmentation. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals exclusively with text discourse parsing without visual or audio modalities.
Worth Their Weights? Evaluating Generative Models for Discourse Parsing The paper compares discriminative and generative language models for rhetorical structure parsing and elementary discourse unit segmentation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on parsing accuracy comparison across model sizes rather than compression techniques.
Worth Their Weights? Evaluating Generative Models for Discourse Parsing The paper compares discriminative and generative language models for rhetorical structure parsing and elementary discourse unit segmentation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses discourse structure parsing without external document retrieval.
Worth Their Weights? Evaluating Generative Models for Discourse Parsing The paper compares discriminative and generative language models for rhetorical structure parsing and elementary discourse unit segmentation. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on natural language discourse parsing, not software code.
Worth Their Weights? Evaluating Generative Models for Discourse Parsing The paper compares discriminative and generative language models for rhetorical structure parsing and elementary discourse unit segmentation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates discourse parsing accuracy rather than safety risks or guardrails.
The Image Still Has a Say: Final-Answer Repair for Multimodal Chain-of-Thought The paper repairs multimodal chain-of-thought final answers using original image features without altering the generated reasoning trace. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an inference intervention method rather than an evaluation benchmark.
The Image Still Has a Say: Final-Answer Repair for Multimodal Chain-of-Thought The paper repairs multimodal chain-of-thought final answers using original image features without altering the generated reasoning trace. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes an attention reallocation mechanism rather than multi-agent or tool-use planning.
The Image Still Has a Say: Final-Answer Repair for Multimodal Chain-of-Thought The paper repairs multimodal chain-of-thought final answers using original image features without altering the generated reasoning trace. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual reasoning readout rather than external document retrieval.
The Image Still Has a Say: Final-Answer Repair for Multimodal Chain-of-Thought The paper repairs multimodal chain-of-thought final answers using original image features without altering the generated reasoning trace. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with visual reasoning, not code generation or program repair.
The Image Still Has a Say: Final-Answer Repair for Multimodal Chain-of-Thought The paper repairs multimodal chain-of-thought final answers using original image features without altering the generated reasoning trace. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or serving latency.
RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval The paper presents a retrieval-aware denoising method for conversational user profiles to improve personalized dense retrieval. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with conversational search rather than autonomous tool-using agents.
RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval The paper presents a retrieval-aware denoising method for conversational user profiles to improve personalized dense retrieval. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on retrieval relevance modeling rather than model compression or serving optimization.
RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval The paper presents a retrieval-aware denoising method for conversational user profiles to improve personalized dense retrieval. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with profile noise in search rather than safety guardrails or red-teaming.
RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval The paper presents a retrieval-aware denoising method for conversational user profiles to improve personalized dense retrieval. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Applies dense retrieval strictly to text conversational history and user profiles.
RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval The paper presents a retrieval-aware denoising method for conversational user profiles to improve personalized dense retrieval. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a retrieval method evaluated at a benchmark, not a general evaluation methodology.
RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval The paper presents a retrieval-aware denoising method for conversational user profiles to improve personalized dense retrieval. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on conversational retrieval, not software engineering or code.
Encoding Context into LLM Weights: What Makes It Context? The paper investigates methods for encoding context into model weights so that language models properly apply the temporary context during subsequent inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on context encoding methods rather than benchmark design.
Encoding Context into LLM Weights: What Makes It Context? The paper investigates methods for encoding context into model weights so that language models properly apply the temporary context during subsequent inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses architectural context handling rather than deployment safety or jailbreak prevention.
Encoding Context into LLM Weights: What Makes It Context? The paper investigates methods for encoding context into model weights so that language models properly apply the temporary context during subsequent inference. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text context encoding without multimodal vision or audio inputs.
Encoding Context into LLM Weights: What Makes It Context? The paper investigates methods for encoding context into model weights so that language models properly apply the temporary context during subsequent inference. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on context conditioning in weights rather than agent planning.
Encoding Context into LLM Weights: What Makes It Context? The paper investigates methods for encoding context into model weights so that language models properly apply the temporary context during subsequent inference. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies test-time weight updates, not program synthesis or code repair.
Encoding Context into LLM Weights: What Makes It Context? The paper investigates methods for encoding context into model weights so that language models properly apply the temporary context during subsequent inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Encodes context into weights as an alternative to context retrieval.
Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively The paper evaluates how language models correct user misconceptions across five languages, showing indirect pushback often fails to fix factual errors. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses factual sycophancy, not coding or software development.
Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively The paper evaluates how language models correct user misconceptions across five languages, showing indirect pushback often fails to fix factual errors. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals exclusively with multilingual text generation without multimodal data.
Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively The paper evaluates how language models correct user misconceptions across five languages, showing indirect pushback often fails to fix factual errors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a labeling framework for pushback but focuses on behavioral sycophancy analysis.
Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively The paper evaluates how language models correct user misconceptions across five languages, showing indirect pushback often fails to fix factual errors. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates sycophancy and pushback phrasing, not retrieval-augmented generation.
Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively The paper evaluates how language models correct user misconceptions across five languages, showing indirect pushback often fails to fix factual errors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on dialogue feedback behavior rather than agent planning or tool execution.
Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively The paper evaluates how language models correct user misconceptions across five languages, showing indirect pushback often fails to fix factual errors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression or inference acceleration.

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