3,694 items tracked: 32 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2610.09870v1 Announce Type: cross Abstract: Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles.
arXiv:2610.09871v1 Announce Type: cross Abstract: Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results.
arXiv:2610.10126v1 Announce Type: cross Abstract: Multi-agent LLM systems coordinate task execution through exchanges of information among agents.
arXiv:2610.10129v1 Announce Type: cross Abstract: As EDA researchers, we conducted eight deliberate trials of agentic algorithm exploration, selecting several topics outside our areas of depth.
arXiv:2610.10179v1 Announce Type: cross Abstract: Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions.
arXiv:2610.10242v1 Announce Type: cross Abstract: SWE-agent ensembles improve issue resolution by combining candidate patches from different agents with complementary strengths.
arXiv:2610.10258v1 Announce Type: cross Abstract: Quantum libraries are now critical infrastructure for quantum algorithm development, yet their correctness remains difficult to test.
arXiv:2610.10355v1 Announce Type: cross Abstract: Text-to-music systems produce increasingly convincing audio, yet evaluation reveals little about whether the result matches user intent.
arXiv:2610.10468v1 Announce Type: cross Abstract: Deployments of research agents are moving to populations of thousands that share one pool of compute, while most current systems organize one project at a time or leave t…
arXiv:2308.00505v4 Announce Type: replace Abstract: Agent Based Models (ABMs) often deal with systems where there is a lack of quantitative data or where quantitative data alone may be insufficient to fully capture the c…
arXiv:2602.00851v4 Announce Type: replace Abstract: AI agents combine normal conversation with autonomous task execution, allowing earlier context to shape how later tasks are executed.
arXiv:2604.11477v2 Announce Type: replace Abstract: The alignment of Multi-Agent Systems (MAS) for autonomous software engineering is constrained by evaluator epistemic uncertainty.
arXiv:2604.17502v5 Announce Type: replace Abstract: Misaligned artificial agents might resist shutdown.
arXiv:2605.12894v2 Announce Type: replace Abstract: Large Language Model (LLM) agents are increasingly deployed in settings where they interact with diverse users, including those who are unclear, impatient, or reluctant…
arXiv:2605.15871v2 Announce Type: replace Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers.
arXiv:2605.21347v4 Announce Type: replace Abstract: Diagnosing failures in LLM agents remains largely manual.
arXiv:2607.06223v2 Announce Type: replace Abstract: Reinforcement learning (RL) improves large language model (LLM) agents on long-horizon search tasks that require multiple intermediate decisions before a final outcome.
arXiv:2607.10286v3 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to pr…
arXiv:2607.14097v3 Announce Type: replace Abstract: We introduce RegNetAgents, an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneous gene regulatory ne…
arXiv:2608.12851v2 Announce Type: replace Abstract: LLM agents increasingly save experience from past tasks as reusable skills through skill evolution.
arXiv:2608.16666v2 Announce Type: replace Abstract: Interval timing is extensively studied as an important aspect of human behaviour.
arXiv:2605.27381v3 Announce Type: replace-cross Abstract: Agents increasingly modify the procedures by which they solve tasks and improve themselves.
arXiv:2606.02908v2 Announce Type: replace-cross Abstract: Multi-turn user-facing agents must infer user intent from incomplete requests, collect missing information through dialogue and tools, and execute valid actions.
arXiv:2606.10684v2 Announce Type: replace-cross Abstract: Agentic search systems must coordinate evidence acquisition and response generation, yet existing approaches either couple both roles under a single agent objecti…
arXiv:2606.13994v2 Announce Type: replace-cross Abstract: LLM-based Agents are becoming increasingly capable and widely deployed, creating growing incentives for adversarial misuse in the real-world.
arXiv:2606.17555v4 Announce Type: replace-cross Abstract: Banks face two threat families with fundamentally different detection requirements: signature-based fraud (card-not-present attacks, account takeover, ATM cloning…
arXiv:2606.17739v2 Announce Type: replace-cross Abstract: Robotics are expected to support environmental monitoring and disaster detection, where decisions must be made under uncertainty, resource limitations, and strict…
arXiv:2607.11119v2 Announce Type: replace-cross Abstract: Robot manipulation is a complex task that requires visual perception, physical reasoning, planning, and closed-loop control.
arXiv:2607.27409v2 Announce Type: replace-cross Abstract: Although coding agents have achieved impressive performance on correctness-oriented benchmarks, their ability to make behavior-preserving non-functional improveme…
arXiv:2609.14767v2 Announce Type: replace-cross Abstract: Does authority in AI teams improve the outcome?
arXiv:2610.08793v1 Announce Type: new Abstract: Enterprise systems increasingly rely on automated workflows, yet many AI-driven solutions remain brittle under non-stationary conditions, evolving policies, and delayed ope…
arXiv:2610.09008v1 Announce Type: new Abstract: Persistent memory allows an LLM agent to carry experience across conversations, but it also turns a local reasoning mistake into a durable one.
arXiv:2610.09107v1 Announce Type: new Abstract: Natural-language feedback in interactive learning often explains why an action failed by pointing to violated requirements.
arXiv:2610.09119v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems are becoming the interface to buildings, answering questions and controlling operations, but a building's readiness for them ha…
arXiv:2610.09142v1 Announce Type: new Abstract: Execution-based task verifiers decide whether an agent succeeded.
arXiv:2610.09197v1 Announce Type: new Abstract: Agent evolvers automate the design of the prompts, skills and workflows around language model agents, yet the optimization process they follow is still designed by hand: a…
arXiv:2610.09218v1 Announce Type: new Abstract: LLM-guided program evolution has enabled discoveries in mathematics and computational optimization, raising the prospect of reinforcement learning (RL) algorithms that self…
arXiv:2610.09348v1 Announce Type: new Abstract: LLM agents that interact with a user across many sessions accumulate histories that exceed their context window, so they store past interactions in an external memory and a…
arXiv:2610.09426v1 Announce Type: new Abstract: Environments are the foundation of recursive self-improvement: they provide the problems agents work on and the feedback used to evaluate progress.
arXiv:2610.09558v1 Announce Type: new Abstract: Drug target discovery requires distinguishing molecules that causally drive disease from those that are merely associated with it.
arXiv:2610.09652v1 Announce Type: new Abstract: Autonomous navigation in dynamic environments requires computing spatiotemporal trajectories that satisfy non-holonomic motion constraints.
arXiv:2610.10042v1 Announce Type: new Abstract: Large language model (LLM) agents can improve their performance by reusing knowledge distilled from past interactions.
arXiv:2610.10088v1 Announce Type: new Abstract: Self-evolving agents distill task-solving experience into skills for future reuse, but these skills can encode incorrect procedures or non-transferable knowledge.
arXiv:2610.10265v1 Announce Type: new Abstract: Personal memory for language agents is usually judged by whether the final an- swer is correct.
arXiv:2610.08825v1 Announce Type: cross Abstract: Autonomous driving has made remarkable progress, with recent AI advances enabling commercial deployments that are reshaping urban mobility.
arXiv:2610.09027v1 Announce Type: cross Abstract: Modern language model systems operate autonomously over increasingly long contexts containing untrusted text and images.
arXiv:2610.09371v1 Announce Type: cross Abstract: Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable.
arXiv:2610.09520v1 Announce Type: cross Abstract: Large vision-language models (VLMs) provide powerful open-world perception and reasoning for autonomous driving, but their high computational cost and inference latency m…
arXiv:2608.02287v2 Announce Type: replace Abstract: Agent skills have become an important mechanism for equipping language-model agents with reusable procedural knowledge.
arXiv:2608.29971v2 Announce Type: replace Abstract: As agentic systems evolve into complex multi agent orchestration workflows, there is a growing and critical need for systematic frameworks that measures an agent's beha…