arXiv AI By Jinnan Li, Zheren Fu, Yue Wang, Jinzhe Li, Yuan Wu, Yi Chang

What if LLMs Ate Their Words: Causal History Effects in Multi-Turn Interaction

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arXiv AI
Sep 21

Outcome-Conditioned End-Effector Geometry Across Vision-Language-Action Policies

The study examines how different vision‑language‑action (VLA) policies execute a manipulation task by comparing the geometry of their end‑effectors across 15,000 closed‑loop LIBERO rollouts. By pairing 3,600 configuration‑matched policy executions, the authors find that when both policies succeed, their end‑effector trajectories are much closer (median DTW distance 0.0120 m) than when only one succeeds (0.0380 m), a pattern consistent across all tasks, policy pairs, and nine representations. Even successful executions remain as far from same‑task demonstrations as the demonstrations are from each other, indicating that task‑associated geometry, rather than training data overlap, drives these differences.

By Xingyu Lin, Zhuang Li, Zhongrun Wu, Shouquan Zhou, Dehui Du
arXiv Machine Learning
Sep 1

The Intervention Gap in Latent World Models

The paper introduces the concept of intervention fidelity in latent world models, measuring whether a model’s open‑loop transitions align with actual environment interventions. Experiments on TD‑MPC2, Cheetah, and DreamerV3 show that high reward fit does not guarantee fidelity, and that self‑supervised models can outperform task‑anchored ones in preserving intervention effects. The authors propose a capture‑gated audit to localize failures and argue that fidelity must be directly audited on the model’s native interface.

By Donna Vakalis
arXiv AI
Aug 12

Do LLMs Benefit From Their Own Words?

arXiv:2602. 24287v2 Announce Type: replace-cross Abstract: In multi-turn conversations, large language models typically condition on the full conversation history: both past user prompts and assistant responses.

By Jenny Y. Huang, Leshem Choshen, Wei Sun, Omar Khattab, Ram\'on Fernandez Astudillo, Mehul Damani, Tamara Broderick, Jacob Andreas