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
TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on speculative decoding speed, not autonomous agentic systems.
TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses general speculative decoding, not software engineering models.
TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on inference acceleration rather than evaluation metrics.
TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or knowledge bases.
When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Analyzes career path prediction models, not planning agents.
When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not examine retrieval-augmented generation architectures.
When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation or software development.
When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on career trajectory tasks rather than LLM benchmarks.
When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Uses textual job descriptions, without vision or speech modalities.
When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model efficiency, quantization, or distillation.
When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction This paper analyzes career path prediction systems, reporting that explicit skill representations provide marginal improvement over text-only job descriptions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not focused on safety, prompt injection, or hallucination.
Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on scaling laws, not safety, guardrails, or hallucination.
Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on multilingual text scaling, not cross-modal processing.
Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not focused on software engineering or code modeling.
Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address external retrieval or grounding techniques.
Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on multilingual mixture-of-experts architecture, not agents.
Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation This paper proposes a joint scaling law evaluating how adding experts in multilingual mixture-of-experts models affects language capacity and loss penalties. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates scaling laws rather than benchmark evaluation reliability.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation or software tooling.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address external document retrieval or RAG pipelines.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Covers speech-text representation alignment, not autonomous agents.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces modality repair layers, not benchmark methodology.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses cross-modal knowledge updates, not AI safety or jailbreaks.
Repairing Speech Access After Text Updates with Frozen Affine Fields This paper introduces AFFIRM, using affine fields on frozen speech models to repair spoken access to recently updated text knowledge. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study model compression, pruning, or quantization.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on speech classification accuracy, not safety or guardrails.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to programming, software engineering, or code.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on audio language identification, not multi-step agents.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve retrieval-augmented generation systems.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates spoken language ID models, not broad LLM benchmarks.
Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages This paper introduces Anuvani LID, a dedicated 115M parameter model for spoken language identification across 22 Indian languages. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on dedicated conformer LID, not model compression methods.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses part-of-speech sequence tagging, not planning agents.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on low-resource NLP labeling rather than LLM benchmarks.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers basic NLP classification, not safety, guardrails, or hallucination.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or knowledge grounding.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to software development, repair, or coding.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Evaluates existing transformer encoders without introducing compression methods.
Neural POS Tagging for the Low-Resource Saraiki This paper introduces an annotated part-of-speech tagging dataset and evaluates pretrained transformer encoders for the low-resource Saraiki language. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses text-only sequence tagging in Saraiki script.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates medical reasoning benchmarks, not code generation.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Operates on text-based medical QA without vision or audio.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval timing rather than parameter reduction or quantization.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval triggering rather than autonomous multi-step agent planning.
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference This paper proposes PUlSE, using internal layer representations to detect factual errors and trigger step-level retrieval during medical reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a retrieval mechanism, not benchmark validation or scoring.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on parameter efficiency or model compression.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent adaptation rather than guardrails or safety.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Covers agent internal memory updates, not document-based RAG.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes agent architectures, not benchmark analysis or judge models.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not focused on coding assistants or software engineering tasks.
MMLA-RSI: Recursive Self-Improvement through RTT and RTMA This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not address vision or cross-modal modalities.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses audio diarization, not safety, guardrails, or hallucination.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation or software maintenance.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on speech speaker diarization, not autonomous multi-step agents.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address document retrieval or knowledge augmentation.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Targets speech diarization benchmarking, not general LLM evaluation.
Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages This paper presents Anuvani-Diarization, evaluating zero-shot speaker diarization across 22 Indian languages with uncertainty-aware silver supervision. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not explore model distillation, quantization, or efficient architectures.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes agent social discourse, not standard LLM evaluation benchmarks.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on social behavior in agents, not retrieval systems.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers conversational behavior, not deployment safety or jailbreak risks.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate code generation or software engineering.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient serving.
Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Investigates text-based forum agents, not multimodal models.
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies sociological and linguistic drift rather than AI safety or guardrails.

7,081–7,140 of 17,313← Previous Next →