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
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	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	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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation or external document retrieval.
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	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	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	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.
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.
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	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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates discourse parsing accuracy rather than safety risks or guardrails.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or serving latency.
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	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.
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 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	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	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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on conversational retrieval, not software engineering or code.
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.
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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Encodes context into weights as an alternative to context retrieval.
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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies test-time weight updates, not program synthesis or code repair.
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	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	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	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.
Understanding Machine Taste: Aesthetic Agreement Across Foundation Models in K-pop The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies creative aesthetic ranking rather than safety, robustness, or guardrails.
Understanding Machine Taste: Aesthetic Agreement Across Foundation Models in K-pop The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on music and video aesthetics, not software engineering.
Understanding Machine Taste: Aesthetic Agreement Across Foundation Models in K-pop The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not touch upon retrieval-augmented generation or document search.
Understanding Machine Taste: Aesthetic Agreement Across Foundation Models in K-pop The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or serving speed.
Understanding Machine Taste: Aesthetic Agreement Across Foundation Models in K-pop The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies aesthetic preference ranking rather than multi-step agent systems.
Geometric Structure Predicts Per-Example Neighborhood Loss Under PCA Truncation The paper analyzes geometric properties of text embeddings to predict how many nearest neighbors an individual example loses under dimensionality reduction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes geometric stability under PCA truncation rather than establishing LLM benchmarks.
Geometric Structure Predicts Per-Example Neighborhood Loss Under PCA Truncation The paper analyzes geometric properties of text embeddings to predict how many nearest neighbors an individual example loses under dimensionality reduction. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text embeddings without image, video, or audio components.
Geometric Structure Predicts Per-Example Neighborhood Loss Under PCA Truncation The paper analyzes geometric properties of text embeddings to predict how many nearest neighbors an individual example loses under dimensionality reduction. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on embedding geometry rather than code generation or repair.
Geometric Structure Predicts Per-Example Neighborhood Loss Under PCA Truncation The paper analyzes geometric properties of text embeddings to predict how many nearest neighbors an individual example loses under dimensionality reduction. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes embedding dimension reduction rather than autonomous agents.
Geometric Structure Predicts Per-Example Neighborhood Loss Under PCA Truncation The paper analyzes geometric properties of text embeddings to predict how many nearest neighbors an individual example loses under dimensionality reduction. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses embedding geometry and compression rather than safety, alignment, or guardrails.
Connecting the Dots: Enduring Performance Gaps in ASR Systems Between Standard and Minority English Varieties The paper tracks automatic speech recognition performance from 2021 to 2025, finding persistent accuracy disparities between standard American English and minority accents. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Investigates speech recognition systems, not software engineering or code.
Connecting the Dots: Enduring Performance Gaps in ASR Systems Between Standard and Minority English Varieties The paper tracks automatic speech recognition performance from 2021 to 2025, finding persistent accuracy disparities between standard American English and minority accents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech recognition rather than retrieval-augmented generation.
Connecting the Dots: Enduring Performance Gaps in ASR Systems Between Standard and Minority English Varieties The paper tracks automatic speech recognition performance from 2021 to 2025, finding persistent accuracy disparities between standard American English and minority accents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on accent fairness rather than model compression or inference efficiency.
Connecting the Dots: Enduring Performance Gaps in ASR Systems Between Standard and Minority English Varieties The paper tracks automatic speech recognition performance from 2021 to 2025, finding persistent accuracy disparities between standard American English and minority accents. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates speech recognition models rather than autonomous agents.
More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026 The paper compares direct prompting, model-distilled supervision, and larger reader datasets for predicting user text highlighting behavior on web pages. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies highlight prediction models rather than autonomous multi-step agents.
More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026 The paper compares direct prompting, model-distilled supervision, and larger reader datasets for predicting user text highlighting behavior on web pages. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Compares training data scaling versus model teachers rather than proposing LLM benchmarks.
More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026 The paper compares direct prompting, model-distilled supervision, and larger reader datasets for predicting user text highlighting behavior on web pages. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on user preference modeling rather than safety, alignment, or guardrails.
More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026 The paper compares direct prompting, model-distilled supervision, and larger reader datasets for predicting user text highlighting behavior on web pages. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Analyzes text sentence highlighting without visual or audio modalities.
More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026 The paper compares direct prompting, model-distilled supervision, and larger reader datasets for predicting user text highlighting behavior on web pages. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not relate to software engineering or code LLMs.
More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026 The paper compares direct prompting, model-distilled supervision, and larger reader datasets for predicting user text highlighting behavior on web pages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on social highlight prediction rather than retrieval-augmented generation.

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