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
PTGPO: Prefix Tree Group Policy Optimization This paper proposes a critic-free reinforcement learning algorithm using prefix trees to assign step-level credit for interactive multi-step tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL training optimization rather than safety or reliability.
PTGPO: Prefix Tree Group Policy Optimization This paper proposes a critic-free reinforcement learning algorithm using prefix trees to assign step-level credit for interactive multi-step tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an RL training algorithm, not an LLM benchmark.
SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability This paper introduces energy-based channel grouping and bit allocation to preserve accuracy when quantizing large language models to two bits. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on model weight compression rather than safety or guardrails.
SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability This paper introduces energy-based channel grouping and bit allocation to preserve accuracy when quantizing large language models to two bits. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on vector quantization for weights, not agent architectures.
SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability This paper introduces energy-based channel grouping and bit allocation to preserve accuracy when quantizing large language models to two bits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates low-bit quantization schemes, not LLM benchmark methodologies.
Equal-information evaluation of credit assignment in modular financial reinforcement learning This paper proposes an evaluation framework to test whether credit assignment techniques in modular financial reinforcement learning truly improve economic decisions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses financial reinforcement learning credit assignment, not LLM-based agents.
Equal-information evaluation of credit assignment in modular financial reinforcement learning This paper proposes an evaluation framework to test whether credit assignment techniques in modular financial reinforcement learning truly improve economic decisions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with financial RL risk controls rather than LLM deployment safety.
Equal-information evaluation of credit assignment in modular financial reinforcement learning This paper proposes an evaluation framework to test whether credit assignment techniques in modular financial reinforcement learning truly improve economic decisions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates RL credit assignment methods, not general LLM benchmarking.
GateSight: Recurrent Memory Anticipates Attention Memory for Query-Agnostic KV Eviction in Hybrid LLMs This paper shows recurrent layers can predict future attention importance, enabling memory eviction in hybrid language models before queries arrive. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on memory management techniques, not evaluation benchmarks.
GateSight: Recurrent Memory Anticipates Attention Memory for Query-Agnostic KV Eviction in Hybrid LLMs This paper shows recurrent layers can predict future attention importance, enabling memory eviction in hybrid language models before queries arrive. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on serving efficiency rather than AI safety or reliability.
GateSight: Recurrent Memory Anticipates Attention Memory for Query-Agnostic KV Eviction in Hybrid LLMs This paper shows recurrent layers can predict future attention importance, enabling memory eviction in hybrid language models before queries arrive. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies KV cache eviction in hybrid architectures, not agent workflows.
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation This paper introduces a multimodal unlearning method using counterfactual inputs and on-policy self-distillation to remove sensitive data without damaging general knowledge. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an unlearning method rather than benchmarking or evaluating LLMs.
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation This paper introduces a multimodal unlearning method using counterfactual inputs and on-policy self-distillation to remove sensitive data without damaging general knowledge. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Covers selective forgetting, not small models, quantization, or inference acceleration.
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation This paper introduces a multimodal unlearning method using counterfactual inputs and on-policy self-distillation to remove sensitive data without damaging general knowledge. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with multimodal unlearning, not code generation or program repair.
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation This paper introduces a multimodal unlearning method using counterfactual inputs and on-policy self-distillation to remove sensitive data without damaging general knowledge. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Addresses model unlearning rather than retrieval-augmented generation systems.
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation This paper introduces a multimodal unlearning method using counterfactual inputs and on-policy self-distillation to remove sensitive data without damaging general knowledge. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on unlearning mechanisms, not agent planning or multi-agent systems.
Last Secure Code Benchmark: Is the Code You Vibe Secure? This paper introduces a benchmark evaluating whether coding agents produce functionally correct code that also avoids real-world security vulnerabilities. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates code security vulnerabilities, not agent planning or tool architectures.
Prior-Fitted and Frozen Scorers for Multimodal Contrastive Learning This paper investigates using frozen, pre-trained Transformer scorers instead of fixed mathematical formulas for multimodal contrastive representation learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Examines representation learning scoring functions, not LLM benchmark evaluation.
Prior-Fitted and Frozen Scorers for Multimodal Contrastive Learning This paper investigates using frozen, pre-trained Transformer scorers instead of fixed mathematical formulas for multimodal contrastive representation learning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies multimodal contrastive learning scorers, not LLM planning agents.
Prior-Fitted and Frozen Scorers for Multimodal Contrastive Learning This paper investigates using frozen, pre-trained Transformer scorers instead of fixed mathematical formulas for multimodal contrastive representation learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers multimodal representation learning, not AI safety or deployment reliability.
Prior-Fitted and Frozen Scorers for Multimodal Contrastive Learning This paper investigates using frozen, pre-trained Transformer scorers instead of fixed mathematical formulas for multimodal contrastive representation learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on frozen contrastive scorers, not model compression or efficient serving.
How Should a Camera-First AI Agent Work? This paper proposes a two-deadline architecture for visual agents to rapidly capture fleeting visual targets before slower semantic reasoning completes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on perception latency management rather than AI safety or guardrails.
How Should a Camera-First AI Agent Work? This paper proposes a two-deadline architecture for visual agents to rapidly capture fleeting visual targets before slower semantic reasoning completes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an agent architecture rather than an LLM evaluation benchmark.
ProgAgent: Learning Tool Orchestration through Programmatic Training This paper presents a training framework that teaches language models to orchestrate tools through executable code instead of sequential prompt accumulation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces training methods rather than general LLM evaluation standards.
ProgAgent: Learning Tool Orchestration through Programmatic Training This paper presents a training framework that teaches language models to orchestrate tools through executable code instead of sequential prompt accumulation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent tool training rather than safety, guardrails, or robustness.
Training a Compact, Strong, and Realistic Text-to-Speech Model This paper introduces a compact text-to-speech model trained with a realism critic to improve prosodic naturalness during zero-shot voice cloning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Develops a speech synthesis model rather than evaluating language models.
Training a Compact, Strong, and Realistic Text-to-Speech Model This paper introduces a compact text-to-speech model trained with a realism critic to improve prosodic naturalness during zero-shot voice cloning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses text-to-speech voice cloning, not autonomous planning agents.
Training a Compact, Strong, and Realistic Text-to-Speech Model This paper introduces a compact text-to-speech model trained with a realism critic to improve prosodic naturalness during zero-shot voice cloning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on audio realism rather than AI deployment safety or guardrails.
LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement This paper presents a multi-agent framework that translates natural language into verified, executable data analytical programs using schema-grounded refinement. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates analytical synthesis systems rather than proposing LLM benchmark methodology.
LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement This paper presents a multi-agent framework that translates natural language into verified, executable data analytical programs using schema-grounded refinement. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on query synthesis accuracy rather than AI safety or guardrails.
Counterfactual Regret for Reflective Agents with Lean-Verified Guarantees This paper establishes mathematical conditions to guarantee long-term performance and low counterfactual regret for reflective language agents under resource constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical agent regret bounds, not benchmarking methodologies.
GREAT-ESDD: Graph Edge Attention Network for Effectively Steered Delivery Decisions This paper develops a graph edge attention neural solver for dynamic pricing and vehicle routing in same-day delivery operations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on delivery routing optimization rather than LLM evaluation.
GREAT-ESDD: Graph Edge Attention Network for Effectively Steered Delivery Decisions This paper develops a graph edge attention neural solver for dynamic pricing and vehicle routing in same-day delivery operations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers logistics routing and pricing, not LLM deployment safety.
GREAT-ESDD: Graph Edge Attention Network for Effectively Steered Delivery Decisions This paper develops a graph edge attention neural solver for dynamic pricing and vehicle routing in same-day delivery operations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses vehicle routing with graph neural networks, not LLM agents.
CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection This paper presents a multi-year network traffic time-series dataset to evaluate long-term anomaly detection and forecasting models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies network data forecasting, not efficient model compression or serving.
CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection This paper presents a multi-year network traffic time-series dataset to evaluate long-term anomaly detection and forecasting models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on network operational anomalies rather than AI deployment safety.
CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection This paper presents a multi-year network traffic time-series dataset to evaluate long-term anomaly detection and forecasting models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates network traffic forecasting models, not language model benchmarks.
CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection This paper presents a multi-year network traffic time-series dataset to evaluate long-term anomaly detection and forecasting models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on network traffic time series, not language model agents.
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies to autonomous driving vehicles, which is explicitly excluded from topic 4.
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers vehicle kinematic planning rather than enterprise AI safety or model robustness.
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates driving kinematic trajectories, not language model capabilities.
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on autonomous driving motion planning, not language model agents.
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focus is on motion planning and style control in driving, not general VLM reasoning.
ASIWorld: Benchmarking Autonomous Agents in a Persistent Survival Sandbox This paper introduces a persistent survival sandbox benchmark to evaluate sustained goal execution and program synthesis in autonomous agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent planning performance rather than safety guardrails or jailbreaks.
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models This paper proposes a depth-progressive framework using 2D generative diffusion models to build 3D seismic velocity models efficiently. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not related to AI safety or enterprise deployment risks.
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models This paper proposes a depth-progressive framework using 2D generative diffusion models to build 3D seismic velocity models efficiently. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses geophysics diffusion modeling, not LLM benchmarking.
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models This paper proposes a depth-progressive framework using 2D generative diffusion models to build 3D seismic velocity models efficiently. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on 2D/3D geophysical seismic modeling, not autonomous agents.
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance This paper evaluates an edge-cloud hybrid architecture using AWS IoT Greengrass for real-time machine learning inference in predictive maintenance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Edge machine learning deployment for telemetry, not an LLM agent.
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance This paper evaluates an edge-cloud hybrid architecture using AWS IoT Greengrass for real-time machine learning inference in predictive maintenance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on edge IoT architecture rather than language model evaluation.
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance This paper evaluates an edge-cloud hybrid architecture using AWS IoT Greengrass for real-time machine learning inference in predictive maintenance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses industrial predictive maintenance latency rather than AI safety or reliability risks.
A Reference Architecture for Integrating Agentic AI into Enterprise Software: Responsibilities, Interactions, and Variability This paper presents a technology-neutral reference architecture for integrating agentic AI capabilities into enterprise software systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant System architecture paper, not a benchmark or evaluation methodology.
AI-Native Product Management: A Vision This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Conceptual framework and vision paper, not an empirical LLM benchmark.
AI-Native Product Management: A Vision This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on product management and organization rather than code generation or software repair.
AI-Native Product Management: A Vision This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant High-level vision paper without technical focus on safety, guardrails, or robustness.
Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies This paper introduces an Indian language text-to-speech benchmark and an inference-time duration estimation method for alignment-free synthesis. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Pertains to text-to-speech audio synthesis, not autonomous multi-step agents.
Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies This paper introduces an Indian language text-to-speech benchmark and an inference-time duration estimation method for alignment-free synthesis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Benchmark is for cross-lingual speech synthesis, not language model evaluation.
Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies This paper introduces an Indian language text-to-speech benchmark and an inference-time duration estimation method for alignment-free synthesis. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not investigate AI safety, guardrails, or reliability risks.
EvoCowork: A Benchmark for Real-World Office Tasks This paper introduces a 240-task benchmark to evaluate recursive self-improvement in agents across diverse real-world office workflows. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on benchmark performance rather than safety guardrails or risk mitigation.
Pre-Registered Genesis Accounting for Recursive Self-Improvement: First Audited Operator-Genesis Events in a Physics-Discovery Runtime This paper introduces an accounting framework to measure recursive self-improvement and audits machine-authored operator generation in a physics runtime. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not concerned with enterprise deployment safety, guardrails, or alignment.
Pre-Registered Genesis Accounting for Recursive Self-Improvement: First Audited Operator-Genesis Events in a Physics-Discovery Runtime This paper introduces an accounting framework to measure recursive self-improvement and audits machine-authored operator generation in a physics runtime. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an accounting methodology for symbolic physics discovery, not LLM evaluation.

8,521–8,580 of 13,754← Previous Next →