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
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with user behavior modeling, not code generation or software engineering.
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on training user behavior modeling, not document retrieval or grounding.
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses user history text logs, not multimodal vision-language models.
Relational Semantic Grounding for Test-Time Discovery This paper proposes a framework using language model attributes to ground and label novel visual classes discovered dynamically at test time. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on test-time visual concept discovery, not LLM agent systems.
Relational Semantic Grounding for Test-Time Discovery This paper proposes a framework using language model attributes to ground and label novel visual classes discovered dynamically at test time. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers open-world visual classification, not deployment safety or guardrails.
Relational Semantic Grounding for Test-Time Discovery This paper proposes a framework using language model attributes to ground and label novel visual classes discovered dynamically at test time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a class discovery method rather than LLM benchmarking.
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a fuzzy control method, not an LLM benchmark.
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies fuzzy reward estimation, not LLM safety or reliability.
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on fuzzy RL control, not model efficiency or distillation.
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers classical fuzzy control with inverse RL, not language agents.
MHWA: Multi-timescale Hierarchical World-Action Model This paper introduces a hierarchical multi-timescale world model to reduce compounding rollout errors and drift in sequential decision-making systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers reinforcement learning world models for gaming, not LLM agents.
MHWA: Multi-timescale Hierarchical World-Action Model This paper introduces a hierarchical multi-timescale world model to reduce compounding rollout errors and drift in sequential decision-making systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an RL architecture rather than language model evaluation.
MHWA: Multi-timescale Hierarchical World-Action Model This paper introduces a hierarchical multi-timescale world model to reduce compounding rollout errors and drift in sequential decision-making systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies hierarchical decision models rather than deployment safety or guardrails.
Self-improving data synthesis can scale embedding models across domains and tasks This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on generating synthetic training data for dense retrieval models.
Self-improving data synthesis can scale embedding models across domains and tasks This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a data synthesis method rather than an evaluation benchmark.
Self-improving data synthesis can scale embedding models across domains and tasks This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers retrieval embedding adaptation rather than safety or reliability.
Self-improving data synthesis can scale embedding models across domains and tasks This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on training data synthesis rather than model compression or efficient serving.
Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems This paper presents a framework that uses history tracking and problem-specific tools to help language agents solve complex combinatorial optimization tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on problem solving optimization rather than AI safety or guardrails.
Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems This paper presents a framework that uses history tracking and problem-specific tools to help language agents solve complex combinatorial optimization tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an optimization agent framework rather than an evaluation benchmark.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL credit assignment rather than model compression or inference efficiency.
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 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	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	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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies KV cache eviction in hybrid architectures, not agent workflows.
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.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on unlearning mechanisms, not agent planning or multi-agent 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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Covers selective forgetting, not small models, quantization, or inference acceleration.
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	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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on frozen contrastive scorers, not model compression or efficient serving.
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.
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.
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.
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.
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.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on audio realism rather than AI deployment safety or guardrails.
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.
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.
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.
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	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	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 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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on network traffic time series, not language model 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.
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	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.

12,001–12,060 of 17,313← Previous Next →