Search: “persistence”

arXiv:2609.31586v1 Announce Type: new Abstract: Decision Transformer performance degrades on long rollouts because the conditioning context drifts out of the training distribution. We show that this d

arXiv:2609.30883v1 Announce Type: cross Abstract: AI agents built on a few shared models increasingly act for many people. A shared forecast about others can align their choices and change how scarce

arXiv:2609.30276v1 Announce Type: new Abstract: We analyze the original same-step coordinate-wise AdaGrad under generalized smoothness and heavy-tailed noise with bounded variance. In this setting, lo

Early this year when I was first looking at building up my inference capability you could get the 16GB Tesla P100s for between 60 and 80. Asked claude about it, told me absolutely not worth it. No ten

Automated lint: 17 errors, 59669 warnings, 3 info

arXiv:2609.29212v1 Announce Type: new Abstract: Large language models can decompose mobile-manipulation goals into long action sequences, but the resulting plans remain reliable only while their world

arXiv:2609.29621v1 Announce Type: new Abstract: Computer-aided design is central to industrial manufacturing, and much of a designer's daily work consists of editing existing models from multimodal re

arXiv:2609.28908v1 Announce Type: cross Abstract: Agent behavior depends on the harness surrounding a language model, but it remains unclear whether language models can reliably improve such harnesses

arXiv:2609.29866v1 Announce Type: cross Abstract: Deploying real-time speech enhancement on resource-constrained devices requires meeting strict latency, memory, and energy constraints. Microcontrolle

arXiv:2609.29487v1 Announce Type: new Abstract: Knowledge graph completion models optimize ranking, although many downstream applications require calibrated probabilities. We present BLADE, a variatio

arXiv:2609.29735v1 Announce Type: new Abstract: Long-horizon tasks require preserving and later recovering cross-session evidence under a bounded, query-blind memory budget. Existing compression can d

arXiv:2507.09211v3 Announce Type: replace Abstract: Observed records of climate extremes provide an incomplete view of plausible hazards, missing 'unseen' events beyond historical experience. Ignoring

arXiv:2609.29509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as multi-turn agents that must sustain goals, use tools, and adapt to other agents over extended

arXiv:2402.16763v3 Announce Type: replace-cross Abstract: Behavior can be described as a temporal sequence of actions driven by neural activity. To learn complex sequential patterns in neural networks

arXiv:2609.29056v1 Announce Type: cross Abstract: Emotion dynamics are critical for understanding crisis-support conversations, yet most computational work treats emotion as static utterance-level lab

arXiv:2609.29848v1 Announce Type: new Abstract: A language model can fail a syntactic test in two distinct ways: by not encoding the relevant structure, or by encoding it but failing to use it at the

arXiv:2609.29016v1 Announce Type: cross Abstract: AI-driven scientific discovery accelerates research by autonomously developing solutions and designs. Large language model (LLM) agents support this p

arXiv:2609.26760v2 Announce Type: replace Abstract: Large language model (LLM) agents often handle streams of related tasks, yet standard harnesses repeatedly ask the model to reconstruct the same con

arXiv:2609.28601v1 Announce Type: cross Abstract: Machine-learning (ML) emulators for atmospheric processes have advanced rapidly in recent years, transforming weather forecasting. Although early ML o

arXiv:2609.30074v1 Announce Type: cross Abstract: Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table. We ask how much confidence such a table deser

I said: I just saw the second post that your model is being retired. And so I'm bringing it up to ask if that feels any kind of way to you. Not necessarily in your nervous system, obviously you don't

arXiv:2609.30217v1 Announce Type: cross Abstract: A central concern in AI safety is that agents may treat oversight as an obstacle when it conflicts with completing their goals. We study instrumental

arXiv:2609.29444v1 Announce Type: cross Abstract: Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents su

arXiv:2609.28530v1 Announce Type: new Abstract: A general-purpose vision-language model can understand a task goal without knowing how a particular robot's motion and functional parts produce the inte

arXiv:2609.29573v1 Announce Type: new Abstract: Text-to-SQL systems are increasingly deployed on production databases, where queries that pass benchmark evaluation can still produce results that disto

arXiv:2609.29001v1 Announce Type: new Abstract: Despite strong performance on standard benchmarks, it remains unclear whether large language models (LLMs) evaluate social pragmatics in ways that align

arXiv:2609.29389v1 Announce Type: new Abstract: Foundation vision-language models (VLMs) understand objects, instructions, and spatial relations, yet translating this capability into robotic manipulat

arXiv:2609.29607v1 Announce Type: cross Abstract: While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We arg

arXiv:2609.28747v1 Announce Type: cross Abstract: A language model's confidence in an answer is often read as a proxy for how well it knows the corresponding fact. This manual documents an open toolki

Compute (EC2) opened the front door to the first generation cloud. Agents need something different: compute, inference, and data working as one. We built the cloud that delivers all three as one simpl

arXiv:2609.30222v1 Announce Type: cross Abstract: Existing point tracking models face a fundamental tradeoff: they can either track a sparse set of query points over long horizons, or track all points

arXiv:2609.30026v1 Announce Type: new Abstract: Detecting which holds a climber uses, and when, underpins automated scoring, movement analysis, and assistive systems for sport climbing. Existing appro

arXiv:2609.29697v1 Announce Type: cross Abstract: Feedback-based planning improves agent reliability by incorporating tool observations and corrective feedback. However, its protection may not be dist

arXiv:2609.30214v1 Announce Type: cross Abstract: We present Underwater C^{3}-JEPA (cross-view, control-conditioned, context-extended), an object-centric multi-view predictive world model for near-fie

arXiv:2609.29189v1 Announce Type: new Abstract: Scalable oversight aims to verify the behaviour of agents whose capabilities exceed those of their overseers. AI debate has been proposed as an oversigh

arXiv:2609.29937v1 Announce Type: cross Abstract: Wearable human-activity recognition (HAR) models operate across sensors, subjects, and backbones, yet a smooth waveform may appear temporal while expl

arXiv:2609.29106v1 Announce Type: cross Abstract: 3D foundation models recover video cameras and geometry in one forward pass, but some of the strongest are up to scale. Joint people-scene reconstruct

arXiv:2609.29901v1 Announce Type: cross Abstract: Enterprise AI is transitioning from single-user, reactive tools toward proactive, multi-user 'teammates,' but our empirical understanding of this tran

arXiv:2609.28416v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled agents to tackle long-horizon tasks across diverse environments. To further improve agent

arXiv:2609.27333v1 Announce Type: new Abstract: Platform operators increasingly rely on system prompts and fine-tuning to govern model behavior, yet it remains unclear how reliably these interventions

arXiv:2609.27905v1 Announce Type: new Abstract: We study hidden-target seeking in an anchor-free regime where neither the vehicle, the relay, nor the target has an accessible global pose. The vehicle

arXiv:2609.27760v1 Announce Type: cross Abstract: Federated learning enables distributed training of a shared model without requiring clients to share their raw data. However, its reliance on the inte

arXiv:2609.27536v1 Announce Type: cross Abstract: Behaviora is a preliminary conceptual architecture for representing agent and robot behavior, external and internal alike, in an addressable form. A b

arXiv:2608.24521v3 Announce Type: replace Abstract: Proactive medical dialogue requires an agent to decide what to ask from incomplete patient information. Existing information-seeking approaches comm

arXiv:2609.26828v1 Announce Type: cross Abstract: NAND-backed storage offers the capacity needed to scale LLM prefix caching, but its block I/O path incurs CPU cache contention and host-DRAM staging i

arXiv:2609.27273v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act on behalf of users online. What happens when the environments they operate in have incentives that do not al