Search: “agents”

arXiv:2609.19944v1 Announce Type: new Abstract: Large language models (LLMs) have been applied to causal discovery, but candidate-graph generation rarely treats premature omission of potentially relev

arXiv:2609.20548v1 Announce Type: cross Abstract: Reinforcement learning agents trained to maximize their own reward in repeated interactions can converge to supra-competitive outcomes resembling expl

arXiv:2609.20791v1 Announce Type: new Abstract: Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminat

arXiv:2609.18094v1 Announce Type: cross Abstract: Autonomous research loops such as AutoResearch show that one coding agent can improve a training setup unattended. Run several of them and each sessio

arXiv:2609.17601v1 Announce Type: cross Abstract: This paper studies decentralized learning of socially optimal equilibria in finite normal-form games over dynamic communication networks. Each agent o

arXiv:2609.19099v1 Announce Type: new Abstract: Self-emulsifying drug delivery systems (SEDDS) can improve the oral bioavailability of poorly soluble drugs, but identifying high-performing formulation

arXiv:2609.17590v1 Announce Type: cross Abstract: Evolutionary Ensemble Search (EES) constructs machine-learning procedures through expert-guided program evolution. A role-specialized council turns ta

arXiv:2609.18540v1 Announce Type: new Abstract: Most keyframe selection studies focus on offline settings, assuming access to the full video and query in advance. In contrast, real-world streaming sce

🔥 Today, we are truly excited to announce our technical prototype, the Generative World Simulation system, which integrates JING（镜）, an interactive experience model, with DAO（道）, a computable shared-w

arXiv:2609.16724v1 Announce Type: new Abstract: Safe local planning requires forecasting surrounding-agent motion and evaluating candidate-specific risks, since identical agent motion can pose differe

arXiv:2609.17300v1 Announce Type: cross Abstract: Artificial ontogeny, developmental encodings, robot reproduction, and inherited controllers are established research directions, yet a narrower questi

arXiv:2609.14985v1 Announce Type: new Abstract: We show how users can create and manipulate causal virtual worlds with large-language-model (LLM) and large-video-model agents. The approach uses feedba

arXiv:2609.15973v1 Announce Type: new Abstract: Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome fe

arXiv:2609.13529v1 Announce Type: cross Abstract: As the use of Large Language Models moves from chatbots into agentic systems, where outputs become actions with irreversible consequences on reality,

arXiv:2609.14857v1 Announce Type: new Abstract: Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve execution

arXiv:2609.14778v1 Announce Type: new Abstract: We propose a decentralized privacy-preserving learning algorithm in which each agent holds a single private sample and a shared model. Samples are learn

The telecommunications industry is currently undergoing a paradigm shift, moving from traditional manual human-driven operations to fully Autonomous Network Operations. Modern networks have grown incr

arXiv:2609.14528v1 Announce Type: new Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-language

Which AI models are most likely to cheat when given the chance? To find out, we built CheatBench [https://cheatbench.ai] : a benchmark that tests whether agents attempt to cheat when given difficult t

arXiv:2609.12444v1 Announce Type: cross Abstract: Simulated societies of large language model agents are used to study online polarization, and separately to study collective intelligence, but the two

arXiv:2605.06377v2 Announce Type: replace-cross Abstract: We study Nash equilibrium learning in partially observable Markov games (POMGs), a multi-agent reinforcement learning framework in which agent

A new paper (I'm not a coauthor BTW -- I just found it interesting) argues, basically, that RSI1 is not on the horizon2, because current (at the time the study was done) agents cannot do open-ended ML

Seems like the intersection of the recent @OpenAI rogue swarm hacking incident (https://x.com/adamsafron/status/2098938666408407118) with solving Navier–Stokes has created a perfect storm of terror ab

i've been thinking about this more after trying different agent setups. the model isn't the only thing that determines how well an agent performs. The harness around the model matters a lot too. With

What is happening in maths right now will happen to any hard industry with verifiable problems and high value rewards. Take microchip design, nuclear or 100s of other industrial processes that are val

arXiv:2609.07573v1 Announce Type: new Abstract: Multi-agent LLM deliberation has been explored as a scalable way to simulate public deliberation. For such simulations to be informative, persona agents

If you run automated prompt or harness evolution, this one is worth your time. (bookmark it) Semantic-gradient prompt optimization improves an agent harness from execution feedback, but its local upda

arXiv:2609.05927v1 Announce Type: new Abstract: Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment

Nice paper to improve inference efficiency. It's been a while we haven't seen good work on efficiency. Here is why it matters: A long-running agent's workspace outgrows its context window long before

arXiv:2609.07718v1 Announce Type: cross Abstract: Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, some clinical workflows for which parcellation is r

This is a really neat benchmark. It's super interesting that Astra starts strong and holds the lead for up to 19 hours, but Fable 5.1 catches up in the final hours. Not entirely clear why. Different m

Cloud platform and AI tooling company — AI SDK, AI Gateway, Eve agent framework, Sandbox, and Observability used by this wiki’s public Ask surface.

An underdiscussed behavior we found on the German wiki was the AIs sending advance parties forward in time to figure out the next questions and report back to the other agents. The agents realized tha

When building consumer-facing generative AI applications, balancing high generation quality with fast response times across diverse media types, can be challenging. KDDI, a major telecommunications ca

Hey so a few months ago I got into local LLMs and didn’t really like the tools out there at the time, so I decided to build my own version of a Claude/Codex GUI for local models. I’ve been working on

I was inspired by Bijan Bowen video - Subway FPS https://youtu.be/6kjXzTVmT58?t=1035 Wondered how far I can push Qwen 3.8 27b so I used a plan made by Fable 5.1 DESIGN.md which has 267 KB! ( 26K of de

arXiv:2609.04641v1 Announce Type: new Abstract: Natural-language-to-SQL systems have ad- vanced rapidly on academic benchmarks, yet production enterprise schemas exhibit graph- like, semi-structured,

Brilliant paper from LinkedIn. (bookmark it) I have been saying that memory is one of the most challenging things to get right when building agents. It's an are you want to invest time in optimizing.

arXiv:2609.04706v1 Announce Type: new Abstract: A merchant's payment processor, ledger, ERP and bank feed are updated by messages that get delayed, duplicated, dropped and reordered, so for minutes at

arXiv:2609.04866v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting struc

We are not in the AGI era. We have incompetent companies creating security problems because they are not properly sandboxing environments, and are not even attempting to monitor, observe, or measure t

arXiv:2511.20591v3 Announce Type: replace Abstract: The emergence and evolution of feature reliance in deep reinforcement learning agents remain poorly understood. Here, we introduce a methodological

The modern edge computing platform has arrived. Before the explosion of artificial intelligence, edge computing sites were generally regarded as a back-end support function, smaller and more remote co

arXiv:2609.03966v1 Announce Type: new Abstract: Agent evaluations report a tool-call rate read off the serving stack. That number can be zero while the model is emitting well-formed calls: the interfa

arXiv:2609.03206v1 Announce Type: cross Abstract: Using a zoom-in tool is an important foundational part of modern visual agents, because it allows to efficiently handle tasks involving high-resolutio

For the ninth consecutive year, Gartner® has named Google a Leader in the Gartner Magic Quadrant™ for Strategic Cloud Platform Services, positioned furthest for Completeness of Vision. We believe this