Search: “agents”

arXiv:2605.12978v1 Announce Type: new Abstract: Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated

arXiv:2605.12947v1 Announce Type: cross Abstract: LLM-enabled AI workflows increasingly produce outputs through iterative generate-evaluate-revise loops. Each iteration can improve the candidate, but

Yesterday we gave an in-person NYC workshop on automating your financial document ingestion pipelines with AI. If you're an investment bank, accounting team, finance AI startup, fintech company, and y

you can see more here (color stuff is at bottom): http://colorfeelings.replit.app - changing embedding model may change results - the similarity range was pretty small so normalizing really stretched

arXiv:2605.11616v1 Announce Type: new Abstract: Functional affordance grounding requires more than recognizing an object: an agent must localize the specific region that supports an interaction, such

arXiv:2605.11477v1 Announce Type: new Abstract: Video understanding in multimodal large language models requires selecting informative frames from long, redundant videos under limited visual-token bud

arXiv:2605.12449v1 Announce Type: new Abstract: While self-supervised pretraining has reduced vision systems' reliance on synthetic data, simulation remains an indispensable tool for closed-loop optim

arXiv:2605.12059v1 Announce Type: cross Abstract: Computational thinking (CT) is increasingly promoted as a core literacy, yet learners and teachers face challenges in connecting abstract program logi

arXiv:2605.12500v1 Announce Type: new Abstract: Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as disti

arXiv:2605.10044v1 Announce Type: cross Abstract: Action chunking emerged as a pivotal technique in imitation learning, enabling policies to predict cohesive action sequences rather than single action

arXiv:2601.02950v3 Announce Type: replace Abstract: Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning pa

arXiv:2605.09441v1 Announce Type: new Abstract: The pursuit of general-purpose embodied agents is hindered by fragmented evaluation protocols that isolate navigation skills and fixate on specific robo

arXiv:2605.09153v1 Announce Type: cross Abstract: Closed-loop traffic simulation requires agents that are both scalable and behaviorally realistic. Recent self-play reinforcement learning approaches d

Chat LangChain has been revamped, and re-open sourced We've been working on a few improvements for a while now, and are very excited to finally open source them again! Want to see how a production Q&A

arXiv:2605.08261v1 Announce Type: cross Abstract: Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically add

arXiv:2605.09869v1 Announce Type: cross Abstract: Zero-shot object navigation has advanced rapidly with open-vocabulary detectors, image--text models, and language-guided exploration. However, even af

arXiv:2605.08681v1 Announce Type: cross Abstract: We study solving large-scale fixed-point equation (x^star=ar F(x^star)) with decomposition. Standard strict decomposition assigns each agent a disjoin

Intelligent trust is not just a bonus for artificial intelligence systems, but a vital factor in their success. Although the AI train doesn’t seem to be slowing down any time soon, security concerns a

arXiv:2605.09932v1 Announce Type: new Abstract: Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains lim

arXiv:2605.09100v1 Announce Type: new Abstract: Text embedding and generative tasks are usually trained separately based on large language models (LLMs) nowadays. This causes a large amount of trainin

arXiv:2605.09487v1 Announce Type: new Abstract: Modern embodied agents achieve impressive performance, but their task knowledge is often stored in neural weights, latent state, or prompt-bound memory,

arXiv:2605.09053v1 Announce Type: new Abstract: Online topological planning has become an effective paradigm for Vision-Language Navigation in Continuous Environments (VLN-CE), but existing methods st

arXiv:2605.08758v1 Announce Type: cross Abstract: Driven by the rapid expansion of e-commerce and small-batch production, the size of the intralogistics load unit of finished goods, semi-finished good

arXiv:2605.08935v1 Announce Type: new Abstract: Coupled spatiotemporal forecasting is important for predicting the future evolution of multiple interacting dynamical systems, such as in climate models

arXiv:2603.05066v2 Announce Type: replace Abstract: Single-task RL agents are typically trained under a fixed reward function, which limits their robustness to reward misspecification and their abilit

arXiv:2605.10515v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to

arXiv:2605.10257v1 Announce Type: new Abstract: Managing disruptions in railway traffic management is a major challenge. Rising traffic density and infrastructure limits increase complexity, making th

arXiv:2510.19788v4 Announce Type: replace Abstract: World models are central to building AI agents capable of flexible reasoning and planning. Yet current evaluations (i) test only properties measurab

arXiv:2605.06936v1 Announce Type: cross Abstract: LLM-based agents are increasingly applied to the 'last mile' of Electronic Design Automation (EDA): repairing residual sign-off Design Rule Check (DRC

arXiv:2602.16928v3 Announce Type: replace-cross Abstract: Much of the advancement in Multi-Agent Reinforcement Learning (MARL) for imperfect-information games has historically depended on the manual,

arXiv:2605.07841v1 Announce Type: cross Abstract: Decentralized machine learning often relies on outsourcing computations, such as gradient evaluations, to untrusted worker nodes. Existing robust aggr

arXiv:2602.21858v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have made significant progress in mobile agent development, yet their capabilities are predominantly confin

arXiv:2605.07195v1 Announce Type: new Abstract: Current end-to-end autonomous driving planners are fundamentally reactive: they condition on historical and present observations to predict future actio

arXiv:2605.06690v1 Announce Type: new Abstract: Recursive reasoning systems alternate between acquiring new evidence and refining an accumulated understanding. Two design choices are typically left im

arXiv:2605.07717v1 Announce Type: cross Abstract: Despite the widespread adoption of agile methods, achieving true agility at scale remains elusive. Large-scale agile frameworks remain largely human-c

arXiv:2605.07462v1 Announce Type: cross Abstract: Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale - a so far unprecedented incident that comes with serious sa

We just published the demo video from our latest @DBOS_Inc user group! Big thanks to Adrian Lyjak from @llama_index for walking through the DBOS integration with LlamaAgents, an open-source Python fra

I feel like we're all circling around the right unified expression of self-optimizing memory+skills+evals and in the next 6 months or so someone will nail it and unlock the next big step change in age

Financial services are becoming more personal, more automated and increasingly AI-driven. However, a big hurdle remains: AI trust. In an industry that handles sensitive financial data, there is little

arXiv:2605.04068v1 Announce Type: new Abstract: The use of artificial intelligence in supply chain forecasting has attracted many scientific studies for several decades. However, the process of select

Episode 3 of Max Agency is here! Join @hwchase17 + @Ramp’s Head of Applied Research Alex Shevchenko and go behind the scenes of Ramp Sheets. @RampLabs Talked to @ramplabs Head of Applied Research Alex

arXiv:2605.03205v1 Announce Type: cross Abstract: Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowled

arXiv:2605.03210v1 Announce Type: cross Abstract: We argue that AI-saturated markets are likely to create Veblen-good premiums, which we term human-provenance premiums, for verified human presence, an

arXiv:2605.04069v1 Announce Type: new Abstract: We introduce LAWS (Learning from Actual Workloads Symbolically), a self-certifying inference caching architecture that builds a growing library of certi

arXiv:2605.04939v1 Announce Type: new Abstract: A cooperative robot swarm is a collective of computationally-limited robots that share a common goal. Each robot can only interact with a small subset o

my posture is terrible but Alex's insights are great, so it evens out Talked to @ramplabs Head of Applied Research Alex Shevchenko on the Max Agency podcast to learn how @Ramp Sheets was built, their