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新论文 · v1arXiv:2611.16694

When Expert Disagreement Hurts: Prestige and Selectivity in LLM Decision Revision

Yupeng Tang, Mingfeng Lin

原文摘要

When language models revise a decision after another agent disagrees, neither reversal counts nor final accuracy shows whether they are fixing mistakes or abandoning correct answers. We introduce a controlled two-pass audit that holds the question, the evidence, and the model's initial answer fixed and varies only the revision context: neutral reflection, anonymous disagreement, or disagreement attributed to a domain expert. For binary decisions the accounting is exact: revision raises accuracy only if it is selective enough, reversing wrong answers more than $a/(1-a)$ times as often as correct ones, where $a$ is initial accuracy (nine times at 90%). Across five models and 1,989 evidence-grounded decisions in medicine, science, and law, no model clears this bar under expert disagreement, and accuracy falls by up to 16.1 points. The expert label, a prestige cue, adds reversals for four of the five models, yet its effect on accuracy ranges from a 5.2-point loss to a 2.4-point gain, so reversal counts are a poor proxy for harm. With identical arguments from both sources, the label still raises reversal for three proprietary models and in a pooled analysis of four newer models. Models also respond to stated source reliability. Revision pipelines should therefore be evaluated with paired, conditional metrics, per model and protocol.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15502

Selective State-Space Neural Tucker Factorization for Temporal-aware Traffic Imputation

Zhenhao Li, Peng Tang

原文摘要

Spatiotemporal traffic data imputation is a key task of intelligent transportation systems (ITS), and urban traffic data inherently exhibits strong long-term temporal dependencies. Therefore, accurate modeling of these long-term temporal dynamics is the core prerequisite for high fidelity traffic data imputation. However, existing traffic data imputation methods still suffer from inherent limitations in long-sequence modeling, such as gradient vanishing, low efficiency, and insufficient capability to capture long-range temporal correlations. To address these issues, this paper proposes a Selective State Space Neural Tucker Factorization model (S3NTF) for robust traffic data imputation. This model combines the Selective State Space mechanism with Neural Tucker Factorization to capture complex spatial correlations and long-term temporal dynamics. A state space-based time encoder is proposed to effectively extracts long sequence dependencies with linear complexity, while the latent spatiotemporal feature interaction learning module models high-order cross modal interactions based on Tucker decomposition. Extensive experiments on six real traffic datasets have shown that our model outperforms existing models in terms of accuracy.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15154

SciSelect-Gym: An Environment and Benchmark for Active Learning and Bayesian Optimization Configuration Selection

Ying-Peng Tang, Tianle Zhang, Wanlong Fang, Ng Jing Xiang, Alvin Chan

原文摘要

In many scientific tasks, scientists repeatedly choose a small set of candidates to test, learn from the results, and use the observations to guide the next selection. Methods based on active learning~(AL) or Bayesian optimization~(BO) have been proposed for these iterative screening campaigns with a surrogate model. Yet, selecting the surrogate model and acquisition function for a new dataset remains challenging due to limited empirical evidence, while the effectiveness of large language models (LLMs) in making such decisions remains unclear. To address these gaps, we introduce SciSelect-Gym, an environment for simulating AL/BO data selection campaigns and evaluating configuration choices. We conducted simulations of AL/BO data selection campaigns across several settings and more than 100 molecular datasets. Using completed simulation campaigns as context improves LLM recommendation shortlists on held-out datasets more consistently than it improves the quality of their first choice. SciSelect-Gym provides a simulation platform and recommendation protocols for learning how to choose configurations before an acquisition campaign. An implemented agent use case connects these campaign records and simulation tools to LLM-generated configuration-selection policies. The code is provided as supplementary material.

cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10439

Who Remembers What? Multi-Granularity Memory Extraction for Multi-User, Multi-Agent Interaction

Yunzhe Wang, Mesut Erhan Unal, Sheng Zhang, Shabnam Ghadar, Peng Tang

原文摘要

Self-evolving agents improve their own skills, memory, and harness code from experience without retraining the base LLM, and group-evolving agents go further by sharing these lessons across agents. Yet most such systems overlook that sharing across users and heterogeneous agents requires deciding who can benefit from each lesson and what information must remain private. We present MixUA, a memory system that distills independent user-agent trajectories into lessons and assigns each lesson to a user-agent pair, user, agent, or global scope according to whom it benefits and what it may disclose. For evaluation, we introduce CrossLearnBench, a mixed-user-agent benchmark that pairs established agent tasks with user preferences and privacy probes that test whether private facts leak into memory other users can read. With five users and seven agents, MixUA raises pass rates by up to 16 points over no memory and 5 over a single global memory, even on tasks previously tried only by other users on other agents. It retains up to 90% of previously stated preferences, and at most 0.02% of privacy probes find a planted private fact in a forbidden scope. We hope MixUA offers a scalable path toward a global system where users and agents co-evolve.

cs.CL · cs.AI · cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06475

Robocasa-Mem: Benchmarking Environment and Process Memory in Long-Horizon Mobile Manipulation

Sihang Chen, Chen Ye, Wei Chen, Xiaohan Lei, Zhipeng Tang, Sha Zhang, Yuan Zhang, Zixuan Chen 等 11 位作者

原文摘要

In the real world, mobile manipulation robots must perform complex object interactions and make multi-step decisions, often relying on task-relevant information that is no longer observable. However, existing benchmarks provide limited evaluation of such memory-dependent decision-making in mobile manipulation. To address this gap, we introduce RoboCasa-Mem, a new benchmark comprising nine mobile manipulation tasks that require agents to use interaction history at key decision points. Specifically, the tasks are grouped into two categories of memory: One is environment memory, which captures spatial locations, object attributes, and object relationships. The other is process memory, which captures single-task progress and multi-task scheduling. Particularly, we introduce Behavior-Conditioned Evaluation. For tasks with multiple valid execution paths, instead of fixing a single target or trajectory, the evaluator derives later task constraints from the robot's earlier actions. This allows us to evaluate whether later behavior is consistent with the robot's own interaction history while reducing shortcuts based on fixed target assignments. We provide 1,800 successful demonstrations for policy training. We also develop a baseline on our benchmark, namely Hierarchical Memory-Augmented Policy(HMAP). Its high-level planner uses structured entity and process memories to generate subtasks. Its low-level VLA controller uses subgoal-conditioned queries to incorporate VGGT-Omega geometry into action generation. Together, RoboCasa-Mem and HMAP baseline support systematic evaluation of memory-dependent behavior and the development of memory-aware mobile manipulation policies.

cs.RO

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arXiv ↗PDF ↗