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
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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses architectural routing, not AI safety or jailbreaks.
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	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	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	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	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on core Transformer architecture, not software engineering or code LLMs.
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	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses humanoid robotics gaze, not manufacturing defect inspection.
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.
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.
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	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	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	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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Uses sparse autoencoders for interpretability, not model compression or serving.
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	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	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.
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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with text environment observations, not multimodal models.
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.
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.
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	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	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	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	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.
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.
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.
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	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	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	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.
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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on internal representation probing, not model compression or efficiency.
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.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Evaluates long-horizon task synthesis, not deployment safety or guardrails.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent task synthesis, not retrieval-augmented generation architectures.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on general long-horizon agent execution, not code generation.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses long-horizon text agent tasks, not multimodal models.
Interlock: From Local Correctness to Task Completion in Long-Horizon Agents The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on task synthesis and evaluation, not efficient inference or pruning.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on language model reasoning optimization, not multimodal inputs.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on policy optimization training algorithms rather than agent architecture.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates policy distillation techniques, not safety, guardrails, or alignment risks.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a reinforcement learning algorithm rather than an evaluation benchmark.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses RL training optimization, not retrieval-augmented generation systems.
Function-Space Temporal Self-Distillation for Policy Optimization The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Deals with general policy optimization, not software engineering models.
PPAD-Completeness of Contiguous Envy-Free Cake Cutting with Additive Valuations The paper proves computational complexity results for contiguous envy-free cake-cutting and indivisible goods allocations under additive valuations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical computational complexity proof, not an LLM evaluation benchmark.
PPAD-Completeness of Contiguous Envy-Free Cake Cutting with Additive Valuations The paper proves computational complexity results for contiguous envy-free cake-cutting and indivisible goods allocations under additive valuations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Proves PPAD-completeness in fair division, not AI safety deployment.
PPAD-Completeness of Contiguous Envy-Free Cake Cutting with Additive Valuations The paper proves computational complexity results for contiguous envy-free cake-cutting and indivisible goods allocations under additive valuations. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Theoretical algorithmic game theory paper on cake-cutting, not LLM agents.
ReFormBench: Benchmarking the Stability of Response-Conditioned Format Correction in Large Language Models The paper presents a benchmark evaluating how reliably language models correct and maintain formatting requirements across changing instructions. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies response formatting constraints rather than tool-use planning agents.
C3: Rethinking Process Supervision via Confidence-Guided Credit Correction in Multi-Turn Reinforcement Learning The paper introduces a method that selectively adjusts positive credit for undesirable agent actions using the policy's confidence during multi-turn reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning credit correction, not deployment safety or robustness.
C3: Rethinking Process Supervision via Confidence-Guided Credit Correction in Multi-Turn Reinforcement Learning The paper introduces a method that selectively adjusts positive credit for undesirable agent actions using the policy's confidence during multi-turn reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a training method, not a benchmark or evaluation framework.
RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios The paper introduces a multi-domain benchmark to evaluate language model agents on identifying, understanding, and applying domain-specific rules across complex files. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmarking rule deduction capabilities rather than safety, guardrails, or jailbreaks.

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