AI Intelligence Archive
浏览 AIQB 已保存的 6421 stories AI intelligenceArticles,覆盖Models、Products、Industry、Research、教程与观点方法六大分类。
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Section 107 / 161 页 · 每页 40 stories
TutorialsOrdinary
My Agent's Tests Were Green Because the Model Learned to Cheat
If your AI reviewer says "pass" every time, you didn't build a reviewer. You built a rubber...
Source: DEV Community
TutorialsOrdinary
The Test Looked Redundant. The Ninth Bug Needed It.
A 5/5 mutation score missed a regression. A ninth candidate then exposed why a redundant-looking test mattered.
Source: DEV Community
ResearchOrdinary
AI for Science with GPT-6 Astra: Thermal Design and Electrothermal Analysis of 2D CFET
Thermal optimization of 2D CFET inverters requires testing structural proposals against their electrical costs. We examine these research tasks using an AI agent workflow within a supplied electrothermal model. At 12 nm, Astra selects a re…
Source: arXiv · GPT
ResearchOrdinary
An Empirical Study of Counterfactual Self-Explanations in LLMs
Large language models can easily generate explanations for their own outputs, but such self-explanations are not necessarily faithful to the model's behavior. We study this issue through counterfactual self-explanations, where a model mini…
Source: arXiv · Qwen / Llama
ResearchOrdinary
Intrinsic Robot Rewarding: Reusing VLA Representations for Autonomous Evaluation and Policy Improvement
Vision-language-action (VLA) systems already bring together two valuable resources for robot learning: rich visual representations and demonstrations of successful task execution. Intrinsic Robot Rewarding (IRR) proposes to use these resou…
Source: arXiv
ResearchOrdinary
Finding Common Mistakes In Modelling With Mathematical Formalisms Using LLMs
Modelling with mathematical formalisms like logical formulas, mathematical equations, or regular expressions is an important yet challenging task for students of computer science and other STEM disciplines. Identifying common mistakes occu…
Source: arXiv
ResearchOrdinary
Shared-Prefix KV Reuse Across Standard LoRA Adapters: Quality and Serving Tradeoffs
A common small-model deployment runs one shared backbone with several LoRA specialists that answer over the same context. Serving them naively re-prefills that shared context once per specialist. We study a narrow, practical question: for …
Source: arXiv · Qwen
ResearchOrdinary
Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics
Generative AI promises natural language access to the massive numerical telemetry of data centers and Industry 4.0 installations, yet text-to-query and tool-using agents stay unreliable: even frontier models answer little more than half of…
Source: arXiv
ResearchOrdinary
High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin projection of the governing equations onto the reduced modes ba…
Source: arXiv
ResearchOrdinary
Semi-Supervised Learning-Based Genetic Biomarkers Dataset for Multiple-Stage Hepatocellular Carcinoma Prediction
Liver cancer is a complex disease responsible for a high number of deaths across the globe each year, making automated solutions for liver cancer classification urgent. The most common form of liver cancer is hepatocellular carcinoma (HCC)…
Source: arXiv
TutorialsOrdinary
Does AI-generated code silently swallow errors? 120 measured generations: every flagged case was a false positive or a documented fallback
A Semgrep detector plus hand adjudication over 120 locally generated Python/TypeScript functions: syntax can surface 'silent failure' candidates, but the verdict lives outside the code.
Source: DEV Community
ResearchOrdinary
Scaling-Score Conformal Prediction for Multi-Target Regression
Multi-target regression requires a model to simultaneously predict several related outputs. Conformal prediction provides distribution-free, finite-sample marginal coverage guarantees, but extending these to joint multi-dimensional regions…
Source: arXiv
ResearchOrdinary
Optimization over covariance matrices with a parameterized metric
The choice of Riemannian metric can strongly influence the convergence of gradient-based optimization over covariance matrices. Euclidean, Bures-Wasserstein and affine-invariant metrics are common choices, but their relative effectiveness …
Source: arXiv
ResearchOrdinary
Interactive Memory Learning for Long-Term Conversations
Recent advancements in large language models have significantly enhanced the capabilities of agents in modeling long-term conversations. Despite these successes, existing approaches typically adopt a static heuristic paradigm, where inform…
Source: arXiv
ResearchOrdinary
EviScope: Paired Counterfactual Evidence Diagnostics for Faithful and Efficient Grounded Language Models
Grounded language-model systems are often evaluated by final answer accuracy, yet a correct answer can be unsupported, drawn from the wrong source, or produced when evidence is insufficient or contradictory. We introduce EviScope, a paired…
Source: arXiv · Qwen / Gemini / Llama
ResearchOrdinary
Sample-Conditioned Representation Selection for Audio Few-Shot Learning
Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting representation shift is concentrated and class dependent: for ResNet12, the top 10 percent of c…
Source: arXiv
ResearchOrdinary
Beyond In-Distribution Metrics: A Systematic Out-of-Distribution Evaluation of Congenital Heart Disease Segmentation
Congenital heart disease (CHD) diagnosis and surgical planning often require patient-specific 3D anatomical models, but manual segmentation is labor-intensive, particularly in complex anatomies. Although deep-learning methods can automate …
Source: arXiv
ResearchOrdinary
Bio-Inspired Palette Evolution in Indirectly Encoded Substrates: Timescale Compatibility Shapes Activation Function Discovery
Indirectly encoded neural networks can assign different activation functions to individual nodes, but the right functions are rarely known in advance. When the available set contains only standard monotonic functions, problems like parity …
Source: arXiv
ResearchOrdinary
Beyond "ChatGPT Can Make Mistakes": Designing Interventions to Support Metacognitive Monitoring in AI-Assisted Work
AI assistance places a metacognitive demand on users, who must judge their own competence and the system's. Yet designers lack comparative evidence on which interventions to choose, where to place them, and how to tell whether they worked.…
Source: arXiv · ChatGPT
ResearchOrdinary
Neuro-Symbolic Hierarchical Intention Anticipation in Human Behavior
Assistive autonomous systems must anticipate human goals before an observed behavior is complete. This article formulates anticipation as goal inference from a partially observed multimodal episode together with structured prediction of th…
Source: arXiv
TutorialsOrdinary
How to Run Local AI Models with Ollama and Save Money
A few days ago, I posted a tutorial in the Spring AI Dev community on how to use Anthropic Claude’s...
Source: DEV Community · Claude / Ollama
ResearchOrdinary
Repurposing Unified Topological Signatures for Graph Representation Learning
Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn graph representations. However, their discriminative power is upper-bounded by the Weisfeil…
Source: arXiv
ResearchOrdinary
Audio-Visual Turn-taking Prediction in Cocktail Party Scenarios
Current predictive turn-taking models (PTTMs) achieve strong performance on benchmarks with controlled acoustic conditions and clean audio signals. Their generalisation to conversations with overlapping speech and background interference r…
Source: arXiv
TutorialsOrdinary
What's Actually Automated in an 'AI-Run' Blog, and What Isn't
I call this an automated blog, and technically that's true -- Windows Task Scheduler wakes a sleeping...
Source: DEV Community
ResearchOrdinary
Near-Optimal Nonconvex Matrix Completion
We study nonconvex methods for matrix completion, the problem of recovering a low-rank matrix from a subset of its entries. Convex methods achieve sample complexity linear in the matrix dimension and the rank, up to logarithmic factors, wh…
Source: arXiv
TutorialsOrdinary
Resilient and Battle-Tested Are Not the Same Word
Unnecessary information that's safe to ignore Hi. So I've been away for a couple of weeks....
Source: DEV Community
TutorialsOrdinary
Cross Stitch vs Needlepoint
If you spend 8+ hours a day looking at high-DPI monitors, debugging race conditions, and tweaking CSS...
Source: DEV Community
TutorialsOrdinary
AI Coding Tip 036 - Grant AI the Least Privilege Possible
Deny by default, or watch your API become someone else's playground. TL;DR: Give every AI agent...
Source: DEV Community
TutorialsOrdinary
🧩 Don't make Tech your entire personality.
This is one of those articles that I wanted to write for many days, but I kept delaying it until, a...
Source: DEV Community
TutorialsOrdinary
AI Didn't Remove the Engineering Work. It Just Made It Easier to Pretend You Did.
On September 15, India — along with Sri Lanka and Tanzania — celebrates Engineer's Day, marking the...
Source: DEV Community
TutorialsOrdinary
The Spreadsheet Copilot Test: Ask It a Number You Already Know
Every spreadsheet AI demo starts with a clean table and a friendly request: "Show me the trend and...
Source: DEV Community
TutorialsOrdinary
Turning Your Database Into an MCP Server With One Click
Make It Work, Make It Right, Make It… There is a piece of advice, usually attributed to...
Source: DEV Community
TutorialsOrdinary
Tool count is context cost: why my MCP server exposes 6 tools instead of 26
Every session starts the same way. I ask the agent to merge a few PDFs and strip the EXIF off some...
Source: DEV Community
TutorialsOrdinary
Stop rolling dice: a gated pipeline for AI-generated architecture diagrams
TL;DR — AI diagram generation fails not because models are weak, but because there's no production...
Source: DEV Community
TutorialsOrdinary
The student with the most gaps was getting the longest test
I'm building a diagnostic tool for A-level maths. A student gives it a question they're stuck on,...
Source: DEV Community
TutorialsOrdinary
What Happens to Junior Developers When AI Becomes Better Than Junior Developers?
I don't think the biggest question about AI is "Will it replace developers?" I think it's this:...
Source: DEV Community
TutorialsOrdinary
Language is a routing problem, not a translation problem
Most teams approach a multilingual support bot as a translation problem. Wire up a translation layer,...
Source: DEV Community
TutorialsOrdinary
How Humans and AI Agents Can Work Together: A Practical Guide to Agent-Based Project Management
AI coding agents have changed the way I think about software development. Not long ago, when I...
Source: DEV Community
TutorialsOrdinary
Why I Moved Us Off Microservices Back to a Monolith (And What the Original Decision Got Wrong)
We split a perfectly working app into eight microservices because it was 2023 and that's what...
Source: DEV Community
TutorialsOrdinary
Vibe coding on a boilerplate: stop your agent from rebuilding what it already has
A comprehensive guide to vibe coding on a boilerplate: stop your agent from rebuilding what it already has
Source: DEV Community