Claude Code

"We removed over 80% of Claude Code's system prompt for Opus 5 and Fable 5"

hackernews · 원본 ID 49043889

Show HN: How well do you use Claude Code?

A bunch of companies that I spoke to had their own claude & codex OTel dashboards that showed spend + seats per month. However, none of the dashboards actually analyzed how the engineers worked with the tools and if...

hackernews · 원본 ID 49042653

3D-Aware VLMs with Implicit and Explicit Geometries

Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial understanding and reasoning. To bridge this gap, we present VLM-IE3D, a unified framework that enhances the 3D spatial awareness of VLMs by equipping them with both implicit and explicit 3D geometries learned from RGB videos. Our VLM-IE3D introduces Implicit Geometry Tokens (IGTs) that capture high-level geometric priors from input videos, a...

arxiv · 원본 ID http://arxiv.org/abs/2607.21595v1

Unified Video Dense Prediction from Disjoint Data

Scene understanding requires simultaneous prediction about geometry, appearance, and semantics. However, existing task-specific annotations are fragmented across incompatible, domain-specific datasets. Current unified systems circumvent this by restricting training to fully co-annotated data, or by incurring the large computational cost of pseudo-labeling. To mitigate this, we introduce UniD, a unified video model that jointly predicts eight dense scene properties-depth, surface normals, semantic segmentation, bou...

arxiv · 원본 ID http://arxiv.org/abs/2607.21592v1

Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning

Diffusion and flow-matching models dominate conditional image generation, yet inference-time scaling for these models is far less developed than for autoregressive language models. Because final quality is highly sensitive to the initial noise seed, many approaches spend extra compute on seed search or resampling under a black-box reward, but typically maintaining a constant memory footprint throughout inference. We show that relaxing this constraint enables an underexplored inference-time scaling axis: by front-l...

arxiv · 원본 ID http://arxiv.org/abs/2607.21591v1

OpenForgeRL: Train Harness-native Agents in Any Environment

Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, whose SFT/RL stacks cannot natively express stateful, multi-process harness inference. To address this, we present OpenForgeRL, an open-source framework for training harness-based agents end-to-end in diverse environments. OpenForgeRL achieve...

arxiv · 원본 ID http://arxiv.org/abs/2607.21557v1

Seeking Help in the Digital Age: A Cross-Platform Analysis of Online Support Systems for Technology-Facilitated Abuse Victims

Technology-facilitated abuse (TFA), the use of digital technologies to stalk, harass, monitor or threaten others, has become a pervasive form of interpersonal harm. As victims turn to online sources for guidance, responses can shape how they assess risks, interpret abuse, and choose protective actions. We present a large-scale evaluation of online support for TFA victims across three channels: web search, peer-support forums, and conversational AI systems. Drawing on a decade of victim narratives from r/Stalking,...

arxiv · 원본 ID http://arxiv.org/abs/2607.21549v1

From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs

Concurrent stateful library APIs expose behavior through evolving resource ownership, lifecycle states, and competing interleavings. Large language models can synthesize executable Rust tests, but their outputs often violate API preconditions, remain shallow, or reduce concurrency to accidental sequential traces. Conversely, model-based and systematic testing techniques provide semantic control but commonly require substantial handwritten code to turn abstract scenarios into executable tests. This paper addresses...

arxiv · 원본 ID http://arxiv.org/abs/2607.21530v1

Boosting Robustness for All-Weather Self-Supervised Depth Estimation in Autonomous Driving

Self-supervised depth estimation is challenging for safe autonomous driving under various adverse weather conditions due to sensor perception degradation. These challenges arise from two main aspects. Firstly, adverse conditions can distort pixel correspondences and violate the assumptions embedded in the self-supervised loss function, leading to erroneous depth predictions. Secondly, while radar is a widely adopted sensor in adverse weather conditions, the sparse distribution of radar points in the Point of View...

arxiv · 원본 ID http://arxiv.org/abs/2607.21526v1

GS-Agent: Creating 4D Physical Worlds With Generative Simulation

Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional computer graphics methods rely on manual creation, requiring extensive human effort to fine-tune materials, motions, and visual fidelity. Recent advances in generative foundation models have sparked interest in learning to generate such 4D worlds from large-scale data; however, existing methods still struggle to ensure physical plausibility and controllability. In this work, we ta...

arxiv · 원본 ID http://arxiv.org/abs/2607.21522v1

Transparent by Design, Usable in Practice? A Formative Usability Study of a Conversational Product Advisor

Large language models can make conversational product advisors fluent but opaque. If they hide the logic behind a ranking and the evidence for a recommendation inside natural-language replies, they challenge users' ability to understand, trust, and steer the results. One response is to build transparency into the advisor. We report a formative, moderated think-aloud usability study of one such system: a chatbot for laptop search with constrained natural-language generation, an on-demand ranking explanation, and a...

arxiv · 원본 ID http://arxiv.org/abs/2607.21513v1

Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry

AI agents are increasingly created inside organizations by non-engineering users through low-code, no-code, and conversational development environments. This democratization enables rapid local innovation, but it also creates a reliability gap: agents that appear to users as simple productivity artifacts may depend on changing models, tools, retrieval sources, permissions, prompts, schedules, and external services. These dependencies can cause silent degradation long after deployment, even when no user directly mo...

arxiv · 원본 ID http://arxiv.org/abs/2607.21495v1