3,698 items tracked: 33 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2610.09335v1 Announce Type: new Abstract: Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability.
arXiv:2610.09374v1 Announce Type: new Abstract: Urban diagnosis integrates heterogeneous observations to identify urban problems, localize affected areas, and investigate contributing factors, informing evidence-based ur…
arXiv:2610.09377v1 Announce Type: new Abstract: Taxonomies are the symbolic representations through which AI systems organize evidence, aggregate patterns, and answer questions over large document collections.
arXiv:2610.09416v1 Announce Type: new Abstract: Flow Language Models (FLMs) have emerged as a continuous-state alternative to discrete diffusion language models, yet the role of their continuous representations in reason…
arXiv:2610.09493v1 Announce Type: new Abstract: A retrieval-augmented model can match a document without relying on it.
arXiv:2610.09497v1 Announce Type: new Abstract: As AI agents work alongside humans in shared workspaces, a mutual awareness challenge arises: agents act at speeds that outpace human monitoring, and users' evolving intere…
arXiv:2610.09560v1 Announce Type: new Abstract: Long-horizon language agents often receive supervision only from terminal task outcomes, leaving little signal for distinguishing productive intermediate behavior from stag…
arXiv:2610.09590v1 Announce Type: new Abstract: Long-running autonomous agents must reuse accumulated reasoning experience without allowing explicit historical memory and LLM context to grow indefinitely.
arXiv:2610.09600v1 Announce Type: new Abstract: Safety interpretability advances the study of Large Language Model (LLM) alignment from behavioral constraints driven by data or algorithms towards a deeper understanding o…
arXiv:2610.09624v1 Announce Type: new Abstract: Tool calling, invoking external tools on demand, is central to agentic LLMs, yet the mechanism that decides whether a model calls a tool or responds directly remains poorly…
arXiv:2610.09644v1 Announce Type: new Abstract: Complementing the many efforts in providing semantic representations of concepts, notions, and entities in materials science, we report and illustrate a process by which we…
arXiv:2610.09667v1 Announce Type: new Abstract: Random selection is widely used in resource allocation and auditing, making reliable implementation essential for AI-agent systems.
arXiv:2610.09681v1 Announce Type: new Abstract: Program verification establishes software correctness through machine-checkable proofs constructed in theorem provers.
arXiv:2610.09856v1 Announce Type: new Abstract: Self-evolving tool-integrated agents learn from tasks and feedback generated within their own training loop.
arXiv:2610.09872v1 Announce Type: new Abstract: Evaluating agents by outcomes alone can obscure the capabilities that produce them.
arXiv:2610.09876v1 Announce Type: new Abstract: Diffusion models generate realistic images but often fail on visual reasoning tasks, such as filling in a Sudoku or drawing the path through a maze.
arXiv:2610.09890v1 Announce Type: new Abstract: Inverse optimization estimates the weights of an objective function that explain observed decisions as optimal solutions, and is used in a variety of fields.
arXiv:2610.09937v1 Announce Type: new Abstract: Artificial intelligence supports building operations in several forms, each with its own barrier.
arXiv:2610.09944v2 Announce Type: replace Abstract: An essential control of AI agents is their ability to manage runtime.
arXiv:2610.09964v1 Announce Type: new Abstract: Large language models (LLMs) can be tuned to influence human attitudes, yet the respective contributions of successive post-training stages remain un-clear.
arXiv:2610.09973v1 Announce Type: new Abstract: When the harmfulness of an LLM agent's output can be quantified, a natural jailbreaking objective is to maximize expected harmfulness over admissible input modifications.
arXiv:2610.10062v1 Announce Type: new Abstract: Tool-using agents are usually scored on whether they finish a task while the tools work.
arXiv:2610.10071v1 Announce Type: new Abstract: LLM-based agents face challenges in personalized interactive tasks due to heterogeneous, multi-typed, and implicitly constrained long-term traces.
arXiv:2610.10120v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in domains where task outcomes can be reliably evaluated, but long-horizon interaction…
arXiv:2610.10184v1 Announce Type: new Abstract: Developing optimization models for production scheduling requires substantial expert effort.
arXiv:2610.10256v1 Announce Type: new Abstract: Reality may establish that an outcome occurred without identifying which evolving procedure produced it or why.
arXiv:2610.10285v1 Announce Type: new Abstract: In the wake of the increasingly public discussion about AI alignment, recent work has tried to propose specific AI architectures that behave safely.
arXiv:2610.10405v2 Announce Type: replace Abstract: Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of misbehavior in such mo…
arXiv:2610.10407v1 Announce Type: new Abstract: As option markets grow and AI advances, agentic systems for option trading are gaining increasing attention.
arXiv:2610.10478v1 Announce Type: new Abstract: How can we predict which base checkpoint is worth an expensive round of agentic post-training?
arXiv:2610.10506v1 Announce Type: new Abstract: Many of the questions now put to large language models have no correct answer to score against: what a policy is worth, which option a user should choose, how to weigh comp…
arXiv:2610.10513v1 Announce Type: new Abstract: Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a langua…
arXiv:2610.08810v1 Announce Type: cross Abstract: Fine-tuned geospatial foundation models (GeoFMs) pretrained on large satellite archives have been shown to improve crop classification accuracy and geographic transferabi…
arXiv:2610.08812v1 Announce Type: cross Abstract: We present Go2-DrivoR, a goal-conditioned adaptation of the end-to-end autonomous driving trajectory planning framework DrivoR for urban navigation with quadrupedal robot…
arXiv:2610.08834v1 Announce Type: cross Abstract: We present JoyAI-Voice~2.0, an end-to-end anthropomorphic speech generation model built upon a fully continuous, dual-encoder architecture.
arXiv:2610.08840v1 Announce Type: cross Abstract: Large language models (LLMs) often abandon a correct answer, or endorse a user's position, once the user pushes back.
arXiv:2610.08871v1 Announce Type: cross Abstract: Language model agents are increasingly deployed to automate everyday digital chores from managing emails and social media to handling banking and bills allowing users to…
arXiv:2610.08892v1 Announce Type: cross Abstract: Multiplicative gamma noise is one of the dominant noise factors in Synthetic Aperture Radar (SAR) and medical ultrasound images.
arXiv:2610.08928v1 Announce Type: cross Abstract: LLM-based agents are entering decision-support roles in defence staff work, where the obligations they must respect are already written down and binding, and where retrai…
arXiv:2610.08933v1 Announce Type: cross Abstract: Deep-space crews cannot rely on real-time ground support for urgent off-nominal events.
arXiv:2610.08944v1 Announce Type: cross Abstract: Scanner variation changes how pathology foundation models represent the same tissue.
arXiv:2610.08954v1 Announce Type: cross Abstract: Video large language models (Vid-LLMs) excel at diverse video-language tasks by reasoning over selected frames.
arXiv:2610.09033v1 Announce Type: cross Abstract: Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment routinely receive inpu…
arXiv:2610.09055v1 Announce Type: cross Abstract: Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-based execution.
arXiv:2610.09064v1 Announce Type: cross Abstract: When we interact with large language models (LLMs), are we having a conversation?
arXiv:2610.09087v1 Announce Type: cross Abstract: Large language models are informing decisions with ever-higher stakes.
arXiv:2610.09092v2 Announce Type: replace-cross Abstract: State Space Models (SSMs) offer an efficient alternative to Transformers for sequence modeling, yet conditioning pre-trained SSMs for iterative generation typical…
arXiv:2610.09138v1 Announce Type: cross Abstract: Autonomous and agentic clients can distribute reconnaissance across many identities so that each request remains valid, low-rate, and benign-looking while the population…
arXiv:2610.09152v1 Announce Type: cross Abstract: Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation.
arXiv:2610.09163v1 Announce Type: cross Abstract: Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when users exhibit non-c…