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
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses orchestration policy learning, not model compression or small-model serving.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an orchestration method, not a novel evaluation methodology or benchmark.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not focus on coding assistants, code generation, or software engineering.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on agentic orchestration rather than document retrieval or grounding.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates spatial reasoning in multimodal models, unrelated to software engineering or code.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Explores internal representation geometry without focusing on model compression or serving efficiency.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies internal spatial representations without retrieval-augmented generation.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Analyzes representation subspaces in MLLMs rather than autonomous agent systems.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates mechanistic spatial representations rather than proposing new benchmark suites.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on internal representations of spatial reasoning rather than safety risks or guardrails.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Analyzes internal representations of MLLMs rather than industrial vision inspection or robotics perception.
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear? The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies paralinguistic personalization and bias rather than autonomous agent systems.
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear? The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Examines speech LLM bias and personalization without retrieval-augmented generation.
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear? The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Evaluates speech models without addressing small models, distillation, or compression.
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear? The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech LLMs and paralinguistics, unrelated to code or software development.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Analyzes code execution runtime efficiency rather than safety, guardrails, or alignment.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies code fix runtime performance without retrieval-augmented generation components.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates patch runtime performance in SWE benchmarks rather than agent planning architectures.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Studies software engineering code patches without multimodal vision or audio components.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Measures runtime performance of generated patches, not language model compression or serving.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies AI-generated text detection rather than autonomous multi-step agent systems.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on artifact reliance in text detectors, not model compression or efficiency.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Investigates machine-text detection artifacts without retrieval-augmented generation.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Examines machine-generated text detectors on scientific literature, not code generation.
Measuring Artifact Reliance in Machine-Generated Text Detectors The paper demonstrates that machine-generated text detectors rely heavily on document formatting and PDF extraction artifacts rather than text quality. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Examines text detectors and PDF extraction artifacts without multimodal models.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes token compression methods rather than novel benchmark suites or evaluation protocols.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates token compression algorithms, unrelated to software engineering or code LLMs.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on token compression layers rather than autonomous tool-using language agents.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text token compression and RoPE positioning without multimodal components.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses inference efficiency and token compression rather than AI safety or reliability risks.
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss The paper proposes compression-layer modifications to reduce accuracy loss when using dynamic LZW vocabularies during language model inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies token representation and vocabulary compression without retrieval-augmented generation.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on linguistic polarity licensing without any retrieval-augmented generation.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Evaluates existing multilingual models without focusing on model compression or efficiency.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies negative polarity items in linguistics, unrelated to code synthesis or programming.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Examines linguistic grammaticality of polarity items, not autonomous multi-step agents.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on multilingual linguistic syntax and semantics without multimodal elements.
BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items The paper presents a multilingual benchmark of minimal pairs to evaluate language model sensitivity to negative polarity items across 33 languages. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses linguistic grammaticality evaluation rather than safety, red-teaming, or guardrails.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on training dialogue user simulators rather than autonomous agent systems.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies simulator feedback for training conversational assistants rather than safety or guardrails.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies conversation feedback simulation without addressing model compression or efficiency.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses conversational assistant feedback rather than software engineering or code models.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Investigates user simulation in multi-turn dialogues, not retrieval-augmented generation.
Training User Simulators with Latent Intrinsic Feedback The paper trains conversational user simulators equipped with latent evaluation thoughts and scalar ratings to provide intrinsic feedback. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text-based dialogue interactions without incorporating multimodal inputs.
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Tests agent execution of generic scripts, but the primary focus is security and prompt injection.
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Assesses frontier model vulnerabilities without focusing on model compression or small models.
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent tool interfaces and files rather than multimodal foundation models.
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces The paper introduces a benchmark evaluating agent susceptibility to prompt and script injections across multi-tool shared workspace environments. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on security attacks against agent tools rather than retrieval-augmented generation systems.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies anti-money laundering transaction graphs, unrelated to code generation or software.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses evaluation sample efficiency rather than model compression or efficient model serving.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses benchmark sampling techniques rather than AI safety risks, guardrails, or alignment.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on benchmark downsampling for AML evaluation, not autonomous agentic frameworks.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Pertains to graph classification evaluation rather than retrieval-augmented generation.
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis The paper presents an importance-weighted downsampling protocol to lower evaluation costs on transaction graph datasets without distorting precision metrics. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on graph-to-text prompt evaluations without vision, audio, or multimodal data.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Presents a general neural architecture improvement rather than code-specific language models.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an internal attention residual architecture, not multi-step autonomous agent systems.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Implements depth-wise attention over internal network layers, not document retrieval systems.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Proposes a general backbone layer architecture rather than multimodal reasoning or VLMs.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general neural network architecture rather than industrial computer vision tasks.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on network layer architecture rather than deployment safety, guardrails, or alignment.
Multi-Stream Attention Residuals The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a model architecture rather than an evaluation framework or benchmark suite.

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