Risks, emerging when developing or using open-source software - Kaspersky
Risks, emerging when developing or using open-source software Kaspersky
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From SWE-ZERO to SWE-HERO: Execution-free to Execution-based Fine-tuning for Software Engineering Agents
arXiv:2604.01496v1 Announce Type: new Abstract: We introduce SWE-ZERO to SWE-HERO, a two-stage SFT recipe that achieves state-of-the-art results on SWE-bench by distilling open-weight frontier LLMs. Our pipeline replaces resource-heavy dependencies with an evolutionary refinement strategy: (1) SWE-ZERO utilizes large-scale, execution-free trajectories to master code semantics and repository-level reasoning, and (2) SWE-HERO applies targeted, execution-backed refinement to transition these semantic intuitions into rigorous engineering workflows. Our empirical results set a new benchmark for open-source models of comparable size. We release a dataset of 300k SWE-ZERO and 13k SWE-HERO trajectories distilled from Qwen3-Coder-480B, alongside a suite of agents based on the Qwen2.5-Coder series.
Air-to-Air Channel Characterization for UAV Communications at 3.4 GHz
arXiv:2604.01582v1 Announce Type: new Abstract: Uncrewed Aerial Vehicle (UAV) networks require accurate Air-to-Air (A2A) channel models, but most existing work focuses on Air-to-Ground links and leaves the sub-6 GHz A2A channel poorly characterized. We present preliminary 3.4 GHz A2A channel measurements collected with a lightweight, reconfigurable, open-source channel sounder built from USRP B210 software-defined radios and a high-precision GNSS-disciplined oscillator mounted on two UAVs. Measurements were conducted at the AERPAW Lake Wheeler testbed using a spherical flight trajectory around a second drone to capture channel behavior over varying altitudes, elevation angles, and relative headings. From these data, we analyze fundamental channel properties, extract channel impulse respons
AI Image Generation in 2026: A Developer's Guide to Building with AI Art APIs
If you are building a product that needs AI-generated images -- whether it is a design tool, a marketing platform, a game, or a chatbot -- you need to choose an API. The landscape in 2026 is crowded, confusing, and changing fast. This is the guide I wish I had when I started building. It covers the major APIs, their real-world performance (not marketing claims), integration patterns that work, and the trade-offs nobody talks about on their pricing pages. The APIs: A Practical Overview OpenAI (DALL-E 3 / gpt-image-1) What it is: OpenAI's image generation API, accessible through the same API platform as GPT-4. Strengths: Best prompt understanding in the industry. DALL-E 3's language model integration means it handles complex, multi-element prompts better than any competitor. Excellent text r
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HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understanding
arXiv:2504.10739v2 Announce Type: replace-cross Abstract: Comprehending extended audiovisual experiences remains challenging for computational systems, particularly temporal integration and cross-modal associations fundamental to human episodic memory. We introduce HippoMM, a computational cognitive architecture that maps hippocampal mechanisms to solve these challenges. Rather than relying on scaling or architectural sophistication, HippoMM implements three integrated components: (i) Episodic Segmentation detects audiovisual input changes to split videos into discrete episodes, mirroring dentate gyrus pattern separation; (ii) Memory Consolidation compresses episodes into summaries with key features preserved, analogous to hippocampal memory formation; and (iii) Hierarchical Memory Retriev
ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems
arXiv:2604.01508v1 Announce Type: new Abstract: Tool using agents often fail for operational reasons even when language understanding is strong. Common causes include invalid arguments, interface drift, weak recovery, and inefficient retry behavior. We introduce ToolMisuseBench, an offline deterministic benchmark for evaluating tool misuse and recovery under explicit step, call, and retry budgets. The benchmark covers CRUD, retrieval, file, and scheduling environments with replayable fault injection. It reports success, invalid call behavior, policy violations, recovery quality, and budgeted efficiency. We release a public dataset with 6800 tasks and a reproducible evaluation pipeline. Baseline results show fault specific recovery gains for schema aware methods, while overall success remai
GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement
arXiv:2604.01832v1 Announce Type: new Abstract: We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge. The system integrates a generative branch, which performs full-stack speech restoration in a self-supervised representation domain and reconstructs the waveform via a neural vocoder, along with a predictive branch that performs spectrogram-domain enhancement, providing complementary cues. Outputs from both branches are fused by a post-processing module, which also performs bandwidth extension to generate the enhanced waveform at 48 kHz, later downsampled to the original sampling rate. This generative-predictive fusion improves robustness and perceptual quality, achieving top performance in the blind-test phase and rank
MOVis: A Visual Analytics Tool for Surfacing Missed Patches Across Software Variants
arXiv:2604.01494v1 Announce Type: new Abstract: Clone-and-own development produces families of related software variants that evolve independently. As variants diverge, important fixes applied in one repository are often missing in others. PaReco has shown that thousands of such missed opportunity (MO) patches exist across real ecosystems, yet its textual output provides limited support for understanding where and how these fixes should be propagated. We present MOVis, a lightweight, interactive desktop tool that visualizes MO patches between a source and target variant. MOVis loads PaReco's MO classifications and presents patched and buggy hunks side-by-side, highlighting corresponding regions and exposing structural differences that hinder reuse. This design enables developers to quickly
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