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
Evaluation Awareness Beyond English: Do LLMs Act Differently When Tested in Indic Languages? The authors evaluate how language models alter their refusal behavior when informed they are being monitored in English and five Indic languages. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not cover model compression, pruning, or efficient inference architectures.
Dexterity from Supervised Play The authors introduce DSP, a framework where robots learn from brief human play and autonomously collect contact-rich data for dexterous manipulation. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Deals with robotic manipulation and model-predictive control, not language model agents.
Dexterity from Supervised Play The authors introduce DSP, a framework where robots learn from brief human play and autonomously collect contact-rich data for dexterous manipulation. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Focuses on general robotic manipulation rather than industrial visual defect inspection.
Dexterity from Supervised Play The authors introduce DSP, a framework where robots learn from brief human play and autonomously collect contact-rich data for dexterous manipulation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a robotic manipulation framework rather than an LLM evaluation benchmark.
Dexterity from Supervised Play The authors introduce DSP, a framework where robots learn from brief human play and autonomously collect contact-rich data for dexterous manipulation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Investigates robotic manipulation policies rather than deployment safety or hallucination.
3DCodeVerse: Exploring 3D Worlds as Code The authors introduce 3DCodeVerse, a dataset and multi-agent framework enabling coding agents to generate, reason over, and verify 3D executable code. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on 3D asset generation rather than AI safety, red-teaming, or guardrails.
3DCodeVerse: Exploring 3D Worlds as Code The authors introduce 3DCodeVerse, a dataset and multi-agent framework enabling coding agents to generate, reason over, and verify 3D executable code. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Focuses on 3D code generation, not industrial manufacturing defect detection.
3DCodeVerse: Exploring 3D Worlds as Code The authors introduce 3DCodeVerse, a dataset and multi-agent framework enabling coding agents to generate, reason over, and verify 3D executable code. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a dataset and harness for 3D code rather than LLM benchmark evaluation methodology.
GeoState: Learning View-Decoupled Geometry States for 3D World Modeling The authors introduce GeoState, a view-decoupled 3D world modeling approach separating scene dynamics from camera motion. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Focuses on autonomous driving 3D datasets, not industrial inspection or manufacturing quality control.
GeoState: Learning View-Decoupled Geometry States for 3D World Modeling The authors introduce GeoState, a view-decoupled 3D world modeling approach separating scene dynamics from camera motion. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Develops 3D visual world representations rather than LLM-based autonomous agents.
GeoState: Learning View-Decoupled Geometry States for 3D World Modeling The authors introduce GeoState, a view-decoupled 3D world modeling approach separating scene dynamics from camera motion. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a predictive 3D model rather than a benchmark or evaluation method for LLMs.
GeoState: Learning View-Decoupled Geometry States for 3D World Modeling The authors introduce GeoState, a view-decoupled 3D world modeling approach separating scene dynamics from camera motion. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not investigate model quantization, pruning, or efficient inference.
GeoState: Learning View-Decoupled Geometry States for 3D World Modeling The authors introduce GeoState, a view-decoupled 3D world modeling approach separating scene dynamics from camera motion. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on view synthesis and 3D modeling rather than AI safety or guardrails.
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Addresses autonomous ML engineering rather than retrieval-augmented generation techniques.
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Trains a 35B model for task automation rather than focusing on compression or efficient serving.
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a model and training method for ML engineering rather than an LLM benchmark.
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on code and text-based ML engineering rather than multimodal perception or reasoning.
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Investigates agent training for engineering tasks, not AI safety or reliability guardrails.
FDE-Gym: The Last Mile of AI Delivery The authors introduce FDE-Gym, a benchmark of 95 customer delivery tasks evaluating AI agents on ambiguous requirements and live software environments. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates software engineering agents without focusing on multimodal reasoning or vision-language models.
FDE-Gym: The Last Mile of AI Delivery The authors introduce FDE-Gym, a benchmark of 95 customer delivery tasks evaluating AI agents on ambiguous requirements and live software environments. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not explore model compression, quantization, or efficient inference methods.
FDE-Gym: The Last Mile of AI Delivery The authors introduce FDE-Gym, a benchmark of 95 customer delivery tasks evaluating AI agents on ambiguous requirements and live software environments. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Benchmarking delivery performance rather than safety guardrails, jailbreaks, or hallucination prevention.
FDE-Gym: The Last Mile of AI Delivery The authors introduce FDE-Gym, a benchmark of 95 customer delivery tasks evaluating AI agents on ambiguous requirements and live software environments. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on customer delivery agent evaluation rather than retrieval-augmented generation architectures.
Rethinking Routing Exploration in Modern Mixture-of-Experts The authors propose MirrorMoE to reconcile an exploration dilemma between fine-grained expert segmentation and shared experts in mixture-of-experts models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on mixture-of-experts architectural design rather than LLM evaluation methodologies.
Rethinking Routing Exploration in Modern Mixture-of-Experts The authors propose MirrorMoE to reconcile an exploration dilemma between fine-grained expert segmentation and shared experts in mixture-of-experts models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on MoE parameter routing exploration rather than AI safety or reliability.
Rethinking Routing Exploration in Modern Mixture-of-Experts The authors propose MirrorMoE to reconcile an exploration dilemma between fine-grained expert segmentation and shared experts in mixture-of-experts models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies internal MoE routing dynamics rather than language model agent systems.
CoDA: Co-Evolution of Data Engines and Action Policies The authors present CoDA, a framework where a simulation data engine and a robot action policy co-evolve using memory of past failures. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Addresses simulation data engines for robotic control, not LLM agent workflows.
CoDA: Co-Evolution of Data Engines and Action Policies The authors present CoDA, a framework where a simulation data engine and a robot action policy co-evolve using memory of past failures. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Focuses on robotics policy learning rather than industrial defect detection or visual quality control.
CoDA: Co-Evolution of Data Engines and Action Policies The authors present CoDA, a framework where a simulation data engine and a robot action policy co-evolve using memory of past failures. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Deals with simulation training for robots, not AI safety or deployment guardrails.
CoDA: Co-Evolution of Data Engines and Action Policies The authors present CoDA, a framework where a simulation data engine and a robot action policy co-evolve using memory of past failures. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes policy training techniques for robots rather than LLM evaluation or benchmarks.
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models The authors propose T2-Z, a method optimizing learnable environment latents at test time to help robotic world models adapt to unobserved physical factors. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Covers robotic world modeling rather than deployment safety, guardrails, or red-teaming.
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models The authors propose T2-Z, a method optimizing learnable environment latents at test time to help robotic world models adapt to unobserved physical factors. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on robotic dynamics optimization rather than multimodal foundation models or cross-modal reasoning.
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models The authors propose T2-Z, a method optimizing learnable environment latents at test time to help robotic world models adapt to unobserved physical factors. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on physical dynamics for robotic manipulation, not language-model agents.
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models The authors propose T2-Z, a method optimizing learnable environment latents at test time to help robotic world models adapt to unobserved physical factors. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Addresses test-time latents for robotics, not industrial manufacturing visual inspection.
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models The authors propose T2-Z, a method optimizing learnable environment latents at test time to help robotic world models adapt to unobserved physical factors. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a robotics world model rather than LLM benchmarking or evaluation methods.
Discovering state-of-the-art quantum circuits with large language models The authors introduce an evolutionary framework using large language models to design and optimize resource-efficient quantum circuits. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Deals with quantum circuit synthesis rather than evaluating or benchmarking language models.
Discovering state-of-the-art quantum circuits with large language models The authors introduce an evolutionary framework using large language models to design and optimize resource-efficient quantum circuits. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on quantum circuit optimization using LLMs, not language model agent systems or planning frameworks.
Discovering state-of-the-art quantum circuits with large language models The authors introduce an evolutionary framework using large language models to design and optimize resource-efficient quantum circuits. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies quantum algorithm discovery rather than AI safety, hallucination, or deployment reliability.
LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation The authors introduce LIBERO-PeRM, a simulation benchmark for studying how embodied robotic agents learn and follow user preferences in manipulation tasks. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on general robotics manipulation rather than LLM-based agent systems.
LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation The authors introduce LIBERO-PeRM, a simulation benchmark for studying how embodied robotic agents learn and follow user preferences in manipulation tasks. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Evaluates household robotic manipulation, not industrial inspection or manufacturing visual quality control.
LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation The authors introduce LIBERO-PeRM, a simulation benchmark for studying how embodied robotic agents learn and follow user preferences in manipulation tasks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a benchmark for embodied robotics rather than LLMs or language-model-based systems.
LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation The authors introduce LIBERO-PeRM, a simulation benchmark for studying how embodied robotic agents learn and follow user preferences in manipulation tasks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not investigate model compression, distillation, quantization, or efficient serving.
LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation The authors introduce LIBERO-PeRM, a simulation benchmark for studying how embodied robotic agents learn and follow user preferences in manipulation tasks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses preference satisfaction in robotics rather than AI safety, guardrails, or hallucination.
Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Bias The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity. PreprintOlder paper (2026) Older paper (2026) — discussed this week	LLMs for code via blog · 16 Oct, 06:19	Not relevant Does not study software engineering, code generation, or repair.
Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Bias The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity. PreprintOlder paper (2026) Older paper (2026) — discussed this week	AI agents via blog · 16 Oct, 06:19	Not relevant Evaluates LLM judges, not agent planning or tool use.
Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Bias The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity. PreprintOlder paper (2026) Older paper (2026) — discussed this week	Multimodal models via blog · 16 Oct, 06:19	Not relevant Focuses on text evaluation benchmarks, not multimodal perception models.
Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Bias The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity. PreprintOlder paper (2026) Older paper (2026) — discussed this week	AI safety and reliability in deployment via blog · 16 Oct, 06:19	Not relevant Assesses general benchmark judge metrics rather than deployment safety mechanisms.
Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Bias The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity. PreprintOlder paper (2026) Older paper (2026) — discussed this week	Retrieval-augmented generation (RAG) via blog · 16 Oct, 06:19	Not relevant Does not study retrieval-augmented generation architectures or document grounding.
Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Bias The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity. PreprintOlder paper (2026) Older paper (2026) — discussed this week	Small and efficient models via blog · 16 Oct, 06:19	Not relevant Focuses on evaluation methodology rather than model efficiency or compression.
Comprehensive Analysis and Experiment of Extreme Faults in MMC Based on IGCT The paper analyzes extreme faults in modular multilevel converters and proposes a protection scheme using integrated gate-commutated thyristors. Peer-reviewedOlder paper (2023) Older paper (2023) — discussed this week	AI safety and reliability in deployment via ieee · 16 Oct, 06:19	Not relevant Electrical engineering paper on power converters, not AI safety.
Sparse and Dense: Learning Confusion Representation Network for 3-D Action Recognition The paper presents a network utilizing self-attention and spatio-temporal convolutions for 3D skeleton-based human action recognition. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 16 Oct, 06:19	Not relevant Covers 3D action recognition for medical IoT, not AI safety.
Sequential Probabilistic Descriptor via Uncertainty-Aware Multi-Modal Fusion for Safety-Critical Place Recognition The paper proposes an uncertainty-aware multimodal descriptor fusing sequential camera and LiDAR data for robotic place recognition. Peer-reviewedOlder paper (2026) Older paper (2026) — discussed this week	AI safety and reliability in deployment via ieee · 16 Oct, 06:19	Not relevant Focuses on robot localization and SLAM, not AI safety guardrails.
Development of a Microprocessor Based Smart and Safety Blind Glass System The paper develops a microprocessor-based smart wearable glass system with GPS monitoring to assist visually impaired individuals. Peer-reviewedOlder paper (2019) Older paper (2019) — discussed this week	AI safety and reliability in deployment via ieee · 16 Oct, 06:19	Not relevant Hardware project for blind assistance, not AI deployment safety.
When Knowledge Changes: Metamorphic Testing of RAG Systems with Mutations The paper proposes a metamorphic testing method with mutation operators to evaluate how retrieval-augmented generation systems handle changing document corpora. Peer-reviewedOlder paper (2026) Older paper (2026) — discussed this week	LLMs for code via acm · 16 Oct, 06:18	Not relevant Focuses on testing RAG consistency rather than coding models.
Modeling and Detection of Critical Slowing Down in Epileptic Dynamics The paper models epileptic dynamics using a multi-stable system to detect critical slowing down and design feedback control for seizure prevention. Peer-reviewedOlder paper (2024) Older paper (2024) — discussed this week	Computer vision for industrial applications via ieee · 16 Oct, 06:18	Not relevant Pertains to epileptic seizure dynamics, not industrial visual defect detection.
Analysis of Backdoor Attacks on Deepfake Detection The paper examines vulnerabilities of deepfake detection models to backdoor attacks and evaluates related defense strategies. Peer-reviewedOlder paper (2024) Older paper (2024) — discussed this week	Computer vision for industrial applications via ieee · 16 Oct, 06:18	Not relevant Focuses on deepfake detection backdoor attacks, not industrial visual inspection.
Cyber Security Threats Detection using Deep Learning The paper studies cybersecurity threat detection using sequential deep learning models evaluated on the KDD network traffic dataset. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Computer vision for industrial applications via ieee · 16 Oct, 06:18	Not relevant Addresses cybersecurity network intrusion detection, not industrial computer vision inspection.
Partial Discharge Localization Method for Uav Inspection Based on Uhf Detection The paper summary contains webpage metadata rather than research text, though it mentions partial discharge localization for drone inspection using UHF detection. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Computer vision for industrial applications via ieee · 16 Oct, 06:18	Not relevant Summary is mostly paywall metadata and concerns UHF electrical discharge localization.
ML Model For Detection of Depression in Menopause Women The paper evaluates machine learning models, finding Random Forest predicts depression in menopausal women with high accuracy. Peer-reviewedOlder paper (2024) Older paper (2024) — discussed this week	Computer vision for industrial applications via ieee · 16 Oct, 06:18	Not relevant Predicts depression in women, unrelated to industrial computer vision.
Neuromorphic implementation of ECG anomaly detection using delay chains The paper proposes parallel delay chains to enable low-power neuromorphic circuits to detect anomalies in long electrocardiogram signals. Peer-reviewedOlder paper (2022) Older paper (2022) — discussed this week	Computer vision for industrial applications via ieee · 16 Oct, 06:18	Not relevant Covers neuromorphic hardware for ECG signals, not industrial vision inspection.
On Early Detection of Anomalous Network Flows The paper evaluates machine learning models to detect active network attacks early with minimal preprocessing of raw traffic data. Peer-reviewed	Computer vision for industrial applications via ieee · 16 Oct, 06:18	Not relevant Deals with network traffic intrusion detection, not industrial vision inspection.

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