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
Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication? The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on semantic reasoning limitations, not model efficiency.
Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication? The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Analyzes natural language sentences without multimodal capabilities.
Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines lexical semantic consistency rather than safety risks.
Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Evaluates semantic relation knowledge, not tool-using agents.
Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies relation prediction, not retrieval from external sources.
Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Tests lexical semantic consistency, not programming assistants.
Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Covers text gloss pairs across languages without multimodal components.
Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on semantic evaluation rather than efficient architectures.
Re-executing Coding-Agent Evaluations: Unchanged Decisions and Unresolved Policy Risk The paper re-evaluates coding-agent benchmark results to measure sensitivity to test execution environments and grading configurations. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Pertains strictly to code and execution environments, not multimodality.
Re-executing Coding-Agent Evaluations: Unchanged Decisions and Unresolved Policy Risk The paper re-evaluates coding-agent benchmark results to measure sensitivity to test execution environments and grading configurations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on benchmark reproducibility, not small or efficient models.
Re-executing Coding-Agent Evaluations: Unchanged Decisions and Unresolved Policy Risk The paper re-evaluates coding-agent benchmark results to measure sensitivity to test execution environments and grading configurations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies coding benchmarks without addressing retrieval-augmented generation.
Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Assesses model classification under irony, not autonomous agent planning.
Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates evaluation validity, not model compression or efficiency.
Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Analyzes text classification registers without multimodal inputs.
Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies content moderation tasks, not code generation or repair.
Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on toxic content classification, not document retrieval systems.
EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines inference compression techniques, not safety or guardrails.
EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Analyzes on-device model compression, not software coding tools.
EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Examines KV-cache compression on hardware, not agent workflows.
EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Investigates text language model caches, not multimodal systems.
EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies KV-cache quantization, not retrieval-augmented generation systems.
Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies text language models, not multimodal inputs.
Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Investigates internal neuron steering, not autonomous tool-using agents.
Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an inference-time steering mechanism, not an evaluation suite.
Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Explores internal model steering without external knowledge retrieval.
Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on general inference steering rather than coding tasks.
LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Focuses on historical document comprehension, not programming tasks.
LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Evaluates document question answering rather than multi-step tool agents.
LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Assesses document QA accuracy without addressing safety risks.
LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes benchmark performance rather than model efficiency or compression.
Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Targets distillation for reasoning performance rather than safety guardrails.
Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Operates entirely on text-based math reasoning without multimodality.
Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a distillation training method, not an evaluation framework.
Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Evaluates math reasoning benchmarks, not software engineering or code.
Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on distillation training, not retrieval-augmented generation systems.
Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Deals with training distillation algorithms, not interactive agents.
Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Develops preference optimization without involving retrieval or external knowledge.
Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Addresses general alignment, not coding assistants or software repair.
Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a training optimization method rather than evaluation benchmarks.
Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies text alignment without multimodal inputs or outputs.
Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on preference alignment, not small models or efficiency.
Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on preference optimization objectives, not agent planning or tools.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Audits text embedding models, not multimodal systems.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on embedding bias in candidate matching, not autonomous agents.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies HR embeddings, not code generation or software engineering.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Evaluates bias impacts, not model compression or efficient serving.
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval This paper audits request independence in open Jev-style retrieval models across multi-passage query states. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Audits retrieval scoring models, not autonomous planning agents.
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval This paper audits request independence in open Jev-style retrieval models across multi-passage query states. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on text passage retrieval without multimodal inputs.
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval This paper audits request independence in open Jev-style retrieval models across multi-passage query states. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies information retrieval models, not software development.
The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval This paper audits request independence in open Jev-style retrieval models across multi-passage query states. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates retrieval scoring consistency, not model compression.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Analyzes visual hallucinations, not multi-step planning agents.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not investigate model quantization or efficient inference.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant General visual hallucination benchmark, not industrial defect inspection.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Studies vision-language hallucinations, not software engineering.
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on visual reasoning errors rather than document retrieval.
VoTSpeech: Decoupling Voice Design from Speech Generation in Instruction-Following TTS This paper presents an instruction-following text-to-speech model that separates voice design from speech generation using an intermediate voice latent. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Deals with acoustic modeling, not software development.
VoTSpeech: Decoupling Voice Design from Speech Generation in Instruction-Following TTS This paper presents an instruction-following text-to-speech model that separates voice design from speech generation using an intermediate voice latent. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Focuses on text-to-speech synthesis, not autonomous agentic systems.
VoTSpeech: Decoupling Voice Design from Speech Generation in Instruction-Following TTS This paper presents an instruction-following text-to-speech model that separates voice design from speech generation using an intermediate voice latent. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a TTS model architecture, not evaluation methodology.
VoTSpeech: Decoupling Voice Design from Speech Generation in Instruction-Following TTS This paper presents an instruction-following text-to-speech model that separates voice design from speech generation using an intermediate voice latent. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on controllable speech synthesis, not deployment safety.
VoTSpeech: Decoupling Voice Design from Speech Generation in Instruction-Following TTS This paper presents an instruction-following text-to-speech model that separates voice design from speech generation using an intermediate voice latent. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not investigate model compression or inference efficiency.

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