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1224 items on 02 Oct 2026
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Where the Agent Development Lifecycle Fits

Coordinating agent capability development with the application it powers The post Where the Agent Development Lifecycle Fits appeared first on Towards Data Science.

Source: Towards Data Science Andrew Hinton
AI Research AI

Hob-VL: A Benchmark for Visually Grounded Boolean Reasoning

arXiv:2610.01605v1 Announce Type: cross Abstract: Reliable visual reasoning requires composing multiple visual observations and returning consistent answers to logically equivalent questions. We introduce Hob-VL, a…

Source: arXiv cs.AI Yuzhou Wang, Emile Anand, Ijay Narang
AI Research AI

Structure-agnostic Causal Representation Learning

arXiv:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods require specifying the…

Source: arXiv cs.AI Arman Behnam, Binghui Wang
AI Research AI

Agent Evaluation Reliability: More Tasks Won't (Always) Fix An Agent Leaderboard

arXiv:2610.00651v1 Announce Type: new Abstract: Agent evaluations are increasingly used to compare LLMs and inform deployment decisions, yet ranks can reflect not only the model but also the effects of the evaluation…

Source: arXiv cs.AI Michael Hardy, Ruhana Azam, Anka Reuel, Mykel Kochenderfer, Sanmi Koyejo
AI Research AI

HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution

arXiv:2610.02089v1 Announce Type: cross Abstract: As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use requires selecting a…

Source: arXiv cs.AI Kyochul Jang, Seohyeon Park, Ohchul Kwon, Sangjun Park, Junhyeok Choi, Seungyeop Yi, Chaeyun Kim, Sangkyu Lee, Idan Szpektor, Avi Caciularu, Jongmin Park, Youngjae Yu
AI Research AI

MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees

arXiv:2610.01641v1 Announce Type: cross Abstract: Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive information can be…

Source: arXiv cs.AI Poushali Sengupta, Sabita Maharjan, Frank Eliassen, Shashi Raj Pandey, Yan Zhang
AI Research AI

When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents

arXiv:2610.00372v1 Announce Type: new Abstract: Large language model agents rely on external harnesses to pass information between the model and its environment and to recover from execution errors. Yet recovery is…

Source: arXiv cs.AI Shuyao Xiao, Shengling Wang, Xuan Chen, Ke Chao, Ming Cui, Feifei Qian, Chaoyang Mei, Fanlin Meng, Ziming Yu, Junxi Yin

1224 items available