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
Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training The paper proposes multi-task pre-training guided by knowledge graphs to improve zero-shot legal reasoning in language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Work is exclusively on text-based statutory legal documents without multimodal data.
OccludeBench: Can VLMs Find What's Missing? The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Examines benchmark visual failures rather than AI safety risks, alignment, or guardrails.
OccludeBench: Can VLMs Find What's Missing? The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates visual occlusion reasoning in VLMs rather than multi-step autonomous agent systems.
OccludeBench: Can VLMs Find What's Missing? The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies benchmark performance of VLMs without focusing on small models or compression.
OccludeBench: Can VLMs Find What's Missing? The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates general visual occlusion reasoning benchmarks rather than industrial inspection or robotics.
OccludeBench: Can VLMs Find What's Missing? The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates object reasoning in visual scenes, unrelated to code generation or software.
OccludeBench: Can VLMs Find What's Missing? The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on visual reasoning about occluded objects without retrieval-augmented generation.
Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies linguistic properties of English discourse particles, unrelated to code or software.
Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Investigates discourse particles in language models without any retrieval components.
Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Analyzes linguistic sensitivity to specific words rather than standard LLM evaluation frameworks.
Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Investigates internal representations of discourse particles without addressing model efficiency.
Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines linguistic particle sensitivity in LLMs rather than autonomous agentic workflows.
Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Pertains strictly to natural language discourse analysis without multimodal integration.
Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models The paper studies language model sensitivity to discourse particles such as 'just' and 'only' in natural conversations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on linguistics of discourse particles, not deployment safety, jailbreaks, or guardrails.
REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Examines dialogue emotion/formality classification without retrieval-augmented generation.
REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Work is strictly on text-based dialogue representations without multimodal modalities.
REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies natural dialogue classification, unrelated to code synthesis or software engineering.
REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on sentiment and formality detection rather than AI safety, red-teaming, or guardrails.
REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Proposes role-aware attention mechanisms without addressing model compression or efficiency.
REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on dialogue classification architectures rather than autonomous multi-step agents.
REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality The paper introduces a transformer architecture incorporating semantic role labeling into attention mechanisms to detect sentiment, emotion, politeness, and formality. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a transformer architecture for classification rather than a benchmark or evaluation method.
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on audio-to-text ASR data augmentation rather than multimodal LLMs or vision-language.
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates speech recognition data augmentation rather than autonomous agent workflows.
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies low-resource training data augmentation rather than model compression or inference efficiency.
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Analyzes ASR augmentation error metrics rather than general LLM evaluation benchmarks.
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech audio data augmentation, not software engineering or code LLMs.
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Analyzes phoneme error distributions in ASR without addressing deployment safety or guardrails.
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR The paper analyzes phoneme-level error patterns from audio and text data augmentation techniques in low-resource automatic speech recognition. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Pertains to low-resource speech recognition without retrieval-augmented generation.
Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion The paper proposes a training-free query expansion method that filters hallucinated terms using corpus-conditioned cross-context reproducibility. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Presents an IR query expansion technique, not model compression or efficient inference.
Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion The paper proposes a training-free query expansion method that filters hallucinated terms using corpus-conditioned cross-context reproducibility. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Pertains strictly to text query expansion and document retrieval, without multimodal aspects.
Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion The paper proposes a training-free query expansion method that filters hallucinated terms using corpus-conditioned cross-context reproducibility. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates query expansion algorithms rather than multi-step tool-using language agents.
Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion The paper proposes a training-free query expansion method that filters hallucinated terms using corpus-conditioned cross-context reproducibility. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a query expansion filtering method rather than general benchmark evaluation methodology.
Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion The paper proposes a training-free query expansion method that filters hallucinated terms using corpus-conditioned cross-context reproducibility. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on information retrieval query expansion rather than software engineering or code synthesis.
QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code The paper introduces a benchmark framework evaluating LLM-generated quantum software unit tests against independent ground-truth checkers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve external document retrieval or retrieval-augmented generation pipelines.
QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code The paper introduces a benchmark framework evaluating LLM-generated quantum software unit tests against independent ground-truth checkers. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Evaluates LLM capabilities without investigating model compression or efficient serving methods.
QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code The paper introduces a benchmark framework evaluating LLM-generated quantum software unit tests against independent ground-truth checkers. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text/code models, containing no multimodal or vision-language components.
QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code The paper introduces a benchmark framework evaluating LLM-generated quantum software unit tests against independent ground-truth checkers. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on quantum code test suites rather than autonomous agent systems or tool workflows.
QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code The paper introduces a benchmark framework evaluating LLM-generated quantum software unit tests against independent ground-truth checkers. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Benchmarking test code generation does not focus on safety, guardrails, or alignment risks.
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Uses a 14B model but does not study distillation, quantization, or inference acceleration.
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates abstention policies rather than proposing new general benchmark suites or evaluation methodologies.
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Work is strictly text-based question answering without vision, audio, or multimodal components.
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies answer selection and abstention for factual QA, not multi-step agent planning or tool use.
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates factual QA datasets (TriviaQA, NQ), not software engineering or code generation.
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection The paper studies policy learning and answer-selection mechanisms to help language models decide when to abstain on factual questions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on calibrated abstention for QA rather than retrieval, chunking, or RAG architecture.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an automated pronunciation scoring system rather than LLM benchmarking.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses speech pronunciation evaluation, not AI deployment safety or guardrails.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech/audio assessment rather than multimodal text-image-video models.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech assessment accuracy rather than model compression or serving efficiency.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Pertains to speech pronunciation assessment, not code models.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Deals with speech scoring, not retrieval-augmented generation.
OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation The paper presents an automatic pronunciation assessment method using Whisper cross-attention to predict word- and utterance-level scores without forced alignment. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech pronunciation scoring, not autonomous AI agents.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a decoding mechanism rather than an LLM evaluation benchmark.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on semantic parsing generation, not document retrieval or grounding.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on formal grammar decoding constraints rather than model efficiency or distillation.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses formal syntax constraints during generation rather than deployment safety risks.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses strictly on neural semantic parsing for text, not multimodal models.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Covers semantic parsing decoding algorithms, not tool-using AI agents.
Structural Automata for Neural Semantic Parsing The paper proposes accepting-path decoding using structural automata to enforce valid prefix completions within length limits for neural semantic parsing. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates abstract meaning representation parsing rather than programming language code generation.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Analyzes multimodal representation geometry rather than deployment safety guardrails.
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors The paper evaluates vision-language models' spatial representations using causally controlled perturbations, showing spatial relations degrade compared to lexical features. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies spatial representations in vision-language models, not code LLMs.

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