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
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 agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on automated scientific discovery and symbolic regression, not LLM agents.
Getting Space Right: Agentic Rewards for Image Generation This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses agentic critic solely as a tool to provide rewards for image generation.
Getting Space Right: Agentic Rewards for Image Generation This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies to text-to-image generation, not industrial inspection or defect detection.
Getting Space Right: Agentic Rewards for Image Generation This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Getting Space Right: Agentic Rewards for Image Generation This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on reward modeling and reinforcement learning for generative models, not LLM evaluation.
PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration This paper presents an on-device communication agent that uses small language models to generate low-latency mobile network configuration rules. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an application and architecture rather than an evaluation benchmark.
PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration This paper presents an on-device communication agent that uses small language models to generate low-latency mobile network configuration rules. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on network configuration optimization rather than safety or jailbreak risks.
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models This paper introduces an adapter that aligns operational numerical weather prediction inputs with ERA5 reference states for pretrained forecasting models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not related to AI safety, hallucination, or deployment guardrails.
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models This paper introduces an adapter that aligns operational numerical weather prediction inputs with ERA5 reference states for pretrained forecasting models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on adapter architectures for weather forecasting, not LLM benchmarks.
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models This paper introduces an adapter that aligns operational numerical weather prediction inputs with ERA5 reference states for pretrained forecasting models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Domain-specific numerical weather prediction adaptation, not an LLM agent.
Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning This paper presents an experience-based learning framework that uses trajectory quality signals rather than final task outcomes to guide agent improvement. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses learning from past executions rather than safety, guardrails, or alignment risks.
Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning This paper presents an experience-based learning framework that uses trajectory quality signals rather than final task outcomes to guide agent improvement. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an experience-learning mechanism rather than a benchmark or evaluation standard.
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Benchmark is for MARL game policies, not language model evaluation.
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Benchmark compute efficiency in JAX, not small LLM compression or serving.
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on traditional multi-agent reinforcement learning games, not LLM-based agents.
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies competitive game mechanics rather than enterprise AI safety or deployment robustness.
Probable but Not Valid: A Psychometric Audit of Decision Models as Synthetic Survey Respondents This paper audits whether decision models and generative language models can serve as valid synthetic respondents for psychometric surveys. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates LLMs as survey respondents, not multi-step autonomous agents.
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical reinforcement learning optimization paper, not about tool-using language model agents.
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on general RL task sampling rather than deployment safety or guardrails.
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on policy training sampling efficiency, not LLM evaluation frameworks.
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses RL task sampling efficiency, not model compression or lightweight serving.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text and terminal environments, not multimodal models.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or inference efficiency.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Synthesizes agent task environments, not retrieval-augmented generation architectures.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on task synthesis rather than safety, guardrails, or hallucination mitigation.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses broadly on terminal-based agent tasks rather than code generation or repair.
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model efficiency, distillation, or quantization.
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate multimodal vision, audio, or document understanding architectures.
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focus is on agent procedural execution rather than retrieval-augmented generation.
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant General agent workflow evaluation, not specialized software engineering or code generation.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies behavioral propensity spillover rather than benchmarking or evaluation methodology.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation, program repair, or software engineering.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies trait generalization across elicitation methods, not autonomous tool-using agents.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Not related to retrieval-augmented generation or external document grounding.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text language models and alignment traits, not multimodality.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, distillation, or serving efficiency.
VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning This paper proposes visual counterfactual on-policy distillation to prioritize visually grounded tokens during vision-language reasoning training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address deployment safety, guardrails, or red-teaming.
VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning This paper proposes visual counterfactual on-policy distillation to prioritize visually grounded tokens during vision-language reasoning training. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focus is on vision-language model distillation, not multi-step tool-using agents.
VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning This paper proposes visual counterfactual on-policy distillation to prioritize visually grounded tokens during vision-language reasoning training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training method rather than an evaluation benchmark or methodology.
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on dataset selection algorithms rather than model compression or inference efficiency.
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with bilevel optimization rather than real-world AI deployment safety.
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on bilevel combinatorial data selection optimization, not LLM agents.
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents optimization mathematics for data selection, not LLM benchmarking methods.
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical RL algorithmic formulation without LLM evaluation or benchmark focus.
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses confounded RL transitions rather than LLM safety, guardrails, or robustness.
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with RL counterfactual transitions rather than model compression or efficient serving.
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on offline goal-conditioned reinforcement learning theory, not LLM agents.
MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents This paper presents a reinforcement learning framework to train language model agents across tools and servers in the Model Context Protocol ecosystem. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on tool execution learning rather than deployment guardrails or safety risks.
MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents This paper presents a reinforcement learning framework to train language model agents across tools and servers in the Model Context Protocol ecosystem. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a training framework rather than a general LLM evaluation benchmark.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies text-based activation steering without multimodal components.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies memory storage in language models, not code generation or program repair.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Examines memory storage mechanisms rather than general LLM evaluation benchmarks.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Analyzes vector memory retention rather than model compression or inference acceleration.
What Survives When Activation Steering Is Used as Memory This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies parametric activation shifts instead of external document retrieval or RAG architectures.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Uses literature as semantic guidance in training rather than real-time retrieval-augmented generation.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses financial factor generation rather than AI safety or reliability risks.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL formulation for factor mining rather than model compression or efficient inference.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on alpha discovery methodology rather than LLM evaluation benchmarks.
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Processes text literature and symbolic expressions without multimodal inputs.
Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering This paper benchmarks language model agents against skilled humans on binary software reverse engineering tasks using interactive tools. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes reverse engineering capability rather than safety guardrails or alignment.

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