Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user authorization. Existing...
AI coding agents increasingly support software development beyond code completion, including planning, implementation, testing, and repository-level task execution. Their practical use, however, often...
Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification. We introduce HappyWor...
As Large Language Model (LLM) agents are applied in continuously interactive environments, driving the evolution of their own capabilities becomes a core problem for achieving long-term autonomy. Curr...
We ask how little geometry a person has to draw to control both where people stand and where the camera moves in a generated video. Our answer is a ground plane and one cylinder per person. A user dra...
Visual segmentation systems encounter objects outside their training distribution during real-world deployment, hindering reliable autonomous systems that depend on scene parsing in the perception sta...
Autonomous endoscopic navigation requires the policy model to predict actions from texture-poor monocular observations, make safe control decisions, and retain evidence of lesions after they leave the...
Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous data, and operating through rigid, reactive proces...
In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes of any chosen policy. We address both forms of uncertainty as a first st...
Automated biomedical evidence synthesis depends on retrieving published studies, but the biomedical literature is systematically skewed toward positive findings. Deeper retrieval can therefore make a...
The paper introduces Jarvis, an offline, edge‑deployable voice assistant designed for autonomous racecars. It combines speech recognition, synthesis, and a lightweight text‑to‑command classifier fine‑tuned from the Mistral 7B model to provide high‑level behavioral commands. Experiments show 97.63 % intent recognition accuracy with an average latency of 1.39 s, outperforming larger online‑hosted models and enabling quick response times for time‑critical driving tasks.
OpenMAS-GCom is a diagnostic benchmark designed to isolate the impact of communication structures, role assignments, and information flows in graph‑enhanced multi‑agent systems (G‑MAS). It evaluates systems by systematically modifying one component—such as rewiring communication edges, removing specialist or critic agents, or corrupting intermediate messages—while keeping tasks, models, prompts, and budget limits constant. The benchmark tests 17 configurations across 29 datasets in six domains, including 400 new G‑MAS‑Complex tasks that require agents to combine and reconcile information from multiple documents.
The paper introduces the Memory Decision Layer (MDL), a zero‑parameter controller that sits between retrieval and generation in large language models. MDL uses a three‑signal complementary encoder—combining relevance, reliability, and task risk—to produce an interpretable decision about the trustworthiness of retrieved memories. By decoupling confidence from consistency and enabling risk inversion and abstention, MDL cuts hallucination rates under conflicting memories by roughly 56% and nearly eliminates them in high‑risk scenarios, all while adding only 0.14 ms per decision.
PRIME introduces a perception feedback mechanism for Vision‑Language‑Action models in autonomous driving, conditioning perceptual queries on a Situational Memory that aggregates past perception, reasoning, navigation goals, and predicted behaviors via cross‑attention. This approach adds only 29.7 M parameters (0.41 % of a 7.3 B‑parameter base model) and enables intent‑driven perceptual attention at minimal computational cost. On the Bench2Drive closed‑loop benchmark, PRIME achieves a state‑of‑the‑art Driving Score of 82.47 and a Success Rate of 60.00 %, outperforming prior models such as ORION.
JudgeSense is a benchmark comprising 880 items from human‑labelled corpora, each presented under two differently worded instructions that ask the same question. The study evaluates 25 judges from six providers across four tasks, measuring how rewording affects agreement with the judge’s own verdicts. Results show that rewording reduces agreement on all tasks, with significant effects on two, and that stability varies across tasks and is not predicted by parameter count.
GraphSkillEvo introduces a graph-structured representation for agent skills, where each node encodes an execution step and edges capture context-dependent transitions. This structure offers clearer workflow guidance and reduces redundancy compared to unstructured natural-language skills. The authors then present a population-based evolutionary optimization framework that explores this structured skill space, achieving higher accuracy than the baseline SkillOpt across five agent benchmarks.
CoLearn is an interactive, agentic tutoring system that learns about each learner through a persistent memory of mastery and misconceptions, updated with a Bayesian Knowledge Tracing model that uses a large language model as an observation function. It generates personalized questions targeting the learner’s weakest topics and recurring misconceptions, and provides a live evidence view for progress visualization and blind A/B comparison. In blind A/B tests, learners preferred questions conditioned on this memory 68‑69% of the time, and simulations show the agent’s belief converges toward the learner’s true mastery.
The paper introduces RAVEL, a retrieval‑aware online reinforcement learning framework designed to improve interactive retrieval under partial evidence. RAVEL begins with supervised question generation, directly observes the top‑4 retrieval candidates, and refines its question policy using rank feedback from the full question‑answer‑retrieval loop. Experiments on the Interactive‑PEDES dataset demonstrate that RAVEL progressively enhances retrieval performance over five interaction rounds, reallocating questioning toward localized open‑ended attributes that yield the greatest gains on challenging queries.
K2-V2 is a fully open, 360‑open large language model built from scratch, designed to serve as a superior base for reasoning adaptation while also supporting conversation and knowledge retrieval. It competes with leading open‑weight models in its size class, outperforming Qwen2.5‑72B and approaching Qwen3‑235B, and incorporates domain knowledge, reasoning, long‑context handling, and tool use throughout training. The authors release the complete training history, data composition, model weights, and LLM360 artifacts to enable community use and continuous training.
The paper proposes a new approach to processing sonar data for remote sensing by using CSV-format data instead of traditional image-based representations. Experiments demonstrate a 91.18% reduction in processing time, improved accuracy of machine‑learning object detection, and higher signal‑to‑noise and peak‑signal‑to‑noise ratios.