The paper introduces PACT, a provenance‑conserving fusion method for typed action admission in human‑robot collaboration. PACT treats evidence countability as a relational variable, using a supplied provenance partition to define countable units and accumulating support only across these units. Experiments on 31,200 evaluations in 48 scene clusters show that PACT achieves a lower normalized risk‑coverage area than singleton aggregation, and in offline human‑robot collaboration it admits 47 of 57 reference‑consistent candidates without any reference‑inconsistent admissions.
arXiv:2610.16895v1 Announce Type: new Abstract: LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating...
arXiv:2611.06653v1 Announce Type: cross Abstract: A robot action can satisfy shape and range constraints yet carry the wrong semantics: axes may be permuted, signs inverted, rotations rescaled, or gr...
arXiv:2606. 12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site.
arXiv:2609.13308v1 Announce Type: cross Abstract: A companion evaluation found that naming the target part in a manipulation prompt increased action accuracy by 0.32-0.63 across eight vision-language...
arXiv:2606. 09919v1 Announce Type: cross Abstract: Perceptual uncertainty is a central challenge for heterogeneous robot teams operating in unstructured outdoor environments, where no single viewpoint affords reliable scene understanding.
arXiv:2607. 13056v1 Announce Type: cross Abstract: Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largely unmodeled.
arXiv:2608. 05670v1 Announce Type: new Abstract: A model's agreement across perturbed inputs is used both as a label-free reliability signal and as a self-training target, on the premise that agreement tracks correctness.
arXiv:2609.00654v1 Announce Type: new Abstract: We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~\cite{sciclaimeval}, which asks systems to verify scien...
The paper introduces the global coherence problem, where AI agents make locally valid decisions that collectively lead to an invalid outcome due to shared state failures. It presents the Observation‑Aliasing Impossibility Theorem, establishing that a policy can guarantee a valid action only when all indistinguishable worlds share an admissible action, and shows that even with additional reasoning, roles, messages, or samples, the missing distinction cannot be recovered. The authors propose a local‑to‑global runtime semantics framework and conduct nine studies demonstrating that missing global state cannot be substituted by local intelligence.
arXiv:2609.26093v1 Announce Type: new Abstract: Vision-language models can answer spatial relation questions confidently even when the image supports an incompatible relation. We formulate relation-g...
RoboJev-27B is an open‑weight vision‑language model that predicts whether a robot has successfully completed a manipulation task by analyzing a task instruction, ten frames of the attempt, and optional auxiliary camera frames. Trained with a rank‑32 LoRA adapter on 52,598 episodes, it achieves 95.2% balanced accuracy and 0.993 AUROC on held‑out episodes of the same robot model, and maintains strong performance on unseen labs, robots, and community‑collected data. The model runs in 0.26 seconds per episode on an H100 GPU and is released with inference code and a large, transparent evaluation dataset for reproducible benchmarking.
CoreSense is a robot‑system integration architecture that traces episodic evidence and uses a conflict‑aware belief gate to decide whether to proceed, re‑observe, abstain, or escalates. The gate evaluates scope, provenance, time, contradiction, and support before making a recommendation. Evaluation on public robot datasets, simulations, and a live cloud deployment shows that belief gating can eliminate protocol‑defined unsafe proceeds while maintaining auditability.