arXiv:2603.27343v1 Announce Type: new Abstract: Task-completion rate is the standard proxy for LLM agent capability, but models with identical completion scores can differ substantially in their ability to track intermediate state. We introduce Working Memory Fidelity-Active Manipulation (WMF-AM), a calibrated…
arXiv:2603.28522v1 Announce Type: cross Abstract: We present LAD, a real-time language–action planner with an interruptible architecture that produces a motion plan in a single forward pass (~20 Hz) or generates textual reasoning alongside a motion plan (~10 Hz). LAD is…
arXiv:2603.27355v1 Announce Type: new Abstract: We present a readiness harness for LLM and RAG applications that turns evaluation into a deployment decision workflow. The system combines automated benchmarks, OpenTelemetry observability, and CI quality gates under a minimal API contract, then…
arXiv:2603.28662v1 Announce Type: cross Abstract: Agentic vision-language models increasingly act through extended interactions, but most evaluations still focus on single-image, single-turn correctness. We introduce AMIGO (Agentic Multi-Image Grounding Oracle Benchmark), a long-horizon benchmark for hidden-target identification over galleries of visually…
arXiv:2603.27360v1 Announce Type: new Abstract: Rebuttal generation is a critical component of the peer review process for scientific papers, enabling authors to clarify misunderstandings, correct factual inaccuracies, and guide reviewers toward a more accurate evaluation. We observe that Large Language…
arXiv:2509.23392v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have achieved impressive performance on challenging tasks, yet their deep reasoning often incurs substantial computational costs. To achieve efficient reasoning, existing reinforcement learning methods still struggle to construct short reasoning path…
arXiv:2603.27404v1 Announce Type: new Abstract: Large Language Models (LLMs) are being increasingly used as autonomous agents in complex reasoning tasks, opening the niche for dialectical interactions. However, Multi-Agent systems implemented with systematically unconstrained systems systematically undergo semantic drift and logical…
arXiv:2603.06082v3 Announce Type: replace Abstract: Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling…
arXiv:2603.27406v1 Announce Type: new Abstract: In this paper, the relationship between probabilistic graphical models, in particular Bayesian networks, and causal diagrams, also called structural causal models, is studied. Structural causal models are deterministic models, based on structural equations or functions,…
arXiv:2408.13516v2 Announce Type: replace-cross Abstract: Few-shot multi-class anomaly detection is crucial in real industrial settings, where only a few normal samples are available while numerous object types must be inspected. This setting is challenging as defect patterns vary widely across…
