arXiv:2602.00185v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) into materials science offers a transformative opportunity to streamline computational workflows, yet current agentic systems remain constrained by rigid, carefully crafted domain-specific tool-calling paradigms and narrowly scoped agents. In this work, we introduce QUASAR, a universal autonomous system for atomistic simulation designed to […]
VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG
arXiv:2604.05418v1 Announce Type: cross Abstract: Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generation (RAG) is a promising remedy by organizing query-relevant visual evidence into a compact context, most existing methods (i) flatten videos into independent segments, breaking their inherent spatio-temporal structure, and (ii) depend on […]
MolDA: Molecular Understanding and Generation via Large Language Diffusion Model
arXiv:2604.04403v2 Announce Type: replace Abstract: Large Language Models (LLMs) have significantly advanced molecular discovery, but existing multimodal molecular architectures fundamentally rely on autoregressive (AR) backbones. This strict left-to-right inductive bias is sub-optimal for generating chemically valid molecules, as it struggles to account for non-local global constraints (e.g., ring closures) and often accumulates structural errors during […]
DRIFT: Decompose, Retrieve, Illustrate, then Formalize Theorems
arXiv:2510.10815v4 Announce Type: replace Abstract: Automating the formalization of mathematical statements for theorem proving remains a major challenge for Large Language Models (LLMs). LLMs struggle to identify and utilize the prerequisite mathematical knowledge and its corresponding formal representation in languages like Lean. Current retrieval-augmented autoformalization methods query external libraries using the informal statement directly, but […]
From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification
arXiv:2504.19959v4 Announce Type: replace-cross Abstract: Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of the total development effort. While the Universal Verification Methodology (UVM) is widely used in industry to improve verification efficiency through structured and reusable testbenches, constructing these testbenches and generating sufficient stimuli remain challenging. These challenges arise […]
URSA: The Universal Research and Scientific Agent
arXiv:2506.22653v2 Announce Type: replace Abstract: Large language models (LLMs) have moved far beyond their initial form as simple chatbots, now carrying out complex reasoning, planning, writing, coding, and research tasks. These skills overlap significantly with those that human scientists use day-to-day to solve complex problems that drive the cutting edge of research. Using LLMs in […]
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling
arXiv:2603.21357v2 Announce Type: replace Abstract: LLM agents fail on the majority of real-world tasks — GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) — yet every failed trajectory is routinely discarded, wasting the dominant source of collected experience. We […]
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models
arXiv:2411.05961v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) are central to Visual Question Answering (VQA) systems and are typically deployed in the cloud due to their high computational demands. However, this cloud-only approach underutilizes edge computational resources and requires significant bandwidth for transmitting raw images. In this paper, we introduce an edge-cloud collaborative VQA […]
PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing
arXiv:2604.05018v1 Announce Type: new Abstract: Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into […]
Closed-Loop Autonomous Software Development via Jira-Integrated Backlog Orchestration: A Case Study in Deterministic Control and Safety-Constrained Automation
arXiv:2604.05000v1 Announce Type: cross Abstract: This paper presents a closed-loop system for software lifecycle management framed as a control architecture rather than a code-generation tool. The system manages a backlog of approximately 1,602 rows across seven task families, ingests 13 structured source documents, and executes a deterministic seven-stage pipeline implemented as seven scheduled automation lanes. […]
RLAIF-SPA: Structured AI Feedback for Semantic-Prosodic Alignment in Speech Synthesis
arXiv:2510.14628v2 Announce Type: replace-cross Abstract: Recent advances in Text-To-Speech (TTS) synthesis have achieved near-human speech quality in neutral speaking styles. However, most existing approaches either depend on costly emotion annotations or optimize surrogate objectives that fail to adequately capture perceptual emotional quality. As a result, the generated speech, while semantically accurate, often lacks expressive and […]
The illusion of reasoning: step-level evaluation reveals decorative chain-of-thought in frontier language models
arXiv:2603.22816v2 Announce Type: replace-cross Abstract: Language models increasingly “show their work” by writing step-by-step reasoning before answering. But are these reasoning steps genuinely used, or decorative narratives generated after the model has already decided? We introduce step-level faithfulness evaluation – removing one reasoning sentence at a time and checking whether the answer changes – requiring […]