Search: Long-horizon - ai.jp.net

Technology #Artificial Intelligence, Language Models 📝 BlogAnalyzed: Jan 3, 2026 05:48

Recursive Language Models: Breaking the LLM Context Length Barrier

Published:Jan 2, 2026 20:54

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1 min read

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MarkTechPost

Analysis

The article introduces Recursive Language Models (RLMs) as a novel approach to address the limitations of traditional large language models (LLMs) regarding context length, accuracy, and cost. RLMs, as described, avoid the need for a single, massive prompt by allowing the model to interact with the prompt as an external environment, inspecting it with code and recursively calling itself. The article highlights the work from MIT and Prime Intellect's RLMEnv as key examples in this area. The core concept is promising, suggesting a more efficient and scalable way to handle long-horizon tasks in LLM agents.

Key Takeaways

•RLMs aim to improve LLMs by addressing the trade-offs between context length, accuracy, and cost.
•RLMs treat the prompt as an external environment, allowing for more flexible interaction.
•The approach involves the model inspecting the prompt with code and recursively calling itself.
•MIT and Prime Intellect's RLMEnv are examples of this approach.

Reference

“RLMs treat the prompt as an external environment and let the model decide how to inspect it with code, then recursively call […]”

Permalink MarkTechPost

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 07:00

Prime Intellect Unveils Recursive Language Models (RLM): Paradigm shift allows AI to manage own context and solve long-horizon tasks

Published:Jan 2, 2026 10:33

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1 min read

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r/singularity

Analysis

This article reports on the unveiling of Recursive Language Models (RLMs) by Prime Intellect, a new approach to handling long-context tasks in LLMs. The core innovation is treating input data as a dynamic environment, avoiding information loss associated with traditional context windows. Key breakthroughs include Context Folding, Extreme Efficiency, and Long-Horizon Agency. The release of INTELLECT-3, an open-source MoE model, further emphasizes transparency and accessibility. The article highlights a significant advancement in AI's ability to manage and process information, potentially leading to more efficient and capable AI systems.

Key Takeaways

•RLMs treat long prompts as dynamic environments, avoiding context rot.
•Context Folding delegates tasks to sub-LLMs and Python scripts.
•RLMs demonstrate extreme efficiency, outperforming standard models on long-context tasks.
•The system can maintain coherence over long-horizon tasks.
•INTELLECT-3, an open-source MoE model, is released alongside the research.

Reference

“The physical and digital architecture of the global "brain" officially hit a new gear.”

Permalink r/singularity

Paper #Robotics, AI, Humanoid Robots, Imitation Learning 🔬 ResearchAnalyzed: Jan 3, 2026 06:10

Coordinated Humanoid Manipulation with Choice Policies

Published:Dec 31, 2025 18:59

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of achieving robust whole-body coordination in humanoid robots, a critical step towards their practical application in human environments. The modular teleoperation interface and Choice Policy learning framework are key contributions. The focus on hand-eye coordination and the demonstration of success in real-world tasks (dishwasher loading, whiteboard wiping) highlight the practical impact of the research.

Key Takeaways

•Proposes a system for coordinated humanoid manipulation using a modular teleoperation interface and Choice Policy.
•Choice Policy, an imitation learning approach, generates and scores multiple candidate actions.
•Demonstrates superior performance compared to diffusion policies and behavior cloning.
•Highlights the importance of hand-eye coordination in long-horizon tasks.
•Validates the approach on real-world tasks like dishwasher loading and whiteboard wiping.

Reference

“Choice Policy significantly outperforms diffusion policies and standard behavior cloning.”

Permalink ArXiv

Paper #LLM 🔬 ResearchAnalyzed: Jan 3, 2026 06:37

Agentic LLM Ecosystem for Real-World Tasks

Published:Dec 31, 2025 14:03

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1 min read

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ArXiv

Analysis

This paper addresses the critical need for a streamlined open-source ecosystem to facilitate the development of agentic LLMs. The authors introduce the Agentic Learning Ecosystem (ALE), comprising ROLL, ROCK, and iFlow CLI, to optimize the agent production pipeline. The release of ROME, an open-source agent trained on a large dataset and employing a novel policy optimization algorithm (IPA), is a significant contribution. The paper's focus on long-horizon training stability and the introduction of a new benchmark (Terminal Bench Pro) with improved scale and contamination control are also noteworthy. The work has the potential to accelerate research in agentic LLMs by providing a practical and accessible framework.

Key Takeaways

•Introduces the Agentic Learning Ecosystem (ALE) for agentic LLM development.
•Releases ROME, an open-source agent trained on a large dataset.
•Proposes Interaction-based Policy Alignment (IPA) for improved long-horizon training.
•Introduces Terminal Bench Pro, a new benchmark for agent evaluation.

Reference

“ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of the ALE infrastructure.”

Permalink ArXiv

Research Paper #Reinforcement Learning, Robotics, Human-in-the-Loop 🔬 ResearchAnalyzed: Jan 3, 2026 17:16

Leveraging Suboptimal Human Interventions in Real-World RL

Published:Dec 30, 2025 15:26

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1 min read

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ArXiv

Analysis

This paper addresses a critical challenge in real-world reinforcement learning: how to effectively utilize potentially suboptimal human interventions to accelerate learning without being overly constrained by them. The proposed SiLRI algorithm offers a novel approach by formulating the problem as a constrained RL optimization, using a state-wise Lagrange multiplier to account for the uncertainty of human interventions. The results demonstrate significant improvements in learning speed and success rates compared to existing methods, highlighting the practical value of the approach for robotic manipulation.

Key Takeaways

•Addresses the problem of suboptimal human interventions in real-world RL.
•Proposes SiLRI, a state-wise Lagrangian reinforcement learning algorithm.
•Formulates the problem as a constrained RL optimization.
•Demonstrates significant improvements in learning speed and success rates.
•Achieves 100% success on long-horizon manipulation tasks.

Reference

“SiLRI effectively exploits human suboptimal interventions, reducing the time required to reach a 90% success rate by at least 50% compared with the state-of-the-art RL method HIL-SERL, and achieving a 100% success rate on long-horizon manipulation tasks where other RL methods struggle to succeed.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 15:54

Latent Autoregression in GP-VAE Language Models: Ablation Study

Published:Dec 30, 2025 09:23

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1 min read

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ArXiv

Analysis

This paper investigates the impact of latent autoregression in GP-VAE language models. It's important because it provides insights into how the latent space structure affects the model's performance and long-range dependencies. The ablation study helps understand the contribution of latent autoregression compared to token-level autoregression and independent latent variables. This is valuable for understanding the design choices in language models and how they influence the representation of sequential data.

Key Takeaways

•Latent autoregression in GP-VAE models improves long-range structure and stability.
•Removing latent autoregression degrades latent structure and leads to unstable behavior.
•The study highlights the role of latent autoregression in organizing long-range dependencies.
•The findings are an empirical analysis of representational structure, not a new architectural proposal.

Reference

“Latent autoregression induces latent trajectories that are significantly more compatible with the Gaussian-process prior and exhibit greater long-horizon stability.”

Permalink ArXiv

Research Paper #Fusion Energy, AI, Plasma Physics 🔬 ResearchAnalyzed: Jan 3, 2026 15:59

AI Predicts Plasma Edge Dynamics for Fusion

Published:Dec 29, 2025 22:19

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1 min read

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ArXiv

Analysis

This paper presents a significant advancement in fusion research by utilizing transformer-based AI models to create a fast and accurate surrogate for computationally expensive plasma edge simulations. This allows for rapid scenario exploration and control-oriented studies, potentially leading to real-time applications in fusion devices. The ability to predict long-horizon dynamics and reproduce key features like high-radiation region movement is crucial for designing plasma-facing components and optimizing fusion reactor performance. The speedup compared to traditional methods is a major advantage.

Key Takeaways

•Developed transformer-based AI models for predicting plasma edge dynamics.
•Achieved significant speedup compared to traditional simulation methods.
•Demonstrated the ability to predict long-horizon dynamics and key features.
•Enables rapid scenario exploration and control-oriented studies in fusion research.

Reference

“The surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration.”

Permalink ArXiv

Research Paper #Robotics, AI, Manipulation, World Models 🔬 ResearchAnalyzed: Jan 3, 2026 18:41

Act2Goal: Long-Horizon Robotic Manipulation with Visual Goals

Published:Dec 29, 2025 15:28

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of long-horizon robotic manipulation by introducing Act2Goal, a novel goal-conditioned policy. It leverages a visual world model to generate a sequence of intermediate visual states, providing a structured plan for the robot. The integration of Multi-Scale Temporal Hashing (MSTH) allows for both fine-grained control and global task consistency. The paper's significance lies in its ability to achieve strong zero-shot generalization and rapid online adaptation, demonstrated by significant improvements in real-robot experiments. This approach offers a promising solution for complex robotic tasks.

Key Takeaways

Reference

“Act2Goal achieves strong zero-shot generalization to novel objects, spatial layouts, and environments. Real-robot experiments demonstrate that Act2Goal improves success rates from 30% to 90% on challenging out-of-distribution tasks within minutes of autonomous interaction.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 18:59

CubeBench: Diagnosing LLM Spatial Reasoning with Rubik's Cube

Published:Dec 29, 2025 09:25

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1 min read

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ArXiv

Analysis

This paper addresses a critical limitation of Large Language Model (LLM) agents: their difficulty in spatial reasoning and long-horizon planning, crucial for physical-world applications. The authors introduce CubeBench, a novel benchmark using the Rubik's Cube to isolate and evaluate these cognitive abilities. The benchmark's three-tiered diagnostic framework allows for a progressive assessment of agent capabilities, from state tracking to active exploration under partial observations. The findings highlight significant weaknesses in existing LLMs, particularly in long-term planning, and provide a framework for diagnosing and addressing these limitations. This work is important because it provides a concrete benchmark and diagnostic tools to improve the physical grounding of LLMs.

Key Takeaways

•CubeBench is a novel benchmark for evaluating spatial reasoning and long-horizon planning in LLMs.
•The benchmark uses the Rubik's Cube to create a controlled environment for testing.
•Experiments revealed significant limitations in existing LLMs, particularly in long-term planning.
•The paper proposes a diagnostic framework to identify cognitive bottlenecks.

Reference

“Leading LLMs showed a uniform 0.00% pass rate on all long-horizon tasks, exposing a fundamental failure in long-term planning.”

Permalink ArXiv

Research Paper #Reinforcement Learning, LLMs 🔬 ResearchAnalyzed: Jan 3, 2026 19:15

Trust Region Masking for Long-Horizon LLM Reinforcement Learning

Published:Dec 28, 2025 20:41

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of off-policy mismatch in long-horizon LLM reinforcement learning, a critical issue due to implementation divergence and other factors. It derives tighter trust region bounds and introduces Trust Region Masking (TRM) to provide monotonic improvement guarantees, a significant advancement for long-horizon tasks.

Key Takeaways

•Addresses the off-policy mismatch problem in long-horizon LLM-RL.
•Derives tighter trust region bounds.
•Introduces Trust Region Masking (TRM) for monotonic improvement guarantees.
•TRM excludes entire sequences if any token violates the trust region.

Reference

“The paper proposes Trust Region Masking (TRM), which excludes entire sequences from gradient computation if any token violates the trust region, providing the first non-vacuous monotonic improvement guarantees for long-horizon LLM-RL.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 16:16

Audited Skill-Graph Self-Improvement for Agentic LLMs

Published:Dec 28, 2025 19:39

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1 min read

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ArXiv

Analysis

This paper addresses critical security and governance challenges in self-improving agentic LLMs. It proposes a framework, ASG-SI, that focuses on creating auditable and verifiable improvements. The core idea is to treat self-improvement as a process of compiling an agent into a growing skill graph, ensuring that each improvement is extracted from successful trajectories, normalized into a skill with a clear interface, and validated through verifier-backed checks. This approach aims to mitigate issues like reward hacking and behavioral drift, making the self-improvement process more transparent and manageable. The integration of experience synthesis and continual memory control further enhances the framework's scalability and long-horizon performance.

Key Takeaways

•Proposes Audited Skill-Graph Self-Improvement (ASG-SI) for agentic LLMs.
•Focuses on creating auditable and verifiable improvements.
•Treats self-improvement as iterative compilation of an agent into a skill graph.
•Integrates experience synthesis and continual memory control.
•Aims to address security and governance challenges in self-improving agents.

Reference

“ASG-SI reframes agentic self-improvement as accumulation of verifiable, reusable capabilities, offering a practical path toward reproducible evaluation and operational governance of self-improving AI agents.”

Permalink ArXiv

Paper #robotics 🔬 ResearchAnalyzed: Jan 3, 2026 19:22

Robot Manipulation with Foundation Models: A Survey

Published:Dec 28, 2025 16:05

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1 min read

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ArXiv

Analysis

This paper provides a structured overview of learning-based approaches to robot manipulation, focusing on the impact of foundation models. It's valuable for researchers and practitioners seeking to understand the current landscape and future directions in this rapidly evolving field. The paper's organization into high-level planning and low-level control provides a useful framework for understanding the different aspects of the problem.

Key Takeaways

•Provides a survey of learning-based approaches to robot manipulation.
•Organizes approaches within a framework of high-level planning and low-level control.
•Highlights the role of foundation models and multimodal learning.
•Identifies open challenges and future research directions, including scalability, data efficiency, and safety.

Reference

“The paper emphasizes the role of language, code, motion, affordances, and 3D representations in structured and long-horizon decision making for high-level planning.”

Permalink ArXiv

Research Paper #Reinforcement Learning, Large Language Models, Context Folding 🔬 ResearchAnalyzed: Jan 3, 2026 19:41

FoldAct: Stable Context Folding for Long-Horizon RL

Published:Dec 28, 2025 00:24

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1 min read

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ArXiv

Analysis

This paper addresses the scalability challenges of long-horizon reinforcement learning (RL) for large language models, specifically focusing on context folding methods. It identifies and tackles the issues arising from treating summary actions as standard actions, which leads to non-stationary observation distributions and training instability. The proposed FoldAct framework offers innovations to mitigate these problems, improving training efficiency and stability.

Key Takeaways

•Addresses the non-stationary observation problem in context folding for long-horizon RL.
•Introduces FoldAct framework with innovations to improve training stability and efficiency.
•Achieves a 5.19x speedup in training.
•Focuses on improving the training of long-horizon search agents.

Reference

“FoldAct explicitly addresses challenges through three key innovations: separated loss computation, full context consistency loss, and selective segment training.”

Permalink ArXiv

Research Paper #Vision-Language-Action Models, Benchmarking, Robotics 🔬 ResearchAnalyzed: Jan 3, 2026 19:56

VLA-Arena: Benchmarking Vision-Language-Action Models

Published:Dec 27, 2025 09:40

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1 min read

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ArXiv

Analysis

This paper introduces VLA-Arena, a comprehensive benchmark designed to evaluate Vision-Language-Action (VLA) models. It addresses the need for a systematic way to understand the limitations and failure modes of these models, which are crucial for advancing generalist robot policies. The structured task design framework, with its orthogonal axes of difficulty (Task Structure, Language Command, and Visual Observation), allows for fine-grained analysis of model capabilities. The paper's contribution lies in providing a tool for researchers to identify weaknesses in current VLA models, particularly in areas like generalization, robustness, and long-horizon task performance. The open-source nature of the framework promotes reproducibility and facilitates further research.

Key Takeaways

•Introduces VLA-Arena, a new benchmark for Vision-Language-Action models.
•Uses a structured task design framework with orthogonal axes for difficulty.
•Identifies limitations in current VLA models, such as poor generalization and robustness.
•Provides an open-source framework to promote reproducibility and further research.

Reference

“The paper reveals critical limitations of state-of-the-art VLAs, including a strong tendency toward memorization over generalization, asymmetric robustness, a lack of consideration for safety constraints, and an inability to compose learned skills for long-horizon tasks.”

Permalink ArXiv

Research Paper #Embodied AI, Navigation, Dialogue Systems 🔬 ResearchAnalyzed: Jan 3, 2026 20:09

VL-LN Bench: Long-Horizon Navigation with Active Dialogs

Published:Dec 26, 2025 19:00

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of existing embodied navigation tasks by introducing a more realistic setting where agents must use active dialog to resolve ambiguity in instructions. The proposed VL-LN benchmark provides a valuable resource for training and evaluating dialog-enabled navigation models, moving beyond simple instruction following and object searching. The focus on long-horizon tasks and the inclusion of an oracle for agent queries are significant advancements.

Key Takeaways

•Proposes a new task, Interactive Instance Object Navigation (IION), that incorporates active dialog.
•Introduces the VL-LN benchmark, a large-scale dataset for training and evaluating dialog-enabled navigation models.
•Demonstrates significant improvements over baselines using the VL-LN benchmark.
•Addresses the limitations of existing navigation tasks by focusing on ambiguity and long-horizon goals.

Reference

“The paper introduces Interactive Instance Object Navigation (IION) and the Vision Language-Language Navigation (VL-LN) benchmark.”

Permalink ArXiv

Research Paper #Software Engineering, LLMs, Context Management 🔬 ResearchAnalyzed: Jan 3, 2026 20:12

Context Management for Long-Horizon SWE-Agents

Published:Dec 26, 2025 17:15

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1 min read

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ArXiv

Analysis

This paper addresses the critical challenge of context management in long-horizon software engineering tasks performed by LLM-based agents. The core contribution is CAT, a novel context management paradigm that proactively compresses historical trajectories into actionable summaries. This is a significant advancement because it tackles the issues of context explosion and semantic drift, which are major bottlenecks for agent performance in complex, long-running interactions. The proposed CAT-GENERATOR framework and SWE-Compressor model provide a concrete implementation and demonstrate improved performance on the SWE-Bench-Verified benchmark.

Key Takeaways

Reference

“SWE-Compressor reaches a 57.6% solved rate and significantly outperforms ReAct-based agents and static compression baselines, while maintaining stable and scalable long-horizon reasoning under a bounded context budget.”

Permalink ArXiv

Research Paper #Financial Forecasting, Time Series Analysis, Deep Learning 🔬 ResearchAnalyzed: Jan 3, 2026 16:33

Bitcoin Price Forecasting with Global Liquidity using TimeXer

Published:Dec 26, 2025 15:36

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of Bitcoin price volatility by incorporating global liquidity as an exogenous variable in a TimeXer model. The integration of macroeconomic factors, specifically aggregated M2 liquidity, is a novel approach that significantly improves long-horizon forecasting accuracy compared to traditional models and univariate TimeXer. The 89% improvement in MSE at a 70-day horizon is a strong indicator of the model's effectiveness.

Key Takeaways

•Bitcoin price forecasting benefits from incorporating global liquidity data.
•The TimeXer architecture, when conditioned on global liquidity, outperforms other models.
•Long-horizon forecasts are significantly improved by considering macroeconomic factors.

Reference

“At a 70-day forecast horizon, the proposed TimeXer-Exog model achieves a mean squared error (MSE) 1.08e8, outperforming the univariate TimeXer baseline by over 89 percent.”

Permalink ArXiv

Paper #UAV Navigation, Vision-and-Language Navigation, Spatiotemporal Modeling 🔬 ResearchAnalyzed: Jan 3, 2026 16:34

LongFly: UAV Navigation with Spatiotemporal Context

Published:Dec 26, 2025 12:09

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of long-horizon vision-and-language navigation (VLN) for UAVs, a critical area for applications like search and rescue. The core contribution is a framework, LongFly, designed to model spatiotemporal context effectively. The focus on distilling historical data and integrating it with current observations is a key innovation for improving accuracy and stability in complex environments.

Key Takeaways

•Proposes LongFly, a framework for long-horizon UAV VLN.
•Employs a history-aware spatiotemporal modeling strategy.
•Includes modules for image compression, trajectory encoding, and multimodal integration.
•Achieves significant performance improvements over existing baselines.

Reference

“LongFly outperforms state-of-the-art UAV VLN baselines by 7.89% in success rate and 6.33% in success weighted by path length.”

Permalink ArXiv

Paper #Robotics/Computer Vision 🔬 ResearchAnalyzed: Jan 3, 2026 23:56

Aerial World Model for UAV Navigation

Published:Dec 26, 2025 06:22

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of autonomous navigation for UAVs by introducing a novel world model (ANWM) that predicts future visual observations. This allows for semantic-aware planning, going beyond simple obstacle avoidance. The use of a physics-inspired module (FFP) to project future viewpoints is a key innovation, improving long-distance visual forecasting and navigation success. The work is significant because it tackles a crucial limitation in current UAV navigation systems by incorporating high-level semantic understanding.

Key Takeaways

Reference

“ANWM significantly outperforms existing world models in long-distance visual forecasting and improves UAV navigation success rates in large-scale environments.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 09:07

Learning Evolving Latent Strategies for Multi-Agent Language Systems without Model Fine-Tuning

Published:Dec 25, 2025 05:00

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1 min read

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ArXiv ML

Analysis

This paper presents an interesting approach to multi-agent language learning by focusing on evolving latent strategies without fine-tuning the underlying language model. The dual-loop architecture, separating behavior and language updates, is a novel design. The claim of emergent adaptation to emotional agents is particularly intriguing. However, the abstract lacks details on the experimental setup and specific metrics used to evaluate the system's performance. Further clarification on the nature of the "reflection-driven updates" and the types of emotional agents used would strengthen the paper. The scalability and interpretability claims need more substantial evidence.

Key Takeaways

•Multi-agent language learning can be improved by evolving latent strategies.
•A dual-loop architecture can separate behavior and language updates.
•Emergent adaptation to emotional agents is a promising research direction.

Reference

“Together, these mechanisms allow agents to develop stable and disentangled strategic styles over long-horizon multi-round interactions.”

Permalink ArXiv ML

Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 00:25

Learning Skills from Action-Free Videos

Published:Dec 24, 2025 05:00

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1 min read

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ArXiv AI

Analysis

This paper introduces Skill Abstraction from Optical Flow (SOF), a novel framework for learning latent skills from action-free videos. The core innovation lies in using optical flow as an intermediate representation to bridge the gap between video dynamics and robot actions. By learning skills in this flow-based latent space, SOF facilitates high-level planning and simplifies the translation of skills into actionable commands for robots. The experimental results demonstrate improved performance in multitask and long-horizon settings, highlighting the potential of SOF to acquire and compose skills directly from raw visual data. This approach offers a promising avenue for developing generalist robots capable of learning complex behaviors from readily available video data, bypassing the need for extensive robot-specific datasets.

Key Takeaways

•SOF learns latent skills from action-free videos using optical flow.
•It bridges the gap between video dynamics and robot actions.
•SOF improves performance in multitask and long-horizon settings.

Reference

“Our key idea is to learn a latent skill space through an intermediate representation based on optical flow that captures motion information aligned with both video dynamics and robot actions.”

Permalink ArXiv AI

Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 08:02

Laser: A Novel Framework for Long-Horizon Agentic Search

Published:Dec 23, 2025 15:53

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1 min read

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ArXiv

Analysis

The research introduces Laser, a novel approach for governing long-horizon agentic search using structured protocols and context registers, which can improve agent performance. The approach likely addresses limitations in current agent architectures and provides a more controlled and interpretable search process.

Key Takeaways

•Laser is designed for long-horizon agentic search tasks.
•It utilizes structured protocols for control and context registers for information management.
•The research aims to improve agent performance through this framework.

Reference

“The paper is available on ArXiv.”

Permalink ArXiv

Research #Inference 🔬 ResearchAnalyzed: Jan 10, 2026 08:28

Stable Long-Horizon Inference: Blending Neural Operators and Traditional Solvers

Published:Dec 22, 2025 18:17

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1 min read

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ArXiv

Analysis

This research explores a promising approach to improve the stability and performance of long-horizon inference in AI models. By hybridizing neural operators and solvers, the authors likely aim to leverage the strengths of both, potentially leading to more robust and reliable predictions over extended time periods.

Key Takeaways

•Addresses the challenge of stable long-horizon inference in AI.
•Combines neural operators and traditional solvers for improved performance.
•Potentially leads to more reliable predictions over extended time frames.

Reference

“The research focuses on the hybridization of neural operators and traditional solvers.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:05

Time-series Forecast for Indoor Zone Air Temperature with Long Horizons: A Case Study with Sensor-based Data from a Smart Building

Published:Dec 22, 2025 05:19

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1 min read

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ArXiv

Analysis

This article presents a case study on forecasting indoor air temperature using time-series data from a smart building. The focus is on long-horizon predictions, which is a challenging but important area for building management and energy efficiency. The use of sensor-based data suggests a practical application of AI in the built environment. The source being ArXiv indicates it's a research paper, likely detailing the methodology, results, and implications of the forecasting model.

Key Takeaways

•Focus on long-horizon time-series forecasting for indoor air temperature.
•Utilizes sensor-based data from a smart building.
•Likely a research paper detailing a specific forecasting model and its performance.

Reference

“The article likely discusses the specific forecasting model used, the data preprocessing techniques, and the evaluation metrics employed to assess the model's performance. It would also probably compare the model's performance with other existing methods.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:04

SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios

Published:Dec 20, 2025 19:08

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1 min read

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ArXiv

Analysis

This article introduces a benchmark, SWE-EVO, for evaluating coding agents in complex, long-term software evolution tasks. The focus on long-horizon scenarios suggests an attempt to move beyond simpler coding tasks and assess agents' ability to handle sustained development and maintenance. The use of the term "benchmarking" implies a comparative analysis of different agents, which is valuable for advancing the field. The source, ArXiv, indicates this is likely a research paper.

Key Takeaways

•SWE-EVO is a new benchmark for evaluating coding agents.
•The benchmark focuses on long-horizon software evolution scenarios.
•The research likely involves comparing different coding agents.

Reference

“”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 13:28

Introducing GPT-5.2-Codex: Enhanced Agentic Coding Model

Published:Dec 19, 2025 05:21

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1 min read

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Simon Willison

Analysis

This article announces the release of GPT-5.2-Codex, an enhanced version of GPT-5.2 optimized for agentic coding. Key improvements include better handling of long-horizon tasks through context compaction, stronger performance on large code changes like refactors, improved Windows environment performance, and enhanced cybersecurity capabilities. The model is initially available through Codex coding agents and will later be accessible via the API. A notable aspect is the invite-only preview for cybersecurity professionals, offering access to more permissive models. While the performance improvement over GPT-5.2 on the Terminal-Bench 2.0 benchmark is marginal (1.8%), the article highlights the author's positive experience with GPT-5.2's ability to handle complex coding challenges.

Key Takeaways

•GPT-5.2-Codex is optimized for agentic coding with improvements in several key areas.
•The model will be available through Codex coding agents and later via API.
•Cybersecurity professionals have access to a more permissive version through an invite-only preview.

Reference

“GPT‑5.2-Codex is a version of GPT‑5.2 further optimized for agentic coding in Codex, including improvements on long-horizon work through context compaction, stronger performance on large code changes like refactors and migrations, improved performance in Windows environments, and significantly stronger cybersecurity capabilities.”

Permalink Simon Willison

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:14

ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation

Published:Dec 18, 2025 08:39

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1 min read

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ArXiv

Analysis

This article introduces a new approach to imitation learning, specifically focusing on long-horizon manipulation tasks. The core idea is to incorporate interaction awareness into a one-shot learning framework. This suggests an advancement in the field by addressing the challenges of complex robotic tasks with limited data. The use of 'interaction-aware' implies a focus on how the robot interacts with its environment, which is crucial for long-horizon tasks. The 'one-shot' aspect highlights the efficiency of the proposed method.

Key Takeaways

•Focuses on long-horizon manipulation tasks.
•Employs a one-shot imitation learning approach.
•Incorporates interaction awareness for improved performance.

Reference

“”

Permalink ArXiv

Research #llm 🏛️ OfficialAnalyzed: Jan 3, 2026 18:05

Introducing GPT-5.2-Codex

Published:Dec 18, 2025 00:00

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1 min read

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OpenAI News

Analysis

The article announces the release of GPT-5.2-Codex, highlighting its advanced coding capabilities. The focus is on its features: long-horizon reasoning, large-scale code transformations, and enhanced cybersecurity. The brevity suggests a press release or announcement rather than an in-depth analysis.

Key Takeaways

•GPT-5.2-Codex is OpenAI's latest coding model.
•Key features include long-horizon reasoning, code transformations, and enhanced cybersecurity.
•The announcement is brief, suggesting a product launch or update.

Reference

“GPT-5.2-Codex is OpenAI’s most advanced coding model, offering long-horizon reasoning, large-scale code transformations, and enhanced cybersecurity capabilities.”

Permalink OpenAI News

Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 11:23

NL2Repo-Bench: Evaluating Long-Horizon Code Generation Agents

Published:Dec 14, 2025 15:12

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1 min read

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ArXiv

Analysis

This ArXiv paper introduces NL2Repo-Bench, a new benchmark for evaluating coding agents. The benchmark focuses on assessing the performance of agents in generating complete and complex software repositories.

Key Takeaways

•NL2Repo-Bench is designed for evaluating long-horizon code generation.
•The benchmark focuses on repository generation, implying broader capabilities than simple code snippets.
•The paper is published on ArXiv, suggesting early-stage research.

Reference

“NL2Repo-Bench aims to evaluate coding agents.”

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Research #Forecasting 🔬 ResearchAnalyzed: Jan 10, 2026 11:57

UrbanAI 2025 Challenge: Comparing AI Model Architectures for Temperature Forecasting

Published:Dec 11, 2025 17:59

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1 min read

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ArXiv

Analysis

This article likely presents a comparison of linear and transformer models within the context of the UrbanAI 2025 challenge. The focus on long-horizon temperature forecasting highlights a practical application of AI in urban environments.

Key Takeaways

•The research investigates AI model performance in a real-world urban challenge.
•The study compares linear and transformer models for temperature prediction.
•The focus is on long-horizon temperature forecasts for practical applications.

Reference

“The research focuses on long-horizon exogenous temperature forecasting.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:49

Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving

Published:Dec 11, 2025 15:26

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1 min read

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ArXiv

Analysis

This article likely discusses a new AI agent designed to solve complex mathematical problems, potentially at the level of mathematical Olympiads. The focus is on the agent's ability to perform long-horizon reasoning, which implies it can handle multi-step problem-solving processes. The source being ArXiv suggests this is a research paper, indicating a focus on novel techniques and experimental results.

Key Takeaways

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:22

AgentProg: Empowering Long-Horizon GUI Agents with Program-Guided Context Management

Published:Dec 11, 2025 07:37

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1 min read

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ArXiv

Analysis

This article introduces AgentProg, a method for improving the performance of GUI agents, particularly those operating over extended periods. The core innovation lies in using program-guided context management. This likely involves techniques to selectively retain and utilize relevant information, preventing the agent from being overwhelmed by the vastness of the context. The source being ArXiv suggests this is a research paper, indicating a focus on novel techniques and experimental validation.

Key Takeaways

Reference

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Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:57

Curriculum Guided Massive Multi Agent System Solving For Robust Long Horizon Tasks

Published:Dec 9, 2025 12:40

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1 min read

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ArXiv

Analysis

This article likely discusses a novel approach to solving complex, long-duration tasks using a multi-agent system. The 'curriculum guided' aspect suggests a structured learning process, potentially breaking down the task into smaller, more manageable sub-tasks. The focus on 'robustness' implies the system is designed to handle uncertainties and variations in the environment. The source, ArXiv, indicates this is a research paper.

Key Takeaways

•Focus on solving long-horizon tasks.
•Utilizes a multi-agent system.
•Employs a curriculum-based learning approach.
•Aims for robustness in task execution.

Reference

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Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:31

MIND-V: Hierarchical Video Generation for Long-Horizon Robotic Manipulation with RL-based Physical Alignment

Published:Dec 7, 2025 02:28

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1 min read

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ArXiv

Analysis

This article introduces MIND-V, a novel approach for generating videos to facilitate long-horizon robotic manipulation. The core of the method lies in hierarchical video generation and reinforcement learning (RL) for physical alignment. The use of RL suggests an attempt to learn optimal control policies for the robot, while the hierarchical approach likely aims to decompose complex tasks into simpler, manageable sub-goals. The focus on physical alignment indicates a concern for the realism and accuracy of the generated videos in relation to the physical world.

Key Takeaways

•Focus on long-horizon robotic manipulation.
•Employs hierarchical video generation.
•Utilizes Reinforcement Learning (RL) for physical alignment.
•Aims for realistic and accurate video generation for robotics.

Reference

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Permalink ArXiv

Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 13:21

PARC: Self-Reflective Coding Agent Advances Long-Horizon Task Execution

Published:Dec 3, 2025 08:15

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1 min read

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ArXiv

Analysis

The announcement of PARC, an autonomous self-reflective coding agent, signifies a promising step towards more robust and efficient AI task completion. This approach, as presented in the ArXiv paper, could significantly enhance the capabilities of AI agents in handling complex, long-term objectives.

Key Takeaways

•PARC focuses on self-reflection to improve the robustness of code execution.
•The agent is tailored for long-horizon tasks, signifying an advancement in complex problem-solving.
•The research's publication on ArXiv suggests an open-access model for further research and development.

Reference

“PARC is an autonomous self-reflective coding agent designed for the robust execution of long-horizon tasks.”

Permalink ArXiv

Research #Robotics 🔬 ResearchAnalyzed: Jan 10, 2026 13:38

GR-RL: Enhancing Robotic Manipulation for Extended Tasks

Published:Dec 1, 2025 15:33

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1 min read

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ArXiv

Analysis

This research explores advancements in robotic manipulation, particularly for tasks requiring prolonged execution and precision. The paper likely investigates novel algorithms or architectures to improve dexterity and accuracy in robotic systems.

Key Takeaways

•Focuses on improving dexterity and precision in robots.
•Addresses challenges in long-horizon robotic manipulation.
•Likely introduces novel methods or architectures.

Reference

“The research focuses on long-horizon robotic manipulation.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:35

PRInTS: Reward Modeling for Long-Horizon Information Seeking

Published:Nov 24, 2025 17:09

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1 min read

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ArXiv

Analysis

The article introduces PRInTS, a reward modeling approach designed for long-horizon information seeking tasks. The focus is on improving the performance of language models in scenarios where information needs to be gathered over an extended period. The use of reward modeling suggests an attempt to guide the model's exploration and decision-making process, potentially leading to more effective and efficient information retrieval.

Key Takeaways

Reference

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Permalink ArXiv

Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 14:40

O-Mem: A New Memory System for Self-Evolving AI Agents

Published:Nov 17, 2025 16:55

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1 min read

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ArXiv

Analysis

This research explores O-Mem, an omni-memory system designed to enhance the capabilities of personalized and self-evolving AI agents. The paper likely focuses on the architecture and potential benefits of this memory system for long-horizon tasks.

Key Takeaways

•O-Mem aims to improve long-horizon task performance.
•The system is designed for personalized and self-evolving agents.
•The research likely presents a novel memory architecture.

Reference

“The article's source is ArXiv, indicating a pre-print research paper.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 04:43

Reinforcement Learning without Temporal Difference Learning

Published:Nov 1, 2025 09:00

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1 min read

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Berkeley AI

Analysis

This article introduces a reinforcement learning (RL) algorithm that diverges from traditional temporal difference (TD) learning methods. It highlights the scalability challenges associated with TD learning, particularly in long-horizon tasks, and proposes a divide-and-conquer approach as an alternative. The article distinguishes between on-policy and off-policy RL, emphasizing the flexibility and importance of off-policy RL in scenarios where data collection is expensive, such as robotics and healthcare. The author notes the progress in scaling on-policy RL but acknowledges the ongoing challenges in off-policy RL, suggesting this new algorithm could be a significant step forward.

Key Takeaways

•Introduces a novel RL algorithm based on divide and conquer.
•Addresses scalability issues associated with traditional TD learning.
•Focuses on off-policy RL, which is crucial for data-scarce environments.

Reference

“Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks.”

Permalink Berkeley AI