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
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses exclusively on text attribution without multimodal capabilities.
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not investigate retrieval-augmented generation or external document grounding.
Who's Behind the Text? Robust Model Authorship Attribution The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on model authorship attribution of text, not agent planning or tool use.
JockeyBench: Evaluating LLM Agents on Horse-Racing Decision-Making The paper introduces a horse-racing decision benchmark to evaluate whether agents can extract profitable signals from noisy historical data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines betting decision evaluation rather than deployment safety, jailbreaks, or guardrails.
L3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents The paper presents a Lean 4 runtime allowing self-extending agents to run verified computations across tools without constant human oversight. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on system runtime architecture rather than LLM evaluation benchmarks.
L3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents The paper presents a Lean 4 runtime allowing self-extending agents to run verified computations across tools without constant human oversight. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses system runtime engineering rather than model compression or inference efficiency.
JevCoder: When Does Decision Delegation Pay Off in Code Repair? The paper evaluates delegating code-repair decisions to low-cost probabilistic judges to reduce token costs without losing accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies existing SWE-bench benchmarks rather than developing new evaluation methodologies.
JevCoder: When Does Decision Delegation Pay Off in Code Repair? The paper evaluates delegating code-repair decisions to low-cost probabilistic judges to reduce token costs without losing accuracy. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent cost and efficiency rather than safety or alignment.
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an architectural mechanism rather than LLM evaluation or benchmarks.
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on forecasting accuracy rather than model compression, quantization, or serving efficiency.
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting The paper proposes a Bayesian formulation of self-attention to address non-stationary temporal dependencies in long-term time series forecasting. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on time series forecasting transformers, not language model agents.
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies agent trajectories generally rather than software engineering or code generation.
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on outcome prediction and training efficiency, not safety or guardrails.
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not address multimodal models or vision-language reasoning.
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses general agent trajectories rather than retrieval-augmented generation systems.
Instruction-Conditioned Motion Control for Robotic Arms: Language-to-Code Decomposition and Reinforcement Learning The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic RL control rather than general LLM benchmarking methods.
Instruction-Conditioned Motion Control for Robotic Arms: Language-to-Code Decomposition and Reinforcement Learning The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Discusses reward discrepancies in RL logs rather than deployment safety guardrails.
Instruction-Conditioned Motion Control for Robotic Arms: Language-to-Code Decomposition and Reinforcement Learning The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic manipulation and code generation, not industrial visual inspection.
Are AI Decision Models Biased? A Case Study of Jev The paper evaluates an artificial intelligence decision model across cognitive reflection, classical bias benchmarks, and pricing tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates decision biases in a static model, not planning agents.
DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates an architectural proposal, not a new evaluation methodology.
DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on autoregressive language modeling, not multimodal processing.
DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses layer routing in Transformers, not retrieval-augmented generation.
DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes a Transformer architecture variant, not an AI agent system.
DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on core Transformer architecture, not software engineering or code LLMs.
DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses architectural routing, not AI safety or jailbreaks.
Eye, Robot: Learning Expressive Gaze Dynamics via a VLM-Guided Transformer-VAE The paper combines a vision-language model with a Transformer-VAE to generate expressive, human-like gaze dynamics for humanoid robots. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a robotics gaze controller, not an LLM benchmark.
Eye, Robot: Learning Expressive Gaze Dynamics via a VLM-Guided Transformer-VAE The paper combines a vision-language model with a Transformer-VAE to generate expressive, human-like gaze dynamics for humanoid robots. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with physical robot motion control, not AI deployment safety.
Eye, Robot: Learning Expressive Gaze Dynamics via a VLM-Guided Transformer-VAE The paper combines a vision-language model with a Transformer-VAE to generate expressive, human-like gaze dynamics for humanoid robots. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on humanoid robotic gaze control, not LLM agents.
Eye, Robot: Learning Expressive Gaze Dynamics via a VLM-Guided Transformer-VAE The paper combines a vision-language model with a Transformer-VAE to generate expressive, human-like gaze dynamics for humanoid robots. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses humanoid robotics gaze, not manufacturing defect inspection.
Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies symbolic matching across knowledge systems, not retrieval-augmented generation.
Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses neuro-symbolic alignment, not code assistants or software agents.
Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Uses sparse autoencoders for interpretability, not model compression or serving.
Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates neuro-symbolic interpretability rather than deployment safety guardrails.
Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on symbolic representations in text models, not multimodal models.
Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates symbolic matching techniques, not general LLM benchmarks.
Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on sparse autoencoder alignment, not planning agents.
Model What Matters: Decision-Guided Selective World Modeling for Language Agents The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning world modeling, not retrieval-augmented generation.
Model What Matters: Decision-Guided Selective World Modeling for Language Agents The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on environment dynamics modeling, not deployment safety or jailbreaks.
Model What Matters: Decision-Guided Selective World Modeling for Language Agents The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with text environment observations, not multimodal models.
Model What Matters: Decision-Guided Selective World Modeling for Language Agents The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Covers general reinforcement learning for agents, not code generation.
Model What Matters: Decision-Guided Selective World Modeling for Language Agents The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an agent training method, not an evaluation benchmark.
Model What Matters: Decision-Guided Selective World Modeling for Language Agents The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on selective world modeling objectives, not model compression.
AWARE-RG: Abstraction Aware Explanation of Resiliency Graphs for Cyber Physical Systems The paper presents a human-in-the-loop framework providing role-bounded explanations of cyber-physical system resilience graphs using Formal Concept Analysis. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with traditional industrial control security graphs, not AI safety.
AWARE-RG: Abstraction Aware Explanation of Resiliency Graphs for Cyber Physical Systems The paper presents a human-in-the-loop framework providing role-bounded explanations of cyber-physical system resilience graphs using Formal Concept Analysis. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on cyber-physical security graphs, not LLM agents.
AWARE-RG: Abstraction Aware Explanation of Resiliency Graphs for Cyber Physical Systems The paper presents a human-in-the-loop framework providing role-bounded explanations of cyber-physical system resilience graphs using Formal Concept Analysis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a formal explanation system, not an LLM benchmark.
HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses pedagogical user simulation, not AI deployment safety or red-teaming.
HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on student behavioral simulation, not retrieval-augmented generation.
HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a simulation technique for tutoring systems, not an LLM benchmark.
HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies tutoring interaction modeling, not code generation or program repair.
HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies to text dialogue and response modeling, not multimodal processing.
HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on behavioral simulation harnesses, not small models or quantization.
HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Simulates student behavior via next-action prediction rather than autonomous planning agents.
Measuring and Reducing Unusable Replies of LLM Agents The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines readability and usability of agent replies rather than safety risks.
Measuring and Reducing Unusable Replies of LLM Agents The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses communication quality in agent outputs, not model efficiency or distillation.
Measuring and Reducing Unusable Replies of LLM Agents The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates general LLM agent replies, not software engineering or coding tasks.
Measuring and Reducing Unusable Replies of LLM Agents The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies reply usability in agent communication, not retrieval-augmented generation.
Measuring and Reducing Unusable Replies of LLM Agents The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text replies from agents, not multimodal understanding.
Learning When to Trust Medical Knowledge: Regression-Safe Routing for Clinical Reasoning The paper introduces a benchmark for medical knowledge poisoning and a routing controller that selectively avoids unreliable retrieved evidence during clinical reasoning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical question answering routing, not multi-step planning agents.
MCAIR: Monitoring and Correcting Agent Unfaithfulness via Internal Representations The paper proposes a runtime monitoring method to detect and correct language model agents that deviate from intended procedures without reporting it. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a runtime monitoring probe rather than an evaluation benchmark.

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